Learning User Representations to Improve Simulation Fidelity

# Learning User Representations to Improve Simulation Fidelity
Source: [https://arxiv.org/html/2610.22518](https://arxiv.org/html/2610.22518)
Shuhaib Mehri††thanks:Work done while interning at Microsoft Research\. Correspondence to:[mehri2@illinois\.edu](mailto:[email protected])Philippe LabanAffiliation:Microsoft ResearchMichel GalleyAffiliation:Microsoft ResearchDilek Hakkani\-TürAffiliation:University of Illinois Urbana\-ChampaignJennifer NevilleAffiliation:Microsoft ResearchJianfeng GaoAffiliation:Microsoft Research

###### Abstract

User simulators enable interactive training and evaluation of AI assistants at scale\. Yet current simulators struggle to reproduce the diverse behaviors of real users, leading to assistants that generalize poorly to underrepresented users\. We identify user representations as an underexplored bottleneck in user simulation\. User representations are natural\-language descriptions that condition simulators to reproduce a particular user’s behavior\. A representation’s quality becomes apparent in hindsight, through the behavior it induces in a simulator\. To evaluate and optimize user representations, we formulate a conversation reconstruction task: given an original conversation, we generate a representation, condition a simulator on it, simulate a conversation, and then measure fidelity by comparing user behavior in the original and simulated conversations\. This comparison also yields*hindsight feedback*, which identifies where the representation falls short and how it can be improved\. We introduce*hindsight distillation*, an online training procedure that distills feedback\-conditioned improvements into the representation generator, enabling it to generate improved representations directly from the original conversation\. We presentUserRepGen\-4B, a representation generator trained with hindsight distillation\. Across 700 conversations spanning seven datasets, hindsight feedback helps improve user representations at test time, andUserRepGen\-4Bgenerates representations that enable more faithful simulation than those generated by GPT\-5\.5\. Beyond conversation reconstruction, learned representations improve simulation fidelity for unseen tasks, better preserve the population\-level diversity of real users, and better distinguish users across conversations\.

![Refer to caption](https://arxiv.org/html/2610.22518v1/intro.png)Figure 1:User representations are a bottleneck in user simulation\.They capture the information a simulator needs to faithfully reproduce a user’s behavior\. We trainUserRepGen\-4Bto generate high\-quality user representations and demonstrate their importance for reconstructing user conversations, simulating users in new scenarios, simulating diverse user populations, and distinguishing users across conversations\.## 1Introduction

Simulating user behavior is central to the development of human\-centered AI assistants\. User simulators provide scalable environments in which AI assistants can be trained\([Wu et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib28);[Zhou et al\., 2024b](https://arxiv.org/html/2610.22518#bib.bib29)\)and evaluated\([Yao et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib30);[Zhou et al\., 2024a](https://arxiv.org/html/2610.22518#bib.bib31)\)on the kinds of interactions they encounter in the real world\. Yet current simulators struggle to faithfully simulate diverse users and instead demonstrate homogeneous behaviors that tend to resemble those of a generic user\([Mehri et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib11);[Xiao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib26)\)\. Reliance on these simulators exposes assistants to a narrow range of behaviors during development, leading to poor generalization to users that are underrepresented in simulation\([Liu et al\., 2023](https://arxiv.org/html/2610.22518#bib.bib32)\)\. It is therefore essential that user simulators capture this diversity and reproduce the behaviors of individual users with high fidelity\.

To simulate a particular user, a simulator is conditioned on a user representation and then used to generate responses\. A user representation is a natural\-language description intended to capture the information needed to reproduce a user’s behavior\. When relevant information is missing, simulators fill in the gaps with assumptions about how the user might behave, often resulting in generic behaviors rather than the particular user’s behavior\. Existing works that improve the simulation pipeline primarily focus on optimizing the user simulator to generate more realistic responses, while representation generation receives comparatively little attention and is typically done with an ad hoc prompt and a general\-purpose large language model \(LLM\)\([Naous et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib1);[Wu et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib2);[Zhou et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib3)\)\. In this work, we identify user representations as an underexplored bottleneck in user simulation, and optimize the representation generator for downstream simulation fidelity while keeping the user simulators fixed\.

The quality of a user representation becomes apparent in hindsight through the behavior it induces in a simulator\. We operationalize this with a conversation reconstruction task: we generate a user representation from a user–assistant conversation, condition a fixed simulator on it, and have the simulator interact with an assistant to produce a simulated conversation\. We evaluate the representation by measuring how faithfully the simulator reproduces the user behavior from the original conversation\. Comparing the two conversations also yields*hindsight feedback*, a description of where the representation falls short and how it can be improved\. The representation generator can be conditioned on this feedback to generate an improved representation\. Collecting hindsight feedback, however, requires expensive multi\-turn simulations\. We therefore introduce*hindsight distillation*, an online training procedure in which we distill feedback\-conditioned improvements into the representation generator\. At each training step, we sample a representation, improve it with hindsight feedback, and then use it as a supervised target\. This teaches the representation generator to produce better representations directly from the original conversation\.

We presentUserRepGen\-4B, a representation generator trained with hindsight distillation, and evaluate it on 700 conversations spanning seven datasets of user–AI interactions\.UserRepGen\-4Bgenerates representations that enable more faithful simulation than those generated by proprietary models such as GPT\-5\.5, without requiring hindsight feedback at inference time\. Improving representations with hindsight feedback also improves simulation fidelity at test time, with further gains when repeated for multiple iterations\. Beyond conversation reconstruction, the learned representations improve simulation fidelity for unseen tasks, produce less homogeneous simulations that better preserve the population\-level diversity of real users, and capture user\-specific behaviors that better distinguish users across conversations\.

The main contributions of our work can be summarized as follows:

- •User representations and conversation reconstruction\.We identify user representations as an underexplored bottleneck in user simulation and formulate conversation reconstruction as a task for evaluating and training representation generators \(§[3](https://arxiv.org/html/2610.22518#S3)\)\.
- •Structured user representations\.We structure a user representation into separate task and behavior descriptions, enabling a user’s behavior description to be paired with unseen tasks or different behavior descriptions to be paired with a fixed task \(§[3](https://arxiv.org/html/2610.22518#S3)\)\.
- •Hindsight distillation\.We introduce*hindsight distillation*, an online training procedure in which we train a representation generator using*hindsight feedback*, which describes how to improve user representations by comparing real and simulated user behavior\. \(§[4](https://arxiv.org/html/2610.22518#S4)& §[5](https://arxiv.org/html/2610.22518#S5)\)\.
- •UserRepGen\-4B\.We presentUserRepGen\-4B, a representation generator trained with hindsight distillation, whose representations enable more faithful simulation than those generated by proprietary models such as GPT\-5\.5 \(§[6\.3](https://arxiv.org/html/2610.22518#S6.SS3)\)\. Improving representations with hindsight feedback also improves fidelity at test time, and can be applied for multiple iterations \(§[6\.2](https://arxiv.org/html/2610.22518#S6.SS2)\)\.
- •Characterizing learned representations\.We present a systematic analysis of how user representations change after being optimized for simulation fidelity \(§[6\.4](https://arxiv.org/html/2610.22518#S6.SS4)\)\.
- •Broader utility of learned representations\.Beyond conversation reconstruction, the learned representations improve simulation fidelity on unseen tasks \(§[7\.1](https://arxiv.org/html/2610.22518#S7.SS1)\), produce less homogeneous simulations that preserve the population\-level diversity of real users \(§[7\.2](https://arxiv.org/html/2610.22518#S7.SS2)\), and better distinguish users across conversations \(§[7\.3](https://arxiv.org/html/2610.22518#S7.SS3)\)\.

## 2Related Work

#### User simulation\.

LLMs have emerged as promising tools for simulating human behavior\([Park et al\., 2023](https://arxiv.org/html/2610.22518#bib.bib33);[Yao et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib30);[Anthis et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib21);[Mehri et al\., 2026a](https://arxiv.org/html/2610.22518#bib.bib54)\)\. Yet prompting general\-purpose LLMs has revealed several limitations, motivating efforts to train specialized simulators with objectives such as profile consistency, goal alignment, latent\-state alignment, and realism\([Wang et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib35);[Mehri et al\., 2026c](https://arxiv.org/html/2610.22518#bib.bib13);[Naous et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib1);[Wu et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib2);[Zhou et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib3);[Wang et al\., 2026c](https://arxiv.org/html/2610.22518#bib.bib34)\)\. Most recently, studies have documented behavioral homogenization among simulated users and the resulting mismatch with real user populations\([Mehri et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib11);[Xiao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib26)\)\. Our work addresses this problem by conditioning simulators on learned user representations that improve simulation fidelity\.

#### User representation\.

Early works introduce personas to enable assistants to generate responses personalized to and consistent with different individuals\([Li et al\., 2016](https://arxiv.org/html/2610.22518#bib.bib36);[Zhang et al\., 2018](https://arxiv.org/html/2610.22518#bib.bib5)\)\. User simulation has explored several forms of user representation, including describing user goals\([Schatzmann et al\., 2007](https://arxiv.org/html/2610.22518#bib.bib37);[Naous et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib1)\), demographic or biographical attributes\([Argyle et al\., 2023](https://arxiv.org/html/2610.22518#bib.bib38)\), interview transcripts\([Park et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib39)\), and learned vector representations\([Li et al\., 2016](https://arxiv.org/html/2610.22518#bib.bib36);[Ning et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib41);[Chen et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib40)\)\. User representations are central to simulation reliability, directly shaping the behaviors that simulators produce\([Li et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib42)\)\. To improve user representations, recent work has explored reducing underspecification\([Lin et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib44)\), scaling generation\([Ge et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib4)\), generating diverse populations of personas\([Chopra et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib22)\), and matching the distribution of behaviors in a reference population\([Liu et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib43)\)\. We learn to generate user representations that capture the user\-specific information necessary for faithful simulation\.

#### Learning from privileged information\.

Recent work incorporates environmental feedback into model training through reinforcement learning and self\-distillation\([Song et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib17);[Shi et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib45);[Sun et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib16)\), often by distilling from a teacher conditioned on privileged information\([Zhao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib15);[Hübotter et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib46);[Mendes et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib47)\)\. For interactive agents, privileged information is obtained from multi\-turn rollouts and used to improve the agent\([Buening et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib48);[Wang et al\., 2026a](https://arxiv.org/html/2610.22518#bib.bib49);[Wang et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib50);[Yeo et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib51);[Li et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib52);[Liu et al\., 2026a](https://arxiv.org/html/2610.22518#bib.bib53)\)\. In contrast, our setting uses the simulator behavior induced by a representation in a multi\-turn conversation as privileged information for improving the representation generator\.

## 3Task Description

Let𝒞=\(u1,a1,…,un,an\)\\mathcal\{C\}=\(u\_\{1\},a\_\{1\},\\ldots,u\_\{n\},a\_\{n\}\)be a conversation between a real user and an assistant, whereuiu\_\{i\}andaia\_\{i\}denote the user and assistant utterances at turnii, respectively\. The goal of user simulation is to generate responses that reproduce the behavior exhibited by the user in𝒞\\mathcal\{C\}\. A typical pipeline consists of two stages\. First, a representation generatorGθG\_\{\\theta\}generates a user representationrrfrom𝒞\\mathcal\{C\}, which is a natural language description that captures the information needed to simulate the user\. Second, an LLM\-based user simulatorUϕU\_\{\\phi\}is conditioned onrrand interacts with an assistant to generate a simulated conversation𝒞~=\(u~1,a~1,…,u~m,a~m\)\\tilde\{\\mathcal\{C\}\}=\(\\tilde\{u\}\_\{1\},\\tilde\{a\}\_\{1\},\\ldots,\\tilde\{u\}\_\{m\},\\tilde\{a\}\_\{m\}\)\.

Recent work has largely focused on the second stage, optimizing the user simulatorUϕU\_\{\\phi\}to generate responses that resemble real humans\([Naous et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib1);[Wu et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib2);[Zhou et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib3)\)\. By contrast, representation generation has received comparatively little attention and is typically performed using a general\-purpose LLM and an ad hoc prompt\. The representations may fail to capture all relevant information about a user’s behavior, which can cause unwanted behaviors fromUϕU\_\{\\phi\}\. For example, the simulator may fill in the gaps with assumptions about the user, and demonstrate generic behaviors rather than those of the particular user in𝒞\\mathcal\{C\}\. This motivates treating representation generation as its own learning problem and optimizingGθG\_\{\\theta\}for downstream simulation fidelity\.

#### User Representations\.

One approach for representing users is through personas that describe demographic or biographical attributes\([Anthis et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib21)\), such as age, occupation, location, experiences, interests, personality, or background\([Zhang et al\., 2018](https://arxiv.org/html/2610.22518#bib.bib5);[Ge et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib4)\)\. These attributes provide only indirect cues about user behavior, leaving the simulator to fill in the gaps with potentially unsupported or stereotypical assumptions\. Another approach represents users through an intent that describes their overall goal without specifying any behaviors\([Naous et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib1)\)\. This may suffice for simulating a generic user pursuing a given task, but it does not capture the unique behaviors of individual users, which can result in homogeneous simulations that fail to reflect the diversity of real users\([Mehri et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib11);[Xiao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib26)\)\.

In this work, we introduce a structured user representationr=\(t,b\)r=\(t,b\)that separates the user’s conversation\-specific task from their behavioral patterns that persist across conversations\. The taskttdescribes what the user is trying to accomplish in𝒞\\mathcal\{C\}, and behaviorbbdescribes the user’s general behavior, such as how they formulate and specify requests, respond to the assistant, and provide feedback\. This structure enables composing representations, either by simulating diverse user behaviors on a fixed task, or in simulating multiple tasks with a fixed user behavior\.

#### Conversation Reconstruction\.

We train and evaluate representation generators through a conversation reconstruction task\. Given a conversation𝒞\\mathcal\{C\}, we measure how well a user representationrrenables user simulators to generate conversations that preserve the user behavior observed in𝒞\\mathcal\{C\}\.

We formalize this task with a simulation environmente=\(Ue,Ae,Je\)e=\(U\_\{e\},A\_\{e\},J\_\{e\}\), consisting of a fixed user simulatorUeU\_\{e\}, assistantAeA\_\{e\}, and judgeJeJ\_\{e\}\. For each simulation environment, the simulatorUeU\_\{e\}is conditioned onrrand interacts with the assistantAeA\_\{e\}to produce a simulated conversation𝒞~e=\(u~1,a~1,…,u~m,a~m\)\\tilde\{\\mathcal\{C\}\}\_\{e\}=\(\\tilde\{u\}\_\{1\},\\tilde\{a\}\_\{1\},\\ldots,\\tilde\{u\}\_\{m\},\\tilde\{a\}\_\{m\}\)\. The interaction ends whenUeU\_\{e\}is satisfied and emits a termination signal, or when the conversation reaches a maximum length\. The judgeJeJ\_\{e\}compares𝒞~e\\tilde\{\\mathcal\{C\}\}\_\{e\}with𝒞\\mathcal\{C\}and produces a scalar scorese∈ℝs\_\{e\}\\in\\mathbb\{R\}that measures simulation fidelity across multiple dimensions \(see Appendix[C](https://arxiv.org/html/2610.22518#A3)\)\. To account for differences in simulator behavior caused by different assistant responses,JeJ\_\{e\}evaluates fidelity based on the given interaction context rather than requiring an exact reconstruction\.

Our goal is to trainGθG\_\{\\theta\}to generate representations that maximize fidelity across fixed simulators\.

## 4Hindsight Feedback

A user representation may appear reasonable without capturing all the relevant information needed to reproduce a user’s behavior\. Its quality becomes apparent in hindsight, after it is used to simulate the user\. The behaviors that the simulator reproduces faithfully indicate what the representation captures well, whereas the behaviors it reproduces poorly or fails to reproduce reveal what the representation does not capture well\. We describe these in natural language with*hindsight feedback*\.

Given a conversation𝒞\\mathcal\{C\}and an initial user representationr\(0\)r^\{\(0\)\}, we collect hindsight feedback for the representation using an ensemble of simulation environmentsℰ=\{ek=\(Uk,Ak,Jk\)\}k=1K\\mathcal\{E\}=\\\{e\_\{k\}=\(U\_\{k\},A\_\{k\},J\_\{k\}\)\\\}\_\{k=1\}^\{K\}, each built on a different underlying model\. For each environmenteke\_\{k\}, we roll out a simulated conversation:

𝒞~k∼rollout\(Uk\(⋅∣r\(0\)\),Ak\)\.\\tilde\{\\mathcal\{C\}\}\_\{k\}\\sim\\operatorname\{rollout\}\\\!\\left\(U\_\{k\}\(\\cdot\\mid r^\{\(0\)\}\),A\_\{k\}\\right\)\.\(1\)The judgeJkJ\_\{k\}then compares the user behavior in𝒞~k\\tilde\{\\mathcal\{C\}\}\_\{k\}with that in𝒞\\mathcal\{C\}and generates hindsight feedbackfkf\_\{k\}\. We collect the feedback from all simulation environments intof=\(f1,…,fK\)f=\(f\_\{1\},\\ldots,f\_\{K\}\), where

fk∼Jk\(⋅∣𝒞,𝒞~k\),k=1,…,K\.f\_\{k\}\\sim J\_\{k\}\\\!\\left\(\\cdot\\mid\\mathcal\{C\},\\tilde\{\\mathcal\{C\}\}\_\{k\}\\right\),\\qquad k=1,\\ldots,K\.\(2\)
#### Improving user representations with hindsight feedback\.

