mlsanityData to 7 Oct 2026

AsymPuzl: An Asymmetric Puzzle for multi-agent cooperation

arXiv 2512.03466 ↗First posted 3 Dec 2025cs.MA cs.AIcited by 0

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Large Language Model (LLM) agents are increasingly studied in multi-turn, multi-agent scenarios, yet most existing setups emphasize open-ended role-play rather than controlled evaluation. We introduce AsymPuzl, a minimal but expressive two-agent puzzle environment designed to isolate communication under information asymmetry. Each agent observes complementary but incomplete views of a symbolic puzzle and must exchange messages to solve it cooperatively. Using a diverse set of current-generation and open-source LLMs, we show that (i) strong models such as GPT-5 and Claude-4.0 reliably converge across puzzle sizes on the solution by sharing complete information in two turns, (ii) weaker models often ignore partner messages or over-correct their hypotheses, and (iii) feedback design is non-trivial: simple self-feedback improves success rates, while detailed joint feedback can hurt performance. These findings show that even in simple cooperative tasks, LLM communication strategies diverge and depend on the granularity of feedback signals. AsymPuzl thus provides a testbed for probing the limits of multi-turn cooperation and opens avenues for studying coordination mechanisms.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 17 of its 17 references that are in the map. First posted on arXiv 3 Dec 2025.

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17 of 17 references.

Closest ancestors · 2

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  1. 2024Autonomous Agents for Collaborative Task under Information Asymmetry21 Jun
  2. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society31 Mar

Other references · 15

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  7. 2025gpt-oss-120b & gpt-oss-20b Model Cardcited by 1,258
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  10. 2025ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoningcited by 186
  11. 2025VGRP-Bench: Visual Grid Reasoning Puzzle Benchmark for Large Vision-Language Modelscited by 26
  12. 2024Step-by-Step Reasoning to Solve Grid Puzzles: Where do LLMs Falter?cited by 18
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  1. 2023Assessing Logical Puzzle Solving in Large Language Models: Insights from a Minesweeper Case Studycited by 14
  2. 2025Multi-Turn Puzzles: Evaluating Interactive Reasoning and Strategic Dialogue in LLMscited by 9
  3. 2025MAEBE: Multi-Agent Emergent Behavior Frameworkcited by 8

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