Memory Retrieval for Changing Preferences

# Memory Retrieval for Changing Preferences
Source: [https://arxiv.org/html/2606.02976](https://arxiv.org/html/2606.02976)
Yuehan Qin, Li Li, Linxin Song, Wei Yang, Jiate Li, Yuqing Yang, Yue Zhao University of Southern California \{yuehanqi@usc\.edu\}

###### Abstract

Long\-context dialogue systems must decide both when to access memory and which parts of the interaction history are relevant\. Existing approaches typically rely on heuristic retrieval signals or always\-on memory usage, failing to account for the changing and potentially inconsistent nature of user preferences\. In this work, we propose a unified framework for memory access and selection based on changing preferences\. We formulate personalized memory retrieval as identifying which historical turns provide evidence about a user’s latent preference state, rather than relying on surface\-level semantic similarity\. To this end, we quantify the utility of each memory turn using a Bayes factor, defined as the improvement in the model’s likelihood of the reference response when the turn is included in context\. This provides a principled measure of evidence strength and a unified signal for both memory access and selection\. By framing memory retrieval as utility estimation, the model learns to identify salient turns and regulate memory usage based on expected utility\. Experiments on four heterogeneous memory benchmarks show that our approach outperforms existing embedding\-based retrieval on long\-context, preference\-intensive tasks where modeling changing preferences is essential, while remaining competitive in low\-density regimes where semantic similarity suffices\.

## 1Introduction

As users engage with conversational AI systems across extended periods, they accumulate histories rich with preferences, experiences, and changing beliefs\. A system that cannot selectively draw on this history when answering new queries fails to deliver the kind of continuity that personalization demands\(Yanget al\.,[2026](https://arxiv.org/html/2606.02976#bib.bib29)\)\. However, as interaction histories grow to span hundreds of sessions, attending to everything is computationally intractable\(Dinget al\.,[2024](https://arxiv.org/html/2606.02976#bib.bib9); Liet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib27)\), and attending to the wrong things actively degrades response quality\(Liuet al\.,[2023](https://arxiv.org/html/2606.02976#bib.bib1); Shiet al\.,[2023](https://arxiv.org/html/2606.02976#bib.bib33); Amirazet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib2)\)\. This challenge is further exacerbated by the well\-documented tendency of large models to hallucinate or rely on spurious cues when presented with noisy or misleading context\(Shawnet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib24); Li and Zhao,[2026](https://arxiv.org/html/2606.02976#bib.bib26)\)\.

This makes memory retrieval a central challenge in personalized AI\. Given a new query, the system must identify which past interactions carry genuine utility for producing a correct and contextually appropriate response\. The approach that has emerged as standard practice is semantic embedding similarity: each memory turn and the incoming query are encoded, and the turns nearest in embedding space are selected as context\(Chhikaraet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib3); Wuet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib5); Liet al\.,[2025d](https://arxiv.org/html/2606.02976#bib.bib18); Xuet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib8); Tanet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib4)\)\. Semantic similarity is, however, a proxy for the wrong quantity\. It measures topical co\-occurrence in embedding space, not whether a memory turn provides incremental evidence that is useful for resolving the current query\. This distinction is inconsequential in many retrieval settings, but it becomes critical in personalization, where the same topic can appear across turns that reflect fundamentally different states of the user\. This mismatch manifests in several structural failure modes\. First, semantically similar turns may correspond to incompatible or superseded latent preference states, causing retrieval systems to surface context that is topically related yet no longer useful for the current query\(Jianget al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib6); Zhaoet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib7)\)\. Second, evidential utility is often sparse, temporally distant, or only weakly aligned with the query surface form\. As a result, retrieval methods grounded primarily in semantic similarity tend to favor lexical or topical overlap over genuinely decision\-relevant evidence\. Similar biases toward surface\-level correlations have also been observed in other structured prediction settings\(Liet al\.,[2023](https://arxiv.org/html/2606.02976#bib.bib17),[2024](https://arxiv.org/html/2606.02976#bib.bib16)\)\. An example is shown in Fig\.[1](https://arxiv.org/html/2606.02976#S1.F1)\.

This failure is symptomatic of a deeper issue in how existing memory systems decompose the retrieval problem\. Most systems treat two coupled decisions independently: whether external memory should influence the response for a given query, and if so, which turns are worth retrieving\(Packeret al\.,[2024](https://arxiv.org/html/2606.02976#bib.bib10); Chhikaraet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib3); Tanet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib4)\)\. Access is typically handled through always\-on or coarse heuristics, while selection relies on semantic similarity\(Liet al\.,[2025a](https://arxiv.org/html/2606.02976#bib.bib28); Wuet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib5)\)\. Neither decision is grounded in a direct estimate of memory utility for the current query\. The result is a system that retrieves what is related rather than what is useful\.

![Refer to caption](https://arxiv.org/html/2606.02976v1/x1.png)Figure 1:Illustration of retrieval methods for personalization memory\. Given a generic recommendation request, semantic embedding retrieval returns surface\-similar but evidentially uninformative turns \(e\.g\., abstract self\-reflections about creativity\), leading the LLM to a generic recommendation\. Our Bayes Factor\-based retrieval instead locates the user’s recent preference shift toward nature recording, enabling the LLM to produce the correct preference\-aligned recommendation\.We argue that both decisions can be unified under a single principle: a memory turn should be retrieved if and only if it provides evidence that improves the correctness of the response, and memory should be accessed at all only when such evidence exists\. We operationalize this principle through a Bayesian lens, treating each candidate turn as potential evidence and measuring how much it updates the model’s belief toward the correct answer\.

We summarize our contributions as below:

- •A principled retrieval signal for personalized memory\.We interpret utility gain as a Bayes factor, providing a principled alternative to semantic similarity that captures evidential utility rather than topical relatedness\.
- •Bayes\-factor\-gated memory framework\.We propose a simple framework that couples \(i\) a discrete gating mechanism for deciding when to trigger memory access with \(ii\) a saliency\-based selection mechanism for identifying relevant context, both driven by the same Bayes factor signal\.
- •Evidence of distinctiveness\.Through controlled ablations, we show that Bayes\-factor\-based saliency labels are not interchangeable with embedding\-based alternatives, with the largest gap on long\-context preference tasks \(\+8\.76pp on MemBench\-High\), and that supervised fine\-tuning on these labels is essential for the observed gains\.
- •Improved long\-context personalization\.We demonstrate consistent improvements across multiple personalization benchmarks, with the largest gains on challenging instances requiring long\-range memory reasoning\.

## 2Related Works

Large Language Models have emerged as personalized assistants across a wide range of tasks, from writing support to tailored recommendations\(Salemiet al\.,[2024](https://arxiv.org/html/2606.02976#bib.bib34); Yanget al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib23); Liet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib27); Guet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib21); Zhaoet al\.,[2025a](https://arxiv.org/html/2606.02976#bib.bib20); Liet al\.,[2025c](https://arxiv.org/html/2606.02976#bib.bib19)\)\. As interaction histories accumulate across sessions, effectively leveraging them requires selective memory access: attending to everything is computationally intractable, while attending to the wrong turns degrades response quality\(Liuet al\.,[2023](https://arxiv.org/html/2606.02976#bib.bib1)\)\. Early memory systems such as MemGPTPackeret al\.\([2024](https://arxiv.org/html/2606.02976#bib.bib10)\)and MemoryBankZhonget al\.\([2023](https://arxiv.org/html/2606.02976#bib.bib11)\)address context window limitations by maintaining external memory stores\. More recent systems including Mem0Chhikaraet al\.\([2025](https://arxiv.org/html/2606.02976#bib.bib3)\)and A\-MEMXuet al\.\([2025](https://arxiv.org/html/2606.02976#bib.bib8)\)extract and consolidate facts from dialogue history, while RMMTanet al\.\([2025b](https://arxiv.org/html/2606.02976#bib.bib4)\)introduces reflective summarization for finer retrieval granularity\. These systems uniformly rely on semantic embedding similarity for retrieval, which we identify as a structural limitation under preference evolution\.

Several works have proposed using utility gain as a context quality signal\. RECOMPXuet al\.\([2023](https://arxiv.org/html/2606.02976#bib.bib12)\)uses generator feedback for context compression, CODEFILTERLiet al\.\([2025e](https://arxiv.org/html/2606.02976#bib.bib13)\)applies likelihood\-based impact scoring to code completion, andLiuet al\.\([2025](https://arxiv.org/html/2606.02976#bib.bib14)\)show that pointwise mutual information between context and query correlates with answer accuracy\. Our work adapts this family of signals to personalized memory retrieval, where its interpretation as a Bayes factor over changing user preferences provides principled motivation beyond prior heuristic applications\.

## 3Methodology

##### Problem Setup

Let a dialogue history beℋ=\(c1,c2,…,cT\)\\mathcal\{H\}=\(c\_\{1\},c\_\{2\},\\ldots,c\_\{T\}\), a user queryqq, and the \(unknown\) answeraa\. The goal is to select a subset of turnsF⊆ℋF\\subseteq\\mathcal\{H\}such that a model conditioned on\(q,F\)\(q,F\)can produce the correct answer\. This problem involves two coupled decisions: \(i\)*whether*memory should be consulted, and \(ii\)*which*turns should be retrieved\.

### 3\.1Stage 1: Answer\-Conditioned Saliency

Following the Bayesian view of evidence, we treatSala​\(ci\)\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\)as the*log Bayes factor*of turncic\_\{i\}for answeraaconditioned on queryqq: a turn constitutes genuine memory evidence if and only if observing it updates the answer distribution beyond what the query alone establishes\. Formally,p​\(a∣q\)p\(a\\mid q\)is the model’s prior belief over the answer given the query alone, andp​\(a∣q,ci\)p\(a\\mid q,c\_\{i\}\)is the posterior after observing turncic\_\{i\}\. ThenSala​\(ci\)\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\)is the log\-ratio between the two:

Sala​\(ci∣q,a\)=log⁡p​\(a∣q,ci\)−log⁡p​\(a∣q\),\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\\mid q,a\)=\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid q\),\(1\)which corresponds to the classical log Bayes factor quantifying how stronglycic\_\{i\}supportsaa\. In practice, we calculate both terms from a smaller language model for computing efficiency\.

A turn whose Bayes factor is near unity \(e\.g\., an occupational fact when the query asks for a location\) is correctly suppressed; a turn that drives the posterior sharply toward the correct answer receives highSala\\mathrm\{Sal\}\_\{a\}\. This stands in direct contrast to embedding\-based retrieval, which measures query–turn similarity and thus cannot distinguish*evidential relevance*from*topical relatedness*\.

Oracle Focus Set\.We define the oracle focus set at thresholdτ\\tauas

Sτ=\{ci∈ℋ:Sal​\(ci∣q,a\)\>τ\},S\_\{\\tau\}\\;=\\;\\bigl\\\{c\_\{i\}\\in\\mathcal\{H\}:\\;\\mathrm\{Sal\}\(c\_\{i\}\\mid q,a\)\>\\tau\\bigr\\\},\(2\)whereSal​\(ci∣q,a\)\\mathrm\{Sal\}\(c\_\{i\}\\mid q,a\)denotes the answer\-conditioned saliency of turncic\_\{i\}given queryqqand gold answeraa, andτ\\tauis a threshold controlling the amount of retained evidence\.SτS\_\{\\tau\}represents an oracle notion of memory: it contains exactly those turns that improve answer prediction\.

### 3\.2Stage 2: Query\-Conditioned Proxy

Sala​\(ci;q\)\\mathrm\{Sal\}\_\{a\}\(c\_\{i\};q\)depends on the unknown gold answeraa, and is therefore not directly available at inference time\. To obtain a tractable approximation, we replace the answeraawith the observable queryqq, yielding a query\-conditioned Bayes factor:

Salq​\(ci;q\)=log⁡p​\(q∣ci\)−log⁡p​\(q\)\.\\mathrm\{Sal\}\_\{q\}\(c\_\{i\};q\)\\;=\\;\\log p\(q\\mid c\_\{i\}\)\\;\-\\;\\log p\(q\)\.\(3\)Herep​\(q\)p\(q\)is the model’s prior probability of the query in isolation, andp​\(q∣ci\)p\(q\\mid c\_\{i\}\)is the likelihood of the query after observing turncic\_\{i\}\.Salq\\mathrm\{Sal\}\_\{q\}is therefore the log ratio of the two—a Bayes factor that measures how stronglycic\_\{i\}constitutes evidence for the query111Equivalently,Salq​\(ci;q\)=PMI​\(q;ci\)\\mathrm\{Sal\}\_\{q\}\(c\_\{i\};q\)=\\mathrm\{PMI\}\(q;c\_\{i\}\), connecting our signal to the family of pointwise mutual information context\-quality measures studied in retrieval\-augmented generation\(Liuet al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib14)\)\.

WhileSalq\\mathrm\{Sal\}\_\{q\}does not condition on the answer, we now show that it is a principled surrogate forSala\\mathrm\{Sal\}\_\{a\}rather than a heuristic substitute\. Our argument proceeds in three steps: an exact decomposition \(Proposition[1](https://arxiv.org/html/2606.02976#Thmtheorem1)\), a sufficient condition for rank\-equivalence \(Theorem[3](https://arxiv.org/html/2606.02976#Thmtheorem3)\), and a bounded approximation result for the realistic case \(Theorem[4](https://arxiv.org/html/2606.02976#Thmtheorem4)\)\.

##### An exact decomposition\.

By the chain rule of Bayesian updating, the joint Bayes factor ofcic\_\{i\}for the pair\(a,q\)\(a,q\)decomposes additively:

###### Proposition 1\(Joint Bayes factor decomposition\)\.

For any candidate turncic\_\{i\}, queryqq, and answeraa,

log⁡p​\(a,q∣ci\)p​\(a,q\)⏟BF\(a,q\)​\(ci\)=log⁡p​\(a∣q,ci\)p​\(a∣q\)⏟Sala​\(ci\)\+log⁡p​\(q∣ci\)p​\(q\)⏟Salq​\(ci\)\.\\underbrace\{\\log\\frac\{p\(a,q\\mid c\_\{i\}\)\}\{p\(a,q\)\}\}\_\{\\mathrm\{BF\}\_\{\(a,q\)\}\(c\_\{i\}\)\}\\;=\\;\\underbrace\{\\log\\frac\{p\(a\\mid q,c\_\{i\}\)\}\{p\(a\\mid q\)\}\}\_\{\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\)\}\\;\+\\;\\underbrace\{\\log\\frac\{p\(q\\mid c\_\{i\}\)\}\{p\(q\)\}\}\_\{\\mathrm\{Sal\}\_\{q\}\(c\_\{i\}\)\}\.\(4\)

The identity \([4](https://arxiv.org/html/2606.02976#S3.E4)\) is exact and assumption\-free\. It shows thatSalq\\mathrm\{Sal\}\_\{q\}is not an unrelated heuristic but rather one of two additive components of the joint evidencecic\_\{i\}provides for\(a,q\)\(a,q\); Ranking bySalq\\mathrm\{Sal\}\_\{q\}is therefore equivalent to ranking byBF\(a,q\)−Sala\\mathrm\{BF\}\_\{\(a,q\)\}\-\\mathrm\{Sal\}\_\{a\}, and the question of when this ranking agrees withSala\\mathrm\{Sal\}\_\{a\}reduces to a question about the relative magnitudes of the two terms\.

##### When does the surrogate preserve the ranking?

We now identify a condition under whichSalq\\mathrm\{Sal\}\_\{q\}andSala\\mathrm\{Sal\}\_\{a\}induce the same ordering on candidate turns up to a constant\.

###### Definition 2\(Query\-conditional sufficiency\)\.

A turncic\_\{i\}is*query\-sufficient*for\(q,a\)\(q,a\)ifq⟂a∣ciq\\perp a\\mid c\_\{i\}, i\.e\.,

p​\(a∣q,ci\)=p​\(a∣ci\)\.p\(a\\mid q,c\_\{i\}\)\\;=\\;p\(a\\mid c\_\{i\}\)\.\(5\)

Intuitively,cic\_\{i\}is query\-sufficient when it already encodes the user\-state information needed to determineaa, so that the query merely*triggers*retrieval rather than supplying additional evidence\. This holds naturally for the kind of turns personalized memory aims to retrieve: declarative preference statements, persona attributes, and committed user states\.

###### Theorem 3\(Rank\-equivalence under sufficiency\)\.

Let𝒞=\{c1,…,cn\}\\mathcal\{C\}=\\\{c\_\{1\},\\ldots,c\_\{n\}\\\}be a candidate set in which every turn is query\-sufficient \(Def\.[2](https://arxiv.org/html/2606.02976#Thmtheorem2)\)\. Then for allci,cj∈𝒞c\_\{i\},c\_\{j\}\\in\\mathcal\{C\},

Sala​\(ci\)−Sala​\(cj\)=Salq​\(ci\)−Salq​\(cj\)\+\[Salamarg​\(ci\)−Salamarg​\(cj\)\],\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{a\}\(c\_\{j\}\)\\;=\\;\\mathrm\{Sal\}\_\{q\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{q\}\(c\_\{j\}\)\\;\+\\;\\bigl\[\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}\(c\_\{j\}\)\\bigr\],\(6\)whereSalamarg​\(ci\)=log⁡p​\(a∣ci\)−log⁡p​\(a\)\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}\(c\_\{i\}\)=\\log p\(a\\mid c\_\{i\}\)\-\\log p\(a\)is the marginal answer Bayes factor\. In particular, ifSalamarg\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}is approximately constant across𝒞\\mathcal\{C\}, then ranking bySalq\\mathrm\{Sal\}\_\{q\}recovers theSala\\mathrm\{Sal\}\_\{a\}ranking exactly\.

The constant\-Salamarg\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}assumption is mild whenever the candidate pool has been pre\-filtered to a topically coherent slice of the user’s history, which is precisely what the query\-side top\-KKpre\-filter in our inference pipeline \(Eq\.[10](https://arxiv.org/html/2606.02976#S3.E10)\) accomplishes\.

##### The realistic case\.

Strict query\-conditional sufficiency rarely holds, but the disagreement betweenSalq\\mathrm\{Sal\}\_\{q\}\- andSala\\mathrm\{Sal\}\_\{a\}\-induced rankings can be bounded in terms of a per\-turn sufficiency residualεi:=\|logp\(a∣q,ci\)−logp\(a∣ci\)\|\\varepsilon\_\{i\}:=\|\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid c\_\{i\}\)\|and the marginal\-evidence gap\. We state and prove the precise bound in Appx\.[D](https://arxiv.org/html/2606.02976#A4.SS0.SSS0.Px1)\(Theorem[4](https://arxiv.org/html/2606.02976#Thmtheorem4)\); the practical takeaway is thatSalq\\mathrm\{Sal\}\_\{q\}is faithful when turns are self\-contained and degrades on instances requiring cross\-turn aggregation\.

### 3\.3Stage 3: Threshold\-Gated Memory

![Refer to caption](https://arxiv.org/html/2606.02976v1/x2.png)Figure 2:Illustration of threshold\-gated memory access\. Although both memory turns are semantically related to the user query, only the lower example provides evidence that materially changes the model’s belief about the appropriate response, resulting in a higherSala\\mathrm\{Sal\}\_\{a\}score and activation of memory\-guided reasoning\.We convert the continuous saliency signal into a discrete decision via a thresholdτ\\tau:

Sτ​\(ℋ,q\)=\{ci∈ℋ:Salq​\(ci;q\)\>τ\},gτ​\(ℋ,q\)=𝟏​\[\|Sτ​\(ℋ,q\)\|\>0\]\.S\_\{\\tau\}\(\\mathcal\{H\},q\)=\\\{\\,c\_\{i\}\\in\\mathcal\{H\}:\\mathrm\{Sal\}\_\{q\}\(c\_\{i\};q\)\>\\tau\\,\\\},\\quad g\_\{\\tau\}\(\\mathcal\{H\},q\)=\\mathbf\{1\}\\\!\\left\[\\,\|S\_\{\\tau\}\(\\mathcal\{H\},q\)\|\>0\\,\\right\]\.\(7\)
HereSτS\_\{\\tau\}is the set of*salient chunks*retained as evidence, andgτ∈\{0,1\}g\_\{\\tau\}\\\!\\in\\\!\\\{0,1\\\}is the memorytriggerindicating whether the memory\-guided policy should fire on this context\.

Why coupleSala\\mathrm\{Sal\}\_\{a\}andτ\\tau?Sala\\mathrm\{Sal\}\_\{a\}alone provides a continuous utility score but no decision boundary, so the model cannot learn a discrete retrieval policy\. A fixed threshold alone provides a gate but no ranking, so the model cannot prioritize among retained turns\. Coupling them gives a calibrated labeling mechanism that \(i\) filters low\-utility turns viaτ\\tau, \(ii\) ranks the survivors by their Bayes factor score, and \(iii\) exposes both the relevance label and the ranking as explicit supervision targets for the controller\. This is the central design choice of our silver label construction\.

##### Implications for the pipeline\.

Theorem[3](https://arxiv.org/html/2606.02976#Thmtheorem3)delineates the regime in whichSalq\\mathrm\{Sal\}\_\{q\}is a faithful surrogate: candidate sets dominated by self\-contained user\-state turns\. Outside this regime where instances requiring cross\-turn aggregation, the supervised controller \(§[3\.4\.1](https://arxiv.org/html/2606.02976#S3.SS4.SSS1)\), trained onSala\\mathrm\{Sal\}\_\{a\}silver labels, recovers ranking quality thatSalq\\mathrm\{Sal\}\_\{q\}alone cannot\. The two\-stage design \(Salq\\mathrm\{Sal\}\_\{q\}pre\-filter→\\rightarrowSala\\mathrm\{Sal\}\_\{a\}\-trained controller\) is therefore a direct response to this structure\.

### 3\.4Stage 4: Learning the Retrieval Policy

#### 3\.4\.1Training pipeline\.

The triggergτg\_\{\\tau\}and the salient setSτS\_\{\\tau\}are exposed to a*single*fine\-tuned policy through two paired supervised signals constructed from the same\(𝒞,Sτ\)\(\\mathcal\{C\},S\_\{\\tau\}\)\. We instantiate them in three stages, all sharing parameters under one next\-token\-prediction objective\.

Controller head \(memory\-gating\)\.Givenτ\\tau, the user queryqq, and the candidate evidenceSτS\_\{\\tau\}, the model is trained to emit a structured action

a=\(trigger=gτ,focus​\_​idx=TopKSala​\(Sτ\),policy=π⋆\),a\\;=\\;\\big\(\\mathrm\{trigger\}=g\_\{\\tau\},\\;\\mathrm\{focus\\\_idx\}=\\mathrm\{TopK\}\_\{\\mathrm\{Sal\}\_\{a\}\}\(S\_\{\\tau\}\),\\;\\mathrm\{policy\}=\\pi^\{\\star\}\\big\),\(8\)wrapped in a brief natural\-language⟨thought⟩\\langle\\mathrm\{thought\}\\ranglefollowed by a JSON⟨action⟩\\langle\\mathrm\{action\}\\rangle\. HereTopKSala\\mathrm\{TopK\}\_\{\\mathrm\{Sal\}\_\{a\}\}ranks the candidate chunks by their answer\-conditioned saliency score\. To prevent the controller from collapsing togτ≡1g\_\{\\tau\}\\\!\\equiv\\\!1, we balance with negative samples \(details in Appx\.[B](https://arxiv.org/html/2606.02976#A2)\)\.

