Agent - Multiagent collaboration
- Notes: Multi-Agents
In this session, our readings cover:
Required Readings: MULTI-AGENT SYSTEMS
Core Component: Multi-Agent Collaboration - Coordination, Communication, and Collective Intelligence
Understanding how multiple agents work together to solve complex problems. Key Concepts: Agent communication protocols, collaborative problem-solving, role-based coordination, multi-agent architectures
| Topic | Slide Deck | Previous Semester |
|---|---|---|
| Agent - Multiagent Collaboration | W11.1.Team5-agent | 25course |
| MultiAgent LLMs | W13-MultiAgentLLMs | 24course |
2025 HIGH-IMPACT PAPERS on this topic
- a. MAR: Multi-Agent Reflexion Improves Reasoning (December 2025)
- Link: https://arxiv.org/abs/2512.20845
- Key Idea: Multi-persona debators prevent degeneration of thought
- Results: 47% EM on HotPot QA, 82.7% on HumanEval
- b. Towards a Science of Scaling Agent Systems (December 2025)
- Link: https://arxiv.org/abs/2512.08296
Quantitative Scaling Laws:
- 180 configurations tested: 5 architectures (single, independent, centralized, decentralized, hybrid) × 3 LLM families × 4 benchmarks
- Key findings:
- Capability saturation: Coordination has diminishing returns above ~45% single-agent baseline
- Error amplification: Independent agents amplify errors 17.2×, centralized reduces to 4.4×
- Task dependency: Centralized excels on parallelizable tasks (+80.8%), decentralized on web navigation (+9.2%)
- Sequential tasks: All multi-agent variants degrade performance by 39-70%
- Predictive framework: 87% accuracy on held-out configurations
- Validated on GPT-5.2 (MAE=0.071)
- c. Multi-Agent Collaboration Mechanisms: A Survey of LLMs (January 2025)
- Link: https://arxiv.org/abs/2501.06322
Framework Dimensions:
- Actors: Agents involved in collaboration
- Types: Cooperation, competition, coopetition
- Structures: Peer-to-peer, centralized, distributed
- Strategies: Role-based, model-based
- Coordination protocols: Communication patterns
- Applications: 5G/6G networks, Industry 5.0, question answering, social/cultural settings
- e. From Single-Agent to Multi-Agent: Legal Agents Review (November 2025)
- Venue: AI Agent Journal 2025
- Link: https://www.oaepublish.com/articles/aiagent.2025.06
- Core tasks: Legal information retrieval, QA, judgment prediction, text generation
- Evaluation benchmarks: LAiW (Chinese practical), UCL-Bench (user-centric), JuDGE (judgment documents)
- Single-agent challenges: Trustworthiness, explainability, factuality
- Multi-agent systems: Collaborative reasoning, specialized roles (researcher, analyst, writer)
- Future directions: Cross-jurisdictional interoperability via legal knowledge graphs, ethical governance
- g. LongVideoAgent: Multi-Agent Reasoning with Long Videos (December 2025)
- Link: https://arxiv.org/abs/2512.20618 Architecture:
- Master agent: Coordinates with step limit, trained via RL
- Grounding agent: Localizes question-relevant segments
- Vision agent: Extracts targeted textual observations from video
- Training: Reinforcement learning to encourage concise, correct, efficient cooperation
- Benchmark: LongTVQA and LongTVQA+ (episode-level datasets from TVQA/TVQA+)
- Results: Significantly outperforms non-agent baselines on hour-long video reasoning