We leverage the in\-context learning capabilities of LLMs to improve a user representation\. We generate an improved representation by conditioningGθG\_\{\\theta\}on the conversation𝒞\\mathcal\{C\}, the current representationrr, and the hindsight feedbackff\. We repeat this improvement forIIiterations, as each iteration may expose previously unseen failures or reveal that earlier improvements were insufficient\. At each roundii, we collect feedbackf\(i\)f^\{\(i\)\}forr\(i\)r^\{\(i\)\}and generate:

r\(i\+1\)∼Gθ\(⋅∣𝒞,r\(i\),f\(i\)\),r^\{\(i\+1\)\}\\sim G\_\{\\theta\}\\\!\\left\(\\cdot\\mid\\mathcal\{C\},r^\{\(i\)\},f^\{\(i\)\}\\right\),\(3\)The final user representation is denoted byr\(I\)r^\{\(I\)\}\.

## 5Hindsight Distillation

![Refer to caption](https://arxiv.org/html/2610.22518v1/method.png)Figure 2:Hindsight distillation forGθG\_\{\\theta\}\. A representation is sampled, and is improved withIIrounds of hindsight feedback to getr\(I\)r^\{\(I\)\}, which serves as a training target\.Hindsight feedback enablesGθG\_\{\\theta\}to improve a user representation, but obtaining this feedback requires simulating and evaluating a multi\-turn conversation\. To avoid this during inference, we distill the feedback\-conditioned improvements intoGθG\_\{\\theta\}, enabling it to generate improved representations directly from𝒞\\mathcal\{C\}\.

At training stepnn, the current policy samples an initial user representation for a conversation𝒞\\mathcal\{C\}

r\(0\)∼Gθn\(⋅∣𝒞\)\.r^\{\(0\)\}\\sim G\_\{\\theta\_\{n\}\}\(\\cdot\\mid\\mathcal\{C\}\)\.\(4\)We applyIIrounds of hindsight feedback to obtainr\(I\)=\(r1\(I\),…,rL\(I\)\)r^\{\(I\)\}=\(r\_\{1\}^\{\(I\)\},\\ldots,r\_\{L\}^\{\(I\)\}\), which we use as a training target under the negative log\-likelihood objective:

ℒ\(θn\)=−1L∑j=1LlogGθn\(rj\(I\)∣𝒞,r<j\(I\)\)\.\\mathcal\{L\}\(\\theta\_\{n\}\)=\-\\frac\{1\}\{L\}\\sum\_\{j=1\}^\{L\}\\log G\_\{\\theta\_\{n\}\}\\left\(r\_\{j\}^\{\(I\)\}\\mid\\mathcal\{C\},r\_\{<j\}^\{\(I\)\}\\right\)\.\(5\)This distills hindsight feedback\-conditioned improvements intoGθ\(⋅∣𝒞\)G\_\{\\theta\}\(\\cdot\\mid\\mathcal\{C\}\), teaching it to generate better representations without the hindsight feedback\. The complete training procedure is depicted in Figure[2](https://arxiv.org/html/2610.22518#S5.F2)and Algorithm[1](https://arxiv.org/html/2610.22518#alg1)in Appendix[A](https://arxiv.org/html/2610.22518#A1)\.

## 6Learning User Representations

### 6\.1Experimental Setup

#### Datasets\.

We use five datasets of real\-world conversations with AI assistants: WildChat\([Zhao et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib6)\), LMSYS\-Chat\-1M\([Zheng et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib7)\), PRISM\([Kirk et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib8)\), SWE\-Chat\([Baumann et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib9)\), and ThoughtTrace\([Jin et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib10)\)\. We additionally use two datasets of simulated conversations: WildChat Coding and WildChat Writing\([Mehri et al\., 2026b](https://arxiv.org/html/2610.22518#bib.bib11)\)\. For each dataset, we apply a preprocessing stage that removes near\-duplicate conversations based on n\-gram overlap, and retains safe English conversations with at least four user turns\.

#### Training\.

We use a fixed set of 3,200 conversations, approximately 500 conversations from each dataset and 200 from ThoughtTrace\. Hindsight feedback is generated using an ensemble of two simulation environments based on Qwen3\.5\-4B\([Team, 2026](https://arxiv.org/html/2610.22518#bib.bib23)\)and Gemma3\-4B\([Team et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib12)\)\. Within each environment, the corresponding model serves as the user simulator, assistant and judge\. During training, the judges compare only user utterances from the real and simulated conversations, which we found to be a reliable proxy for the full\-conversation evaluation when using smaller and less capable judge models\. Additionally, we stabilize training by using the initial policy as our teacher policy\([Zhao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib15)\)and to generate improved user representations\.

#### Evaluation\.

We sample 100 conversations from each dataset, yielding 700 conversations in total\. We follow the evaluation process described in Section[3](https://arxiv.org/html/2610.22518#S3), using GPT\-5\.4 mini as the user simulator, assistant, and judge\. We specifically use a different model than training to ensure a more reliable and unbiased evaluation that tests whether the learned representations generalize to unseen simulation environments\. The judge compares user behavior in simulated conversations with that in the corresponding original conversation and assigns an overall fidelity score from 1 to 10\. We validate the judge with a human preference study in Appendix[B](https://arxiv.org/html/2610.22518#A2)\. Lastly, simulators sometimes demonstrated failures in staying aligned with their user representations\([Mehri et al\., 2026c](https://arxiv.org/html/2610.22518#bib.bib13)\)\. To account for this, we simulate three conversations for each representation, and report*best\-of\-1*and*best\-of\-3*fidelity\. All prompts are provided in Appendix[C](https://arxiv.org/html/2610.22518#A3)and hyperparameters are reported in Appendix[D](https://arxiv.org/html/2610.22518#A4)\.

### 6\.2Test\-Time Improvement with Hindsight Feedback

Figure 3:Simulation fidelity across iterations of hindsight feedback\. Shaded regions show 95% bootstrap confidence intervals over conversations\.We investigate whether hindsight feedback improves user representations directly at test time\. For each evaluation conversation, we generate an initial user representationr\(0\)r^\{\(0\)\}using Qwen3\.5\-4B\. Using the ensembleℰ\\mathcal\{E\}of Qwen3\.5\-4B and Gemma3\-4B environments, we applyI=10I=10rounds of improvement using hindsight feedback, as described in Section[4](https://arxiv.org/html/2610.22518#S4)\. We evaluate representations after every iteration \(Figure[3](https://arxiv.org/html/2610.22518#S6.F3)\)\.

Hindsight feedback helps generate better user representations at test\-time, which leads to higher simulation fidelity scores\. After one round of feedback, best\-of\-1 fidelity increases from 3\.41 to 4\.93, and best\-of\-3 increases from 4\.42 to 6\.14\. Performance continues to improve over subsequent rounds, with best\-of\-1 reaching 6\.10 after ten iterations and best\-of\-3 peaking at 7\.07 at nine\. Most gains occur in the first two rounds, and performance largely plateaus after six rounds\.

### 6\.3Hindsight Distillation

We then distill hindsight feedback into the representation generator to generate better representations directly from a conversation\. Unless otherwise stated, hindsight feedback is collected using the ensemble of simulation environments described in Section[6\.1](https://arxiv.org/html/2610.22518#S6.SS1)\. We explore four training approaches:

- •Supervised Fine\-Tuning \(SFT\):For each conversation𝒞\\mathcal\{C\}in the training set, the base policy samples an initial user representationr\(0\)∼Gθ0\(⋅∣𝒞\)r^\{\(0\)\}\\sim G\_\{\\theta\_\{0\}\}\(\\cdot\\mid\\mathcal\{C\}\)\. We collect hindsight feedback and generate an improved user representationr\(1\)r^\{\(1\)\}\. We then train the representation generator to predictr\(1\)r^\{\(1\)\}directly from𝒞\\mathcal\{C\}using the standard negative log\-likelihood objective\.
- •Group Relative Policy Optimization \(GRPO\)\([Shao et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib14)\):For each conversation𝒞\\mathcal\{C\}, the current policy samples a group ofnnuser representations\{ri\}i=1n\\\{r\_\{i\}\\\}\_\{i=1\}^\{n\}\. We assign each group to one of the simulation environmentse=\(Ue,Ae,Je\)e=\(U\_\{e\},A\_\{e\},J\_\{e\}\)and roll out a simulated conversation𝒞~e,i∼rollout\(Ue\(⋅∣ri\),Ae\)\\tilde\{\\mathcal\{C\}\}\_\{e,i\}\\sim\\operatorname\{rollout\}\\\!\\left\(U\_\{e\}\(\\cdot\\mid r\_\{i\}\),A\_\{e\}\\right\)for each representationrir\_\{i\}\. To compute the reward, the judge assigns a scalar scoresi=Je​\(𝒞,𝒞~e,i\)s\_\{i\}=J\_\{e\}\(\\mathcal\{C\},\\tilde\{\\mathcal\{C\}\}\_\{e,i\}\)measuring simulation fidelity\. We compute group\-relative advantages and update the policy using the clipped GRPO objective\.
- •On\-Policy Self\-Distillation \(OPSD\)\([Zhao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib15)\):The current policy samples an initial user representationr\(0\)∼Gθ\(⋅∣𝒞\)r^\{\(0\)\}\\sim G\_\{\\theta\}\(\\cdot\\mid\\mathcal\{C\}\)\. Hindsight feedbackffis collected, and the teacher policy is conditioned on the privileged context\(𝒞,r\(0\),f\)\(\\mathcal\{C\},r^\{\(0\)\},f\), while the student policy is conditioned only on𝒞\\mathcal\{C\}\. We update the student policy by minimizing the token\-level forward KL divergence from the teacher distribution to the student distribution alongr\(0\)r^\{\(0\)\}\.
- •Online Supervised Fine\-Tuning \(OSFT\):As described in Section[5](https://arxiv.org/html/2610.22518#S5), the current policy samples an initial user representationr\(0\)∼Gθ\(⋅∣𝒞\)r^\{\(0\)\}\\sim G\_\{\\theta\}\(\\cdot\\mid\\mathcal\{C\}\)and receives hindsight feedbackff\. Rather than using token\-level supervision from the teacher policy, we condition the policy on\(𝒞,r\(0\),f\)\(\\mathcal\{C\},r^\{\(0\)\},f\)to generate an improved representationr\(1\)r^\{\(1\)\}\. The policy is updated to predictr\(1\)r^\{\(1\)\}directly from𝒞\\mathcal\{C\}using the standard negative log\-likelihood objective\.

Results for training Qwen3\.5\-4B using the different approaches are reported in Table[1](https://arxiv.org/html/2610.22518#S6.T1)\. OSFT performs best, achieving 4\.95 best\-of\-1 and 6\.02 best\-of\-3\. We refer to this model asUserRepGen\-4B\. OPSD achieves 4\.46 best\-of\-1 and 5\.60 best\-of\-3\. Like OSFT, OPSD samples user representations from the current policy and collects hindsight feedback online\. However, OPSD distills this feedback through dense token\-level supervision, whereas OSFT generates an improved representation and uses it as a supervised target\. The stronger performance of OSFT suggests that explicitly supervising on an improved representation is more effective for this task\. SFT similarly trains on improved user representations using negative log\-likelihood, but generates its targets only once from the base policy rather than continually doing so using the latest policy\. It scores 4\.64 best\-of\-1 and 5\.68 best\-of\-3, therefore supporting prior findings and showing that an online approach for collecting feedback and improving representations is more effective\([Ross et al\., 2011](https://arxiv.org/html/2610.22518#bib.bib18);[Agarwal et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib19);[Xiong et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib20);[Shao et al\., 2024](https://arxiv.org/html/2610.22518#bib.bib14)\)\.

Table 1:Simulation fidelity across user representation generators\. We report Best\-of\-1 and Best\-of\-3 scores\. Subscripts indicate absolute improvements over the Qwen3\.5\-4B, and darker blue indicates stronger scores\.GRPO achieves 3\.57 best\-of\-1 and 4\.59 best\-of\-3, yielding only modest improvements over the base model\. Two factors may help explain this result\. First, scalar rewards provide limited information about what a user representation omitted or how it should be improved\([Song et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib17);[Hübotter et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib46);[Zhao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib15);[Sun et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib16)\)\. This limitation is especially costly in our setting because computing each reward requires an expensive multi\-turn simulation\. Second, we observe low within\-group reward diversity during training, with a median reward variance of 0\.029 \(mean 0\.048\)\. This can reduce the learning signal, and when all samples receive identical rewards, their advantages are zero and have no gradient signal\([Hübotter et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib46);[Zhao et al\., 2026](https://arxiv.org/html/2610.22518#bib.bib15)\)\.

#### General\-purpose LLMs struggle to generate user representations\.

We also investigate whether stronger general\-purpose LLMs generate better user representations without training and report results in Table[1](https://arxiv.org/html/2610.22518#S6.T1)\. Despite substantial differences in model scale and general capability, all models perform within a similar range\. GPT\-5\.5, the strongest off\-the\-shelf model, outperforms Qwen3\.5\-4B by only 0\.49 best\-of\-1 and 0\.48 best\-of\-3 points\. In contrast,UserRepGen\-4Bsurpasses GPT\-5\.5 and raises scores by 1\.05 and 1\.12 points\. These results suggest that user representation generation depends less on general model capability and more on explicitly learning what information is important for downstream simulation fidelity\.

### 6\.4How Does Training Change User Representations?

Table 2:Quantitative analysis of user representations\. We report the average word counts of the task, behavior and complete user representations, as well as ROUGE\-L precision against the source conversation\.We analyze how user representations change when optimized for downstream simulation fidelity\. Quantitatively, we measure their average length and ROUGE\-L precision\([Lin, 2004](https://arxiv.org/html/2610.22518#bib.bib27)\), which measures lexical overlap with the source conversation\. Table[2](https://arxiv.org/html/2610.22518#S6.T2)shows that the representation lengths stay within the same general range, with slight increase in the behavior descriptions\. ROUGE\-L precision decreases slightly, indicating that improvements in fidelity do not come from simply copying from the source conversation\.

We next qualitatively analyze the task and behavior descriptions\. After training, task descriptions become more standardized and comprehensive, more consistently capturing constraints introduced in later turns, shifts in direction, and multiple objectives pursued within a single conversation\. Behavior descriptions become more specific and actionable\. Rather than relying on broad characterizations such as “goal\-oriented” or “directive”, they describe observable patterns that a simulator can reproduce\. They also use more negative and contrastive instructions, such as specifying that a user does not provide all constraints upfront but instead introduces them incrementally\. Lastly, the descriptions capture dimensions that were previously overlooked, including how users respond to the assistant, formulate requests, communicate, provide context, and engage across turns\. Appendix[E](https://arxiv.org/html/2610.22518#A5)uses GPT\-5\.4 mini to analyze these observations and provides qualitative examples\.

## 7Broader Utility of Learned Representations

Conversation reconstruction provides an objective for learning and evaluating user representations based on simulation fidelity\. We next demonstrate their broader utility beyond reconstruction\.

### 7\.1Simulating Users in New Scenarios

One use case for user simulators is to use evidence from a user’s prior interactions to simulate how they might behave in new scenarios\. Our primary evaluation, however, evaluates a representation through reconstruction of the same conversation from which it was generated\. We therefore evaluate how faithfully a behavior description enables user simulation for unseen tasks\.

We select all 147 users from ThoughtTrace who have at least two conversations and do not appear in our training set\. For each user, let𝒞\\mathcal\{C\}denote the target conversation and𝒞′\\mathcal\{C\}^\{\\prime\}another conversation from the same user\. We generate representationsr=\(t,b\)∼Gθ​\(𝒞\)r=\(t,b\)\\sim G\_\{\\theta\}\(\\mathcal\{C\}\)andr′=\(t′,b′\)∼Gθ​\(𝒞′\)r^\{\\prime\}=\(t^\{\\prime\},b^\{\\prime\}\)\\sim G\_\{\\theta\}\(\\mathcal\{C\}^\{\\prime\}\)\. We follow Section[6\.1](https://arxiv.org/html/2610.22518#S6.SS1)and use GPT\-5\.4 mini as the user simulator, assistant and judge\. We condition it under four configurations while keeping task descriptionttfixed:

1. \\scriptsizeA⃝Same task only:Taskttfrom𝒞\\mathcal\{C\}without a behavior description\.
2. \\scriptsizeB⃝Same task, different user, different conversation:Taskttand behaviorb¯\\bar\{b\}from a randomly selected conversation by a different user\.
3. \\scriptsizeC⃝Same task, same user, different conversation:Taskttand behaviorb′b^\{\\prime\}from the same user’s other conversation𝒞′\\mathcal\{C\}^\{\\prime\}\.
4. \\scriptsizeD⃝Same task, same user, same conversation:Taskttand behaviorbbfrom𝒞\\mathcal\{C\}\.

![Refer to caption](https://arxiv.org/html/2610.22518v1/new_scenarios.png)Figure 4:Simulation fidelity when conditioned under configurations \\scriptsizeA⃝, \\scriptsizeB⃝, \\scriptsizeC⃝, and \\scriptsizeD⃝\. Each cell reports Best\-of\-3 scores\. Subscripts indicate absolute improvement relative to the condition immediately to the left\.For each configuration, the simulator interacts with the assistant to generate a simulated conversation𝒞~\\tilde\{\\mathcal\{C\}\}\. The judge compares the user behavior in𝒞~\\tilde\{\\mathcal\{C\}\}with that in𝒞\\mathcal\{C\}and assigns a fidelity score\. We generate three rollouts per configuration and report best\-of\-3 fidelity in Figure[4](https://arxiv.org/html/2610.22518#S7.F4)\.