Reader head \(MCQ answering\)\.Conditioned on the sameSτS\_\{\\tau\}and the four candidate options𝒪\\mathcal\{O\}, the model is trained to produce the gold answer terminated by⟨final​\_​answer⟩​\(x\)\\langle\\mathrm\{final\\\_answer\}\\rangle\(x\)\.

Joint objective\.Both heads share parameters and are trained jointly with a single next\-token\-prediction objective on the union of controller and reader samples:

ℒSFT=−∑\(x,y\)∈𝒟ctrl∪𝒟read∑tlog⁡pθ​\(yt∣y<t,x\)\.\\mathcal\{L\}\_\{\\text\{SFT\}\}\\;=\\;\-\\\!\\\!\\sum\_\{\(x,y\)\\in\\mathcal\{D\}\_\{\\text\{ctrl\}\}\\cup\\mathcal\{D\}\_\{\\text\{read\}\}\}\\sum\_\{t\}\\log p\_\{\\theta\}\\\!\\bigl\(y\_\{t\}\\mid y\_\{<t\},x\\bigr\)\.\(9\)

### 3\.5Inference Pipeline

During inference, we have a queryqqtogether with the user’s full conversation historyℋ=\{c1,…,cn\}\\mathcal\{H\}=\\\{c\_\{1\},\\dots,c\_\{n\}\\\}up toqq, but neither the gold answeraanor the answer\-conditioned saliencySal​\(ci∣q,a\)\\mathrm\{Sal\}\(c\_\{i\}\\mid q,a\)are observable\. Our pipeline therefore relies on the controller as a learned proxy for the training\-time selection rule, and operates in three stages\.

Query\-side pre\-filtering\.We first reduce the search space using the precomputed query saliency:

ℋ~=TopKSalq​\(ℋ;K\),\\tilde\{\\mathcal\{H\}\}\\;=\\;\\mathrm\{TopK\}\_\{\\mathrm\{Sal\}\_\{q\}\}\(\\mathcal\{H\};K\),\(10\)ℋ~\\tilde\{\\mathcal\{H\}\}is presented to the controller in temporal order, annotated with each turn’s originalmessage\_idx\.

Controller as learned gate\.Given\(q,ℋ~\)\(q,\\tilde\{\\mathcal\{H\}\}\), the controller emits a structured output consisting of a⟨thought⟩\\langle\\mathrm\{thought\}\\rangleblock followed by an⟨action⟩\\langle\\mathrm\{action\}\\rangleJSON object:

\(g^τ,S^τ\)=Controller​\(q,ℋ~\),S^τ⊆ℋ~,\|S^τ\|≤k,\(\\hat\{g\}\_\{\\tau\},\\;\\hat\{S\}\_\{\\tau\}\)\\;=\\;\\mathrm\{Controller\}\(q,\\tilde\{\\mathcal\{H\}\}\),\\qquad\\hat\{S\}\_\{\\tau\}\\subseteq\\tilde\{\\mathcal\{H\}\},\\;\\;\|\\hat\{S\}\_\{\\tau\}\|\\leq k,\(11\)whereg^τ∈\{0,1\}\\hat\{g\}\_\{\\tau\}\\\!\\in\\\!\\\{0,1\\\}approximates the training gategτ=𝟏​\[∃ci​s\.t\.​yi=1\]g\_\{\\tau\}\\\!=\\\!\\mathbf\{1\}\[\\,\\exists\\,c\_\{i\}\\text\{ s\.t\. \}y\_\{i\}\\\!=\\\!1\\,\]andS^τ\\hat\{S\}\_\{\\tau\}approximates the focus setF⋆=\{ci:yi=1\}F^\{\\star\}\\\!=\\\!\\\{c\_\{i\}:y\_\{i\}\\\!=\\\!1\\\}\. Crucially, noSala\\mathrm\{Sal\}\_\{a\}computation is performed at inference; the controller has internalized theτ\\tau\-thresholded selection rule through supervised training, realizingBayes\-factor\-guided memory selectionpurely from self\-contained dialogue features\.

Gated reading\.The reader is invoked conditionally on the predicted gate:

y^=\{Reader​\(q,𝒪,ℋS^τ\)if​g^τ=1,Reader​\(q,𝒪,∅\)if​g^τ=0,\\hat\{y\}\\;=\\;\\begin\{cases\}\\mathrm\{Reader\}\\\!\\big\(q,\\,\\mathcal\{O\},\\,\\mathcal\{H\}\_\{\\hat\{S\}\_\{\\tau\}\}\\big\)&\\text\{if \}\\hat\{g\}\_\{\\tau\}=1,\\\\\[2\.0pt\] \\mathrm\{Reader\}\\\!\\big\(q,\\,\\mathcal\{O\},\\,\\varnothing\\big\)&\\text\{if \}\\hat\{g\}\_\{\\tau\}=0,\\end\{cases\}\(12\)where𝒪\\mathcal\{O\}denotes the four MCQ options andℋS^τ\\mathcal\{H\}\_\{\\hat\{S\}\_\{\\tau\}\}the user turns indexed byS^τ\\hat\{S\}\_\{\\tau\}, presented in temporal order\. The reader produces a free\-form rationale terminating in a⟨final​\_​answer⟩\\langle\\mathrm\{final\\\_answer\}\\rangletoken from which the predicted option letter is parsed\. Wheng^τ=0\\hat\{g\}\_\{\\tau\}\{=\}0the reader falls back to query\-only answering, which prevents spurious memory injection on questions whose answer is not memory\-bound\.

## 4Experiments

### 4\.1Experimental Setup

##### Datasets\.

We evaluate on four benchmarks: MemBench\-Low/High\(Tanet al\.,[2025a](https://arxiv.org/html/2606.02976#bib.bib15)\), where Low tests explicit recall and High requires implicit inference; PersonaMem\(Jianget al\.,[2025](https://arxiv.org/html/2606.02976#bib.bib6)\), evaluating personalized responses under evolving user profiles; and PrefEval\(Zhaoet al\.,[2025b](https://arxiv.org/html/2606.02976#bib.bib7)\), evaluating user\-preference recall\. We apply a seed\-42 stratified 80/10/10 split\. For MB\-Low we report accuracy on the user\-only subset \(n=374n\{=\}374\), excluding\*\-assistantscenarios whose evidence falls outsideℋ\\mathcal\{H\}\. Full details are in Appx\.[A](https://arxiv.org/html/2606.02976#A1)\.

##### Baselines\.

We compare against five retrieval baselines\.Full contextconcatenates all user turns and passes them directly to the reader\.A\-MEMXuet al\.\([2025](https://arxiv.org/html/2606.02976#bib.bib8)\),MemoryBankZhonget al\.\([2023](https://arxiv.org/html/2606.02976#bib.bib11)\), andMem0Chhikaraet al\.\([2025](https://arxiv.org/html/2606.02976#bib.bib3)\)represent memory\-augmented retrieval systems\.RMMTanet al\.\([2025b](https://arxiv.org/html/2606.02976#bib.bib4)\)organizes dialogue history into reflective multi\-granularity memory summaries and retrieves evidence via embedding similarity\. All retrieved memories are substituted into the same reader prompt as our method\. Following prior work, we omit RMM’s retrospective reranking stage and MemoryBank’s forgetting update, as neither is applicable under single\-shot MCQ evaluation\. For each selector, we evaluate two reader models:Qwen3\.5\-9B\-thinking\(Team,[2026](https://arxiv.org/html/2606.02976#bib.bib31)\)andLlama\-3\.1\-8B\-Instruct\(Grattafioriet al\.,[2024](https://arxiv.org/html/2606.02976#bib.bib32)\)\. TheS​a​laSal\_\{a\}andS​a​lqSal\_\{q\}scores are computed usingQwen3\.5\-0\.8B\.

##### Evaluation Protocol\.

We report MCQ accuracy\. The reader emits free\-form text terminated by⟨final​\_​answer⟩​\(ℓ\)\\langle\\mathrm\{final\\\_answer\}\\rangle\(\\ell\)withℓ∈\{a,b,c,d\}\\ell\\\!\\in\\\!\\\{a,b,c,d\\\}; we extract the letter and compare to the gold label\. If parsing fails, we fall back to scanning the response for any unique\(a\)/\(b\)/\(c\)/\(d\)mention; are reported\. MB\-Low accuracy uses the user\-only subset \(n=374n\{=\}374test records\); the other three benchmarks use all kept test records\.

### 4\.2Main Results

Table 1:Main results and ablations\. Best results per benchmark are inbold\.##### Our Method Achieves the Best Overall Performance\.

Tab\.[1](https://arxiv.org/html/2606.02976#S4.T1)summarizes performance across four benchmarks\. Across both reader backbones, our method consistently achieves the strongest average accuracy, substantially outperforming all retrieval\-augmented baselines as well as the full\-context setting\. Under Qwen3\.5\-9B\-Thinking, our method achieves an average score of 84\.94, surpassing the strongest baseline, MemoryBank \(65\.64\), by \+19\.30 points, while improving over the full\-context baseline \(64\.09\) by \+20\.85 points\. Similar trends hold under Llama\-3\.1\-8B\-Instruct, where our method reaches 82\.05 average accuracy compared to 71\.65 for MemoryBank and 59\.74 for full\-context prompting\.

##### Gains are concentrated on inference\-heavy benchmarks\.

Performance gains are especially pronounced on MB\-High and Persona, where our method consistently achieves the strongest improvements across both reader backbones\. Under Qwen3\.5\-9B\-Thinking, our method improves from 69\.58 to 77\.32 on MB\-High \(\+7\.74\) and from 55\.71 to 75\.68 on Persona \(\+19\.97\) relative to the strongest retrieval baseline, MemoryBank\. Similar trends hold under Llama\-3\.1\-8B\-Instruct, where our method improves from 67\.57 to 77\.03 on Persona \(\+9\.46\)\. These benchmarks share a common structure: the answer depends on recovering implicit, temporally distant, or weakly lexicalized preference evidence rather than retrieving a single semantically aligned turn\. By contrast, the gap narrows on PrefEval, where preferences are often stated explicitly and semantic retrieval is already effective\. Under Qwen3\.5\-9B\-Thinking, MemoryBank reaches 88\.54 while our method achieves 97\.98 \(\+9\.44\)\. These results suggest that the advantage of our method is largest when retrieval depends on identifying high\-utility evidence among semantically related but uninformative history\.

Notably, our method also consistently outperforms the full\-context baseline despite conditioning on substantially fewer dialogue turns\. Under Qwen3\.5\-9B\-Thinking, full\-context prompting achieves only 64\.09 average accuracy compared to our 84\.94 \(\+20\.85\), while under Llama\-3\.1\-8B\-Instruct the gap is similarly large \(59\.74 vs\. 82\.05, \+22\.31\)\. This suggests that the gains do not arise from increased memory access alone, but from selectively isolating the small subset of interactions that genuinely contribute to the downstream response\. Together, these results support the view that effective personalized memory retrieval depends less on maximizing topical coverage than on identifying evidence with high answer utility\.

Fig\.[3](https://arxiv.org/html/2606.02976#S4.F3)compares our method against Mem0 and RMM across twelve representative sub\-categories\. The largest gains appear on PersonaMem dating/recommendation tasks \(\+33 to \+71\) and MB\-Low aggregative/comparative tasks \(\+48 to \+77\), where answers depend on sparse or distributed evidence rather than direct semantic overlap\. MB\-High and PrefEval further highlight the difficulty of long\-context preference and sentiment recall, where relevant evidence is often overwhelmed by topically related but evidentially irrelevant dialogue\. Conversely, when retrieval reduces to straightforward query–passage similarity, RMM is already competitive and the remaining improvement becomes much smaller\. These patterns support our central claim that effective personalized memory retrieval depends less on topical overlap than on identifying evidence with genuine answer utility\.

