arXiv AI
One Readout, Many Repairs: Diffusion-Guided Hierarchical Search for Tool-Agent Repair
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arXiv AI
Sep 1
Repair or Resample? Rethinking Failure Debugging in LLM Multi-Agent Systems
arXiv:2608.25920v2 Announce Type: replace Abstract: As large language model (LLM)-based multi-agent systems (MASs) are increasingly applied to long-horizon complex tasks, their reliability has emerge...
arXiv AI
Sep 25
Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents
The paper introduces Growing Harness, a training method that transforms recurring control logic in large language model agents into reusable executable code, reducing reliance on the model for task-specific decisions. By using strategy-free scaffolds, failure-guided code repair, and success-first gating, the approach learns a shared harness that improves performance across multiple benchmarks and model sizes. Experiments on BrowseComp-Plus and WebArena-Verified show significant gains in success rates and substantial reductions in LLM calls and inference cost compared to traditional tool‑calling agents.
arXiv AI
Aug 3
Autonomous Repair for Multi-Agent Systems via Monte-Carlo Tree Search
arXiv:2607. 29055v1 Announce Type: cross Abstract: Multi-agent systems (MAS) are increasingly deployed to solve complex tasks.
Hugging Face Trending Papers
Aug 12
Diagnosis Before Recovery: Turning Agent Failures into Selective Self-Correction
Self-correction is particularly useful when a failure constrains the next repair. Coding agents benefit from this property because compilers, tests, and execution traces turn many failures into typed recovery signals, but broad language-agent tasks often expose only a coarse task failure.
arXiv AI
Aug 7
SearchAuditor: Auditing and Attributing Failures in Long-Horizon Search Agents
arXiv:2608. 05212v1 Announce Type: new Abstract: Deep search agents tackle challenging questions through long-horizon web interactions, a process that is both complex and fragile: small reasoning errors may propagate through long, noisy trajectories into fluent but incorrect answers.
arXiv AI
Jun 12
Evoflux: Inference-Time Evolution of Executable Tool Workflows for Compact Agents
arXiv:2606. 12674v1 Announce Type: new Abstract: Compact language models (LMs) reduce cost, latency, and deployment risk for tool agents.