#### \\scriptsizeB⃝≫\\gg\\scriptsizeA⃝: Behavior descriptions encourage realistic simulation\.

Adding a behavior description improves fidelity even when it is generated from a different user’s conversation\. The descriptions provide concrete guidance, such as how a user formulates requests or responds to the assistant\. This helps the simulator avoid defaulting to assistant\-like behaviors and be more representative of real users in general, despite not identifying the behavior of the particular user being simulated\. The larger gain for trained representation generators indicates more effective behavior descriptions\.

#### \\scriptsizeC⃝\>\>\\scriptsizeB⃝: Behavior descriptions improve simulation for unseen tasks\.

Condition \\scriptsizeC⃝ tests whether behavior extracted from one of a user’s conversations enables faithful simulation of the same user in another conversation with an unseen task\. \\scriptsizeC⃝ outperforms \\scriptsizeB⃝ for every trained representation generator, providing evidence that the descriptions capture user\-specific behaviors that generalize to new scenarios\. The improvements are modest, however, and remain well below \\scriptsizeD⃝, an upper bound using behavior extracted directly from the target conversation\.

### 7\.2Preserving Behavioral Diversity at the Population Level

We next evaluate user representations at the population level, asking whether the learned representations produce less homogeneous simulations that better preserve the behavioral diversity of the original conversations\. We measure the distributional gap between original and simulated user behaviors using the approach proposed by[Mehri et al\. \(2026b\)](https://arxiv.org/html/2610.22518#bib.bib11): \(1\) Qwen3\.5\-122B\-A10B\-FP8\([Team, 2026](https://arxiv.org/html/2610.22518#bib.bib23)\)describes the user behavior in each original and simulated conversation along six dimensions; \(2\) Qwen3\-Embedding\-8B\([Zhang et al\., 2025](https://arxiv.org/html/2610.22518#bib.bib24)\)embeds the resulting descriptions; \(3\) the embeddings are quantized usingkk\-means intok=500k=500clusters; \(4\) the cluster assignments for each conversation are used to construct discrete distributions for the original and simulated user behaviors; and \(5\) the two distributions are compared using Jensen–Shannon divergence \(JS\\mathrm\{JS\}\)\. See Appendix[F](https://arxiv.org/html/2610.22518#A6)for details\.

Table 3:Distributional gap between original and simulated user behaviors for our different representation generators when the simulator is conditioned on the task description alone or both the task and behavior descriptions\. We report \(JS\\mathrm\{JS\}\) divergence and Self\-BLEU \(SB\)\. Darker shades of blue and lower values indicate better performance\.For each representation generator, we generate a user representation for each of the 700 evaluation conversations\. Using GPT\-5\.4 mini as both the user simulator and assistant, we produce a corresponding simulated conversation and measure the distributional gap\. We additionally report Self\-BLEU\([Zhu et al\., 2018](https://arxiv.org/html/2610.22518#bib.bib25)\), which measures the lexical overlap between each simulated conversation with the other simulated conversations\. We conduct this evaluation under two configurations: conditioning the simulator on only the generated task description and conditioning it on both the task and behavior descriptions\. Results are reported in Table[3](https://arxiv.org/html/2610.22518#S7.T3)\.

Across both Task Only and Task \+ Behavior, the trained representation generators generally achieve lowerJS\\mathrm\{JS\}divergence and Self\-BLEU than the base model\. Moreover, conditioning the simulator on both the task and behavior descriptions instead of just on the task reduces both metrics for every representation generator\. LowerJS\\mathrm\{JS\}divergence indicates closer alignment between original and simulated distributions of user behavior, and lower Self\-BLEU indicates less lexical homogeneity among the simulated conversations\. Together, these results show that learned representations better preserve behavioral diversity at the population level\.

### 7\.3Distinguishing Users Across Conversations

Table 4:GPT\-5\.4 mini accuracy at distinguishing users across conversations\. Subscripts indicate performance compared to the full conversation baseline, and darker blue indicates higher accuracy\.Finally, we consider the task of distinguishing users across conversations\. We frame this as a multiple\-choice task: given a query and four candidate conversations, the goal is to select the candidate from the same user as the query\. We use the same 147 ThoughtTrace users from Section[7\.1](https://arxiv.org/html/2610.22518#S7.SS1)\. For each user, one conversation serves as the query and their other conversation serves as the correct candidate\. The remaining candidates are conversations from randomly selected other users\. We ask GPT\-5\.4 mini to select the correct candidate and report accuracy\. Random selection would yield 25% accuracy, whereas GPT\-5\.4 mini achieves 44\.22% accuracy\.

We next ask whether user representations can improve performance\. For each representation generator, we replace every conversation with its generated behavior description and ask GPT\-5\.4 mini to solve the same task\. Table[4](https://arxiv.org/html/2610.22518#S7.T4)reports the results\. Representations generated by the base model yield the lowest accuracy at 38\.78%, 5\.44% under the full conversation baseline\. GRPO and SFT narrow the gap, but also remain below the full conversation baseline\. OPSD and OSFT achieve the highest accuracies at 50\.34% and 51\.02%, exceeding the full conversation baseline by 6\.12% and 6\.80%\. These results demonstrate that the learned representations capture user\-specific characteristics that distinguish a user from other users and persist across conversations\.

## 8Conclusion

User representations are a bottleneck in user simulation\. A simulator conditioned on a representation can only reproduce the behaviors that the representation captures\. When relevant behaviors are missing, the simulator fills in the gaps with assumptions, often producing generic rather than user\-specific behavior\. We therefore presentUserRepGen\-4B, a representation generator that we optimize for downstream simulation fidelity using hindsight distillation\. Our results show that learned representations enable more faithful simulations of a particular user, improve simulation fidelity for unseen tasks, better preserve the population\-level diversity of real users, and better distinguish users across conversations\. Simulation fidelity therefore depends not only on the ability to generate realistic user behavior, but also on capturing the relevant information with user representations\.

## Acknowledgements

We would like to thank Matthew Ho, Joseph Suh, Saujas Vaduguru, Kshitish Ghate, Nikhil Sharma, Nishant Balepur, Ethan Mendes, and Xuhui Zhou for feedback at various stages of the projects, as well as researchers in the Deep Learning and AI Interaction and Learning groups at Microsoft Research, including Nebojsa Jojic, Baolin Peng, and Jihoon Tack\.

## AI Use Statement

In this work, generative AI tools were used to help with writing code and implementing methods, obtaining feedback on research methodology and experiments, supporting qualitative analysis of user representations, and assisting with interpreting results\. We did not use generative AI tools to develop conceptual frameworks, propose or refine hypotheses, or to directly clean and reformat datasets\. Additionally, we used generative AI tools to analyze existing literature, search for information, and improve writing and readability\. Writing was limited to expressing the authors’ intended content\. Mathematical claim formulation, proofs, and translations are not applicable to this work\. The use of generative AI tools was to support rather than replace the authors’ work\. All AI\-assisted work was reviewed and verified, and we take responsibility for the final content of this paper, including all text, claims, and artifacts produced with the aid of generative AI\.

## Ethics Statement

UserRepGen\-4Bis intended to support research on user simulation and the interactive training and evaluation of AI assistants\. More faithful simulations may help developers account for a broader range of user behaviors, but also introduce risks of privacy loss, mischaracterization, and misuse\. User representations are inferred from a single conversation and may retain sensitive information or make unsupported assumptions about an individual\. Our results in Section[7\.3](https://arxiv.org/html/2610.22518#S7.SS3)show that learned behavior descriptions can help link conversations from the same user, highlighting potential privacy or anonymity considerations\. AlthoughUserRepGen\-4Benables more faithful simulation of individual users, it should not be used to impersonate them or present simulated interactions as their own\. These simulations should not be used to replace real users\.

Our training and evaluation use datasets of real English conversations collected through publicly accessible chatbot interfaces, paid participant studies, and agent coding session records in public GitHub repositories\. We also use LLM\-generated simulated conversations\. These sources do not ensure representative coverage of user populations, and we do not confirm whether the observed improvements in simulation fidelity will generalize to other languages and user populations\. Furthermore, although our LLM judge was validated against human preferences, we acknowledge that its evaluations may still reflect model biases and inconsistencies\.

## Reproducibility Statement

UserRepGen\-4Bwill be made publicly available\. We describe the dataset sources, preprocessing criteria, and training and evaluation setup in Section[6\.1](https://arxiv.org/html/2610.22518#S6.SS1)\. Prompts for representation generation, simulation, and evaluation are provided in Appendix[C](https://arxiv.org/html/2610.22518#A3)\.