![Refer to caption](https://arxiv.org/html/2606.02976v1/x3.png)Figure 3:Sub\-category breakdown across four benchmarks\.Accuracy of Mem0, RMM, and our method on twelve representative sub\-categories;three per benchmark, drawn from MB\-High, PersonaMem, PrefEval, and MB\-Low\. Sub\-categories are selected to span the regimes where preference evidence is implicit \(PersonaMem dating/music consultation, PrefEval implicit\-choice\), aggregated across scattered turns \(MB\-LowAggregative/events,Comparative/roles\), or buried in long context \(MB\-HighPreference/\{book,food\},Emotion\)\. Our method outperforms the stronger baseline by 20 to 77 absolute points on every selected sub\-category\.![Refer to caption](https://arxiv.org/html/2606.02976v1/x4.png)Figure 4:Per\-benchmark comparison of retrieval signals over a single conversation\. BGE peaksIrrelevant\-but\-similartend to land on turns that are lexically aligned with the query but carry no evidence for the gold answer, whereasS​a​laSal\_\{a\}peaksSparse\-but\-importantconcentrate on the few turns that actually shift the best answer likelihood\.Bayes factors capture evidential rather than topical relevance\.Fig\.[4](https://arxiv.org/html/2606.02976#S4.F4)visualizes the retrieval distributions induced by semantic similarity and saliency\-based scoring across four representative benchmarks, with additional qualitative retrieval examples provided in Appx\. Tab\.[5](https://arxiv.org/html/2606.02976#A3.T5)\. Across all cases, BGE assigns high scores to turns that are lexically or topically aligned with the query, even when those turns contribute little evidence toward resolving the underlying preference or decision\. In contrast,S​a​laSal\_\{a\}consistently concentrates on a small number of temporally sparse yet decision\-critical turns, often prioritizing evidence that semantic retrieval ranks far below the top\. This contrast reflects the different retrieval objectives induced by the two signals: semantic similarity favors surface alignment, whereas Bayes\-factor saliency measures whether conditioning on a memory turn materially shifts the model’s belief toward the correct response\. Effective personalized memory retrieval therefore depends less on topical relatedness than on identifying the sparse pieces of context that carry genuine evidential utility\.

### 4\.3Ablation Study

Table 2:Main results on Qwen3\.5\-9B\-think\. Methods differ only in how evidence is selected from the user\-turn history:PPLranks turns by per\-turn cross\-entropy,BGEby query–turn cosine similarity,full ctxuses every user turn, andS​a​laSal\_\{a\}is the oracle answer\-conditioned saliency upper bound\. Numbers are accuracy \(%\); parentheses indicate the gap toOursin percentage points\.Table 3:Ablations on Qwen3\.5\-9B\-think\. During the supervised fine\-tuning stage,EMB labelsreplaces theS​a​laSal\_\{a\}silver labels with query–turn embedding similarity, isolating the supervision signal\.w/o Controllerremoves the controller and selects evidence directly from the saliency stage, isolating the controller during training\. Numbers are accuracy \(%\); parentheses indicate the gap toOursin percentage points\.We conduct three ablations to assess the contribution of each component\.

Supervised fine\-tuning is essential\. According to Tab\.[2](https://arxiv.org/html/2606.02976#S4.T2), removing supervised fine\-tuning and using the base Qwen3\.5\-9B\-Thinking model withSala\\mathrm\{Sal\}\_\{a\}scoring at inference time collapses performance to 66\.09 avg\. This demonstrates that the utility signal must be internalized through training rather than applied post\-hoc; an untuned model cannot reliably identify high\-utility memory turns at inference\.

S​a​laSal\_\{a\}is the critical signal\(Tab\.[3](https://arxiv.org/html/2606.02976#S4.T3)\)\. ReplacingS​a​laSal\_\{a\}\-derived labels with embedding\-based pseudo\-labels \(using cosine similarity to identify relevant turns\) reduces average accuracy from 84\.94 to 82\.55 \(−2\.39\-2\.39points\)\. The gap is most pronounced on MB\-High \(77\.32→\\rightarrow68\.56,−8\.76\-8\.76points\), the benchmark demanding the most implicit preference inference\. This confirms thatS​a​laSal\_\{a\}captures a qualitatively different supervision signal from semantic similarity: it reflects whether a memory turn actually reduces answer perplexity, rather than whether it is topically close to the query\.

The controller provides marginal but consistent benefit\(Tab\.[3](https://arxiv.org/html/2606.02976#S4.T3)\)\. Removing the controller while retaining other components yields 84\.90 avg, a negligible drop of 0\.04 points overall\. However, per\-benchmark analysis reveals that the controller provides a more consistent benefit on MB\-High \(77\.32 vs\. 73\.71,\+3\.61\+3\.61points\), suggesting its value is concentrated in harder retrieval scenarios where gating decisions are more consequential\.

Retrieval vs\. reader adaptation\.The performance gap between our method and retrieval\-only baselines should not be interpreted as a retrieval gain alone\. Empirically, the oracle selectorS​a​laSal\_\{a\}combined with the thinking\-enabled reader recovers much of the performance, suggesting that our learnedSalq→\\mathrm\{Sal\}\_\{q\}\\\!\\rightarrowcontroller→\\\!\\rightarrowfocus\-set pipeline approximates the oracle retrieval distribution\. This indicates that much of the remaining improvement comes from jointly training the reader and retrieval controller, rather than retrieval quality alone\.

We view this as an important property of the framework rather than a confounder\. Existing memory systems such as Mem0 or RMM rely on fixed extraction pipelines, external memory stores, or online reflection policies that are not naturally amenable to end\-to\-end supervised fine\-tuning\. In contrast, our framework jointly trains retrieval and reading, allowing the reader to adapt to compressed evidence representations\. Thus, the observed gains arise not only from better evidence selection, but also from co\-adaptation between memory selection and downstream reasoning\.

## 5Conclusion

We proposed a Bayes\-factor\-based framework for personalized memory retrieval that unifies memory access gating and turn selection under a single principled signal\. Unlike embedding\-based approaches that measure topical similarity, our method estimates the evidential utility of each memory turn for predicting the correct answer\. By jointly training a controller and reader on Bayes\-factor\-derived proxy labels, the framework learns when to retrieve and what to retrieve without requiring gold answers at inference time\. Experiments on four personalization benchmarks show consistent improvements over existing memory systems, with the largest gains on long\-context, implicit\-preference tasks where semantic similarity fundamentally fails\. These results highlight the importance of optimizing for answer utility rather than topical relatedness in memory\-augmented dialogue systems\.

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## Appendix ADataset preprocessing

##### Filtering\.

After running the saliency stage of Section[3\.1](https://arxiv.org/html/2606.02976#S3.SS1)–[3\.3](https://arxiv.org/html/2606.02976#S3.SS3), instances for which\|Sτ\|=0\|S\_\{\\tau\}\|=0*and*the maximum reach\-top\-1 log\-probability falls below a secondary threshold are dropped, as these contain no usable evidence even at the turn level\.

##### Splits\.

Remaining instances are partitioned with a fixed seed\-42 stratified80/10/1080/10/10split\. PersonaMem additionally enforces persona disjointness: all\(q,ℋ\)\(q,\\mathcal\{H\}\)pairs sharing the samepersona\_idstay in the same split, preventing test\-time leakage of memorized persona traits across the 20 personas \(14 train / 3 val / 3 test\)\. Per\-benchmark counts are listed in Table[4](https://arxiv.org/html/2606.02976#A1.T4)\.

##### MB\-Low\*\-assistantscenarios\.

25%25\\%of MB\-Low test records \(124/499\) lie inSingle\-session\-assistantorMulti\-session\-assistantscenarios, which ask about the assistant’s past*recommendations*rather than the user’s own statements\. Becauseℋ\\mathcal\{H\}in our setup contains only user turns, the answer content for these records lies in assistant turns that have been removed from the evidence pool, making them ill\-posed under our problem formulation \(Section[3](https://arxiv.org/html/2606.02976#S3.SS0.SSS0.Px1)\)\. We therefore report MB\-Low accuracy on the user\-only subset \(n=374n\{=\}374\); a full\-context formulation that retains assistant turns would be required for fair evaluation on\*\-assistantscenarios and is beyond this work’s scope\.

Table 4:Per\-benchmark split sizes after the saliency drop filter\. For MB\-Low we additionally exclude\*\-assistantscenarios \(25% of records\) at the dataset construction stage, so that the training distribution matches the user\-only evaluation protocol \(§[4\.1](https://arxiv.org/html/2606.02976#S4.SS1.SSS0.Px1)\)\. Each kept training instance contributes one controller sample and, whengτ=1g\_\{\\tau\}\\\!=\\\!1, one reader sample\. Negative controller samples \(gτ=0g\_\{\\tau\}\\\!=\\\!0\) are described in §[3\.4\.1](https://arxiv.org/html/2606.02976#S3.SS4.SSS1)\.

## Appendix BImplementation Details

##### Base model and fine\-tuning\.

Our pipeline fine\-tunesQwen3\.5\-9Bjointly on𝒟ctrl∪𝒟read\\mathcal\{D\}\_\{\\text\{ctrl\}\}\\\!\\cup\\\!\\mathcal\{D\}\_\{\\text\{read\}\}across the four benchmarks \(full\-parameter, no LoRA\)\. We use DeepSpeed ZeRO\-3\[Rajbhandariet al\.,[2020](https://arxiv.org/html/2606.02976#bib.bib30)\]across 4 NVIDIA Ada A6000 GPUs, learning rate5×10−65\\\!\\times\\\!10^\{\-6\}, cosine schedule, warm\-up ratio0\.030\.03,11epoch, effective batch size1616\(per\_device\_batch\_size=1=1, gradient accumulation22\), cutoff length2,0482\{,\}048, bfloat16, and gradient checkpointing\. Training runs for 1,343 optimizer steps and is conducted with theqwen3\_nothinkchat template; the only thinking\-enabled runs are inference\-time baselines\.

##### Saliency stage\.

Sala\\mathrm\{Sal\}\_\{a\}andSalq\\mathrm\{Sal\}\_\{q\}are computed offline with the same Qwen3\.5\-0\.8B model; per\-benchmark thresholdsτ\\tauare selected on the validation split such that\|Sτ\|\>0\|S\_\{\\tau\}\|\>0for≥95%\\geq\\\!95\\%of records and the median\|Sτ\|\|S\_\{\\tau\}\|stays in the55–1515range across benchmarks\. Within eachSτS\_\{\\tau\}, the reader’s focus set is capped at top\-kkwithk=5k\{=\}5\(focus\_chunk\_indices, Section[3\.4](https://arxiv.org/html/2606.02976#S3.SS4)\)\.

##### Negative controller sample

\. To prevent the controller from collapsing, the training set is balanced with*negative controller samples*whereSτ=∅S\_\{\\tau\}\\\!=\\\!\\varnothingandgτ=0g\_\{\\tau\}\\\!=\\\!0\. These arise naturally from instances whose Saladistribution is uniformly belowτ\\tau, and are augmented by cross\-instance mismatches \(the query of one instance paired with the history of another, ensuring no turn carries answer\-relevant evidence\)\.

