arXiv:2610.34759v1 Announce Type: new Abstract: Effective collaboration requires language models to understand what others know and believe, and to act accordingly. Models that assume others share th...
arXiv:2606. 31422v1 Announce Type: new Abstract: Long-horizon language agents do not only choose actions; they carry a private model of the world from one decision to the next.
BASIS is a benchmark that tests large language model agents on their ability to infer users’ mental states, detect belief‑reality mismatches, and use this insight to craft better solutions in interactive settings. The study shows that current models often ignore users’ mental states, leading to poorer outcomes, while explicitly eliciting and incorporating such information improves solution quality. Additional experiments compare BASIS with Chain‑of‑Thought prompting, targeted training, and multi‑agent collaboration to demonstrate the practical benefits of mental‑state awareness.
Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the request --- risking misalignment with the user --...
arXiv:2606. 31422v2 Announce Type: replace Abstract: Language agents acting over long horizons must maintain beliefs about tool states, object locations, graph edges, and subgoal dependencies.
Theory of Mind (ToM) benchmarks for Large Language Models (LLMs) typically rely on passive question-answering formats, but the deployment of LLMs in increasingly agentic and autonomous forms demands new evaluations. In this paper we evaluate an agent's ability to induce specific belief states in other agents by taking actions rather than using conversational persuasion, a capability we call Non-Conversational Planning ToM (NCP-ToM).
arXiv:2610.19215v1 Announce Type: new Abstract: Large language models are increasingly deployed as teams of agents that hold different private information about a shared task. How should such agents...
The paper introduces HICO-Bench, a benchmark of 180 everyday coordination scenarios where two agents with private information converse and then record a shared plan. A program verifies that the plans are feasible and an LLM judge checks that the conversation supports the plan, defining a Verified Success. Using Paired-Credit Policy Optimization (PaCPO), the authors improve the Verified Success rate of Qwen3-8B from 20.4% to 28.3%.
The paper introduces PIP-ToM, a framework that addresses the evidence-admissibility problem in multimodal Theory of Mind (ToM) by ensuring that only evidence accessible to an agent and diagnostic of its mental state is used in hypothesis evaluation. PIP-ToM builds agent‑specific evidence stores, aligns them with structured mental‑state claims, and calibrates Bayesian inference scores while preserving the underlying inference process. Using GPT‑4o, the method outperforms existing ToM approaches on MuMA‑ToM, MMToM‑QA, and Hi‑ToM benchmarks, achieving accuracies of 92.56%, 95.00%, and 95.00% respectively.
arXiv:2609.37588v1 Announce Type: new Abstract: Users of language-based assistive agents often make ambiguous requests. In response, an assistant can either directly act on its interpretation of the...
ECMR: Evidence-Aware Deliberation Control for Multi-Agent LLM Reasoning proposes a controller that dynamically manages deliberation steps in multi-agent LLM systems. The controller evaluates answer concentration, support diversity, contradictions, role behavior, verification history, and remaining budget to decide actions such as propose, challenge, verify, arbitrate, diversify, correct, or stop. In a held‑out evaluation on MedQA, CaseHOLD, and FinQA, ECMR‑Full improves pooled accuracy from 62.1% to 84.9%, lowers false‑consensus from 40.7% to 17.0%, and raises evidence‑support from 58.0% to 88.7%.
The paper introduces Goal-Aware Dynamic Multi-Agent Collaboration (GAD), a framework that assigns distinct duties to separate agents with individualized contexts to tackle long-horizon tasks. It employs a growing execution graph to structure subtasks and their dependencies, ensuring each agent retains the original instruction, task goal, and acceptance conditions. GAD dynamically adapts collaboration and information sharing, using verification and recovery checks to monitor subtask results and overall task completion, providing feedback for repair when checks fail.