{"work":{"id":"0ae29ba2-3e94-41be-b6e9-71cf059f885b","openalex_id":null,"doi":null,"arxiv_id":"2511.20639","raw_key":null,"title":"Latent Collaboration in Multi-Agent Systems","authors":null,"authors_text":"Zou, J","year":2025,"venue":"cs.CL","abstract":"Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on text-based mediation for reasoning and communication, we take a step forward by enabling models to collaborate directly within the continuous latent space. We introduce LatentMAS, an end-to-end training-free framework that enables pure latent collaboration among LLM agents. In LatentMAS, each agent first performs auto-regressive latent thoughts generation through last-layer hidden embeddings instead of text. Then, a shared latent working memory preserves and transfers each agent's internal representations and latent thoughts, ensuring lossless information exchange without re-encoding. We provide detailed theoretical analyses showing that LatentMAS achieves higher expressiveness and lossless information preservation with lower overall complexity than standard text-based MAS. In addition, empirical evaluations across 9 comprehensive benchmarks spanning math and science reasoning, commonsense understanding, and code generation show that LatentMAS outperforms advanced single agents and text-based MAS baselines, achieving up to 14.6% higher accuracy, reducing output token usage by 70.8%-83.7%, and providing 4$\\times$-4.3$\\times$ faster end-to-end inference. Code and data are fully open-sourced at https://github.com/Gen-Verse/LatentMAS.","external_url":"https://arxiv.org/abs/2511.20639","cited_by_count":null,"metadata_source":"pith","metadata_fetched_at":"2026-06-28T19:32:34.778919+00:00","pith_arxiv_id":"2511.20639","created_at":"2026-05-09T04:30:10.530345+00:00","updated_at":"2026-06-28T19:32:34.778919+00:00","title_quality_ok":true,"display_title":"arXiv preprint arXiv:2511.20639 , year=","render_title":"arXiv preprint arXiv:2511.20639 , year="},"hub":{"state":{"work_id":"0ae29ba2-3e94-41be-b6e9-71cf059f885b","tier":"hub","tier_reason":"10+ Pith inbound or 1,000+ external citations","pith_inbound_count":17,"external_cited_by_count":null,"distinct_field_count":6,"first_pith_cited_at":"2026-04-04T17:30:23+00:00","last_pith_cited_at":"2026-05-29T22:47:04+00:00","author_build_status":"not_needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"not_needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-06-29T17:09:06.572212+00:00","tier_text":"hub"},"tier":"hub","role_counts":[{"context_role":"background","n":5},{"context_role":"extension","n":1}],"polarity_counts":[{"context_polarity":"background","n":5},{"context_polarity":"extend","n":1}],"runs":{},"summary":{},"graph":{},"authors":[]}}