arXiv (All)
open access
IterSynth: Rethinking Deep Search Agents via Role-Decoupled Iterative Synthesis
Deep search requires LLM agents to decompose complex queries, search for evidence, and synthesize grounded answers, yet existing ReAct-style agents suffer from two limitations: role coupling, where one policy must handle planning, evidence use, and synthesis; and context accumulation, where growing
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Not Worth Another Token: Marginal Value Estimation for Efficient Deep Research Agents#821977
- DeepInstructor: An Agentic AI Instructor for Experience-Driven Idea Evaluation#1036788
- Total Recall QA: A Verifiable Evaluation Suite for Deep Research Agents#1069764
- IDRBench: Benchmarking the Interactive Capabilities of Deep Research Agents#1076026
- Learning to Route in Visual Space via Multi-Step Embedding Retrieval#1143344
Record · ID 1077373
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive