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Quantifying Claim Smoothing by LLM Research Agents

Paper recorded by Signals 4 on 2026-10-26 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.CL · 自然语言处理 · first seen 2026-10-27

Abstract

Deep research, or any use of large language models (LLMs) to read a large document collection and produce insights, requires synthesizing information from different sources. In this work, we find that LLMs produce long-form research reports that read as confident syntheses but which fail to accurately reflect the underlying documents. We call this failure smoothing. In collapsing diverse and poten

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#136 most recent of 660 cs.CL papers we have recorded · ↑ newer: Understanding Jev-Style System One Models: Does Structure Reduce Selec · ↓ older: Atomic Security Concerns: Towards a Foundational Language for Security
Cite this page: Quantifying Claim Smoothing by LLM Research Agents: the #136 most recent of 660 cs.CL papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/quantifying-claim-smoothing-by-llm-research-agents.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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