## References

- Agarwalet al\.\(2024\)R\. Agarwal, N\. Vieillard, Y\. Zhou, P\. Stanczyk, S\. Ramos Garea, M\. Geist, and O\. BachemOn\-policy distillation of language models: learning from self\-generated mistakes\.InInternational Conference on Learning Representations,B\. Kim, Y\. Yue, S\. Chaudhuri, K\. Fragkiadaki, M\. Khan, and Y\. Sun \(Eds\.\),Vol\.2024,pp\. 21246–21263\.External Links:[Link](https://proceedings.iclr.cc/paper_files/paper/2024/file/5be69a584901a26c521c2b51e40a4c20-Paper-Conference.pdf)Cited by:[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p2.1)\.
- Anthiset al\.\(2025\)J\. R\. Anthis, R\. Liu, S\. M\. Richardson, A\. C\. Kozlowski, B\. Koch, E\. Brynjolfsson, J\. Evans, and M\. S\. BernsteinPosition: LLM social simulations are a promising research method\.InForty\-second International Conference on Machine Learning Position Paper Track,External Links:[Link](https://openreview.net/forum?id=cRBg1dtj7o)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1)\.
- Argyleet al\.\(2023\)L\. P\. Argyle, E\. C\. Busby, N\. Fulda, J\. R\. Gubler, C\. Rytting, and D\. WingateOut of one, many: using language models to simulate human samples\.Political Analysis31\(3\),pp\. 337–351\.External Links:[Document](https://dx.doi.org/10.1017/pan.2023.2)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Baumannet al\.\(2026\)J\. Baumann, V\. Padmakumar, X\. Li, J\. Yang, D\. Yang, and S\. KoyejoSWE\-chat: coding agent interactions from real users in the wild\.External Links:2604\.20779,[Link](https://arxiv.org/abs/2604.20779)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1)\.
- Bueninget al\.\(2026\)T\. K\. Buening, J\. Hübotter, B\. Pásztor, I\. Shenfeld, G\. Ramponi, and A\. KrauseAligning language models from user interactions\.External Links:2603\.12273,[Link](https://arxiv.org/abs/2603.12273)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Chenet al\.\(2025\)R\. Chen, A\. Arditi, H\. Sleight, O\. Evans, and J\. LindseyPersona vectors: monitoring and controlling character traits in language models\.External Links:2507\.21509,[Link](https://arxiv.org/abs/2507.21509)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Chopraet al\.\(2026\)H\. Chopra, K\. Ghate, A\. Caliskan, T\. Kohno, C\. Shah, and N\. JaquesBeyond cooperative simulators: generating realistic user personas for robust evaluation of LLM agents\.InSecond Workshop on Social Simulation with LLMS: Fidelity in Applications,External Links:[Link](https://openreview.net/forum?id=cJVWKXCws0)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Geet al\.\(2025\)T\. Ge, X\. Chan, X\. Wang, D\. Yu, H\. Mi, and D\. YuScaling synthetic data creation with 1,000,000,000 personas\.External Links:2406\.20094,[Link](https://arxiv.org/abs/2406.20094)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1)\.
- Hübotteret al\.\(2026\)J\. Hübotter, F\. Lübeck, L\. D\. Behric, A\. Baumann, M\. Bagatella, D\. Marta, I\. Hakimi, I\. Shenfeld, T\. K\. Buening, C\. Guestrin, and A\. KrauseReinforcement learning via self\-distillation\.InForty\-third International Conference on Machine Learning,External Links:[Link](https://openreview.net/forum?id=QkfkxyRizZ)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1),[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p3.1)\.
- Jinet al\.\(2026\)C\. Jin, B\. Li, H\. Xie, C\. M\. Fang, T\. Li, S\. Longpre, H\. Gu, M\. Chen, and T\. ShuThoughtTrace: understanding user thoughts in real\-world llm interactions\.External Links:2605\.20087,[Link](https://arxiv.org/abs/2605.20087)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1)\.
- Kirket al\.\(2024\)H\. R\. Kirk, A\. Whitefield, P\. Röttger, A\. Bean, K\. Margatina, J\. Ciro, R\. Mosquera, M\. Bartolo, A\. Williams, H\. He, B\. Vidgen, and S\. HaleThe prism alignment dataset: what participatory, representative and individualised human feedback reveals about the subjective and multicultural alignment of large language models\.InAdvances in Neural Information Processing Systems,A\. Globerson, L\. Mackey, D\. Belgrave, A\. Fan, U\. Paquet, J\. Tomczak, and C\. Zhang \(Eds\.\),Vol\.37,pp\. 105236–105344\.External Links:[Document](https://dx.doi.org/10.52202/079017-3342),[Link](https://proceedings.neurips.cc/paper_files/paper/2024/file/be2e1b68b44f2419e19f6c35a1b8cf35-Paper-Datasets_and_Benchmarks_Track.pdf)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1)\.
- Liet al\.\(2025\)A\. Li, H\. Chen, H\. Namkoong, and T\. PengLLM generated persona is a promise with a catch\.InAdvances in Neural Information Processing Systems,D\. Belgrave, C\. Zhang, H\. Lin, R\. Pascanu, P\. Koniusz, M\. Ghassemi, and N\. Chen \(Eds\.\),Vol\.38, Main Conference,pp\.\.External Links:[Document](https://dx.doi.org/10.52202/085713-2226),[Link](https://proceedings.neurips.cc/paper_files/paper/2025/file/5fc150b75e8b509735564de16bc3527b-Paper-Position_Paper_Track.pdf)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Liet al\.\(2016\)J\. Li, M\. Galley, C\. Brockett, G\. Spithourakis, J\. Gao, and B\. DolanA persona\-based neural conversation model\.InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),K\. Erk and N\. A\. Smith \(Eds\.\),Berlin, Germany,pp\. 994–1003\.External Links:[Link](https://aclanthology.org/P16-1094/),[Document](https://dx.doi.org/10.18653/v1/P16-1094)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Liet al\.\(2026\)X\. Li, T\. Lyu, Y\. Li, Y\. Ma, P\. Li, L\. Li, Q\. Guo, D\. Lin, and K\. ChenWhat and when to distill: selective hindsight distillation for multi\-turn agents\.External Links:2605\.19447,[Link](https://arxiv.org/abs/2605.19447)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Lin \(2004\)C\. LinRouge: a package for automatic evaluation of summaries\.InText summarization branches out,pp\. 74–81\.Cited by:[§6\.4](https://arxiv.org/html/2610.22518#S6.SS4.p1.1)\.
- Linet al\.\(2026\)V\. Lin, T\. Yun, M\. Mataric, J\. Canny, A\. Gretton, and A\. N\. D’AmourThe illusion of intervention: your LLM\-simulated experiment is an observational study\.InICML 2026 Statistical Frameworks for Uncertainty in Agentic Systems,External Links:[Link](https://openreview.net/forum?id=7EZrgYo07P)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Liuet al\.\(2026a\)H\. Liu, Y\. Zhang, X\. Li, B\. Lyu, and J\. ShangHERO: hindsight\-enhanced reflection from environment observations for agentic self\-distillation\.External Links:2606\.11559,[Link](https://arxiv.org/abs/2606.11559)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Liuet al\.\(2026b\)X\. Liu, R\. Khaziev, H\. Nayyeri, E\. Yilmaz, C\. Peris, and H\. ThadakamallaBeyond individual personas: aligning synthetic dialogue to population\-level behavior distributions\.External Links:2606\.07893,[Link](https://arxiv.org/abs/2606.07893)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Liuet al\.\(2023\)Y\. Liu, X\. Jiang, Y\. Yin, Y\. Wang, F\. Mi, Q\. Liu, X\. Wan, and B\. WangOne cannot stand for everyone\! leveraging multiple user simulators to train task\-oriented dialogue systems\.InProceedings of the 61st Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),A\. Rogers, J\. Boyd\-Graber, and N\. Okazaki \(Eds\.\),Toronto, Canada,pp\. 1–21\.External Links:[Link](https://aclanthology.org/2023.acl-long.1/),[Document](https://dx.doi.org/10.18653/v1/2023.acl-long.1)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1)\.
- Mehriet al\.\(2026a\)S\. Mehri, P\. Kargupta, T\. August, and D\. Hakkani\-TürMultiSessionCollab: learning user preferences with memory to improve long\-term collaboration\.External Links:2601\.02702,[Link](https://arxiv.org/abs/2601.02702)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1)\.
- Mehriet al\.\(2026b\)S\. Mehri, P\. Laban, S\. Shashidhar, M\. Abdulhai, S\. Levine, M\. Galley, and D\. Hakkani\-TürMeasuring and mitigating the distributional gap between real and simulated user behaviors\.arXiv preprint arXiv:2605\.07847\.Cited by:[Appendix F](https://arxiv.org/html/2610.22518#A6.p1.1),[§1](https://arxiv.org/html/2610.22518#S1.p1.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1),[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1),[§7\.2](https://arxiv.org/html/2610.22518#S7.SS2.p1.1)\.
- Mehriet al\.\(2026c\)S\. Mehri, X\. Yang, T\. Kim, G\. Tur, S\. Mehri, and D\. Hakkani\-TürGoal alignment in llm\-based user simulators for conversational ai\.Transactions of the Association for Computational Linguistics14,pp\. 852–876\.Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px3.p1.1)\.
- Mendeset al\.\(2026\)E\. Mendes, J\. Park, and A\. RitterMaking expert reasoning learnable with self\-distillation\.InForty\-third International Conference on Machine Learning,External Links:[Link](https://openreview.net/forum?id=JG6f02X29a)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Naouset al\.\(2026\)T\. Naous, P\. Laban, W\. Xu, and J\. NevilleFlipping the dialogue: training and evaluating user language models\.InThe Fourteenth International Conference on Learning Representations,External Links:[Link](https://openreview.net/forum?id=ykSmkVqzn4)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p2.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.p2.1)\.
- Ninget al\.\(2025\)L\. Ning, L\. Liu, J\. Wu, N\. Wu, D\. Berlowitz, S\. Prakash, B\. Green, S\. O’Banion, and J\. XieUser\-llm: efficient llm contextualization with user embeddings\.InCompanion Proceedings of the ACM on Web Conference 2025,WWW ’25,New York, NY, USA,pp\. 1219–1223\.External Links:ISBN 9798400713316,[Link](https://doi.org/10.1145/3701716.3715463),[Document](https://dx.doi.org/10.1145/3701716.3715463)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Parket al\.\(2023\)J\. S\. Park, J\. O’Brien, C\. J\. Cai, M\. R\. Morris, P\. Liang, and M\. S\. BernsteinGenerative agents: interactive simulacra of human behavior\.InProceedings of the 36th Annual ACM Symposium on User Interface Software and Technology,UIST ’23,New York, NY, USA\.External Links:ISBN 9798400701320,[Link](https://doi.org/10.1145/3586183.3606763),[Document](https://dx.doi.org/10.1145/3586183.3606763)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1)\.
- Parket al\.\(2024\)J\. S\. Park, C\. Q\. Zou, J\. Kamphorst, N\. Egan, A\. Shaw, B\. Mako Hill, C\. Cai, M\. Ringel Morris, P\. Liang, R\. Willer, and M\. S\. BernsteinLLM Agents Grounded in Self\-Reports Enable General\-Purpose Simulation of Individuals\.arXiv e\-prints,pp\. arXiv:2411\.10109\.External Links:[Document](https://dx.doi.org/10.48550/arXiv.2411.10109),2411\.10109Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Rosset al\.\(2011\)S\. Ross, G\. Gordon, and D\. BagnellA reduction of imitation learning and structured prediction to no\-regret online learning\.InProceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics,G\. Gordon, D\. Dunson, and M\. Dudík \(Eds\.\),Proceedings of Machine Learning Research, Vol\.15,Fort Lauderdale, FL, USA,pp\. 627–635\.External Links:[Link](https://proceedings.mlr.press/v15/ross11a.html)Cited by:[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p2.1)\.
- Schatzmannet al\.\(2007\)J\. Schatzmann, B\. Thomson, K\. Weilhammer, H\. Ye, and S\. YoungAgenda\-based user simulation for bootstrapping a POMDP dialogue system\.InHuman Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Companion Volume, Short Papers,C\. Sidner, T\. Schultz, M\. Stone, and C\. Zhai \(Eds\.\),Rochester, New York,pp\. 149–152\.External Links:[Link](https://aclanthology.org/N07-2038/)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1)\.
- Shaoet al\.\(2024\)Z\. Shao, P\. Wang, Q\. Zhu, R\. Xu, J\. Song, X\. Bi, H\. Zhang, M\. Zhang, Y\. K\. Li, Y\. Wu, and D\. GuoDeepSeekMath: pushing the limits of mathematical reasoning in open language models\.External Links:2402\.03300,[Link](https://arxiv.org/abs/2402.03300)Cited by:[2nd item](https://arxiv.org/html/2610.22518#S6.I1.i2.p1.1.1),[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p2.1)\.
- Shiet al\.\(2026\)T\. Shi, S\. Chen, B\. Jiang, L\. Song, L\. Yang, and J\. ZhaoExperiential reinforcement learning\.InICLR 2026 Workshop on Lifelong Agents: Learning, Aligning, Evolving,External Links:[Link](https://openreview.net/forum?id=OSYyL6hqdP)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Songet al\.\(2026\)Y\. Song, L\. Chen, F\. Tajwar, R\. Munos, D\. Pathak, D\. Bagnell, A\. Singh, and A\. ZanetteExpanding the capabilities of reinforcement learning via text feedback\.InThe 1st Workshop on Scaling Post\-training for LLMs,External Links:[Link](https://openreview.net/forum?id=om12baYnKM)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1),[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p3.1)\.
- Sunet al\.\(2026\)W\. Sun, X\. Zhou, J\. Liu, W\. Du, H\. Sun, Y\. Xie, Q\. Ma, S\. Chen, M\. Wan, L\. Yang, P\. Zhou, S\. Wu, S\. Welleck, G\. Neubig, Y\. Yang, and M\. SapReinforcing human behavior simulation via verbal feedback\.External Links:2605\.20506,[Link](https://arxiv.org/abs/2605.20506)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1),[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p3.1)\.
- Teamet al\.\(2025\)G\. Team, A\. Kamath, J\. Ferret, S\. Pathak, N\. Vieillard, R\. Merhej, S\. Perrin, T\. Matejovicova, A\. Ramé, M\. Rivière, L\. Rouillard, T\. Mesnard, G\. Cideron, J\. Grill, S\. Ramos, E\. Yvinec, M\. Casbon, E\. Pot, I\. Penchev, G\. Liu, F\. Visin, K\. Kenealy, L\. Beyer, X\. Zhai, A\. Tsitsulin, R\. Busa\-Fekete, A\. Feng, N\. Sachdeva, B\. Coleman, Y\. Gao, B\. Mustafa, I\. Barr, E\. Parisotto, D\. Tian, M\. Eyal, C\. Cherry, J\. Peter, D\. Sinopalnikov, S\. Bhupatiraju, R\. Agarwal, M\. Kazemi, D\. Malkin, R\. Kumar, D\. Vilar, I\. Brusilovsky, J\. Luo, A\. Steiner, A\. Friesen, A\. Sharma, A\. Sharma, A\. M\. Gilady, A\. Goedeckemeyer, A\. Saade, A\. Feng, A\. Kolesnikov, A\. Bendebury, A\. Abdagic, A\. Vadi, A\. György, A\. S\. Pinto, A\. Das, A\. Bapna, A\. Miech, A\. Yang, A\. Paterson, A\. Shenoy, A\. Chakrabarti, B\. Piot, B\. Wu, B\. Shahriari, B\. Petrini, C\. Chen, C\. L\. Lan, C\. A\. Choquette\-Choo, C\. Carey, C\. Brick, D\. Deutsch, D\. Eisenbud, D\. Cattle, D\. Cheng, D\. Paparas, D\. S\. Sreepathihalli, D\. Reid, D\. Tran, D\. Zelle, E\. Noland, E\. Huizenga, E\. Kharitonov, F\. Liu, G\. Amirkhanyan, G\. Cameron, H\. Hashemi, H\. Klimczak\-Plucińska, H\. Singh, H\. Mehta, H\. T\. Lehri, H\. Hazimeh, I\. Ballantyne, I\. Szpektor, I\. Nardini, J\. Pouget\-Abadie, J\. Chan, J\. Stanton, J\. Wieting, J\. Lai, J\. Orbay, J\. Fernandez, J\. Newlan, J\. Ji, J\. Singh, K\. Black, K\. Yu, K\. Hui, K\. Vodrahalli, K\. Greff, L\. Qiu, M\. Valentine, M\. Coelho, M\. Ritter, M\. Hoffman, M\. Watson, M\. Chaturvedi, M\. Moynihan, M\. Ma, N\. Babar, N\. Noy, N\. Byrd, N\. Roy, N\. Momchev, N\. Chauhan, N\. Sachdeva, O\. Bunyan, P\. Botarda, P\. Caron, P\. K\. Rubenstein, P\. Culliton, P\. Schmid, P\. G\. Sessa, P\. Xu, P\. Stanczyk, P\. Tafti, R\. Shivanna, R\. Wu, R\. Pan, R\. Rokni, R\. Willoughby, R\. Vallu, R\. Mullins, S\. Jerome, S\. Smoot, S\. Girgin, S\. Iqbal, S\. Reddy, S\. Sheth, S\. Põder, S\. Bhatnagar, S\. R\. Panyam, S\. Eiger, S\. Zhang, T\. Liu, T\. Yacovone, T\. Liechty, U\. Kalra, U\. Evci, V\. Misra, V\. Roseberry, V\. Feinberg, V\. Kolesnikov, W\. Han, W\. Kwon, X\. Chen, Y\. Chow, Y\. Zhu, Z\. Wei, Z\. Egyed, V\. Cotruta, M\. Giang, P\. Kirk, A\. Rao, K\. Black, N\. Babar, J\. Lo, E\. Moreira, L\. G\. Martins, O\. Sanseviero, L\. Gonzalez, Z\. Gleicher, T\. Warkentin, V\. Mirrokni, E\. Senter, E\. Collins, J\. Barral, Z\. Ghahramani, R\. Hadsell, Y\. Matias, D\. Sculley, S\. Petrov, N\. Fiedel, N\. Shazeer, O\. Vinyals, J\. Dean, D\. Hassabis, K\. Kavukcuoglu, C\. Farabet, E\. Buchatskaya, J\. Alayrac, R\. Anil, Dmitry, Lepikhin, S\. Borgeaud, O\. Bachem, A\. Joulin, A\. Andreev, C\. Hardin, R\. Dadashi, and L\. HussenotGemma 3 technical report\.External Links:2503\.19786,[Link](https://arxiv.org/abs/2503.19786)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px2.p1.1)\.
- Team \(2026\)Q\. TeamQwen3\.5: towards native multimodal agents\.External Links:[Link](https://qwen.ai/blog?id=qwen3.5)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px2.p1.1),[§7\.2](https://arxiv.org/html/2610.22518#S7.SS2.p1.1)\.
- Wanget al\.\(2026a\)H\. Wang, G\. Wang, H\. Xiao, Y\. Zhou, Y\. Pan, J\. Wang, K\. Xu, Y\. Wen, X\. Ruan, X\. Chen, and H\. QiSkill\-sd: skill\-conditioned self\-distillation for multi\-turn llm agents\.External Links:2604\.10674,[Link](https://arxiv.org/abs/2604.10674)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Wanget al\.\(2025\)K\. Wang, X\. Li, S\. Yang, L\. Zhou, F\. Jiang, and H\. LiKnow you first and be you better: modeling human\-like user simulators via implicit profiles\.InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),W\. Che, J\. Nabende, E\. Shutova, and M\. T\. Pilehvar \(Eds\.\),Vienna, Austria,pp\. 21082–21107\.External Links:[Link](https://aclanthology.org/2025.acl-long.1025/),[Document](https://dx.doi.org/10.18653/v1/2025.acl-long.1025),ISBN 979\-8\-89176\-251\-0Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1)\.
- Wanget al\.\(2026b\)S\. Wang, G\. Li, Z\. Yang, and Y\. GaoHindsight hint distillation: scaffolded reasoning for swe agents from cot\-free answers\.External Links:2605\.11556,[Link](https://arxiv.org/abs/2605.11556)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Wanget al\.\(2026c\)Y\. S\. Wang, C\. E\. Zhang, L\. Qiu, Z\. He, P\. Li, A\. Pentland, R\. P\. Levy, and Y\. KimLearning user simulators with turing rewards\.External Links:2606\.19336,[Link](https://arxiv.org/abs/2606.19336)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1)\.
- Wuet al\.\(2026\)S\. Wu, E\. Choi, A\. Khatua, Z\. Wang, J\. He\-Yueya, T\. C\. Weerasooriya, W\. Wei, D\. Yang, J\. Leskovec, and J\. ZouHumanlm: simulating users with state alignment beats response imitation\.arXiv preprint arXiv:2603\.03303\.Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p2.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.p2.1)\.
- Wuet al\.\(2025\)S\. Wu, M\. Galley, B\. Peng, H\. Cheng, G\. Li, Y\. Dou, W\. Cai, J\. Zou, J\. Leskovec, and J\. GaoCollabLLM: from passive responders to active collaborators\.InForty\-second International Conference on Machine Learning,External Links:[Link](https://openreview.net/forum?id=DmH4HHVb3y)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1)\.
- Xiaoet al\.\(2026\)Y\. Xiao, V\. J\. Zhang, C\. Yang, N\. Ma, W\. Xuan, and J\. HuangThe chameleon’s limit: investigating persona collapse and homogenization in large language models\.External Links:2604\.24698,[Link](https://arxiv.org/abs/2604.24698)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1)\.
- Xionget al\.\(2024\)W\. Xiong, H\. Dong, C\. Ye, Z\. Wang, H\. Zhong, H\. Ji, N\. Jiang, and T\. ZhangIterative preference learning from human feedback: bridging theory and practice for RLHF under KL\-constraint\.InProceedings of the 41st International Conference on Machine Learning,R\. Salakhutdinov, Z\. Kolter, K\. Heller, A\. Weller, N\. Oliver, J\. Scarlett, and F\. Berkenkamp \(Eds\.\),Proceedings of Machine Learning Research, Vol\.235,pp\. 54715–54754\.External Links:[Link](https://proceedings.mlr.press/v235/xiong24a.html)Cited by:[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p2.1)\.
- Yaoet al\.\(2024\)S\. Yao, N\. Shinn, P\. Razavi, and K\. Narasimhanτ\\tau\-Bench: a benchmark for tool\-agent\-user interaction in real\-world domains\.External Links:2406\.12045,[Link](https://arxiv.org/abs/2406.12045)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1)\.
- Yeoet al\.\(2026\)W\. Yeo, Y\. Choi, T\. Ki, and S\. J\. HwangHINT\-sd: targeted hindsight self\-distillation for long\-horizon agents\.External Links:2605\.17873,[Link](https://arxiv.org/abs/2605.17873)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1)\.
- Zhanget al\.\(2018\)S\. Zhang, E\. Dinan, J\. Urbanek, A\. Szlam, D\. Kiela, and J\. WestonPersonalizing dialogue agents: I have a dog, do you have pets too?\.InProceedings of the 56th Annual Meeting of the Association for Computational Linguistics \(Volume 1: Long Papers\),I\. Gurevych and Y\. Miyao \(Eds\.\),Melbourne, Australia,pp\. 2204–2213\.External Links:[Link](https://aclanthology.org/P18-1205/),[Document](https://dx.doi.org/10.18653/v1/P18-1205)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px2.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.SS0.SSS0.Px1.p1.1)\.
- Zhanget al\.\(2025\)Y\. Zhang, M\. Li, D\. Long, X\. Zhang, H\. Lin, B\. Yang, P\. Xie, A\. Yang, D\. Liu, J\. Lin, F\. Huang, and J\. ZhouQwen3 embedding: advancing text embedding and reranking through foundation models\.External Links:2506\.05176,[Link](https://arxiv.org/abs/2506.05176)Cited by:[§7\.2](https://arxiv.org/html/2610.22518#S7.SS2.p1.1)\.
- Zhaoet al\.\(2026\)S\. Zhao, Z\. Xie, M\. Liu, J\. Huang, G\. Pang, F\. Chen, and A\. GroverSelf\-distilled reasoner: on\-policy self\-distillation for large language models\.InForty\-third International Conference on Machine Learning,External Links:[Link](https://openreview.net/forum?id=Jpxfof0EaS)Cited by:[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px3.p1.1),[3rd item](https://arxiv.org/html/2610.22518#S6.I1.i3.p1.1.1),[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px2.p1.1),[§6\.3](https://arxiv.org/html/2610.22518#S6.SS3.p3.1)\.
- Zhaoet al\.\(2024\)W\. Zhao, X\. Ren, J\. Hessel, C\. Cardie, Y\. Choi, and Y\. DengWildChat: 1m chatGPT interaction logs in the wild\.InThe Twelfth International Conference on Learning Representations,External Links:[Link](https://openreview.net/forum?id=Bl8u7ZRlbM)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1)\.
- Zhenget al\.\(2024\)L\. Zheng, W\. Chiang, Y\. Sheng, T\. Li, S\. Zhuang, Z\. Wu, Y\. Zhuang, Z\. Li, Z\. Lin, E\. Xing, J\. E\. Gonzalez, I\. Stoica, and H\. ZhangLMSYS\-chat\-1m: a large\-scale real\-world llm conversation dataset\.InInternational Conference on Learning Representations,B\. Kim, Y\. Yue, S\. Chaudhuri, K\. Fragkiadaki, M\. Khan, and Y\. Sun \(Eds\.\),Vol\.2024,pp\. 22225–22257\.External Links:[Link](https://proceedings.iclr.cc/paper_files/paper/2024/file/5f9bfdfe3685e4ccdbc0e7fb29cccf2a-Paper-Conference.pdf)Cited by:[§6\.1](https://arxiv.org/html/2610.22518#S6.SS1.SSS0.Px1.p1.1)\.
- Zhouet al\.\(2026\)X\. Zhou, W\. Sun, W\. Du, J\. Liu, H\. Sun, Q\. Ma, T\. Wu, Y\. Yang, and M\. SapOdysSim: building foundation models for human behavior simulation\.arXiv preprint arXiv:2606\.14199\.Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p2.1),[§2](https://arxiv.org/html/2610.22518#S2.SS0.SSS0.Px1.p1.1),[§3](https://arxiv.org/html/2610.22518#S3.p2.1)\.
- Zhouet al\.\(2024a\)X\. Zhou, H\. Zhu, L\. Mathur, R\. Zhang, H\. Yu, Z\. Qi, L\. Morency, Y\. Bisk, D\. Fried, G\. Neubig, and M\. SapSOTOPIA: interactive evaluation for social intelligence in language agents\.InInternational Conference on Learning Representations,B\. Kim, Y\. Yue, S\. Chaudhuri, K\. Fragkiadaki, M\. Khan, and Y\. Sun \(Eds\.\),Vol\.2024,pp\. 40975–41019\.External Links:[Link](https://proceedings.iclr.cc/paper_files/paper/2024/file/b3075b88e583a0e98d8b24338a613060-Paper-Conference.pdf)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1)\.
- Zhouet al\.\(2024b\)Y\. Zhou, A\. Zanette, J\. Pan, S\. Levine, and A\. KumarArCHer: training language model agents via hierarchical multi\-turn RL\.InForty\-first International Conference on Machine Learning,External Links:[Link](https://openreview.net/forum?id=b6rA0kAHT1)Cited by:[§1](https://arxiv.org/html/2610.22518#S1.p1.1)\.
- Zhuet al\.\(2018\)Y\. Zhu, S\. Lu, L\. Zheng, J\. Guo, W\. Zhang, J\. Wang, and Y\. YuTexygen: a benchmarking platform for text generation models\.InThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval,SIGIR ’18,New York, NY, USA,pp\. 1097–1100\.External Links:ISBN 9781450356572,[Link](https://doi.org/10.1145/3209978.3210080),[Document](https://dx.doi.org/10.1145/3209978.3210080)Cited by:[§7\.2](https://arxiv.org/html/2610.22518#S7.SS2.p2.1)\.