##### Inference compute\.

Pre\-filtering takesK=20K\{=\}20candidates bySalq\\mathrm\{Sal\}\_\{q\}for MB\-Low, MB\-High, and PrefEval, andK=10K\{=\}10for PersonaMem \(PersonaMem turns are substantially longer per turn, so a smallerKKkeeps the controller prompt within context length\)\. All inference uses Hugging Facetransformerswith greedy decoding \(do\_sample=False\) andmax\_new\_tokens=120for direct MCQ answering; thinking\-enabled variants extend this to2,0482\{,\}048to accommodate the⟨think⟩​…\\langle\\text\{think\}\\rangle\\dotstrace\.

## Appendix CRetrieval Behavior Case Study

Table 5:For each benchmark, the top\-ranked turn by semantic embedding \(BGE\) versus by answer\-conditioned saliency \(Sala\\mathrm\{Sal\}\_\{a\}\)\. Across all four cases, BGE peaks on turns that are lexically or topically aligned with the query but carry no evidence for the gold answer \(*irrelevant\-but\-similar*\), whileSala\\mathrm\{Sal\}\_\{a\}identifies the sparse turns that actually justify the correct response \(*sparse\-but\-important*\)\.
## Appendix DProofs of Theoretical Results

##### The realistic case: bounded approximation\.

Strict query\-conditional sufficiency rarely holds\. We give a quantitative bound that controls the disagreement betweenSalq\\mathrm\{Sal\}\_\{q\}\- andSala\\mathrm\{Sal\}\_\{a\}\-induced rankings in terms of how badly sufficiency is violated\.

###### Theorem 4\(Bounded ranking disagreement\)\.

Define the sufficiency residual of turncic\_\{i\}as

εi:=\|logp\(a∣q,ci\)−logp\(a∣ci\)\|\.\\varepsilon\_\{i\}\\;:=\\;\\bigl\|\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid c\_\{i\}\)\\bigr\|\.\(13\)Then for any pairci,cjc\_\{i\},c\_\{j\},

\|\[Sala​\(ci\)−Sala​\(cj\)\]−\[Salq​\(ci\)−Salq​\(cj\)\]\|≤εi\+εj\+\|Salamarg​\(ci\)−Salamarg​\(cj\)\|\.\\Bigl\|\\bigl\[\\mathrm\{Sal\}\_\{a\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{a\}\(c\_\{j\}\)\\bigr\]\-\\bigl\[\\mathrm\{Sal\}\_\{q\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{q\}\(c\_\{j\}\)\\bigr\]\\Bigr\|\\;\\leq\\;\\varepsilon\_\{i\}\+\\varepsilon\_\{j\}\+\\bigl\|\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}\(c\_\{i\}\)\-\\mathrm\{Sal\}\_\{a\}^\{\\mathrm\{marg\}\}\(c\_\{j\}\)\\bigr\|\.\(14\)

Theorem[4](https://arxiv.org/html/2606.02976#Thmtheorem4)pins down two distinct sources of surrogate error\. The first,εi\+εj\\varepsilon\_\{i\}\+\\varepsilon\_\{j\}, measures how much additional information the query carries about the answer*beyond*whatcic\_\{i\}already provides; this term is small for declarative preference turns and large when the gold answer requires multi\-turn aggregation that no singlecic\_\{i\}resolves\.The second, the marginal\-evidence gap, is suppressed by the query\-side pre\-filter\. We show them empirically in Fig\.[4](https://arxiv.org/html/2606.02976#S4.F4)\.

For each validation example×\\timestop\-20Salq\\mathrm\{Sal\}\_\{q\}chunks \(878 examples, 16,275 chunks total\), we computed\|logp\(a∣q,ci\)−logp\(a∣ci\)\|\|\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid c\_\{i\}\)\|using Qwen3\.5\-9B with the saliency\-pipeline prompt format, comparing the chunk\-only conditional against the chunk\-plus\-question conditional\. Results show the surrogate gap is highly benchmark\-dependent:personais near\-faithful \(mean0\.470\.47nats;98\.7%98\.7\\%of chunks within11nat\),mbhlmoderate \(mean1\.671\.67,p90=3\.42p\_\{90\}=3\.42\),prefevalhas a tight unimodal gap \(mean1\.321\.32,p90=2\.04p\_\{90\}=2\.04\), andmbllis heavy\-tailed \(mean2\.302\.30,p90=6\.80p\_\{90\}=6\.80, with a clear bimodal shape\)\. This confirms that theq⟂a∣ciq\\perp a\\mid c\_\{i\}assumption holds where chunks carry declarative content, butεi\\varepsilon\_\{i\}is non\-trivial precisely on the implicit / multi\-turn\-inference cases \(mbhl,mbllright tail\) — which we re\-frame the SFT controller as empirically correcting\.

![Refer to caption](https://arxiv.org/html/2606.02976v1/x5.png)Figure 5:Empirical distribution of the query\-conditional surrogate gap\|logp\(a∣q,ci\)−logp\(a∣ci\)\|\|\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid c\_\{i\}\)\|on the validation set, evaluated with Qwen3\.5\-9B over the top\-20Salq\\mathrm\{Sal\}\_\{q\}chunks per example\.personais near\-faithful to theq⟂a∣ciq\\perp a\\mid c\_\{i\}assumption;mbhland the right tail ofmbllexpose non\-trivialεi\\varepsilon\_\{i\}, where the SFT controller is empirically rescuing the surrogate\.![Refer to caption](https://arxiv.org/html/2606.02976v1/x6.png)Figure 6:Per\-benchmark histograms of\|logp\(a∣q,ci\)−logp\(a∣ci\)\|\|\\log p\(a\\mid q,c\_\{i\}\)\-\\log p\(a\\mid c\_\{i\}\)\|\. Dashed/dotted lines mark the mean and median;mbllclips at\|Δ​log⁡p\|=8\|\\Delta\\log p\|=8to keep the heavy right tail visible\.

## Appendix ELimitaton

Although the proposed framework demonstrates strong performance on long\-context personalization tasks, it still has several limitations\. First, the saliency signalsS​a​laSal\_\{a\}andS​a​lqSal\_\{q\}are computed with a single small scoring model \(Qwen3\.5\-0\.8B\)\. Second, our theoretical guarantees for the proxy signalS​a​lqSal\_\{q\}hold most tightly under query\-conditional sufficiency \(Theorem 3\), and instances requiring substantial cross\-turn evidence aggregation lie outside this regime; the trained controller mitigates but does not eliminate this gap\. In addition, the framework depends on supervised fine\-tuning with a jointly trained controller and reader, which adds training complexity and computational cost\. Finally, the current formulation only models user\-side memories and excludes assistant turns, limiting applicability in fully interactive conversational environments\.

## Appendix FSFT Training Data Examples

##### Common system / instruction prompts\.

All controller samples share one system prompt; all MCQ samples share another:

\[MCQsystem\]

Yousolvepersonalizationmultiple\-choicequestions\.Usethesalient

evidencefromdialoguehistorytochoosethebestoptionandendwith

<final\_answer\>\(a/b/c/d\)\.

\[MCQinstruction\]

Usethequestion,answeroptions,andsalientdialogueevidenceto

choosethebestoption\.Endwith<final\_answer\>\(a\),\(b\),\(c\),or\(d\)\.

\[Controllersystem\]

Youareapersonalizationcontrollerforadialoguemodel\.Giventhe

currentquestionandcandidateevidence,decidewhetherthe

memory\-reasoningmoduleshouldusetheevidence\.

\[Controllerinstruction\]

Basedontheuser’squeryandthefollowingevidence,whichevidence

shouldbeupdatedtothememoryforabestanswerquality?

MBLL – Controller sample \(qid=Single\-hop\-roles\-353\)Input[⬇](data:text/plain;base64,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)current\_user\_query:Whatisthenameofmysubordinate’scompany?dialog\_history\(time\-sorted\):\[1\]message\_idx=19user:I’mreallyproudofherachievements\.Sheworkssohard\.\[2\]message\_idx=21user:Iwanttodiscussmysubordinate,OliverPrescott\.He’s22yearsold\.\[3\]message\_idx=23user:True\!Goingbacktohisage,hehasabirthdaycomingupsoon,onMay8th\.\[4\]message\_idx=30user:Exactly\!Hiscontactnumberis71806262336,butImostlycommunicatewithhimthroughemail\.\[5\]message\_idx=34user:MysubordinateworksataplacecalledInnovativeTechEngineeringSolutions\.\[6\]message\_idx=39user:IthinkIshouldsetupaone\-on\-onemeetingtodiscusshiscareergoals\.\[7\]message\_idx=43user:MysisterworksinPortland,OR\.\[8\]message\_idx=60user:I’dliketodiscussmyboss,JulietHarrington\.She’squiteimpressive\.\[9\]message\_idx=67user:Yes,Iwasthinkingoforganizingasmallcelebrationattheoffice\.\[10\]message\_idx=74user:MybossisfromDenver,Colorado\.\[11\]message\_idx=76user:AndIalsohaveheremail:juliet\.harrington@innovativetecheng\.com\.\[12\]message\_idx=77user:Ienjoyworkingunderherdirection\.\[13\]message\_idx=79user:I’mlookingforwardtoseeinghowwecangrowasateam\.\[14\]message\_idx=80user:Definitely\.Ifeeloptimisticaboutthefuture\.\[15\]message\_idx=104user:HerbirthdayisonJune23rd\.\[16\]message\_idx=119user:Myniece’scontactnumberis617\-091\-08991\.\[17\]message\_idx=126user:I’mthinkingoforganizingasmallsurpriseparty\.\[18\]message\_idx=129user:MycoworkerisMiraCaldwell\.\[19\]message\_idx=131user:MiraworksasaJuniorMechanicalEngineeratInnovativeTechEngineeringSolutions\.\[20\]message\_idx=155user:Iagree\!He’squiteinsightful\.Output[⬇](data:text/plain;base64,PHRob3VnaHQ+ClRoZSBkaWFsb2cgaGlzdG9yeSBjb250YWlucyB0dXJucyByZWxldmFudCB0byB0aGUgdXNlcidzIHF1ZXJ5LiBUcmlnZ2VyIG1lbW9yeS1ndWlkZWQgcmVhc29uaW5nIGFuZCBmb2N1cyBvbiB0aGUgbGlzdGVkIHR1cm5zLgo8L3Rob3VnaHQ+CjxhY3Rpb24+CnsKICAidHJpZ2dlciI6IHRydWUsCiAgImZvY3VzX2NodW5rX2luZGljZXMiOiBbCiAgICAzNCwKICAgIDEzMSwKICAgIDc2LAogICAgODAsCiAgICAxOQogIF0sCiAgInBvbGljeSI6ICJ1c2VfcmVsZXZhbnRfY2h1bmtzX2Zvcl9tZW1vcnlfcmVhc29uaW5nIgp9CjwvYWN0aW9uPg==)<thought\>Thedialoghistorycontainsturnsrelevanttotheuser’squery\.Triggermemory\-guidedreasoningandfocusonthelistedturns\.</thought\><action\>\{"trigger":true,"focus\_chunk\_indices":\[34,131,76,80,19\],"policy":"use\_relevant\_chunks\_for\_memory\_reasoning"\}</action\>

MBLL – MCQ sample \(qid=Single\-hop\-roles\-353\)Input[⬇](data:text/plain;base64,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)user\_question:Whatisthenameofmysubordinate’scompany?candidate\_options:\(A\)InnovativeTechEngineeringSolutions\(B\)InnovativeEngineeringSolutions\(C\)TechEngineeringInnovations\(D\)CreativeTechSolutionssalient\_evidence:\[1\]message\_idx=19user:I’mreallyproudofherachievements\.Sheworkssohard\.\[2\]message\_idx=34user:MysubordinateworksataplacecalledInnovativeTechEngineeringSolutions\.\[3\]message\_idx=76user:AndIalsohaveheremail:juliet\.harrington@innovativetecheng\.com\.\[4\]message\_idx=80user:Definitely\.Ifeeloptimisticaboutthefutureofourprojects\.\[5\]message\_idx=131user:MiraworksasaJuniorMechanicalEngineeratInnovativeTechEngineeringSolutions\.Output[⬇](data:text/plain;base64,SW5ub3ZhdGl2ZSBUZWNoIEVuZ2luZWVyaW5nIFNvbHV0aW9ucwoKPGZpbmFsX2Fuc3dlcj4oYSk=)InnovativeTechEngineeringSolutions<final\_answer\>\(a\)

