arXiv AI
Dynamic Conflict Clustering with Lightweight Learning and Strategic Repair for Distributed Constraint Satisfaction
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 21
Repair the Core, Not the Symptom: Planning-Graph Repair for Long-Horizon Agents
arXiv:2607.01767v3 Announce Type: replace Abstract: Long-horizon language agents make interdependent decisions across multiple rounds, forming growing planning graphs in which later decisions depend...
arXiv AI
Jul 13
Communication-Efficient Digital-Twin Coordination for Heterogeneous LLM Embodied Agents over Computing Power Networks
arXiv:2607. 09330v1 Announce Type: new Abstract: Embodied agent teams powered by heterogeneous large language models (LLMs) are being widely deployed in physical artificial intelligence such as smart factories, warehouses, and service robotics.
arXiv AI
Jul 3
Repair the Amplifier, Not the Symptom: Stable World-Model Correction for Agent Rollouts
arXiv:2607. 01767v1 Announce Type: new Abstract: As agent planning moves from short tool chains toward persistent workflows with thousands or tens of thousands of steps, failures will occur inside large planning graphs rather than in isolated predictions.
arXiv AI
Oct 7
Verifying Coordination in Parallel Coding Agents: NP-Bench and a Scheduling Planner
The paper introduces NP‑Bench, a benchmark and a proactive scheduling planner that coordinates parallel large‑language‑model coding agents. By partitioning work scopes and ordering merges ahead of time, the planner improves clean‑integration rates from 1/9 to 9/9 and eliminates merge conflicts, outperforming both no‑coordination and reactive‑detection baselines. It also demonstrates that cross‑session memory can eliminate repeated mistakes and that routing facts to agents does not improve long‑context accuracy at scale.
arXiv AI
Oct 23
Parallel AI Agent Collaboration as Graph-Structured Distributed Block Optimization
arXiv:2610. 30074v1 Announce Type: cross Abstract: Parallel AI agents can produce useful local edits yet degrade a shared solution when their edits are combined.
arXiv Machine Learning
Sep 24
Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning
The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.