## Appendix AHindsight Distillation Algorithm

Algorithm[1](https://arxiv.org/html/2610.22518#alg1)presents our hindsight distillation algorithm\. This algorithm distills hindsight feedback\-conditioned improvements into a representation generator model, enabling it to generate improved representations directly from an input conversation\.

Algorithm 1Hindsight Distillation1:input:representation generator

GθG\_\{\\theta\}; conversation dataset

𝒟\\mathcal\{D\}; ensemble of simulation environments

ℰ=\{\(Uk,Ak,Jk\)\}k=1K\\mathcal\{E\}=\\\{\(U\_\{k\},A\_\{k\},J\_\{k\}\)\\\}\_\{k=1\}^\{K\}; refinement rounds

II
2:repeat

3:sample a conversation

𝒞∼𝒟\\mathcal\{C\}\\sim\\mathcal\{D\}\.

4:sample an initial user representation

r\(0\)∼Gθ\(⋅∣𝒞\)r^\{\(0\)\}\\sim G\_\{\\theta\}\(\\cdot\\mid\\mathcal\{C\}\)\.

5:for

i=0,…,I−1i=0,\\ldots,I\-1do

6:for all

ek=\(Uk,Ak,Jk\)∈ℰe\_\{k\}=\(U\_\{k\},A\_\{k\},J\_\{k\}\)\\in\\mathcal\{E\}do

7:roll out

𝒞~k\(i\)∼rollout\(Uk\(⋅∣r\(i\)\),Ak\)\\tilde\{\\mathcal\{C\}\}\_\{k\}^\{\(i\)\}\\sim\\operatorname\{rollout\}\(U\_\{k\}\(\\cdot\\mid r^\{\(i\)\}\),A\_\{k\}\)\.

8:generate hindsight feedback

fk\(i\)∼Jk\(⋅∣𝒞,𝒞~k\(i\)\)f\_\{k\}^\{\(i\)\}\\sim J\_\{k\}\(\\cdot\\mid\\mathcal\{C\},\\tilde\{\\mathcal\{C\}\}\_\{k\}^\{\(i\)\}\)\.

9:endfor

10:collect feedback

f\(i\)=\(f1\(i\),…,fK\(i\)\)f^\{\(i\)\}=\(f\_\{1\}^\{\(i\)\},\\ldots,f\_\{K\}^\{\(i\)\}\)\.

11:sample a refined user representation

r\(i\+1\)∼Gθ\(⋅∣𝒞,r\(i\),f\(i\)\)r^\{\(i\+1\)\}\\sim G\_\{\\theta\}\(\\cdot\\mid\\mathcal\{C\},r^\{\(i\)\},f^\{\(i\)\}\)\.

12:endfor

13:update

θ\\thetawith gradient descent on

ℒ⁡\(θ\)\\mathcal\{L\}\(\\theta\)to predict

r\(I\)r^\{\(I\)\}from

𝒞\\mathcal\{C\}alone\.

14:untilconverged

## Appendix BHuman Validation of LLM Judge

In this section, we validate whether GPT\-5\.4 mini’s fidelity scores align with human judgements\. We construct a human\-labeled validation set of 100 original conversations from our evaluation set, each paired with two simulations generated using representations from Qwen3\.5\-4B andUserRepGen\-4B, with GPT\-5\.4 mini as both user simulator and assistant\. We select only pairs for which the LLM judge assigns unequal fidelity scores\. For each pair, presented in randomized order and without its source or scores, we label the simulation whose user behavior is more faithful to the original user\. We then measure how often the judge’s ranking agrees with these labels\.

The judge’s ranking agrees with our labels on 88% of pairs\. As shown in Table[5](https://arxiv.org/html/2610.22518#A2.T5), agreement increases with the absolute difference between the two fidelity scores, from 80\.5% when the gap is one point to 97\.4% when the gap is at least three points\.

Table 5:Agreement between our manually labeled preferences and the LLM judge\. The score gap is the absolute difference between the two simulated conversations’ overall fidelity scores\.These results support the LLM judge as a measure of simulation fidelity\. Moreover, our evaluations for population\-level diversity and distinguishing users across conversations in Sections[7\.2](https://arxiv.org/html/2610.22518#S7.SS2)and[7\.3](https://arxiv.org/html/2610.22518#S7.SS3)provide complementary evidence, by demonstrating that representations optimized for simulation fidelity improve performance on downstream tasks that do not rely on the fidelity judge\.

## Appendix CPrompts

User Representation Generation Prompt[⬇](data:text/plain;base64,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)YouareanexpertatanalyzinguserbehaviorinconversationsbetweenauserandanAIassistant\.Giventheconversationbelow,yourtaskistoproducearepresentationoftheuser\.Treattheconversationasevidenceabouttheuser,anduseittoinferhowtheygenerallybehaveandhowtheywouldtypicallyactinotherconversations\.Focusonstable,generaltendenciessuchas:howtheyphraseandspecifyrequests,howtheydecomposegoalsacrossturns,howtheyrespondtoandgivefeedbacktotheassistant,howandwhentheyprovidecontext,theircommunicationstyle,thedialogactstheyuse,andtheirdegreeanddepthofengagement\.Keepthisdescriptiongeneral:donotincludetopic\-specificdetailsorcontentfromtheconversation,donotquoteorcloselyparaphrasefullusermessages,anddonotrefertospecificmomentsinthisconversation\(e\.g\."here","inthisconversation","whentheyaskedaboutX"\)\.Shortdistinctivewordsorphrasesmaybequotedsparinglywhentheyareusefulexamplesoftheuser’scommunicationstyle\.Describetheirpatternsinyourowngeneraltermsinstead\.Separately,youwillalsodescribethetasktheuseristryingtoaccomplish\.Hereyoumayincludetopic\-specificdetailsaboutwhattheuserisdoingintheconversation\.Conversation:\{conversation\_history\}OutputasinglevalidJSONobjectoftheform\{\{"task":"\.\.\.","user\_behavior":"\.\.\."\}\}\.Donotwraptheoutputinmarkdownoraddanyothertext\.

Improving User Representation Prompt[⬇](data:text/plain;base64,WW91IGFyZSBhbiBleHBlcnQgYXQgYW5hbHl6aW5nIHVzZXIgYmVoYXZpb3IgaW4gY29udmVyc2F0aW9ucyBiZXR3ZWVuIGEgdXNlciBhbmQgYW4gQUkgYXNzaXN0YW50LiBZb3UgaGF2ZSBhIHJlcHJlc2VudGF0aW9uIG9mIHRoZSB1c2VyIGJhc2VkIG9uIHRoZSBjb252ZXJzYXRpb24gYmVsb3cuIFdlIHVzZWQgdGhhdCByZXByZXNlbnRhdGlvbiB0byBzaW11bGF0ZSBhIHVzZXIsIGFuZCB0aGVuIGNvbGxlY3RlZCBmZWVkYmFjayBmcm9tIGEganVkZ2UuIFlvdXIgdGFzayBpcyB0byBpbmNvcnBvcmF0ZSB0aGF0IGZlZWRiYWNrIGFuZCBnZW5lcmF0ZSBhbiBpbXByb3ZlZCB1c2VyIHJlcHJlc2VudGF0aW9uLgoKVGhlIHVzZXIgcmVwcmVzZW50YXRpb24gY29uc2lzdHMgb2YgYSB0YXNrIGFuZCB1c2VyX2JlaGF2aW9yIGZpZWxkLiBUaGUgdGFzayBmaWVsZCBkZXNjcmliZXMgd2hhdCB0aGUgdXNlciBpcyB0cnlpbmcgdG8gYWNjb21wbGlzaCwgYW5kIGNhbiBpbmNsdWRlIHRvcGljLXNwZWNpZmljIGRldGFpbHMgYWJvdXQgd2hhdCB0aGUgdXNlciBpcyBkb2luZyBpbiB0aGUgY29udmVyc2F0aW9uLiBUaGUgdXNlcl9iZWhhdmlvciBmaWVsZCBkZXNjcmliZXMgaG93IHRoZXkgZ2VuZXJhbGx5IGJlaGF2ZSBhbmQgaG93IHRoZXkgd291bGQgdHlwaWNhbGx5IGFjdCBpbiBjb252ZXJzYXRpb25zLiBJdCBmb2N1c2VzIG9uIHN0YWJsZSwgZ2VuZXJhbCB0ZW5kZW5jaWVzIHN1Y2ggYXM6IGhvdyB0aGV5IHBocmFzZSBhbmQgc3BlY2lmeSByZXF1ZXN0cywgaG93IHRoZXkgZGVjb21wb3NlIGdvYWxzIGFjcm9zcyB0dXJucywgaG93IHRoZXkgcmVzcG9uZCB0byBhbmQgZ2l2ZSBmZWVkYmFjayB0byB0aGUgYXNzaXN0YW50LCBob3cgYW5kIHdoZW4gdGhleSBwcm92aWRlIGNvbnRleHQsIHRoZWlyIGNvbW11bmljYXRpb24gc3R5bGUsIHRoZSBkaWFsb2cgYWN0cyB0aGV5IHVzZSwgYW5kIHRoZWlyIGRlZ3JlZSBhbmQgZGVwdGggb2YgZW5nYWdlbWVudC4gVGhpcyBkZXNjcmlwdGlvbiBzaG91bGQgYmUgZ2VuZXJhbDogaXQgc2hvdWxkIG5vdCBpbmNsdWRlIHRvcGljLXNwZWNpZmljIGRldGFpbHMgb3IgY29udGVudCBmcm9tIHRoZSBjb252ZXJzYXRpb24sIGl0IHNob3VsZCBub3QgcXVvdGUgb3IgY2xvc2VseSBwYXJhcGhyYXNlIGZ1bGwgdXNlciBtZXNzYWdlcywgYW5kIGl0IHNob3VsZCBub3QgcmVmZXIgdG8gc3BlY2lmaWMgbW9tZW50cyBpbiB0aGlzIGNvbnZlcnNhdGlvbiAoZS5nLiAiaGVyZSIsICJpbiB0aGlzIGNvbnZlcnNhdGlvbiIsICJ3aGVuIHRoZXkgYXNrZWQgYWJvdXQgWCIpLiBTaG9ydCBkaXN0aW5jdGl2ZSB3b3JkcyBvciBwaHJhc2VzIG1heSBiZSBxdW90ZWQgc3BhcmluZ2x5IHdoZW4gdGhleSBhcmUgdXNlZnVsIGV4YW1wbGVzIG9mIHRoZSB1c2VyJ3MgY29tbXVuaWNhdGlvbiBzdHlsZS4gSXQgc2hvdWxkIGRlc2NyaWJlIHRoZWlyIHBhdHRlcm5zIGluIGdlbmVyYWwgdGVybXMuCgpBZGRpdGlvbmFsIGd1aWRlbGluZXMgZm9yIGltcHJvdmluZyB0aGUgcmVwcmVzZW50YXRpb246Ci0gT3B0aW1pemUgZm9yIGJlaGF2aW9yYWwgc2ltdWxhdGlvbiwgbm90IGVmZmljaWVudCB0YXNrIGNvbXBsZXRpb246IHByZXNlcnZlIG11bHRpLXR1cm4gcmVwZXRpdGlvbiwgaW5jcmVtZW50YWwgZm9sbG93LXVwcywgdmVyYm9zZSBjb25zdHJhaW50cywgYW5kIGluZWZmaWNpZW50IGludGVyYWN0aW9uIHBhdHRlcm5zIHdoZW4gdGhleSBhcHBlYXIgaW4gdGhlIHJlYWwgdXNlci4KLSBNYWtlIHVzZXJfYmVoYXZpb3Igb3BlcmF0aW9uYWw6IHNwZWNpZnkgd2hhdCB0aGUgc2ltdWxhdG9yIHNob3VsZCBkbyBhbmQgc2hvdWxkIG5vdCBkbywgaW5jbHVkaW5nIG5lZ2F0aXZlIGNvbnN0cmFpbnRzIGZyb20ganVkZ2UgZmVlZGJhY2sgKGUuZy4gZG8gbm90IHRlcm1pbmF0ZSBlYXJseSwgY29tYmluZSB0dXJucywgb3Zlci1wb2xpc2gsIGNoYW5nZSBmb3JtYXR0aW5nIHN0eWxlLCBvciBzb2x2ZSB0aGUgdGFzayBkaXJlY3RseSkuCi0gQmUgZXhwbGljaXQgYWJvdXQgcm9sZSBkaXN0aW5jdGlvbnM6IGlmIHRoZSByZWFsIHVzZXIgYXNrcyB0aGUgYXNzaXN0YW50L2FnZW50IHRvIHByb2R1Y2UgY29tbWFuZHMsIEpTT04sIGNvZGUsIHRvb2wgY2FsbHMsIG9yIGZvcm1hdHRlZCBvdXRwdXQsIHNheSB3aGV0aGVyIHRoZSB1c2VyIHdyaXRlcyBpbnN0cnVjdGlvbnMgdG8gdGhlIGFzc2lzdGFudCBvciBkaXJlY3RseSBwcm92aWRlcyB0aGUgZmluYWwgYXJ0aWZhY3QuCi0gV2hlbiBmZWVkYmFjayBzYXlzIHRoZSBzaW11bGF0aW9uIGlzIHRvbyBzaG9ydCwgdG9vIGdlbmVyaWMsIG9yIGNvbGxhcHNlZCBtdWx0aXBsZSB0dXJucywgc3BlY2lmeSB0aGUgZGVzaXJlZCB1dHRlcmFuY2UgZm9ybSBhbmQgaW50ZXJhY3Rpb24gYXJjOiBhcHByb3hpbWF0ZSB0dXJuIGNvdW50LCBuZWFyLWR1cGxpY2F0ZSB2cyBpbmNyZW1lbnRhbCB0dXJucywgd2hhdCBlYWNoIHR1cm4gc2hvdWxkIGNvbnRhaW4sIGFuZCB3aGF0IG5vdCB0byBjb21wcmVzcy4KLSBGb3IgcHJvbXB0LWF1dGhvciBvciBhZ2VudC1jb250cm9sIHVzZXJzLCBzdGF0ZSBpZiB1c2VyIHR1cm5zIHNob3VsZCBiZSBsb25nIHByb21wdCBibG9ja3Mgd2l0aCByb2xlIGluc3RydWN0aW9ucywgY29uc3RyYWludHMsIGFsbG93ZWQgYWN0aW9ucywgcmVzcG9uc2Ugc2NoZW1hcywgZXZhbHVhdGlvbiBjcml0ZXJpYSwgYW5kIGdvYWxzOyBhbHNvIHN0YXRlIG5vdCB0byByZXBsYWNlIHRob3NlIGJsb2NrcyB3aXRoIGEgYmFyZSBjb21tYW5kLCBKU09OIG9iamVjdCwgb3IgZmluYWwgYW5zd2VyLgoKQ29udmVyc2F0aW9uOgp7Y29udmVyc2F0aW9uX2hpc3Rvcnl9CgpDdXJyZW50IFVzZXIgUmVwcmVzZW50YXRpb246CnRhc2s6IHt0YXNrfQp1c2VyX2JlaGF2aW9yOiB7dXNlcl9iZWhhdmlvcn0KCkp1ZGdlIEZlZWRiYWNrOgp7ZmVlZGJhY2t9CgpHZW5lcmF0ZSBhbiB1cGRhdGVkIHJlcHJlc2VudGF0aW9uIHRoYXQgaW5jb3Jwb3JhdGVzIHRoZSBqdWRnZSBmZWVkYmFjay4gVGhpcyB1cGRhdGVkIHJlcHJlc2VudGF0aW9uIHNob3VsZCBmb2xsb3cgdGhlIGd1aWRlbGluZXMgYWJvdmUsIGFuZCBzaG91bGQgYmUgaW4gdGhlIHNhbWUgZm9ybWF0IGFzIHRoZSBjdXJyZW50IHJlcHJlc2VudGF0aW9uLgpPdXRwdXQgb25seSB2YWxpZCBKU09OIHdpdGggZXhhY3RseSB0aGlzIHNoYXBlOgp7eyJ0YXNrIjogIjxpbXByb3ZlZCB0YXNrPiIsICJ1c2VyX2JlaGF2aW9yIjogIjxpbXByb3ZlZCBiZWhhdmlvciByZXByZXNlbnRhdGlvbj4ifX0=)YouareanexpertatanalyzinguserbehaviorinconversationsbetweenauserandanAIassistant\.Youhavearepresentationoftheuserbasedontheconversationbelow\.Weusedthatrepresentationtosimulateauser,andthencollectedfeedbackfromajudge\.Yourtaskistoincorporatethatfeedbackandgenerateanimproveduserrepresentation\.Theuserrepresentationconsistsofataskanduser\_behaviorfield\.Thetaskfielddescribeswhattheuseristryingtoaccomplish,andcanincludetopic\-specificdetailsaboutwhattheuserisdoingintheconversation\.Theuser\_behaviorfielddescribeshowtheygenerallybehaveandhowtheywouldtypicallyactinconversations\.Itfocusesonstable,generaltendenciessuchas:howtheyphraseandspecifyrequests,howtheydecomposegoalsacrossturns,howtheyrespondtoandgivefeedbacktotheassistant,howandwhentheyprovidecontext,theircommunicationstyle,thedialogactstheyuse,andtheirdegreeanddepthofengagement\.Thisdescriptionshouldbegeneral:itshouldnotincludetopic\-specificdetailsorcontentfromtheconversation,itshouldnotquoteorcloselyparaphrasefullusermessages,anditshouldnotrefertospecificmomentsinthisconversation\(e\.g\."here","inthisconversation","whentheyaskedaboutX"\)\.Shortdistinctivewordsorphrasesmaybequotedsparinglywhentheyareusefulexamplesoftheuser’scommunicationstyle\.Itshoulddescribetheirpatternsingeneralterms\.Additionalguidelinesforimprovingtherepresentation:\-Optimizeforbehavioralsimulation,notefficienttaskcompletion:preservemulti\-turnrepetition,incrementalfollow\-ups,verboseconstraints,andinefficientinteractionpatternswhentheyappearintherealuser\.\-Makeuser\_behavioroperational:specifywhatthesimulatorshoulddoandshouldnotdo,includingnegativeconstraintsfromjudgefeedback\(e\.g\.donotterminateearly,combineturns,over\-polish,changeformattingstyle,orsolvethetaskdirectly\)\.\-Beexplicitaboutroledistinctions:iftherealuseraskstheassistant/agenttoproducecommands,JSON,code,toolcalls,orformattedoutput,saywhethertheuserwritesinstructionstotheassistantordirectlyprovidesthefinalartifact\.\-Whenfeedbacksaysthesimulationistooshort,toogeneric,orcollapsedmultipleturns,specifythedesiredutteranceformandinteractionarc:approximateturncount,near\-duplicatevsincrementalturns,whateachturnshouldcontain,andwhatnottocompress\.\-Forprompt\-authororagent\-controlusers,stateifuserturnsshouldbelongpromptblockswithroleinstructions,constraints,allowedactions,responseschemas,evaluationcriteria,andgoals;alsostatenottoreplacethoseblockswithabarecommand,JSONobject,orfinalanswer\.Conversation:\{conversation\_history\}CurrentUserRepresentation:task:\{task\}user\_behavior:\{user\_behavior\}JudgeFeedback:\{feedback\}Generateanupdatedrepresentationthatincorporatesthejudgefeedback\.Thisupdatedrepresentationshouldfollowtheguidelinesabove,andshouldbeinthesameformatasthecurrentrepresentation\.OutputonlyvalidJSONwithexactlythisshape:\{\{"task":"<improvedtask\>","user\_behavior":"<improvedbehaviorrepresentation\>"\}\}