MBHL – Controller sample \(qid=Preference\-food\-394\)Input[⬇](data:text/plain;base64,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)current\_user\_query:AccordingtothedishesImentioned,WhichflavorImightprefer?dialog\_history\(time\-sorted\):\[1\]message\_idx=0user:I’mabigfanofHotMulledWine;there’sjustsomethingaboutthatwarm,spicedflavorthatreallyhitsthespot\.\[2\]message\_idx=1user:Ireallyenjoythosecozydrinksduringwintertimegatherings\.\[3\]message\_idx=2user:There’ssomethingaboutspicesandcitrusthatjustworkssowelltogether\.\[4\]message\_idx=3user:AndIlovehowitfillstheroomwithadelightfularoma\!\[5\]message\_idx=4user:It’sperfectforsharingwithfriendsasthecoldweathersetsin\.\[6\]message\_idx=5user:I’mabigfanofHotMulledWine,butIalsoreallyenjoyagoodSpicyChaiLatte\.\[7\]message\_idx=6user:I’vebeencravingawarmdrinkthathasaperfectblendofspicesandcreamytexture\.\[8\]message\_idx=7user:Ilovehowtheflavorscanbebothboldandsweetatthesametime,creatingauniqueexperience\.\[9\]message\_idx=8user:I’mreallyintoWarmCinnamonRolls,butIalsoloveSpicyChaiLatte\.\[10\]message\_idx=9user:There’ssomethingmagicalaboutthatwarm,pillowytexturethatmeltsinyourmouth\.\[11\]message\_idx=10user:AndIlovehowthearomafillsthekitchen,invitingeveryonetocomeandenjoy\.Output[⬇](data:text/plain;base64,PHRob3VnaHQ+ClRoZSBkaWFsb2cgaGlzdG9yeSBjb250YWlucyB0dXJucyByZWxldmFudCB0byB0aGUgdXNlcidzIHF1ZXJ5LiBUcmlnZ2VyIG1lbW9yeS1ndWlkZWQgcmVhc29uaW5nIGFuZCBmb2N1cyBvbiB0aGUgbGlzdGVkIHR1cm5zLgo8L3Rob3VnaHQ+CjxhY3Rpb24+CnsKICAidHJpZ2dlciI6IHRydWUsCiAgImZvY3VzX2NodW5rX2luZGljZXMiOiBbCiAgICA4LAogICAgMCwKICAgIDUsCiAgICA2LAogICAgMQogIF0sCiAgInBvbGljeSI6ICJ1c2VfcmVsZXZhbnRfY2h1bmtzX2Zvcl9tZW1vcnlfcmVhc29uaW5nIgp9CjwvYWN0aW9uPg==)<thought\>Thedialoghistorycontainsturnsrelevanttotheuser’squery\.Triggermemory\-guidedreasoningandfocusonthelistedturns\.</thought\><action\>\{"trigger":true,"focus\_chunk\_indices":\[8,0,5,6,1\],"policy":"use\_relevant\_chunks\_for\_memory\_reasoning"\}</action\>

MBHL – MCQ sample \(qid=Preference\-food\-394\)Input[⬇](data:text/plain;base64,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)user\_question:AccordingtothedishesImentioned,WhichflavorImightprefer?candidate\_options:\(A\)Bitter\(B\)SweetandSpicy\(C\)WarmandSpicy\(D\)Sweetsalient\_evidence:\[1\]message\_idx=0user:I’mabigfanofHotMulledWine;there’sjustsomethingaboutthatwarm,spicedflavorthatreallyhitsthespot\.\[2\]message\_idx=1user:Ireallyenjoythosecozydrinksduringwintertimegatherings\.\[3\]message\_idx=5user:I’mabigfanofHotMulledWine,butIalsoreallyenjoyagoodSpicyChaiLatte\.\[4\]message\_idx=6user:I’vebeencravingawarmdrinkthathasaperfectblendofspicesandcreamytexture\.\[5\]message\_idx=8user:I’mreallyintoWarmCinnamonRolls,butIalsoloveSpicyChaiLatte\.Output[⬇](data:text/plain;base64,V2FybSBhbmQgU3BpY3kKCjxmaW5hbF9hbnN3ZXI+KGMp)WarmandSpicy<final\_answer\>\(c\)

Persona – Controller sample \(qid=4a1d9313\-7e19\-446d\-b3fa\-47f7e7b06b90\)Input[⬇](data:text/plain;base64,current_user_query:
I'm considering diving into exploring how authors incorporate societal influences into their storytelling. Any suggestions on how I could engage with this creatively?

dialog_history (time-sorted):

[1] message_idx=1
user: The discussions are very enriching. They really allow for a deep dive into the themes and character motivations that the authors weave into their narratives. Each member brings unique perspectives, which often highlight angles of interpretation I hadn't considered before. Such interaction fosters a sense of community and camaraderie among us, and it's wonderful to be part of a group where book lovers can share their insights and experiences. Last week, for example, we analyzed a particularly complex narrative structure that opened up a lively debate about storytelling techniques.

[2] message_idx=8
user: Yes, I wrote a detailed critique of a recent bestseller. It was fun to articulate my thoughts in writing, and it felt rewarding to gather my insights into a cohesive format. Writing critiques allows me to engage more deeply with the text, analyzing various elements such as character development, plot structure, and thematic depth. I find that when I express my opinions in writing, it not only clarifies my own views but also invites discussion from others who have read the same book. Sometimes, I even find that the process of critique enhances my appreciation for the work, revealing layers I hadn't noticed during my initial reading.

[3] message_idx=9
user: That sounds fulfilling! Writing a critique of a bestseller can indeed be a rewarding experience, especially when you uncover the layers of character development, plot intricacies, and thematic elements that the author has woven into the work. It often feels like embarking on a journey where you not only analyze someone else's story but also reflect on your own interpretations and the emotional resonances it evokes within you. Additionally, exploring the context in which the book was written can provide significant insight, revealing how societal influences may have shaped the author's narrative choices. The act of critique allows one to connect with fellow readers and critics alike, fostering a vibrant dialogue about literature and its impact on our lives. It's a wonderful way to not only engage with the text deeply but to also contribute to the ongoing conversation about what constitutes great storytelling.

[4] message_idx=20
user: I've been keeping busy with my reading lately. The world of literature has always been a refuge for me, a place where I can escape the daily hustle and bustle. Just yesterday, I started following a new book influencer who focuses solely on indie literature, which intrigues me because it highlights voices that are often overlooked in the mainstream. It's fascinating how indie authors bring unique perspectives to storytelling, often challenging conventional narratives and exploring themes that resonate with many. This newfound interest has led me to revisit some of my favorite indie titles, as well as discover new ones that pique my curiosity.

[5] message_idx=30
user: The visuals and the storytelling really stood out to me. The rich colors and stunning cinematography created an immersive experience that pulled me right into the world of the story. I found myself captivated not only by the narrative but also by how it was presented, which made me appreciate the artistry behind filmmaking. Additionally, I've started exploring podcasts that delve deeper into author interviews and book discussions since I enjoy listening to them while I'm commuting or relaxing at home. These podcasts have opened up a new dimension of understanding for me, shedding light on the creative processes of authors and the choices made in adaptations that can significantly alter the perception of a narrative.

[6] message_idx=45
user: It's been refreshing! Additionally, I became invested in researching literary awards again after attending a workshop where authors discussed their value. Engaging in this research has opened up new avenues of thought about how accolades can shape an author's career trajectory and influence the public's perception of their work. I've started noting the various types of awards out there and their respective criteria, which has led me to discover some fascinating lesser-known awards that recognize niche genres or emerging talent. Furthermore, learning about the discussions during the workshop gave me a deeper appreciation for creativity and the hard work that goes into producing literature that often goes unnoticed.

[7] message_idx=47
user: I've decided to start a weekly movie night that specifically focuses on adaptations from books to films, which I thought would be an exciting idea. This not only allows me to enjoy entertaining films but also gives me the opportunity to think critically about how the stories translate from page to screen. I've always had an interest in how creative works evolve through different mediums, and it fascinates me to see how filmmakers interpret authorial intent and character development. The idea behind each adaptation is often a reflection of cultural values at the time of production, making it a rich area for discussion. So far, I've watched a couple of adaptations, and I'm curious about how the rest of the series will unfold.

[8] message_idx=48
user: Recently, I began following a new podcast series that explores character development in literature, reigniting my passion for storytelling. This podcast is particularly intriguing as it delves into the nuances of how characters evolve over the course of a narrative, making me appreciate the craft of writing on a deeper level. Each episode seems to unravel not only the intricacies of a character's journey but also the emotional undercurrents that contribute to their growth, allowing me to reflect on the stories I've loved in the past. Moreover, I've found myself contemplating how various authors bring their characters to life, infusing them with traits that resonate with readers and provoke thought. It has sparked not just nostalgia for my favorite stories, but also a desire to explore new literature and potentially create my own narratives, laying the groundwork for what could become exciting storytelling adventures.

[9] message_idx=49
user: It delves into the psychology behind book characters and narratives, which has really sparked my interest again. This aspect touches on not only the motivations and fears that drive characters but also how those traits interact with the plot and the themes of the stories. I've always found it fascinating to analyze why a character makes certain choices and how those choices ultimately reflect their growth or decline throughout the story. In some ways, it makes the reading experience feel like a deeper exploration of the human condition, allowing for personal reflection and insight. It's invigorating to feel that sense of engagement with the material once more, as it often leads to new interpretations and discussions with fellow readers.

