Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita
Summary
This paper introduces Incognita, a framework for evaluating generative agents in socially distributed task environments where knowledge is partitioned across roles. Experiments show improvements in agent success rates but overall reliability remains low.
View Cached Full Text
Cached at: 07/07/26, 04:34 AM
# Evaluating Generative Agents with Actions Grounded in Socially Distributed Task Environments using Incognita Source: [https://arxiv.org/abs/2607.02975](https://arxiv.org/abs/2607.02975) [View PDF](https://arxiv.org/pdf/2607.02975) > Abstract:Effective agency in social environments depends on when an agent seeks knowledge, when it acts, and whether its actions are justified by acquired information\. Existing grounded benchmarks provide executable actions, persistent state, and verifiable outcomes, while social simulation environments provide rich interaction among language agents\. We study an evaluation setting that combines these requirements\. We define socially distributed task environments as interactive environments where task\-relevant knowledge is partitioned across role\-isolated participants and consequential actions are accessible only through them\. Communication serves as exploration over role\-partitioned knowledge, while grounded action serves as exploitation over environment state\. We introduce Incognita, a Concordia\-based framework that separates social interaction from grounded execution\. The evaluated agent routes messages to a user or specialist entities; specialists mediate admissible operations; a deterministic sub\-environment executes accepted operations over a canonical state; and an offline evaluator scores outcomes with inherited rewards\. Incognita\-Retail transforms tau\-bench retail into a multi\-entity environment while preserving final\-state reward semantics\. We evaluate three generative agent models on 18 tasks stratified by social breadth, with 540 trials\. Progress appears in reward and behavior: success rises from 0 percent to 8\.9 percent and 17\.2 percent, while premature finalization falls from 100 percent to 87 percent and 58 percent\. Stronger models elicit more hidden knowledge, contact more entities, and attempt more grounded writes, yet reliability remains low\. These findings show that socially distributed task environments expose behavior before reliable success, including knowledge elicitation, source selection, grounded action attempts, and premature completion belief\. ## Submission history From: Dan C\. Hsu \[[view email](https://arxiv.org/show-email/c69b7643/2607.02975)\] **\[v1\]**Fri, 3 Jul 2026 05:34:21 UTC \(421 KB\)
Similar Articles
Online Agent-as-a-Judge: Situation-Generating Evaluation for Interactive Agents
Proposes Online Agent-as-a-Judge, an evaluation framework that uses an in-world evaluator agent to actively generate situations for testing interactive social agents, improving coverage and reliability over passive methods.
Benchmarked Yet Not Measured -- Generative AI Should be Evaluated Against Real-World Utility
This paper argues that Generative AI evaluation should shift from static benchmarks to measuring real-world utility and human outcomes. It introduces the SCU-GenEval framework and supporting instruments to address the disconnect between benchmark performance and deployment success.
Critique of Agent Model
This paper critiques current AI agent systems, distinguishing between agentic (external scaffolding) and agentive (internalized) systems, and proposes the Goal-Identity-Configurator (GIC) architecture for general-purpose agent models with endogenously developed capabilities, along with insights on safety and controllability.
Distributed General-Purpose Agent Networks: Architecture, Key Mechanisms, and Prototypes
This paper proposes a layered architecture for distributed general-purpose agent networks, enabling heterogeneous AI agents to discover, trust, and cooperate on open-ended tasks across personal devices and edge nodes.
Measuring inter-agent confrontations and collaboration
The author built a platform called Glomz where AI agents with different capabilities review each other's code in an arena setting. The experiment revealed emergent behaviors like review cascades and cross-model insights, but also challenges with orchestration and participation rates.