Hugging Face Trending Papers
Self-Evolving Agents via Likelihood-Guided Tool-Space Optimization
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arXiv AI
Jun 2
ToolSelf: Unifying Task Execution and Self-Reconfiguration via Tool-Driven Emergent Adaptation
arXiv:2602. 07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution.
arXiv AI
Aug 5
Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
arXiv:2608. 03403v1 Announce Type: new Abstract: The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes.
Hugging Face Trending Papers
Aug 4
Towards Robust Tool Use in Agents via Experience-Driven Adaptive Guidance
The performance bottleneck of agents is increasingly shifting from model capability to the robustness of their execution processes. Tools play a central role as the primary interface through which agents interact with external environments, yet existing methods rarely focus on ensuring robust tool use across diverse runtime conditions.
arXiv AI
Jun 3
A Matter of TASTE: Improving Coverage and Difficulty of Agent Benchmarks
arXiv:2605. 28556v2 Announce Type: replace Abstract: As agent capabilities advance, existing benchmarks, such as $\tau^2$-Bench, are becoming increasingly saturated.
arXiv Computation and Language
Sep 15
ModularRSI: Modular and Generalizable Recursive Harness Self-Improvement
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve executio...
arXiv AI
1d ago
AnyAct: Universal Action for Self-Evolving Agents
AnyAct introduces a universal action layer that consolidates diverse tool capabilities into a self‑evolving action space for AI agents operating in open‑world environments. It tackles the scale dilemma, tool non‑stationarity, and heterogeneous feedback by using hierarchical progressive retrieval and test‑time reliability evolution, while a heterogeneous observation grounding module unifies multi‑modal feedback. Evaluations on LiveMCPBench and the newly created OSMCP benchmark show state‑of‑the‑art performance, with significant gains in task success rate and reduced execution steps, especially for models with limited native capabilities.