arXiv (All)
open access
Why Your Deep Research Agent Fails? On Hallucination Evaluation in Full Research Trajectory
Diagnosing failure patterns in Deep Research Agents (DRAs) remains a critical challenge. Existing benchmarks predominantly rely on end-to-end evaluation, obscuring intermediate hallucinations that accumulate throughout the research trajectory. To bridge this gap, we propose a shift from outcome-base
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
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.#308866
- APEx: Distillation of Agent Procedural Experience for Adaptive Deep Research Question Answering#822643
- Multi-Turn Evaluation of Deep Research Agents Under Process-Level Feedback#267646
- SCIRIGOR:Evaluating Open-Ended Scientific Analysis Beyond Final Scores#973792
Record · ID 1155106
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