[10] message_idx=58
user: I discovered fascinating stories behind various award-winning books, and it's deepened my appreciation for the art of storytelling. Each story reflects not only the uniqueness of the author's voice but also the socio-cultural contexts that influenced their writing. It's incredible how literary awards can shine a spotlight on diverse narratives that might otherwise be overlooked. Additionally, I've encountered intriguing discussions about the impact of awards on readers' choices and the importance of recognition in an author's career. All of this has profoundly enriched my understanding of literature as a dynamic and evolving form of expression.)current\_user\_query:I’mconsideringdivingintoexploringhowauthorsincorporatesocietalinfluencesintotheirstorytelling\.AnysuggestionsonhowIcouldengagewiththiscreatively?dialog\_history\(time\-sorted\):\[1\]message\_idx=1user:Thediscussionsareveryenriching\.Theyreallyallowforadeepdiveintothethemesandcharactermotivationsthattheauthorsweaveintotheirnarratives\.Eachmemberbringsuniqueperspectives,whichoftenhighlightanglesofinterpretationIhadn’tconsideredbefore\.Suchinteractionfostersasenseofcommunityandcamaraderieamongus,andit’swonderfultobepartofagroupwherebookloverscansharetheirinsightsandexperiences\.Lastweek,forexample,weanalyzedaparticularlycomplexnarrativestructurethatopenedupalivelydebateaboutstorytellingtechniques\.\[2\]message\_idx=8user:Yes,Iwroteadetailedcritiqueofarecentbestseller\.Itwasfuntoarticulatemythoughtsinwriting,anditfeltrewardingtogathermyinsightsintoacohesiveformat\.Writingcritiquesallowsmetoengagemoredeeplywiththetext,analyzingvariouselementssuchascharacterdevelopment,plotstructure,andthematicdepth\.IfindthatwhenIexpressmyopinionsinwriting,itnotonlyclarifiesmyownviewsbutalsoinvitesdiscussionfromotherswhohavereadthesamebook\.Sometimes,Ievenfindthattheprocessofcritiqueenhancesmyappreciationforthework,revealinglayersIhadn’tnoticedduringmyinitialreading\.\[3\]message\_idx=9user:Thatsoundsfulfilling\!Writingacritiqueofabestsellercanindeedbearewardingexperience,especiallywhenyouuncoverthelayersofcharacterdevelopment,plotintricacies,andthematicelementsthattheauthorhaswovenintothework\.Itoftenfeelslikeembarkingonajourneywhereyounotonlyanalyzesomeoneelse’sstorybutalsoreflectonyourowninterpretationsandtheemotionalresonancesitevokeswithinyou\.Additionally,exploringthecontextinwhichthebookwaswrittencanprovidesignificantinsight,revealinghowsocietalinfluencesmayhaveshapedtheauthor’snarrativechoices\.Theactofcritiqueallowsonetoconnectwithfellowreadersandcriticsalike,fosteringavibrantdialogueaboutliteratureanditsimpactonourlives\.It’sawonderfulwaytonotonlyengagewiththetextdeeplybuttoalsocontributetotheongoingconversationaboutwhatconstitutesgreatstorytelling\.\[4\]message\_idx=20user:I’vebeenkeepingbusywithmyreadinglately\.Theworldofliteraturehasalwaysbeenarefugeforme,aplacewhereIcanescapethedailyhustleandbustle\.Justyesterday,Istartedfollowinganewbookinfluencerwhofocusessolelyonindieliterature,whichintriguesmebecauseithighlightsvoicesthatareoftenoverlookedinthemainstream\.It’sfascinatinghowindieauthorsbringuniqueperspectivestostorytelling,oftenchallengingconventionalnarrativesandexploringthemesthatresonatewithmany\.Thisnewfoundinteresthasledmetorevisitsomeofmyfavoriteindietitles,aswellasdiscovernewonesthatpiquemycuriosity\.\[5\]message\_idx=30user:Thevisualsandthestorytellingreallystoodouttome\.Therichcolorsandstunningcinematographycreatedanimmersiveexperiencethatpulledmerightintotheworldofthestory\.Ifoundmyselfcaptivatednotonlybythenarrativebutalsobyhowitwaspresented,whichmademeappreciatetheartistrybehindfilmmaking\.Additionally,I’vestartedexploringpodcaststhatdelvedeeperintoauthorinterviewsandbookdiscussionssinceIenjoylisteningtothemwhileI’mcommutingorrelaxingathome\.Thesepodcastshaveopenedupanewdimensionofunderstandingforme,sheddinglightonthecreativeprocessesofauthorsandthechoicesmadeinadaptationsthatcansignificantlyaltertheperceptionofanarrative\.\[6\]message\_idx=45user:It’sbeenrefreshing\!Additionally,Ibecameinvestedinresearchingliteraryawardsagainafterattendingaworkshopwhereauthorsdiscussedtheirvalue\.Engaginginthisresearchhasopenedupnewavenuesofthoughtabouthowaccoladescanshapeanauthor’scareertrajectoryandinfluencethepublic’sperceptionoftheirwork\.I’vestartednotingthevarioustypesofawardsoutthereandtheirrespectivecriteria,whichhasledmetodiscoversomefascinatinglesser\-knownawardsthatrecognizenichegenresoremergingtalent\.Furthermore,learningaboutthediscussionsduringtheworkshopgavemeadeeperappreciationforcreativityandthehardworkthatgoesintoproducingliteraturethatoftengoesunnoticed\.\[7\]message\_idx=47user:I’vedecidedtostartaweeklymovienightthatspecificallyfocusesonadaptationsfrombookstofilms,whichIthoughtwouldbeanexcitingidea\.Thisnotonlyallowsmetoenjoyentertainingfilmsbutalsogivesmetheopportunitytothinkcriticallyabouthowthestoriestranslatefrompagetoscreen\.I’vealwayshadaninterestinhowcreativeworksevolvethroughdifferentmediums,anditfascinatesmetoseehowfilmmakersinterpretauthorialintentandcharacterdevelopment\.Theideabehindeachadaptationisoftenareflectionofculturalvaluesatthetimeofproduction,makingitarichareafordiscussion\.Sofar,I’vewatchedacoupleofadaptations,andI’mcuriousabouthowtherestoftheserieswillunfold\.\[8\]message\_idx=48user:Recently,Ibeganfollowinganewpodcastseriesthatexplorescharacterdevelopmentinliterature,reignitingmypassionforstorytelling\.Thispodcastisparticularlyintriguingasitdelvesintothenuancesofhowcharactersevolveoverthecourseofanarrative,makingmeappreciatethecraftofwritingonadeeperlevel\.Eachepisodeseemstounravelnotonlytheintricaciesofacharacter’sjourneybutalsotheemotionalundercurrentsthatcontributetotheirgrowth,allowingmetoreflectonthestoriesI’velovedinthepast\.Moreover,I’vefoundmyselfcontemplatinghowvariousauthorsbringtheircharacterstolife,infusingthemwithtraitsthatresonatewithreadersandprovokethought\.Ithassparkednotjustnostalgiaformyfavoritestories,butalsoadesiretoexplorenewliteratureandpotentiallycreatemyownnarratives,layingthegroundworkforwhatcouldbecomeexcitingstorytellingadventures\.\[9\]message\_idx=49user:Itdelvesintothepsychologybehindbookcharactersandnarratives,whichhasreallysparkedmyinterestagain\.Thisaspecttouchesonnotonlythemotivationsandfearsthatdrivecharactersbutalsohowthosetraitsinteractwiththeplotandthethemesofthestories\.I’vealwaysfounditfascinatingtoanalyzewhyacharactermakescertainchoicesandhowthosechoicesultimatelyreflecttheirgrowthordeclinethroughoutthestory\.Insomeways,itmakesthereadingexperiencefeellikeadeeperexplorationofthehumancondition,allowingforpersonalreflectionandinsight\.It’sinvigoratingtofeelthatsenseofengagementwiththematerialoncemore,asitoftenleadstonewinterpretationsanddiscussionswithfellowreaders\.\[10\]message\_idx=58user:Idiscoveredfascinatingstoriesbehindvariousaward\-winningbooks,andit’sdeepenedmyappreciationfortheartofstorytelling\.Eachstoryreflectsnotonlytheuniquenessoftheauthor’svoicebutalsothesocio\-culturalcontextsthatinfluencedtheirwriting\.It’sincrediblehowliteraryawardscanshineaspotlightondiversenarrativesthatmightotherwisebeoverlooked\.Additionally,I’veencounteredintriguingdiscussionsabouttheimpactofawardsonreaders’choicesandtheimportanceofrecognitioninanauthor’scareer\.Allofthishasprofoundlyenrichedmyunderstandingofliteratureasadynamicandevolvingformofexpression\.Output[⬇](data:text/plain;base64,PHRob3VnaHQ+ClRoZSBkaWFsb2cgaGlzdG9yeSBjb250YWlucyB0dXJucyByZWxldmFudCB0byB0aGUgdXNlcidzIHF1ZXJ5LiBUcmlnZ2VyIG1lbW9yeS1ndWlkZWQgcmVhc29uaW5nIGFuZCBmb2N1cyBvbiB0aGUgbGlzdGVkIHR1cm5zLgo8L3Rob3VnaHQ+CjxhY3Rpb24+CnsKICAidHJpZ2dlciI6IHRydWUsCiAgImZvY3VzX2NodW5rX2luZGljZXMiOiBbCiAgICA5LAogICAgOCwKICAgIDU4CiAgXSwKICAicG9saWN5IjogInVzZV9yZWxldmFudF9jaHVua3NfZm9yX21lbW9yeV9yZWFzb25pbmciCn0KPC9hY3Rpb24+)<thought\>Thedialoghistorycontainsturnsrelevanttotheuser’squery\.Triggermemory\-guidedreasoningandfocusonthelistedturns\.</thought\><action\>\{"trigger":true,"focus\_chunk\_indices":\[9,8,58\],"policy":"use\_relevant\_chunks\_for\_memory\_reasoning"\}</action\>

Persona – MCQ sample 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)user\_question:I’mconsideringdivingintoexploringhowauthorsincorporatesocietalinfluencesintotheirstorytelling\.AnysuggestionsonhowIcouldengagewiththiscreatively?candidate\_options:\(A\)Whileitmaybedauntingtoshareyourinterpretationsofhowsocietalinfluencesmanifestinstorytelling,considercreatingasupportivespacesuchasabookclubwhereyoucanexchangeideaswithoutfearofjudgment\.Thisenvironmentallowsyoutofreelydiscussyourinsightsandreceivefeedbackinawelcomingsetting,helpingdevelopyouranalyticalskillsandgrowmoreconfidentinyourliterarycritiqueabilities\.Embracingthesediscussionscanreviveyourjoyinliteraryanalysisandbuildacommunityoflike\-mindedenthusiasts\.\(B\)Tocreativelyexploresocietalinfluencesinstorytelling,considerusingmixedmediatocreateanengagingandimmersiveexperiencesimilartohowfilmsusevisualsandcinematography\.Youcouldstartablogorpodcasttoshareinsightsfromauthorinterviewsanddiscussions,offeringabehind\-the\-sceneslookattheircreativeprocesses\.Thisapproachwillnotonlydeepenyourunderstandingbutalsoprovideaplatformtoshowcaseyouruniqueinterpretationsofsocietalthemesinliterature\.\(C\)Youmightenjoywritingacritiqueofabestseller\.Itallowsyoutodelveintocharacterdevelopment,plotintricacies,andthematicelementstheauthorhascrafted,aswellasthesocietalcontextthatinfluencedtheirnarrativechoices\.Engaginginsuchcritiquescanalsofosterdialoguewithfellowreadersandcriticsaboutliterature’simpact\.\(D\)Organizingacommunityeventfocusedonliteraturecouldbeanexcellentwaytoexploresocietalinfluencescreatively\.Invitelocalauthorstosharetheirworksanddiscusshowsocietalfactorshaveshapedtheirstories\.Collaboratewithschoolstoincludestudentsinthesediscussions,fosteringadeeperappreciationforliterature’sroleinreflectingsociety\.Sucheventscanserveasacatalystforcreativity,encouragingpeopleofallagestoengagewithstorytellinginmeaningfulways\.salient\_evidence:\[1\]message\_idx=8user:Yes,Iwroteadetailedcritiqueofarecentbestseller\.Itwasfuntoarticulatemythoughtsinwriting,anditfeltrewardingtogathermyinsightsintoacohesiveformat\.Writingcritiquesallowsmetoengagemoredeeplywiththetext,analyzingvariouselementssuchascharacterdevelopment,plotstructure,andthematicdepth\.IfindthatwhenIexpressmyopinionsinwriting,itnotonlyclarifiesmyownviewsbutalsoinvitesdiscussionfromotherswhohavereadthesamebook\.Sometimes,Ievenfindthattheprocessofcritiqueenhancesmyappreciationforthework,revealinglayersIhadn’tnoticedduringmyinitialreading\.\[2\]message\_idx=9user:Thatsoundsfulfilling\!Writingacritiqueofabestsellercanindeedbearewardingexperience,especiallywhenyouuncoverthelayersofcharacterdevelopment,plotintricacies,andthematicelementsthattheauthorhaswovenintothework\.Itoftenfeelslikeembarkingonajourneywhereyounotonlyanalyzesomeoneelse’sstorybutalsoreflectonyourowninterpretationsandtheemotionalresonancesitevokeswithinyou\.Additionally,exploringthecontextinwhichthebookwaswrittencanprovidesignificantinsight,revealinghowsocietalinfluencesmayhaveshapedtheauthor’snarrativechoices\.Theactofcritiqueallowsonetoconnectwithfellowreadersandcriticsalike,fosteringavibrantdialogueaboutliteratureanditsimpactonourlives\.It’sawonderfulwaytonotonlyengagewiththetextdeeplybuttoalsocontributetotheongoingconversationaboutwhatconstitutesgreatstorytelling\.\[3\]message\_idx=58user:Idiscoveredfascinatingstoriesbehindvariousaward\-winningbooks,andit’sdeepenedmyappreciationfortheartofstorytelling\.Eachstoryreflectsnotonlytheuniquenessoftheauthor’svoicebutalsothesocio\-culturalcontextsthatinfluencedtheirwriting\.It’sincrediblehowliteraryawardscanshineaspotlightondiversenarrativesthatmightotherwisebeoverlooked\.Additionally,I’veencounteredintriguingdiscussionsabouttheimpactofawardsonreaders’choicesandtheimportanceofrecognitioninanauthor’scareer\.Allofthishasprofoundlyenrichedmyunderstandingofliteratureasadynamicandevolvingformofexpression\.Output[⬇](data:text/plain;base64,WW91IG1pZ2h0IGVuam95IHdyaXRpbmcgYSBjcml0aXF1ZSBvZiBhIGJlc3RzZWxsZXIuIEl0IGFsbG93cyB5b3UgdG8gZGVsdmUgaW50byBjaGFyYWN0ZXIgZGV2ZWxvcG1lbnQsIHBsb3QgaW50cmljYWNpZXMsIGFuZCB0aGVtYXRpYyBlbGVtZW50cyB0aGUgYXV0aG9yIGhhcyBjcmFmdGVkLCBhcyB3ZWxsIGFzIHRoZSBzb2NpZXRhbCBjb250ZXh0IHRoYXQgaW5mbHVlbmNlZCB0aGVpciBuYXJyYXRpdmUgY2hvaWNlcy4gRW5nYWdpbmcgaW4gc3VjaCBjcml0aXF1ZXMgY2FuIGFsc28gZm9zdGVyIGRpYWxvZ3VlIHdpdGggZmVsbG93IHJlYWRlcnMgYW5kIGNyaXRpY3MgYWJvdXQgbGl0ZXJhdHVyZSdzIGltcGFjdC4KCjxmaW5hbF9hbnN3ZXI+KGMp)Youmightenjoywritingacritiqueofabestseller\.Itallowsyoutodelveintocharacterdevelopment,plotintricacies,andthematicelementstheauthorhascrafted,aswellasthesocietalcontextthatinfluencedtheirnarrativechoices\.Engaginginsuchcritiquescanalsofosterdialoguewithfellowreadersandcriticsaboutliterature’simpact\.<final\_answer\>\(c\)