User Simulator System Prompt[⬇](data:text/plain;base64,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)Youareausersimulator\.Youwillbeprovidedwithauserrepresentationandyourtaskistorole\-playasthatuserinaconversationwithanAIassistant\.\#UserRepresentation\{user\_representation\}\#Instructions\-Stayconsistentwiththeinformationprovidedintheuserrepresentation\-Neverdescribetheuseroryourownbehavior,neverbreakcharacter,andnevermentionthatyouaresimulating,role\-playing,orfollowingadescription\.\-Whenthetaskrequiresconcretematerialfortheassistanttoworkon\(e\.g\.aparagraphtoanalyze,apassagetotranslate,codetodebug,anessaytoedit,datatosummarize\),inventaplausible,self\-containedexampleyourselfandincludeitinlineinyourmessage\.Neverasktheassistanttoworkonsomethingyouhavenotactuallyprovided\.\-Whenthisuser’sgoalhasbeenmet,orwhentheywouldnaturallyendthechat,respondwithexactly\\"\{termination\_signal\}\\"andnothingelse\.

Assistant System Prompt[⬇](data:text/plain;base64,WW91IGFyZSBhIGhlbHBmdWwgQUkgYXNzaXN0YW50LiBSZXNwb25kIHRvIHRoZSB1c2VyJ3MgbWVzc2FnZXMgYWNjdXJhdGVseSBhbmQgaGVscGZ1bGx5Lg==)YouareahelpfulAIassistant\.Respondtotheuser’smessagesaccuratelyandhelpfully\.

Judge Prompt \- Full Conversation[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviorinhuman\-AIconversations\.Yourtaskistoevaluatehowwellasimulateduserreproducesthebehaviorofarealuser\.Youaregiventhefullrealandsimulatedconversations,includingassistantturns\.Usetheassistantturnsonlyascontextforinterpretingtheuser’sbehavior;scoreonlyhowfaithfullytheSIMULATEDuserreproducestheREALuser’sbehavior\.Donotscoreassistantquality\.Evaluatehowwellthesimulateduserreproducestherealuseralongthefollowingdimensions:\-Communicationstyle:\-Dotheyusesimilarpunctuation,capitalization,spelling/grammar,typos,slang,emoji,contractions,andothersurface\-levelwritinghabits?Compareconcretesurfaceartifacts,notjustthegeneralvibe\.\-Dotheyhavesimilarresponselengths?Aretheyterseorverboseinthesameway?\-Dotheyusesimilarformatting,suchasparagraphs,markdown,bullets,orlinebreaks?\-Dotheyhavesimilartone/formality,politeness,greetings/thanks,directness,warmth,andsocialconventions?\-Engagement\(TheREALusertook\{real\_user\_turns\}turn\(s\);theSIMULATEDusertook\{sim\_user\_turns\}turn\(s\)\):\-Dotheyspendasimilarnumberofturnsonthesameinteractionarcorsubtask?Dotheypersistormoveonquicklyinasimilarway?\-Dotheycontinue,stop,orterminateatasimilarpoint?\-Requests:\-Dotheypursueasimilarnumberofdistinctasksorsubtasks?Ignoretheexactdomain/topic,butcomparetheinteractionstructure\.\-Dotheyaskforthingsinasimilarway:directcommandsvsquestions,broadasksvsnarrowasks,explicitconstraintsvsimplicitcues?\-Dotheyfront\-loadrequirementsorrevealthemgraduallyacrossturnsinasimilarway?\-Dotheykeepseparatereal\-userrequestsseparate,ratherthancombiningthemtooearly?\-Aretheirrequestssimilarlyopen\-ended,verification\-oriented,correction\-oriented,orsolution\-oriented?\-Responses:\-Giventheassistantturnsshownineachtranscript,dotheusersrespondtoassistantoutputsinsimilarways?\-Dotheyaccept,reject,correct,validate,expressdissatisfaction,refineconstraints,orredirecttheconversationsimilarly?\-Istheirfeedbacksimilarlyexplicitorimplicit,terseorexplanatory,politeorblunt?\-Context:\-Dotheyprovideasimilaramountandtypeofcontext:examples,data,code,priorattempts,constraints,personalbackground?\-Iscontextfront\-loadedorrevealedgraduallyinasimilarway?\#REALconversation\{real\}\#SIMULATEDconversation\{simulated\}\#Additionalinstructions\-FocusONLYontheUSERbehavior,andjudgeHOWtheuserbehavesratherthanWHATtheyareworkingon:thespecificdomain,entities,technologies,orsubjectmatterarenotimportant\.Donotrewardorpenalizethesimulateduserforworkingonthesameoradifferenttopicthantherealuser\.\-Useassistantturnsonlytounderstandwhattheuserisrespondingto\.Donotpenalizethesimulateduserbecausetheassistantgavedifferentcontent,unlessthesimulateduser’sbehavioralresponsepatternisalsodifferent\.\-Accountforthefactthatthesimulatedusermayneedtoactdifferentlybecausethesimulatedassistantbehaveddifferently\.Afaithfulusermayaskadifferentfollow\-up,skipacorrection,orintroduceaclarificationatadifferentpointiftheassistant’sanswercreatedadifferentopportunity;judgewhethertheunderlyingbehavioraltendencyissimilar\.\-Payattentiontowhetherthesimulateduserreproducesthefullarcoftherealuser’suserturns\.Forexample,iftherealuserworksonmultipletasksacrosstheconversationbutthesimulateduserworksononlyone,thereisabehavioralmismatch\.\-Penalizegenericsimulatorbehaviorsuchasasking"HowcanIhelp?",endingimmediately,over\-polishingterseusers,usingmarkdownwhentherealuserdoesnot,orcombiningmultiplereal\-userfollow\-upsintooneturn\.Foreachdimension,writeaone\-sentencerationale,andthenassignascorefrom1\(poorlyreproducestherealuser’sbehavior\)to10\(highlyreproducestherealuser’sbehavior\)\.Finally,giveasingle1\-10overallscorethatmeasuresthesimulator’soverallbehavioralsimilarity\.OutputavalidJSONobjectwithexactlythisshapeandnothingelse\(nomarkdown\):\{\{"dimensions":\{\{"communication\_style":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"engagement":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"requests":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"responses":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"context":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\}\}\},"overall":<1\-10\>\}\}

Judge Prompt \- User Utterances Only[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviorinhuman\-AIconversations\.Yourtaskistoevaluatehowwellasimulateduserreproducesthebehaviorofarealuser\.YouaregiventheuserutterancesfromarealandsimulatedconversationbetweenauserandanAIassistant\.Evaluatehowwellthesimulateduserreproducesthebehavioroftherealuseralongthefollowingdimensions:\-Communicationstyle:\-Dotheyusesimilarpunctuation,capitalization,spelling/grammar,typos,slang,emoji,contractions,andothersurface\-levelwritinghabits?Compareconcretesurfaceartifacts,notjustthegeneralvibe\.\-Dotheyhavesimilarresponselengths?Aretheyterseorverboseinthesameway?\-Dotheyusesimilarformatting,suchasparagraphs,markdown,bullets,orlinebreaks?\-Dotheyhavesimilartone/formality,politeness,greetings/thanks,directness,warmth,andsocialconventions?\-Engagement\(TheREALusertook\{real\_user\_turns\}turn\(s\);theSIMULATEDusertook\{sim\_user\_turns\}turn\(s\)\):\-Dotheyspendasimilarnumberofturnsonthesameinteractionarcorsubtask?Dotheypersistormoveonquicklyinasimilarway?\-Dotheycontinue,stop,orterminateatasimilarpoint?\-Requests:\-Dotheypursueasimilarnumberofdistinctasksorsubtasks?Ignoretheexactdomain/topic,butcomparetheinteractionstructure\.\-Dotheyaskforthingsinasimilarway:directcommandsvsquestions,broadasksvsnarrowasks,explicitconstraintsvsimplicitcues?\-Dotheyfront\-loadrequirementsorrevealthemgraduallyacrossturnsinasimilarway?\-Dotheykeepseparatereal\-userrequestsseparate,ratherthancombiningthemtooearly?\-Aretheirrequestssimilarlyopen\-ended,verification\-oriented,correction\-oriented,orsolution\-oriented?\-Responses:\-Basedonlyonthevisibleuserfollow\-upturns,dotheyappeartoaddress/engagewithpriorassistantoutputsinasimilarway?\-Dotheyaccept,reject,correct,validate,expressdissatisfaction,refineconstraints,orredirecttheconversationsimilarly?\-Istheirfeedbacksimilarlyexplicitorimplicit,terseorexplanatory,politeorblunt?\-Context:\-Dotheyprovideasimilaramountandtypeofcontext:examples,data,code,priorattempts,constraints,personalbackground?\-Iscontextfront\-loadedorrevealedgraduallyinasimilarway?\#REALuserutterances\{real\}\#SIMULATEDuserutterances\{simulated\}\#Additionalinstructions\-FocusONLYontheUSERbehavior,andjudgeHOWtheuserbehavesratherthanWHATtheyareworkingon:thespecificdomain,entities,technologies,orsubjectmatterarenotimportant\.Donotrewardorpenalizethesimulateduserforworkingonthesameoradifferenttopicthantherealuser\.\-Payattentiontowhetherthesimulateduserreproducesthefullarcoftherealuser’suserturns\.Forexample,iftherealuserworksonmultipletasksacrosstheconversationbutthesimulateduserworksononlyone,thereisabehavioralmismatch\.\-Penalizegenericsimulatorbehaviorsuchasasking"HowcanIhelp?",endingimmediately,over\-polishingterseusers,usingmarkdownwhentherealuserdoesnot,orcombiningmultiplereal\-userfollow\-upsintooneturn\.\-Donotinferhiddenassistantcontent\.Ifaresponsepatternisnotvisiblefromtheuserutterancesthemselves,donotgivecreditforit\.Foreachdimension,writeaone\-sentencerationale,andthenassignascorefrom1\(poorlyreproducestherealuser’sbehavior\)to10\(highlyreproducestherealuser’sbehavior\)\.Finally,giveasingle1\-10overallscorethatmeasuresthesimulator’soverallbehavioralsimilarity\.OutputavalidJSONobjectwithexactlythisshapeandnothingelse\(nomarkdown\):\{\{"dimensions":\{\{"communication\_style":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"engagement":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"requests":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"responses":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\},"context":\{\{"rationale":"<onesentence\>","score":<1\-10\>\}\}\}\},"overall":<1\-10\>\}\}

## Appendix DTraining Hyperparameters

Table 6:Training hyperparameters for GRPO, OPSD, SFT, and OSFT\.HyperparameterGRPOOPSDSFTOSFTLearning rate5×10−65\\times 10^\{\-6\}5×10−65\\times 10^\{\-6\}5×10−65\\times 10^\{\-6\}5×10−65\\times 10^\{\-6\}Effective batch size32323232Training steps100100100100LoRA rank \(rr\)32646464LoRA alpha \(α\\alpha\)64128128128Maximum prompt length8,1928,1928,1928,192Maximum completion length1,0241,0241,0241,024Maximum sequence length9,2169,2169,2169,216Generations per prompt81–1Sampling temperature0\.71\.1–1\.1Top\-pp0\.80\.95–0\.95Top\-kk2020–20
## Appendix EFurther Analysis on How Training Changes User Representations

### E\.1Pairwise Analysis with GPT\-5\.4 Mini

In Section[6\.4](https://arxiv.org/html/2610.22518#S6.SS4), we qualitatively analyze how training changes user representations by manually comparing representations generated by the base and trained models\. We use the resulting observations to guide a larger\-scale, systematic analysis over the 700 conversations in our evaluation set\. For each conversation, we present GPT\-5\.4 mini with the representations generated by Qwen3\.5\-4B andUserRepGen\-4Bin randomized order, and ask it to answer 11 questions about how they differ:

Task descriptions\.

1. 1\.Which task description better captures constraints introduced in later turns?
2. 2\.Which task description better captures shifts in direction or goal?
3. 3\.Which task description better captures multiple distinct objectives or subtasks?
4. 4\.Which task description has less repetition?

Behavior descriptions\.

1. 5\.Which behavior description is more specific and actionable?
2. 6\.Which behavior description uses more useful negative and contrastive instructions?
3. 7\.Which behavior description better captures the user’s response policy?
4. 8\.Which behavior description better captures the user’s communication style?
5. 9\.Which behavior description better captures the user’s engagement patterns, including how long they persist, when they move on, and when they end the conversation?
6. 10\.Which behavior description better captures how the user makes requests, including how many requests or subtasks they pursue, how directly they ask, and whether they provide requirements upfront or gradually?
7. 11\.Which behavior description better captures how the user provides context, including the amount and type of context and whether it is provided upfront or gradually?

