{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/comm-collaborative-multi-agent-multi","title":"CoMM: Collaborative Multi-Agent, Multi-Reasoning-Path Prompting for Complex Problem Solving","arxiv_id":"2404.17729","date":"2024-04-26","proceeding":null,"authors":["Pei Chen","Boran Han","Shuai Zhang"],"abstract":"Large Language Models (LLMs) have shown great ability in solving traditional natural language tasks and elementary reasoning tasks with appropriate prompting techniques. However, their ability is still limited in solving complicated science problems. In this work, we aim to push the upper bound of the reasoning capability of LLMs by proposing a collaborative multi-agent, multi-reasoning-path (CoMM) prompting framework. Specifically, we prompt LLMs to play different roles in a problem-solving team, and encourage different role-play agents to collaboratively solve the target task. In particular, we discover that applying different reasoning paths for different roles is an effective strategy to implement few-shot prompting approaches in the multi-agent scenarios. Empirical results demonstrate the effectiveness of the proposed methods on two college-level science problems over competitive baselines. Our further analysis shows the necessity of prompting LLMs to play different roles or experts independently. We release the code at: https://github.com/amazon-science/comm-prompt","url_abs":"https://arxiv.org/abs/2404.17729v1","url_pdf":"https://arxiv.org/pdf/2404.17729v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"comm-collaborative-multi-agent-multi","repo_url":"https://github.com/amazon-science/comm-prompt","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.17729","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.17729"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/amazon-science/comm-prompt","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":3},"by_repo_kind":{"official":{"samples":3,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"f681312e5b7db660","entry":"call_chat_gpt","repo":"amazon-science/comm-prompt","repo_kind":"official","path":"run_pathways.py","file_url":"https://github.com/amazon-science/comm-prompt/blob/HEAD/run_pathways.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f681312e5b7db660"}},{"code_sha256_prefix":"31e1472282690919","entry":"chatgpt","repo":"amazon-science/comm-prompt","repo_kind":"official","path":"run_pathways.py","file_url":"https://github.com/amazon-science/comm-prompt/blob/HEAD/run_pathways.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"31e1472282690919"}},{"code_sha256_prefix":"57fd67bab7ab8ce8","entry":"find_answer_letter","repo":"amazon-science/comm-prompt","repo_kind":"official","path":"run_pathways.py","file_url":"https://github.com/amazon-science/comm-prompt/blob/HEAD/run_pathways.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"57fd67bab7ab8ce8"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}