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τ2\tau^2-bench: : Evaluating Conversational Agents in a Dual-Control Environment

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 8.0Download Report PDF
BenchmarkEvaluationDual ControlConversational AI AgentsUser Simulation
Abstract:

Existing benchmarks for conversational AI agents simulate single-control environments, where only the AI agent can use tools to interact with the world, while the user remains a passive information provider. This differs from real-world scenarios like technical support, where users need to actively participate in modifying the state of the (shared) world. In order to address this gap, we introduce τ2\tau^2-bench, with four key contributions:

  1. A novel Telecom dual-control domain modeled as a Dec-POMDP, where both agent and user make use of tools to act in a shared, dynamic environment that tests both agent coordination and communication,

  2. A compositional task generator that programmatically creates diverse, verifiable tasks from atomic components, ensuring domain coverage and controlled complexity,

  3. A reliable user simulator tightly coupled with the environment, whose behavior is constrained by tools and observable states, improving simulation fidelity,

  4. Fine-grained analysis of agent performance through multiple ablations including separating errors arising from reasoning vs communication/coordination.

In particular, our experiments show significant performance drops when agents shift from no-user to dual-control, highlighting the challenges of guiding users. Overall, τ2\tau^2-bench provides a controlled testbed for agents that must both reason effectively and guide user actions.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces τ²-bench, a benchmark for evaluating conversational agents in dual-control environments where both agent and user actively use tools to modify shared state. It sits within the 'Dual-Control Environment Benchmarks' leaf of the taxonomy, which contains only one sibling paper (AssistEditor). This indicates a relatively sparse research direction within the broader field of conversational agent evaluation. The paper's core contributions—Dec-POMDP formalization, compositional task generation, and tightly coupled user simulation—target systematic evaluation of coordination and communication in shared-control scenarios.

The taxonomy reveals that dual-control benchmarks form a small subset of the broader 'Benchmark Design and Evaluation Frameworks' branch, which also includes task-specific workflow automation evaluation. Neighboring branches address multi-agent coordination architectures, including oracle-based coordination, decentralized synthesis, and dialogue-based shared control systems spanning robotics, safety-critical applications, and data collection. The paper's telecom domain and Dec-POMDP formalization connect it to formal multi-agent coordination work, while its emphasis on conversational grounding aligns with dialogue-based shared control research. The taxonomy's scope notes clarify that this work differs from single-control benchmarks and pure architectural studies.

Among 25 candidates examined across three contributions, none were found to clearly refute the paper's claims. The Dec-POMDP formalization examined 5 candidates with no refutations, suggesting this formal modeling approach may be relatively novel for dual-control conversational benchmarks. The compositional task generator and user simulator contributions each examined 10 candidates, also with no refutations. This limited search scope indicates that within the top-25 semantically similar papers, no substantial prior work directly overlaps with these specific technical contributions. However, the small candidate pool means the analysis cannot rule out relevant work outside this search window.

Based on the limited literature search, the paper appears to occupy a relatively unexplored niche within conversational agent evaluation. The sparse taxonomy leaf (only one sibling) and absence of refuting candidates among 25 examined papers suggest the dual-control benchmark approach with compositional generation and coupled simulation may be distinctive. However, the analysis is constrained by the top-K semantic search methodology and does not constitute an exhaustive survey of all potentially relevant prior work in multi-agent systems, dialogue evaluation, or simulation-based benchmarking.

Taxonomy

Core-task Taxonomy Papers
7
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Evaluating conversational agents in dual-control environments with shared tool use. This emerging field addresses scenarios where multiple agents or an agent and a human must coordinate control over shared resources or tools through dialogue. The taxonomy organizes work into two main branches: Benchmark Design and Evaluation Frameworks, which focuses on creating testbeds and metrics for assessing agent performance in these complex settings, and Multi-Agent Coordination and Control Architectures, which explores the underlying mechanisms and algorithms that enable effective collaboration. Within the first branch, researchers have developed specialized dual-control environment benchmarks that simulate realistic scenarios of shared tool manipulation and conversational grounding, while the second branch examines how agents can negotiate control, resolve conflicts, and maintain coherent dialogue during joint task execution. Early foundational work such as Dialogue Shared Control[6] and Safe Shared Control[8] established basic principles, while more recent efforts like Dual Control Dialogue[2] and AssistEditor[3] have introduced richer interactive settings. Recent developments reveal contrasting emphases between synthetic benchmark construction and real-world applicability. Some studies prioritize controlled experimental conditions using synthetic data generation approaches like Matrix Synthetic Data[5] or oracle-based evaluation methods such as Black Box Oracle[4], enabling systematic measurement of agent capabilities under varied conditions. Others focus on naturalistic human-agent collaboration scenarios where dialogue must adapt to unpredictable user intentions and tool states. Tau Squared Bench[0] situates itself within the Benchmark Design branch, specifically targeting dual-control environment evaluation. Compared to closely related work like AssistEditor[3], which emphasizes collaborative editing tasks, Tau Squared Bench[0] appears to offer a broader framework for assessing conversational coordination across diverse shared-tool scenarios, providing structured metrics for both dialogue quality and control effectiveness in settings where agents must dynamically negotiate resource access.

Claimed Contributions

Dual-control environment formalized as Dec-POMDP

The authors introduce a dual-control setup where both the AI agent and the simulated user possess distinct tools to observe, act upon, and verify the state of a shared environment. This is formalized using a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), enabling realistic simulations of collaborative scenarios like technical support.

4 retrieved papers
Compositional task generator

The authors develop a programmatic task generator that automatically composes a vast and diverse set of verifiable tasks from atomic base scenarios defined by initialization, solution, and assertion functions. This method ensures provable correctness, provides complete domain coverage, and allows explicit control over task complexity.

9 retrieved papers
Reliable user simulator tightly coupled with environment

The authors enhance user simulation reliability by tightly coupling the user simulator to the environment. User behavior is constrained by available tools and observable state, leading to more predictable and consistent interactions with substantially lower error rates compared to existing domains.

9 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Dual-control environment formalized as Dec-POMDP

The authors introduce a dual-control setup where both the AI agent and the simulated user possess distinct tools to observe, act upon, and verify the state of a shared environment. This is formalized using a Decentralized Partially Observable Markov Decision Process (Dec-POMDP), enabling realistic simulations of collaborative scenarios like technical support.

Contribution

Compositional task generator

The authors develop a programmatic task generator that automatically composes a vast and diverse set of verifiable tasks from atomic base scenarios defined by initialization, solution, and assertion functions. This method ensures provable correctness, provides complete domain coverage, and allows explicit control over task complexity.

Contribution

Reliable user simulator tightly coupled with environment

The authors enhance user simulation reliability by tightly coupling the user simulator to the environment. User behavior is constrained by available tools and observable state, leading to more predictable and consistent interactions with substantially lower error rates compared to existing domains.

Table of Contents