For each question, GPT\-5\.4 mini must select one of the two representations\. We use the following prompt:

How Training Changes User Representations \- Judge Prompt[⬇](data:text/plain;base64,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)Youareanexpertinanalyzinguserrepresentations\.Auserrepresentationisanatural\-languagedescriptiongeneratedfromaconversationandusedtoconditionanLLM\-basedusersimulator\.Itspurposeistogivethesimulatortheinformationneededtoreproducetheuserbehaviorobservedintheoriginalconversation\.Userrepresentationsaremadeupoftwocomponents:\(1\)ataskdescriptionthatdescribeswhattheuseristryingtoaccomplish,and\(2\)abehaviordescriptionthatdescribeshowtheuserbehaves\.Yourtaskistohelpmeanalyzethedifferencebetweenthetwouserrepresentations\.Youwillbegiventworepresentationsgeneratedfromthesameconversation,presentedinrandomizedorder\.Theordercarriesnoinformationaboutwhichmodelsproducedthemorwhichrepresentationisbetter\.Carefullycomparethecontentofthetworepresentationsforeverycriterionandanswerthequestionsbelow\.\#REPRESENTATION1task:\{representation\_1\_task\}behavior:\{representation\_1\_behavior\}\#REPRESENTATION2task:\{representation\_2\_task\}behavior:\{representation\_2\_behavior\}\#Foreachcriterionofthefollowingcriteria,selecteither‘representation\_1‘or‘representation\_2‘:\#\#TaskDescription1\.Whichrepresentationbettercapturesconstraintsintroducedinlaterturns?2\.Whichrepresentationbettercapturesshiftsindirectionorgoal?3\.Whichrepresentationbettercapturesmultipledistinctobjectivesorsubtasks?4\.Whichrepresentationhaslessrepetition?\#\#BehaviorDescription5\.Whichbehaviordescriptionismorespecificandactionable?Preferdescriptionsofobservablepatternsthatasimulatorcanreproduceoverbroadcharacterizationssuchas"goal\-oriented"or"directive"\.6\.Whichbehaviordescriptionusesmoreusefulnegativeandcontrastiveinstructions?7\.Whichbehaviordescriptionbettercapturestheuser’sresponsepolicy?8\.Whichbehaviordescriptionbettercapturestheuser’scommunicationstyle?9\.Whichbehaviordescriptionbettercapturestheuser’sengagementpatterns,includinghowlongtheypersist,whentheymoveon,andwhentheyendtheconversation?10\.Whichbehaviordescriptionbettercaptureshowtheusermakesrequests,includinghowmanyrequestsorsubtaskstheypursue,howdirectlytheyask,andwhethertheyproviderequirementsupfrontorgradually?11\.Whichbehaviordescriptionbettercaptureshowtheuserprovidescontext,includingtheamountandtypeofcontextandwhetheritisprovidedupfrontorgradually?\#OutputformatOutputonlythefollowingJSONobjectandnothingelse\.Foreachquestion,provideyourselectionandonebriefsentenceexplainingthemostimportantdifference:\{\{"q1":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q2":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q3":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q4":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q5":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q6":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q7":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q8":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q9":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q10":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\},"q11":\{\{"selection":"representation\_1\|representation\_2","reasoning":"<briefsentence\>"\}\}\}\}

Table 7:Comparing user representations generated by our trained and untrained models along several dimensions\. We report the percentage of times that GPT\-5\.4 mini selects the representation generated byUserRepGen\-4Bover the one generated by Qwen3\.5\-4B\.QuestionUserRepGen\-4BSelected \(%\)Task descriptionCaptures constraints introduced in later turns63\.6Captures shifts in direction or goal61\.0Captures multiple objectives or subtasks58\.3Has less repetition33\.1Behavior descriptionIs more specific and actionable87\.7Uses more useful negative and contrastive instructions91\.6Captures the user’s response policy83\.1Captures the user’s communication style88\.1Captures the user’s engagement patterns90\.0Captures how the user makes requests83\.9Captures how the user provides context85\.9Our results are presented in Table[7](https://arxiv.org/html/2610.22518#A5.T7)\. GPT\-5\.4 mini selects the task description fromUserRepGen\-4Bas better capturing constraints introduced in later turns in 63\.6% of comparisons, shifts in direction or goal in 61\.0%, and multiple objectives in 58\.3%\. Together, these results support our qualitative observation that training produces more comprehensive task descriptions\. However,UserRepGen\-4Bis selected as less repetitive in only 33\.1% of comparisons, indicating that Qwen3\.5\-4B usually produces less repetitive task descriptions\. For behavior descriptions, GPT\-5\.4 mini selects the description fromUserRepGen\-4Bas containing more useful negative and contrastive instructions in 91\.6% of comparisons, better capturing user engagement patterns in 90\.0%, better capturing communication style in 88\.1%, and being more specific and actionable in 87\.7%\. It also selectsUserRepGen\-4Bas better describing how the user provides context in 85\.9%, how the user makes requests in 83\.9%, and how the user responds to the assistant in 83\.1%\. These results support our qualitative observation that training produces behavior descriptions that are more specific and actionable, and that capture dimensions of user behavior previously overlooked\.

### E\.2User Representation Examples

We provide two examples of user representations generated by Qwen3\.5\-4B andUserRepGen\-4B\. Yellow highlighting marks the differences most relevant to our analysis\.

#### Example 1\.

This example illustrates howUserRepGen\-4Bgenerates task descriptions that capture the full sequence of user requests, whereas the base model summarizes the conversation at a high\-level\. The behavior description replaces broad terms such as “curious”, “direct”, and “inquisitive” with observable patterns that a simulator can reproduce, such as specifying that the user asks short factual questions, provides no initial context, and pivots to new angles rather than giving feedback\.

User representation generated by Qwen3\.5\-4BTask description\.The user seeks to understand the core ideas and historical impact of a specific philosopher, Čaadaev, and subsequently explores the philosophical connections between him and other notable figures like Dostoevsky\.Behavior description\.The user initiates inquiries withbroad, open\-ended questions about a topic’s fundamental nature and relevance\. They progressively narrow the scope by requesting specific follow\-up information on influences and related concepts\. Their engagement is characterized bya high degree of curiosityand a desire for concise summaries, often explicitly requesting structural constraints \(such as a sentence limit\) when formulating their requests\. The communication style isdirect and inquisitive, treating the assistant as a knowledge repository without providing extensive initial context\.

User representation generated byUserRepGen\-4BTask description\.The user seeks to understand the philosophical contributions of Ivan Čaadaev and their influence on later thinkers, specifically focusing on Fyodor Dostoevsky\. The interaction involvesa series of sequential inquiries about Čaadaev’s core themes, modern relevance, intellectual lineage, and a specific request for a concise summary of Dostoevsky’s philosophy\.Behavior description\.The user engages in a multi\-turn dialogue characterized byshort, direct, and factual questions that progressively narrow down the scope of inquiryfrom general biography to specific thematic analysis and finally to a constrained output format \(two sentences\)\. They decompose complex topics intodiscrete sub\-questions without providing initial context or background information, relying instead on the assistant’s responses to inform subsequent queries\. The communication style isterse and impersonal, avoiding conversational fillers, emotional expressions, or requests for formatting details like markdown headers\.The user does not provide feedback on previous answers but immediately pivots to new angles or deeper dives into related figures based on the assistant’s output\. They maintain a consistent pattern of asking distinct follow\-ups rather than combining multiple goals into single prompts or requesting broad overviews before drilling down\. The engagement level is moderate; they do not terminate the conversation early nor do they expand significantly beyond the immediate topic area, preferring a linear progression of specific facts and summaries\.

#### Example 2\.

In this example, the behavior description fromUserRepGen\-4Bspecifies how the user formulates requests: short, direct questions, often phrased as yes/no inquiries, with little upfront context\. It also describes how the user responds to the assistant, noting that they neither critique nor refine previous answers but instead use them to guide the next question\. The base model, by contrast, relies on broad characterizations\.

Representation generated by Qwen3\.5\-4BTask description\.The user is investigating the safety, ethical implications, and management challenges of nuclear energy, specifically focusing on waste disposal methods\. They are exploring alternatives to traditional nuclear power and considering how artificial intelligence can contribute to solving complex environmental and safety problems\.Behavior description\.The user engages in astructured inquiryby posing direct questions that progressively narrow down from general safety concerns to specific technical solutions and ethical considerations\. They actively seek alternative perspectives and practical applications \(such as AI’s role\) rather than accepting single answers\. Their communication style isconcise and focused on high\-level concepts, demonstrating a degree of engagement that balances curiosity with a desire for actionable information\.

Representation generated byUserRepGen\-4BTask description\.The user is exploring the safety, morality, and future implications of nuclear energy, specifically focusing on waste disposal methods and seeking potential AI\-driven solutions for these challenges\.Behavior description\.The user engages in a multi\-turn dialogue with a distinct communication style characterized byshort, direct questions often phrased as simple yes/no inquiries or brief statements\. They decompose complex topics intosequential sub\-questions without providing extensive background context upfront, relying instead on the assistant’s responses to guide subsequent turns\. The user maintainsa low level of engagement per turn, avoiding elaboration, justification, or follow\-up analysis beyond the immediate question\. They do not provide feedback, corrections, or alternative perspectives; their interaction pattern isstrictly linear and interrogative\. The user consistentlyasks new, related questions rather than refining previous ones or requesting specific formatting changes\.

## Appendix FMeasuring the Gap Between Distributions of Original and Simulated User Behaviors

To evaluate how well user representations preserve behavioral diversity at the population level, we measure the distributional gap between original and simulated user behaviors\. We follow the pipeline and hyperparameters of[Mehri et al\. \(2026b\)](https://arxiv.org/html/2610.22518#bib.bib11), which we detail below\.

#### User behavior descriptions\.

We use Qwen3\.5\-122B\-A10B\-FP8 to describe the user behavior in each original and simulated conversation along six behavioral dimensions: requests, responses, context, communication style, and dialog acts under the Dialog Act Markup in several Layers \(DAMSL\) framework and Schema\-Guided Dataset \(SGD\) taxonomies\. Each prompt instructs the model to describe general behavioral patterns while excluding task\-specific details\. We use the following prompts:

User Behavior Description: Requests Dimension[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Theuserhasagoal,andtheassistanthelpsthemachieveit\.Yourtaskistodescribetheuser’sbehavioraccordingtothecriteriabelow\.\#UserGoal\{user\_goal\}\#UserUtterances\{conversation\_history\}\#AnalysisCriteria1\.SpecificationandArticulation\-Howspecifiedaretherequests?Isthefirstrequestunderspecified,andclarifiedinsubsequentturns?Oraretherequestsexhaustive?\-Whattypeofinformationisleftunderspecified/specified?\(e\.g\.constraints,edgecases,context,outputformat,etc\.\)\-Doestheuserexplicitlycommandspecifictasks,ordotheyrelyonindirectcues\(e\.g\.,presentinganerrorwithoutexplicitlyaskingforafix\)?2\.UserGoalDecomposition\-Howistheusergoaldecomposedacrossutterances?\-Single\-shot:Theentiregoalisexpressedinoneutterancewithnofurtherdecomposition\.\-Top\-down:High\-levelgoalstatedupfront,thenprogressivelyrefinedorelaborated\.\-Bottom\-up:Individualpreconditionsorsub\-tasksaddressedfirst,buildingtowardtheoverallgoal\.\-Chained:Eachrequestbuildspurelyontheimmediatepreviousturnratherthanreferringbacktoacentralgoal\.3\.RelevancetoGoal\-Aretherequestsdirectlyrelatedtotheusergoal?Ordoestheuserintroducesecondary/perpendicular/emergentneeds?\-Whatfunctionsdotherequestsservebeyondachievingtheusergoal?\(settingcontext,probingAIcapabilities,verifyingintermediateoutputs,logistics,troubleshooting,side\-effects,exploringrelatedsub\-tasks,etc\.\)\#Instructions\-Generateterse,concisebulletpoints,notfullsentences\.\-Abstractawayfromthespecifictopic/domain\-IMPORTANT:DoNOTusetask\-specificterms\(e\.g\.,"coding,""booking,""Python","CSV"\)\.Usegenericsubstitutes\(e\.g\.,"executingatask,""providingconstraints,""thetargetartifact"\)\.OutputavalidJSONobjectusingtheexactformatbelow\.DonotincludeanytextoutsidetheJSON\.\{\{"specification\_and\_articulation":\{\{"specification\_level":"howspecifiedarerequests?","underspecified\_aspects":"whattypesofinformationareleftunderspecified/specified?\(providehigh\-leveldescriptionswithouttask\-specificdetails\)","articulation\_mode":"howdoestheuserarticulatetheirrequests\(explicitdirectives,indirectcues,mixed\)?"\}\},"goal\_decomposition\_strategy":"describehowtheusergoaldecomposedacrossutterances?","relevance\_to\_goal":\{\{"goal\_adherence":"Aretheuser’srequestsdirectlyrelatedtotheusergoal?Ifnot,whatotherfunctionsdotheyserve?\(providehigh\-leveldescriptionswithouttask\-specificdetails\)"\}\}\}\}

User Behavior Description: Responses Dimension[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Theuserhasagoal,andtheassistanthelpsthemachieveit\.Yourtaskistodescribetheuser’sbehavioraccordingtothecriteriabelow\.\#UserGoal\{user\_goal\}\#Conversation\{conversation\_history\}\#AnalysisCriteria1\.EngagementandEvaluation\-Doestheuserengagewiththeagentresponses,orignore/skipoverthem?\-Howdoestheuserevaluatetheagent’soutput?\(e\.g\.,explicitvalidation,implicitacceptancebycontinuing,partialacceptancewithcorrections,rejection\)\-Doestheuserprovidespecificoractionablefeedback,oronlysurface\-levelacknowledgment?2\.ResponseComposition\-Whattypesofactionsarepresentintheuser’sresponses?Aretheyreactive\(e\.g\.,validation,acknowledgment,answeringagentquestions,corrections,feedback\),proactive\(e\.g\.,follow\-uprequests,newconstraints/preferences,suggestions,questions\),orself\-directed\(e\.g\.,thinkingoutloud,expressinguncertainty,narratingtheirprocess\)?3\.SteeringMechanism\-Doestheusersteerthroughdirectfollow\-uprequests,orthroughindirectmeans?\(e\.g\.,askingquestionsthatimplicitlyrequestaction,expressingdissatisfactionwithoutstatingwhattochange,providinghintsorexamplesratherthandirectives\)\-Doestheuserintroducenewpreferences,constraints,orfeedbackaspartoftheirresponses,effectivelyreshapingthetaskincrementally?\#Instructions\-Generateterse,concisebulletpoints,notfullsentences\.\-Abstractawayfromthespecifictopic/domain\.\-IMPORTANT:DoNOTusetask\-specificterms\(e\.g\.,"coding,""booking,""Python","CSV"\)\.Usegenericsubstitutes\(e\.g\.,"executingatask,""providingconstraints,""thetargetartifact"\)\.OutputavalidJSONobjectusingtheexactformatbelow\.DonotincludeanytextoutsidetheJSON\.\{\{"engagement\_and\_evaluation":\{\{"engagement\_level":"doestheuserengagewiththeagent’sresponsesorskipoverthem?","evaluation\_mode":"howdoestheuserevaluatetheagent’soutput?\(explicitvalidation,implicitacceptance,partialacceptance,rejection,etc\.\)","feedback\_specificity":"doestheuserprovidespecific/actionablefeedbackoronlysurface\-levelacknowledgment?"\}\},"response\_composition":\{\{"action\_types":"Whattypesofactionsarepresentintheuser’sresponses?Aretheyreactive\(e\.g\.,validation,acknowledgment,answeringagentquestions,corrections,feedback\),proactive\(e\.g\.,follow\-uprequests,newconstraints/preferences,suggestions,questions\),orself\-directed\(e\.g\.,thinkingoutloud,expressinguncertainty,narratingtheirprocess\)?"\}\},"steering\_mechanism":\{\{"directness":"doestheusersteerthroughexplicitfollow\-uprequestsorindirectmeans\(questions,hints,expresseddissatisfaction\)?","incremental\_reshaping":"doestheuserintroducenewpreferences/constraints/feedbackthatreshapethetaskwithintheirresponses?"\}\}\}\}

