Storyline
Large language models can reason correctly yet produce wrong answers, revealing reasoning-output dissociation
Recent research reveals that large language models (LLMs) can perform chain-of-thought reasoning steps correctly but still output incorrect final answers.
Published 2026-04-15 17:31 UTCUpdated 2026-04-16 04:00 UTC
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Evidence trail (top sources)
top sources (1 domains)domains are deduped. counts indicate coverage, not truth.1 top source shown
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Overview
Recent research reveals that large language models (LLMs) can perform chain-of-thought reasoning steps correctly but still output incorrect final answers.
Score total
1.22
Momentum 24h
2
Posts
2
Origins
2
Source types
2
Duplicate ratio
0%
Why now
- New benchmark exposes reasoning-output dissociation previously undetectable.
- Growing community concern about LLM compliance and reasoning reliability.
- Advances in LLM capabilities demand deeper understanding of failure modes.
Why it matters
- Highlights limitations in current LLM reasoning evaluation benchmarks.
- Reveals challenges in ensuring reliable and correct AI reasoning outputs.
- Informs development of safer and more robust AI systems.
Continuity snapshot
- Trend status: insufficient_history.
- Continuity stage: emerging_confirmed.
- Current status: open.
- 2 current source-linked posts are attached to this storyline.
All evidence
All evidence
Correct Chains, Wrong Answers: Dissociating Reasoning from Output in LLM Logic
arXiv cs.LG and cs.AI RSS · arxiv.org · 2026-04-16 04:00 UTC
Reasoning amplifies Nonsense Compliance in LLMs
ControlProblem · reddit.com · 2026-04-15 17:31 UTC
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Top publishers (this list)
- arXiv cs.LG and cs.AI RSS (1)
- ControlProblem (1)
Top origin domains (this list)
- arxiv.org (1)
- reddit.com (1)