arXiv:2606. 11662v1 Announce Type: new Abstract: Deep search requires agents to answer complex questions through multi-step web search, browsing, evidence comparison, and synthesis.
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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.
arXiv:2609.01294v1 Announce Type: new Abstract: Deep-research agents answer complex questions by interacting with search and browsing tools, yet they often search along a single evolving trajectory....
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. 11926v1 Announce Type: cross Abstract: Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction.
The paper introduces Strategy Accumulation and Guided Execution (SAGE), a two-stage framework that makes automated fine-tuning of large language models cumulative. In the first stage, a multi-agent pipeline uses Monte Carlo Tree Search to explore training strategies while a Distillation Agent records task-specific insights and cross-task confidence scores into a structured repository. In the second stage, SAGE retrieves relevant experience from this repository to guide training on new tasks, achieving a 12.4‑percentage‑point improvement over a baseline pipeline without accumulated experience on nine unseen tasks.
Scientific progress depends on a repeated loop of exploration, experimentation, and abstraction. Researchers test candidate directions, interpret the evidence, and carry the resulting lessons into later attempts.