arXiv:2608. 08677v1 Announce Type: new Abstract: Skill evolution improves agent skills through feedback over time, with failed trajectories often providing informative signals by revealing incomplete or misleading behaviors.
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arXiv:2608. 05628v1 Announce Type: new Abstract: Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment.
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not...
The paper introduces Agentic Reasoning for Tree Search (ARTS), a method that uses a reasoning language model to navigate the hypothesis‑experiment space in scientific discovery. Unlike traditional approaches that conflate hypothesis quality with execution quality and prune search logs, ARTS evaluates prior execution logs to distinguish implementation failures from poor hypotheses and selects the next hypothesis to pursue. By employing test‑time training to embed search‑tree knowledge into model weights, ARTS achieves a 15.3% relative improvement over leading algorithms on 22 benchmark tasks and enables smaller models like Qwen3‑4B to match or exceed the performance of larger closed‑source models at lower inference cost.
arXiv:2606. 01993v1 Announce Type: cross Abstract: Abundant procedural knowledge on the Web holds great potential for helping agents solve long-horizon tasks.
Although agent skills equip LLMs with reusable procedural knowledge, manual maintenance suffers from high costs, unscalability, and misalignment. Real-world deployments thus require autonomous, on-demand skill evolution at test time, constrained by limited interaction budgets and a lack of training or validation sets.
Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis. A central challenge is deciding how to search when several directions look plausible but only some will later lead to reliable evidence.