👥 Multi-Agent¶

🧠 NeurIPS2025 · 17 paper notes

📌 Same area in other venues: 🧪 ICML2026 (15) · 💬 ACL2026 (39) · 🔬 ICLR2026 (15) · 🤖 AAAI2026 (26)

🔥 Top topics: Agents ×14 · LLM ×3 · Reasoning ×2

3D-Agent: Tri-Modal Multi-Agent Collaboration for Scalable 3D Object Annotation: This paper proposes Tri-MARF, a tri-modal multi-agent framework comprising a VLM annotation agent (multi-view, multi-candidate description generation), an information aggregation agent (BERT clustering + CLIP weighting + UCB1 Multi-Armed Bandit selection), and a point cloud gating agent (Uni3D text–point cloud alignment for hallucination filtering). The system achieves a CLIPScore of 88.7 (surpassing human annotation at 82.4), a throughput of 12k objects/hour, and has annotated approximately 2 million 3D models.
Adaptive Coopetition: Leveraging Coarse Verifier Signals for Resilient Multi-Agent LLM Reasoning: This paper proposes the Adaptive Coopetition (AdCo) framework, which employs a UCB multi-armed bandit strategy with coarse-grained verifier signals to enable multiple LLM agents to adaptively switch between cooperative and competitive modes during inference, achieving a 20% relative improvement on mathematical reasoning benchmarks.
Automated Composition of Agents: A Knapsack Approach for Agentic Component Selection: This paper formalizes the agent component selection problem as an online knapsack problem and proposes the Composer Agent framework, which evaluates true component capabilities via sandbox testing (rather than static semantic retrieval) and dynamically selects optimal component combinations under budget constraints using the ZCL online algorithm. The approach achieves up to a 31.6% improvement in single-agent tool selection success rate, and boosts multi-agent sub-agent selection success rate from 37% to 87%.
Belief-Calibrated Multi-Agent Consensus Seeking for Complex NLP Tasks: This paper proposes the Belief-Calibrated Consensus Seeking (BCCS) framework, which incorporates three modules—belief-calibrated consensus judgment, conflict-aware collaborator assignment, and leader selection—to enable multi-agent systems to reach more stable consensus on complex NLP tasks, yielding improvements of 2.23% and 3.95% on difficult subsets of MATH and MMLU, respectively.
Communicating Plans, Not Percepts: Scalable Multi-Agent Coordination with Embodied World Models: This paper proposes an "intention communication" architecture based on lightweight world models, enabling multi-agent coordination by generating and sharing future trajectory plans. The approach comprehensively outperforms end-to-end emergent communication methods in both scalability and performance.
Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?: This work establishes, both theoretically and empirically, that the performance gains attributed to Multi-Agent Debate (MAD) stem primarily from majority voting (ensembling) rather than the debate process itself. The debate dynamics are shown to constitute a martingale—meaning debate does not systematically improve correctness in expectation—and this theoretical insight motivates a principled improvement to MAD by biasing updates toward correct signals.
GauDP: Reinventing Multi-Agent Collaboration through Gaussian-Image Synergy in Diffusion Policies: GauDP is proposed to enable scalable, perception-enhanced multi-agent collaborative imitation learning by constructing a globally consistent 3D Gaussian field from decentralized RGB observations of multiple agents and dynamically allocating Gaussian attributes back to each agent's local viewpoint.
Large Language Models Miss the Multi-Agent Mark: This position paper systematically surveys 1,400+ papers to argue that current LLM-based multi-agent systems (MAS LLMs) deviate from foundational MAS theory along four dimensions: LLMs lack native social behavior, environment design is LLM-centric, asynchronous coordination and standard communication protocols are absent, and emergent behaviors lack quantification. The paper warns that the field risks reinventing the wheel while ignoring 40 years of MAS research.
Lessons Learned: A Multi-Agent Framework for Code LLMs to Learn and Improve: This paper proposes the LessonL framework, enabling multiple small LLM agents to reflect on both successful and failed cases through mutually shared "lessons," collaboratively optimizing code performance. A combination of three 7B–14B models achieves code optimization results on par with GPT-4o and approaching o3.
MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting: This paper proposes MASFIN, a multi-agent system that decomposes financial forecasting into multiple sub-tasks (macroeconomic analysis, industry analysis, technical analysis, sentiment analysis, etc.), with specialized LLM agents collaborating to produce more accurate and interpretable financial predictions than single-model approaches.
MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision: MAS-ZERO is the first inference-time automatic MAS design framework. Through a meta-agent that iteratively designs, critiques, and refines MAS configurations (including task decomposition and sub-MAS assignment), it requires no validation set or training, and outperforms both manual and automatic MAS baselines on reasoning (+16.69%), programming (+16.66%), and search agent (+5.45%) tasks while maintaining a Pareto-optimal accuracy–cost trade-off.
MedAgentBoard: Benchmarking Multi-Agent Collaboration with Conventional Methods for Diverse Medical Tasks: This paper proposes MedAgentBoard, a comprehensive benchmark that systematically evaluates multi-agent collaboration, single-LLM, and conventional methods across diverse medical tasks, revealing that multi-agent collaboration does not consistently outperform strong single models or specialized conventional approaches.
MetaMind: Modeling Human Social Thoughts with Metacognitive Multi-Agent Systems: This paper proposes MetaMind — a multi-agent framework inspired by psychological metacognition theory — that significantly enhances the social reasoning capabilities of LLMs through three-stage collaboration: a ToM Agent (mental state hypothesis generation), a Moral Agent (social norm-constrained refinement), and a Response Agent (response generation with self-verification). MetaMind achieves state-of-the-art performance on multiple social intelligence benchmarks, approaching human-level performance for the first time.
Multi-Agent Collaboration via Evolving Orchestration: This paper proposes a "Puppeteer" multi-agent collaboration paradigm in which a centralized orchestrator learns via RL to dynamically select which agent to activate at each reasoning step. The approach simultaneously improves performance and efficiency on both closed-domain and open-domain tasks, and reveals that evolved topologies tend toward more compact cyclic structures.
R&D-Agent-Quant: A Multi-Agent Framework for Data-Centric Factors and Model Joint Optimization: This paper proposes R&D-Agent(Q), a data-driven multi-agent framework that automates the joint optimization of factor mining and model innovation for quantitative strategies through five collaborative modules (Specification, Synthesis, Implementation, Validation, and Analysis), achieving approximately 2× the annualized return of traditional factor libraries in real stock markets at a cost of under $10.
The PokeAgent Challenge: Competitive and Long-Context Learning at Scale: This paper introduces the PokéAgent Challenge, a large-scale dual-track AI benchmark built on Pokémon competitive battling and RPG speedrunning. Validated through the NeurIPS 2025 competition, it demonstrates that specialist RL methods substantially outperform general-purpose LLM approaches, and reveals that the capabilities measured by Pokémon battling are nearly orthogonal to those assessed by 49 existing LLM benchmarks.
Thought Communication in Multiagent Collaboration: This paper proposes ThoughtComm, a framework that formalizes multiagent communication as a latent variable generative model. It proves that both shared and private thoughts are identifiable under nonparametric conditions, extracts latent thoughts via a sparsity-regularized autoencoder, and feeds them back to each agent through prefix injection. ThoughtComm achieves an average improvement of 19.06% over the current SOTA Multiagent Finetuning on mathematical reasoning benchmarks.