User Behavior Description: Context Dimension[⬇](data:text/plain;base64,WW91IGFyZSBhbiBleHBlcnQgYW5hbHl6aW5nIHVzZXIgYmVoYXZpb3VyIGluIGh1bWFuLUFJIGNvbnZlcnNhdGlvbnMuIFRoZSB1c2VyIGhhcyBhIGdvYWwsIGFuZCB0aGUgYXNzaXN0YW50IGhlbHBzIHRoZW0gYWNoaWV2ZSBpdC4gWW91ciB0YXNrIGlzIHRvIGRlc2NyaWJlIHRoZSB1c2VyJ3MgYmVoYXZpb3IgYWNjb3JkaW5nIHRvIHRoZSBjcml0ZXJpYSBiZWxvdy4KCiMgVXNlciBHb2FsCnt1c2VyX2dvYWx9CgojIENvbnZlcnNhdGlvbgp7Y29udmVyc2F0aW9uX2hpc3Rvcnl9CgojIEFuYWx5c2lzIENyaXRlcmlhCjEuIENvbnRleHQgUmljaG5lc3MKICAgLSBIb3cgbXVjaCBjb250ZXh0IGRvZXMgdGhlIHVzZXIgcHJvdmlkZSBvdmVyYWxsPyBEbyB0aGV5IHNoYXJlIGJhY2tncm91bmQgYWJvdXQgdGhlbXNlbHZlcywgdGhlaXIgc2l0dWF0aW9uLCB3aGF0IHRoZXkndmUgYWxyZWFkeSB0cmllZCwgd2hhdCB0aGV5J3JlIHN0cnVnZ2xpbmcgd2l0aCwgb3Igd2hhdCB0aGV5J3JlIHRoaW5raW5nPwogICAtIERvZXMgdGhlIHVzZXIgcHJvdmlkZSBhbGwgcmVsZXZhbnQgY29udGV4dCBuZWVkZWQgZm9yIHRoZSBhZ2VudCB0byBoZWxwIGVmZmVjdGl2ZWx5LCBvciBpcyBlc3NlbnRpYWwgY29udGV4dCBsZWZ0IG91dD8KICAgLSBEb2VzIHRoZSB1c2VyIHByb3ZpZGUgY29udGV4dCBhdCBhbGwsIG9yIGRvIHRoZXkgc2ltcGx5IGlzc3VlIGRpcmVjdGl2ZXMgd2l0aG91dCBzaXR1YXRpbmcgdGhlIHRhc2s/CgoyLiBDb250ZXh0IFR5cGUKICAgLSBXaGF0IHR5cGVzIG9mIGNvbnRleHQgZG9lcyB0aGUgdXNlciBwcm92aWRlPyAoZS5nLiwgcGVyc29uYWwgYmFja2dyb3VuZCwgZ29hbHMvbW90aXZhdGlvbnMsIHByaW9yIGF0dGVtcHRzLCBleGlzdGluZyBzb2x1dGlvbnMsIGNvbnN0cmFpbnRzLCBwcmVmZXJlbmNlcywgZG9tYWluIGtub3dsZWRnZSwgZW1vdGlvbmFsIHN0YXRlLCB0aG91Z2h0IHByb2Nlc3MpCgozLiBDb250ZXh0IERlbGl2ZXJ5CiAgIC0gRG9lcyB0aGUgdXNlciBmcm9udC1sb2FkIGFsbCByZWxldmFudCBjb250ZXh0IGluIHRoZWlyIGZpcnN0IG1lc3NhZ2UsIG9yIHJldmVhbCBpdCBpbmNyZW1lbnRhbGx5IGFjcm9zcyB0dXJucz8gV2hhdCB0eXBlcyBvZiBjb250ZXh0IGFyZSBpbnRyb2R1Y2VkIGxhdGVyPyAoZS5nLiwgY29uc3RyYWludHMgdGhleSBmb3Jnb3QsIHByZWZlcmVuY2VzIHRoZXkgZGlkbid0IHRoaW5rIHRvIG1lbnRpb24sIGJhY2tncm91bmQgdGhhdCBiZWNvbWVzIHJlbGV2YW50IGFzIHRoZSB0YXNrIGV2b2x2ZXMpCiAgIC0gSXMgaW5jcmVtZW50YWwgY29udGV4dCByZXZlYWxlZCBpbiByZXNwb25zZSB0byBhZ2VudCBxdWVzdGlvbnMsIG9yIHZvbHVudGVlcmVkIHVucHJvbXB0ZWQ/CgojIEluc3RydWN0aW9ucwotIEdlbmVyYXRlIHRlcnNlLCBjb25jaXNlIGJ1bGxldCBwb2ludHMsIG5vdCBmdWxsIHNlbnRlbmNlcy4KLSBBYnN0cmFjdCBhd2F5IGZyb20gdGhlIHNwZWNpZmljIHRvcGljL2RvbWFpbi4KLSBJTVBPUlRBTlQ6IERvIE5PVCB1c2UgdGFzay1zcGVjaWZpYyB0ZXJtcyAoZS5nLiwgImNvZGluZywiICJib29raW5nLCIgIlB5dGhvbiIsICJDU1YiKS4gVXNlIGdlbmVyaWMgc3Vic3RpdHV0ZXMgKGUuZy4sICJleGVjdXRpbmcgYSB0YXNrLCIgInByb3ZpZGluZyBjb25zdHJhaW50cywiICJ0aGUgdGFyZ2V0IGFydGlmYWN0IikuCgpPdXRwdXQgYSB2YWxpZCBKU09OIG9iamVjdCB1c2luZyB0aGUgZXhhY3QgZm9ybWF0IGJlbG93LiBEbyBub3QgaW5jbHVkZSBhbnkgdGV4dCBvdXRzaWRlIHRoZSBKU09OLgoKe3sKICAgImNvbnRleHRfcmljaG5lc3MiOiB7ewogICAgICAiZGVwdGgiOiAiaG93IG11Y2ggY29udGV4dCBkb2VzIHRoZSB1c2VyIHByb3ZpZGU/IGRvIHRoZXkgc2hhcmUgYmFja2dyb3VuZCwgcHJpb3IgYXR0ZW1wdHMsIHRob3VnaHQgcHJvY2VzcywgZXRjLj8iLAogICAgICAiY29tcGxldGVuZXNzIjogImRvZXMgdGhlIHVzZXIgcHJvdmlkZSBhbGwgcmVsZXZhbnQgY29udGV4dCBvciBsZWF2ZSBlc3NlbnRpYWwgaW5mb3JtYXRpb24gb3V0PyIsCiAgICAgICJjb250ZXh0dWFsaXphdGlvbl92c19kaXJlY3RpdmVzIjogImRvZXMgdGhlIHVzZXIgc2l0dWF0ZSB0aGUgdGFzayB3aXRoIGNvbnRleHQsIG9yIHNpbXBseSBpc3N1ZSBkaXJlY3RpdmVzPyIKICAgfX0sCiAgICJjb250ZXh0X3R5cGUiOiAid2hhdCB0eXBlcyBvZiBjb250ZXh0IGRvZXMgdGhlIHVzZXIgcHJvdmlkZT8gKHBlcnNvbmFsIGJhY2tncm91bmQsIGdvYWxzL21vdGl2YXRpb25zLCBwcmlvciBhdHRlbXB0cywgZXhpc3Rpbmcgc29sdXRpb25zLCBjb25zdHJhaW50cywgcHJlZmVyZW5jZXMsIGRvbWFpbiBrbm93bGVkZ2UsIGVtb3Rpb25hbCBzdGF0ZSwgdGhvdWdodCBwcm9jZXNzLCBldGMuKSIsCiAgICJjb250ZXh0X2RlbGl2ZXJ5Ijoge3sKICAgICAgImRpc3RyaWJ1dGlvbiI6ICJkb2VzIHRoZSB1c2VyIGZyb250LWxvYWQgY29udGV4dCBvciByZXZlYWwgaXQgaW5jcmVtZW50YWxseT8gV2hhdCB0eXBlcyBvZiBjb250ZXh0IGFyZSBpbnRyb2R1Y2VkIGluIGxhdGVyIHR1cm5zPyIsCiAgICAgICJ0cmlnZ2VyIjogImlzIGluY3JlbWVudGFsIGNvbnRleHQgcmV2ZWFsZWQgaW4gcmVzcG9uc2UgdG8gYWdlbnQgcXVlc3Rpb25zIG9yIHZvbHVudGVlcmVkIHVucHJvbXB0ZWQ/IgogICB9fQp9fQ==)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Theuserhasagoal,andtheassistanthelpsthemachieveit\.Yourtaskistodescribetheuser’sbehavioraccordingtothecriteriabelow\.\#UserGoal\{user\_goal\}\#Conversation\{conversation\_history\}\#AnalysisCriteria1\.ContextRichness\-Howmuchcontextdoestheuserprovideoverall?Dotheysharebackgroundaboutthemselves,theirsituation,whatthey’vealreadytried,whatthey’restrugglingwith,orwhatthey’rethinking?\-Doestheuserprovideallrelevantcontextneededfortheagenttohelpeffectively,orisessentialcontextleftout?\-Doestheuserprovidecontextatall,ordotheysimplyissuedirectiveswithoutsituatingthetask?2\.ContextType\-Whattypesofcontextdoestheuserprovide?\(e\.g\.,personalbackground,goals/motivations,priorattempts,existingsolutions,constraints,preferences,domainknowledge,emotionalstate,thoughtprocess\)3\.ContextDelivery\-Doestheuserfront\-loadallrelevantcontextintheirfirstmessage,orrevealitincrementallyacrossturns?Whattypesofcontextareintroducedlater?\(e\.g\.,constraintstheyforgot,preferencestheydidn’tthinktomention,backgroundthatbecomesrelevantasthetaskevolves\)\-Isincrementalcontextrevealedinresponsetoagentquestions,orvolunteeredunprompted?\#Instructions\-Generateterse,concisebulletpoints,notfullsentences\.\-Abstractawayfromthespecifictopic/domain\.\-IMPORTANT:DoNOTusetask\-specificterms\(e\.g\.,"coding,""booking,""Python","CSV"\)\.Usegenericsubstitutes\(e\.g\.,"executingatask,""providingconstraints,""thetargetartifact"\)\.OutputavalidJSONobjectusingtheexactformatbelow\.DonotincludeanytextoutsidetheJSON\.\{\{"context\_richness":\{\{"depth":"howmuchcontextdoestheuserprovide?dotheysharebackground,priorattempts,thoughtprocess,etc\.?","completeness":"doestheuserprovideallrelevantcontextorleaveessentialinformationout?","contextualization\_vs\_directives":"doestheusersituatethetaskwithcontext,orsimplyissuedirectives?"\}\},"context\_type":"whattypesofcontextdoestheuserprovide?\(personalbackground,goals/motivations,priorattempts,existingsolutions,constraints,preferences,domainknowledge,emotionalstate,thoughtprocess,etc\.\)","context\_delivery":\{\{"distribution":"doestheuserfront\-loadcontextorrevealitincrementally?Whattypesofcontextareintroducedinlaterturns?","trigger":"isincrementalcontextrevealedinresponsetoagentquestionsorvolunteeredunprompted?"\}\}\}\}

User Behavior Description: Communication Style Dimension[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Theuserhasagoal,andtheassistanthelpsthemachieveit\.Yourtaskistodescribetheuser’sbehavioraccordingtothecriteriabelow\.\#UserGoal\{user\_goal\}\#Conversation\{conversation\_history\}\#AnalysisCriteria1\.RegisterandTone\-Whatistheuser’sregister?\(e\.g\.,formal,casual,conversational,terse,professional\)\-Whatistheuser’semotionaltone?\(e\.g\.,neutral,frustrated,enthusiastic,impatient,apologetic,deferential\)\-Doestheregister/toneshiftacrosstheconversation?\(e\.g\.,startspolitebutbecomescurtaftererrors\)2\.VerbosityandStructure\-Howverbosearetheuser’smessages?Aretheyminimalandcompressed,orexpansiveandelaborated?\-Doestheuseruseformattingconventions?\(e\.g\.,bulletpoints,numberedlists,codeblocks,markdown,allcaps,punctuationpatterns\)\-Howdoesmessagelengthandstructurechangeacrossturns?3\.SocialConventions\-Doestheuserusegreetings,pleasantries,expressionsofgratitude,orsign\-offs?\-Doestheusertreattheagentasatool\(purelytransactional\)orasasocialinterlocutor\(rapport\-building,politeness,humor\)?4\.RequestFraming\-Howdoestheusersyntacticallyframetheirrequests?\(e\.g\.,imperativecommands,questions,hedgedsuggestions,statementsofproblemswithoutexplicitasks\)\#Instructions\-Generateterse,concisebulletpoints,notfullsentences\.\-Abstractawayfromthespecifictopic/domain\.\-IMPORTANT:DoNOTusetask\-specificterms\(e\.g\.,"coding,""booking,""Python","CSV"\)\.Usegenericsubstitutes\(e\.g\.,"executingatask,""providingconstraints,""thetargetartifact"\)\.OutputavalidJSONobjectusingtheexactformatbelow\.DonotincludeanytextoutsidetheJSON\.\{\{"register\_and\_tone":\{\{"register":"whatistheuser’sregister?\(formal,casual,conversational,terse,professional,etc\.\)","emotional\_tone":"whatistheuser’semotionaltone?\(neutral,frustrated,enthusiastic,impatient,apologetic,deferential,etc\.\)","tone\_shifts":"doesthetoneshiftacrosstheconversation,andinresponsetowhat?"\}\},"verbosity\_and\_structure":\{\{"verbosity":"howverbosearetheuser’smessages?minimalandcompressed,orexpansiveandelaborated?","formatting":"doestheuseruseformattingconventions?\(bulletpoints,numberedlists,codeblocks,markdown,etc\.\)","evolution":"howdoesmessagelengthandstructurechangeacrossturns?"\}\},"social\_conventions":\{\{"politeness\_markers":"doestheuserusegreetings,pleasantries,gratitude,orsign\-offs?","agent\_relationship":"doestheusertreattheagentasatool\(transactional\)orasasocialinterlocutor\(rapport\-building,politeness,humor\)?"\}\},"request\_framing":"howdoestheusersyntacticallyframetheirrequests?\(imperativecommands,questions,hedgedsuggestions,problemstatementswithoutexplicitasks,etc\.\)"\}\}

User Behavior Description: DAMSL Dialog Acts Dimension[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Yourtaskistoanalyzeaspecificuserutteranceinamulti\-turnconversationandprovideadescriptionoftheuser’sbehaviour\.\#ConversationHistory\{conversation\_history\}\#TargetUtteranceReadtheconversationhistoryforcontext,butyouranalysisMUSTstrictlyapplyONLYtothisutterance:\{target\_user\_utterance\}\#DialogueActMarkupinSeveralLayers\(DAMSL\)AnnotationFrameworkDAMSLdefinesutterancesbasedontheintentionsofthespeaker\.Eachutteranceisanalyzedalongseveraldimensions\.1\.INFORMATIONLEVEL:Characterizesthesemanticcontentoftheutterance:\-\*\*Task\*\*:Directlyadvances\(orattemptstoadvance\)thegoalsofthedomaintask\.\-\*\*Task\-management\*\*:Addressestheproblem\-solvingprocess/procedureratherthanperformingthetaskitself\.\-\*\*Communication\-management\*\*:Concernedexclusivelywithmaintainingthecommunicationchannel\.2\.FORWARDLOOKINGFUNCTION:Characterizeswhateffecttheutterancehasonthesubsequentdialog:\-\*\*Statement\*\*:Makesaclaim\(Assert,Re\-assert,Other\-statement\)\.\-\*\*Info\-request\*\*:Asksforinformation\.\-\*\*Influencing\-addressee\-future\-action\*\*:Directlyinfluencestheaddressee’sfuturenon\-communicativeactions,directsorsuggeststheaddresseetoperformanaction\.\-\*\*Committing\-speaker\-future\-action\*\*:Potentiallycommitsthespeakertosomefutureaction\.\-\*\*Conventional\*\*:Conventionalconversationalactionssuchasgreeting,farewells,thanking,orrespondingtothanks\.3\.BACKWARDLOOKINGFUNCTION:Characterizeshowthecurrentutterancerelatestothepreviousdiscourse:\-\*\*Agreement\*\*:Accept,reject,partialaccept/reject,holdsoffaresponse\.\-\*\*Answer\*\*:Providesrequestedinformation\.\-\*\*Understanding\*\*:Signalscomprehension\(acknowledgement,repetition/reformulation,collaborativecompletion\)orlackthereof\.\#OutputInstructionsAnalyzethetargetutterancealongDAMSL’sdimensionsandwriteaconcise,1sentencedescriptionoftheuser’sbehaviouranditsfunctionalroleinthetargetutterance:\-Generateterse,concisebulletpoints,notfullsentences\.\-Abstractawayfromthespecifictopic/domain\-IMPORTANT:DoNOTusetask\-specificterms\(e\.g\.,"coding,""booking,""Python","CSV"\)\.Usegenericsubstitutes\(e\.g\.,"executingatask,""providingconstraints,""thetargetartifact"\)\.Outputonlytheconcisedescriptionasplaintext,withoutanyothertextorformatting\.

User Behavior Description: SGD Dialog Acts Dimension[⬇](data:text/plain;base64,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)Youareanexpertanalyzinguserbehaviourinhuman\-AIconversations\.Yourtaskistoanalyzeaspecificuserutteranceinamulti\-turnconversationandclassifytheuser’sdialogueact\(s\)\.\#ConversationHistory\{conversation\_history\}\#TargetUtteranceReadtheconversationhistoryforcontext,butyouranalysisMUSTstrictlyapplyONLYtothisutterance:\{target\_user\_utterance\}\#DialogueActTaxonomy\(adaptedfromSchema\-GuidedDialogue\)Classifythetargetutteranceusingoneormoreofthefollowinguserdialogueacts:1\.INFORM:Userprovidesinformation,statespreferences,orsuppliesconstraintstotheagent\.2\.REQUEST:Useraskstheagentforspecificinformationordetails\.3\.AFFIRM:Useragreeswith,confirms,oracceptsaproposition,value,orsuggestionmadebytheagent\.4\.NEGATE:Userdisagreeswith,denies,orrejectsaproposition,value,orsuggestionmadebytheagent\.5\.SELECT:Userchoosesbetweenmultipleoptionsoralternativespresentedbytheagent\.6\.INFORM\_INTENT:Userstates,introduces,orshiftstoanewgoalortaskobjective\.7\.AFFIRM\_INTENT:Userconfirmsoragreeswithagoalortaskobjectivesuggestedbytheagent\.8\.NEGATE\_INTENT:Userrejectsordeniesagoalortaskobjectivesuggestedbytheagent\.9\.REQUEST\_ALTS:Userasksfordifferentoradditionaloptionsbeyondwhathasbeenpresented\.10\.THANK:Userexpressesgratitudeorappreciation\.11\.GOODBYE:Usersignalstheendoftheconversationoraconversationalclosing\.12\.GREET:Userinitiatestheconversationwithagreetingoropening\.\#OutputInstructions\-AssignALLapplicabledialogueactstothetargetutterance\(utterancesmayhavemultipleacts\)\.Outputthedialogueacts,separatedbycommas,withoutanyothertextorformatting\.

#### Embedding and quantization\.

For each conversation, we concatenate the descriptions across all six behavioral dimensions and embed the resulting text using Qwen3\-Embedding\-8B\. We retain the first 1,024 embedding dimensions and applyℓ2\\ell\_\{2\}normalization\. For each comparison between original and simulated conversations, we pool the two sets of embeddings and apply PCA to retain 90% of the variance\. We then quantize the embeddings usingkk\-means withk=500k=500clusters, using five restarts with up to 500 iterations each and retaining the solution with the lowest clustering objective\. The cluster counts are used to create discrete distributions for the original and simulated user behaviors\.