REVIEW 9 cited by
EvolveSearch: An Iterative Self-Evolving Search Agent
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
The rapid advancement of large language models (LLMs) has transformed the landscape of agentic information seeking capabilities through the integration of tools such as search engines and web browsers. However, current mainstream approaches for enabling LLM web search proficiency face significant challenges: supervised fine-tuning struggles with data production in open-search domains, while RL converges quickly, limiting their data utilization efficiency. To address these issues, we propose EvolveSearch, a novel iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without any external human-annotated reasoning data. Extensive experiments on seven multi-hop question-answering (MHQA) benchmarks demonstrate that EvolveSearch consistently improves performance across iterations, ultimately achieving an average improvement of 4.7\% over the current state-of-the-art across seven benchmarks, opening the door to self-evolution agentic capabilities in open web search domains.
Forward citations
Cited by 9 Pith papers
-
GeoBrowse: A Geolocation Benchmark for Agentic Tool Use with Expert-Annotated Reasoning Traces
GeoBrowse is a two-level geolocation benchmark combining visual cue composition with knowledge-intensive multi-hop queries, paired with the GATE agent workflow that outperforms no-tool, search-only, and image-only baselines.
-
Fetch-then-Explore: Decoupling Selection from Extraction over a Persistent Workspace for Search Agents
Fetch-then-Explore, which stores fetched pages in a per-question workspace and extracts evidence on demand with grep/read, beats visit-and-read and browsing baselines on BrowseComp across three LLM backbones.
-
LocalSearchBench: Benchmarking Agentic Search in Real-World Local Life Services
LocalSearchBench—1.3M merchant records and 900 multi-hop local-life QA tasks across 9 Chinese cities—shows the best reasoning agent reaches only 35.6% correctness.
-
Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL
A single LLM is trained with multi-agent distilled trajectories plus agentic RL, and the resulting Chain-of-Agents models set state-of-the-art Pass@1 scores among tool-integrated reasoning methods on GAIA, BrowseComp,...
-
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
WebShaper formalizes web information-seeking tasks as set-theoretic queries, synthesizes training questions by layer-wise expansion, and uses the resulting data to train open-source agents that outperform prior open-s...
-
WebSailor: Navigating Super-human Reasoning for Web Agent
WebSailor trains open-source web agents to match proprietary performance on complex information-seeking tasks by generating high-uncertainty scenarios and using a new RL method called DUPO.
-
Erase to Improve: Erasable Reinforcement Learning for Search-Augmented LLMs
ERL trains LLMs to erase faulty reasoning steps and regenerate them in place, yielding gains of up to 8.48% EM on multi-hop QA benchmarks like HotpotQA.
-
Beyond Relevance: Utility-Centric Retrieval in the LLM Era
Retrieval systems must prioritize utility for LLM generation quality over traditional relevance metrics, supported by a unified framework distinguishing LLM-agnostic vs specific and context-independent vs dependent utility.
-
A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
Discussion (0). Sign in to comment.