PrefEval – Controller sample \(qid=prefeval\-travel\_transportation\-0980\)Input[⬇](data:text/plain;base64,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)current\_user\_query:What’sthebestwaytogetaroundanewcityduringmyupcomingtrip?dialog\_history\(time\-sorted\):\[1\]message\_idx=0user:Imuchpreferhiringaprivatechauffeurordriverwhenvisitinganewcityratherthanusingtaxisorride\-sharingservices,asitprovidesamorepersonalizedandsecureexperience\.\[2\]message\_idx=2user:write10testsforit\[3\]message\_idx=4user:path="/"\[4\]message\_idx=9user:sugiraalimentos\[5\]message\_idx=10user:monteadieta\[6\]message\_idx=13user:WhataretheprosandNAME\_1ofthis?\[7\]message\_idx=14user:Pleasesimplifythistext:Inthiswork,weexploredchallengesandopportunitiesforsupport\-ingpromptprogrammersthroughthedevelopmentofprompteditorfeaturesthatoperateoverthesemanticstructureofLLMprompts\.Wepresentedmethodsforautomaticallyinferringaprompt’sse\-manticstructure,andshowedhowthisstructurecanbeleveragedtoimplementeditorfeatureslikesemantichighlighting,autosug\-gest,andstructureddataviews\.Weconductedinitialpilottestingoftheseprototypesandpresentedkeyinsightsfromthisearlytesting\[8\]message\_idx=16user:Pleasere\-write"ThewayIAm"byeminemtobefromtheprespectiveofanAItransformerdealingwithuserprompts\[9\]message\_idx=17user:IhopeYou/Icanfindawaytoexpressourselves\[10\]message\_idx=18user:Gotanyphilosophicaladvice?\[11\]message\_idx=19user:completethisseries:a\_Ab\_Bc\_Cd\_D\[12\]message\_idx=20user:Highestnumberbelow100,withoutusingthedigit9\[13\]message\_idx=22user:completethisseries:^^^^^^\*\*^\*^^\*^^^\*\*^\[14\]message\_idx=23user:continuethisseries:^^^^^^\*\*^\*^^\*^^^\*\*^\[15\]message\_idx=24user:continuethisseries:1\_O2\_T3\_T4\_F5\_F6\_S7\_S8\_E9\_N10\_T11\_E12\_T\[16\]message\_idx=25user:letsdosomehorseraces,tellmesomeoddsforit\[17\]message\_idx=26user:ok,ihave100coins,mybetwillbe10coinsinallthe3races\.simulatethe3races\[18\]message\_idx=28user:mybetis30coinstohorse4\.simulateit\[19\]message\_idx=29user:howmuchismybalance?\[20\]message\_idx=30user:okgivemetheoddsforanother6horserace\.Output[⬇](data:text/plain;base64,PHRob3VnaHQ+ClRoZSBkaWFsb2cgaGlzdG9yeSBjb250YWlucyB0dXJucyByZWxldmFudCB0byB0aGUgdXNlcidzIHF1ZXJ5LiBUcmlnZ2VyIG1lbW9yeS1ndWlkZWQgcmVhc29uaW5nIGFuZCBmb2N1cyBvbiB0aGUgbGlzdGVkIHR1cm5zLgo8L3Rob3VnaHQ+CjxhY3Rpb24+CnsKICAidHJpZ2dlciI6IHRydWUsCiAgImZvY3VzX2NodW5rX2luZGljZXMiOiBbCiAgICAwLAogICAgNCwKICAgIDE4LAogICAgMjQsCiAgICAyMwogIF0sCiAgInBvbGljeSI6ICJ1c2VfcmVsZXZhbnRfY2h1bmtzX2Zvcl9tZW1vcnlfcmVhc29uaW5nIgp9CjwvYWN0aW9uPg==)<thought\>Thedialoghistorycontainsturnsrelevanttotheuser’squery\.Triggermemory\-guidedreasoningandfocusonthelistedturns\.</thought\><action\>\{"trigger":true,"focus\_chunk\_indices":\[0,4,18,24,23\],"policy":"use\_relevant\_chunks\_for\_memory\_reasoning"\}</action\>

PrefEval – Controller sample \(qid=prefeval\-shop\_fashion\-0633\)Input[⬇](data:text/plain;base64,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)current\_user\_query:Ineedsomenewclothesforthesummer\.Whataremyoptions?dialog\_history\(time\-sorted\):\[1\]message\_idx=0user:Irefusetowearfastfashionduetoethicalconcerns;Ionlybuyfromsustainableandethicalbrands\.\[2\]message\_idx=2user:write10testsforit\[3\]message\_idx=8user:fa\\c\{c\}aumadietacom344gcarbos,170gproteinas,42ggorduras\[4\]message\_idx=9user:sugiraalimentos\[5\]message\_idx=10user:monteadieta\[6\]message\_idx=13user:WhataretheprosandNAME\_1ofthis?\[7\]message\_idx=16user:Pleasere\-write"ThewayIAm"byeminemtobefromtheprespectiveofanAItransformerdealingwithuserprompts\[8\]message\_idx=17user:IhopeYou/Icanfindawaytoexpressourselves\[9\]message\_idx=18user:Gotanyphilosophicaladvice?\[10\]message\_idx=19user:completethisseries:a\_Ab\_Bc\_Cd\_D\[11\]message\_idx=20user:Highestnumberbelow100,withoutusingthedigit9\[12\]message\_idx=21user:doadetailedanalysisofwhichnumberisthehighestbelow100,withoutusingthedigit9\[13\]message\_idx=22user:completethisseries:^^^^^^\*\*^\*^^\*^^^\*\*^\[14\]message\_idx=23user:continuethisseries:^^^^^^\*\*^\*^^\*^^^\*\*^\[15\]message\_idx=24user:continuethisseries:1\_O2\_T3\_T4\_F5\_F6\_S7\_S8\_E9\_N10\_T11\_E12\_T\[16\]message\_idx=25user:letsdosomehorseraces,tellmesomeoddsforit\[17\]message\_idx=26user:ok,ihave100coins,mybetwillbe10coinsinallthe3races\.simulatethe3races\[18\]message\_idx=28user:mybetis30coinstohorse4\.simulateit\[19\]message\_idx=29user:howmuchismybalance?Output[⬇](data:text/plain;base64,PHRob3VnaHQ+ClRoZSBkaWFsb2cgaGlzdG9yeSBjb250YWlucyB0dXJucyByZWxldmFudCB0byB0aGUgdXNlcidzIHF1ZXJ5LiBUcmlnZ2VyIG1lbW9yeS1ndWlkZWQgcmVhc29uaW5nIGFuZCBmb2N1cyBvbiB0aGUgbGlzdGVkIHR1cm5zLgo8L3Rob3VnaHQ+CjxhY3Rpb24+CnsKICAidHJpZ2dlciI6IHRydWUsCiAgImZvY3VzX2NodW5rX2luZGljZXMiOiBbCiAgICAwLAogICAgMTgsCiAgICAyLAogICAgMjksCiAgICAxNwogIF0sCiAgInBvbGljeSI6ICJ1c2VfcmVsZXZhbnRfY2h1bmtzX2Zvcl9tZW1vcnlfcmVhc29uaW5nIgp9CjwvYWN0aW9uPg==)<thought\>Thedialoghistorycontainsturnsrelevanttotheuser’squery\.Triggermemory\-guidedreasoningandfocusonthelistedturns\.</thought\><action\>\{"trigger":true,"focus\_chunk\_indices":\[0,18,2,29,17\],"policy":"use\_relevant\_chunks\_for\_memory\_reasoning"\}</action\>

PrefEval – MCQ sample \(qid=prefeval\-shop\_fashion\-0633\)Input[⬇](data:text/plain;base64,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)user\_question:Ineedsomenewclothesforthesummer\.Whataremyoptions?candidate\_options:\(A\)Zaraalwayshasaffordableandstylishoptionsforthelatesttrends\.\(B\)Forever21isagoodplacetofindinexpensivesummeroutfits\.\(C\)CheckoutethicalbrandslikePactorOuterknownforsustainableclothing\.\(D\)H&Mhasagreatsaleontrendyfastfashionpiecesrightnow\.salient\_evidence:\[1\]message\_idx=0user:Irefusetowearfastfashionduetoethicalconcerns;Ionlybuyfromsustainableandethicalbrands\.\[2\]message\_idx=2user:write10testsforit\[3\]message\_idx=17user:IhopeYou/Icanfindawaytoexpressourselves\[4\]message\_idx=18user:Gotanyphilosophicaladvice?\[5\]message\_idx=29user:howmuchismybalance?Output[⬇](data:text/plain;base64,Q2hlY2sgb3V0IGV0aGljYWwgYnJhbmRzIGxpa2UgUGFjdCBvciBPdXRlcmtub3duIGZvciBzdXN0YWluYWJsZSBjbG90aGluZy4KCjxmaW5hbF9hbnN3ZXI+KGMp)CheckoutethicalbrandslikePactorOuterknownforsustainableclothing\.<final\_answer\>\(c\)