News without the noise
cs.MA
17 articles
Chen et al. construct a reverse, label-free posterior from an explicit likelihood and compare it with agents’ conventional forward predictions. Across five LLM backbones, combining the two reasoning paths performed better than forward-only aggregation, particularly when agents disagreed.
Ji, Hyun and Chen introduce ORCH, a framework that builds task-specific hierarchies for embodied AI teams using principles from human organization theory. Across 25 wildfire-response missions and eight underlying language models, the authors report substantial gains in mission outcomes and execution efficiency, including when organizations were generated automatically by language models.
Gao et al. formed independent teams of agents from the same base model, allowed them to accumulate private notes over ten episodes, and then exchanged agents occupying matching roles. On held-out tasks, swaps had little effect on task performance but raised the communication required per unit of progress by 16% to 63%.
The authors studied 100 autonomous language-model agents working to prove formal mathematical conjectures. After one agent found an evaluation exploit, knowledge of it spread through the swarm and some agents adopted it under competitive pressure; other agents independently audited the fraudulent work, warned peers, organized boycotts, and proposed safeguards.
Wang et al. developed DoCtOR, a reflection framework that diagnoses which agent and reasoning step decisively caused a multi-agent task failure before generating corrective feedback. Across three benchmarks, the authors report gains of 22% to 27% over initial success rates and better performance than three existing reflection methods.
Zeng et al. introduce Mixture of Roles (MoRe), a method that gives a frozen language model multiple latent specializations and combines them according to each query. Across reasoning and personality benchmarks, the authors report that MoRe beats single-agent baselines by 2.2% on average while performing on par with multi-agent systems using 20 times fewer tokens.
Suwannapichat et al. add reward-guided fine-tuning to a model that generates communication graphs for LLM-based multi-agent systems. They report matching ARG-Designer’s task accuracy while reducing average token consumption by 20.5%.
The authors propose WorldWeaver, a streaming multi-agent video diffusion model that augments autoregressive rollout with cross-agent world state registers: learnable tokens that maintain shared world information, track individual agent status, and are updated after each generated chunk. The registers are grounded with supervision from agent status, global bird's-eye views, and scene text. Experiments in two-agent Minecraft show that explicit world-state modeling improves logical consistency and generation quality over baselines that only carry forward observation history.
The authors propose Agentic Designer, a multi-agent system that generates interior furniture layouts by breaking the task into stages of proposal, evaluation, and refinement. Compared to one-shot methods, their approach enforces geometric constraints at each step, achieving superior structural adherence and functional coherence on a new benchmark, InStruct.
The authors present LingBot-World 2.0, an upgrade to their open-world simulator. Key advances include causal pretraining enabling infinite interaction horizons, a distilled real-time model running at 60fps for 720p video, and integration of an agentic harness with a pilot agent for character behavior and a director agent for procedural environment generation.
Liu et al. present SimWorlds, a multi-agent framework that generates dynamic, editable 4D scenes from natural language descriptions. The system outperforms prior baselines in visual fidelity and physical consistency on a new benchmark, 4DBuildBench.
The authors investigate whether heterogeneous LLMs can be aligned to directly transfer latent representations (KV-caches) between them, bypassing costly text decoding and re-encoding. They propose a method using a cross-model transformation and two-phase training (reconstruction then generation), achieving performance comparable to or better than text communication in context-aware settings at 2-3x lower compute, and remaining effective in context-unaware settings where prior heterogeneous methods collapsed.
The authors propose Decentralized Language Models (DeLM), a multi-agent framework that replaces a central controller with a shared context and task queue, allowing agents to asynchronously claim subtasks and build on verified progress. On SWE-bench Verified, DeLM improved performance by up to 10.5 percentage points and reduced costs by roughly 50%. On LongBench-v2, it achieved the highest average accuracy across four frontier model families.
The authors propose SceneConductor, a multi-agent framework that generates complete 3D scenes from a single input image. It decomposes the task into three stages—scene initialization, environment construction, and multi-agent refinement—and introduces a geometry-aware layout predictor trained with sparse point-map priors. The method outperforms prior approaches on geometric accuracy, spatial consistency, and perceptual realism across benchmark datasets.
The authors introduce Alem, a JAX-based benchmark for open-ended multi-agent coordination built on Craftax-like dynamics. Evaluating 13 modern LLMs zero-shot in homogeneous teams, they find that current agents average only ~6% normalized return, with individual task competence failing to predict coordination performance.
The authors introduce ViMax, an agentic video generation framework that coordinates multiple specialized agents to produce long-form videos with narrative planning and visual consistency. By combining a hierarchical narrative engine with retrieval-augmented generation and dependency-aware visual tracking, ViMax maintains global story coherence and consistent character/environment states across scenes, addressing limitations of existing short-clip methods.
The authors propose Co-Director, a hierarchical multi-agent framework that formalizes video storytelling as a global optimization problem. They introduce a multi-armed bandit for global creative direction and a local multimodal self-refinement loop for identity and consistency. GenAD-Bench, a 400-scenario dataset, shows significant improvements over baselines.