RESEARCHMonitorWATCHLIST
Reasoning Shift: How Context Silently Shortens LLM Reasoning
arXiv cs.LG — Machine Learning
Factual evidence
What the source reports
An arXiv paper finds that adding lengthy, irrelevant context to prompts silently reduces LLM test-time reasoning steps and accuracy.
Inspect the evidence
- Inclusion basis
- Enterprise AI
- Publisher and source type
- arXiv cs.LG — Machine Learning · RESEARCH
- Published by source
- 30 September 2026
- Collected by OneBench
- 1 Oct 2026, 03:02 UK
- Original headline
- Reasoning Shift: How Context Silently Shortens LLM Reasoning ↗
Stored source excerpt
arXiv:2604.01161v4 Announce Type: replace Abstract: Large language models (LLMs) exhibiting test-time scaling behavior, such as extended reasoning traces and self-verification, have demonstrated remarkable performance on…
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