arXiv AI
Search Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
Hugging Face Trending Papers
Sep 21
When More Evidence Hurts: Publication-Bias Drift and Principled Stopping for Biomedical Causal Search
Automated biomedical evidence synthesis depends on retrieving published studies, but the biomedical literature is systematically skewed toward positive findings. Deeper retrieval can therefore make a...
arXiv AI
Sep 23
When More Evidence Hurts: Publication-Bias Drift and Principled Stopping for Biomedical Causal Search
arXiv:2609.24101v1 Announce Type: new Abstract: Automated biomedical evidence synthesis depends on retrieving published studies, but the biomedical literature is systematically skewed toward positive...
arXiv AI
Jul 17
L-MARS: Legal Multi-Agent System with Agentic Search and Citation-Faithfulness Audit
arXiv:2509. 00761v4 Announce Type: replace Abstract: Large language models are increasingly deployed for legal question answering, where evaluations typically focus on multiple-choice accuracy.
arXiv AI
Aug 18
LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents
arXiv:2608. 16185v1 Announce Type: cross Abstract: LLM agents increasingly answer questions over dynamic raw-document collections, where files may change before preprocessing, and relevant evidence (spans, sections, pages, or tables) is query-dependent.
arXiv Computation and Language
Sep 15
CITECHOICE: A Causal Audit of How Document Presentation Redistributes Citation Credit in Agentic Search
CITECHOICE is a causal audit that examines how the presentation of documents in an agentic search engine redistributes citation credit. Using 129 everyday‑query transcripts, the study compares structured versus prose renderings of the same source while keeping all other transcript elements fixed. The results show that structured rendering increases the target’s citation count by about half a citation per answer without adding total citations or diminishing competitors’ credit, while also revealing that rank position has a larger effect on citation rates than presentation order alone.
arXiv Machine Learning
Jul 21
DRNOISE: Benchmarking Deep Research Agents in Misleading Evidence Environments
arXiv:2607. 17291v1 Announce Type: new Abstract: Deep research agents increasingly operate over the open web, where relevant records coexist with redundant summaries, outdated reports, and misleading documents.