Thought Branches: Interpreting LLM Reasoning Requires Resampling
arxiv.org · 11,841 words · saved by 1 readers
N/A
# link_4pzlbiecp4.pdf ## Metadata - PDFFormatVersion=1.7 - IsLinearized=false - IsAcroFormPresent=false - IsXFAPresent=false - IsCollectionPresent=false - IsSignaturesPresent=false - Author=Uzay Macar; Paul C. Bogdan; Senthooran Rajamanoharan; Neel Nanda - Creator=arXiv GenPDF (tex2pdf:a6404ea) - Custom.DOI=https://doi.org/10.48550/arXiv.2510.27484 - Custom.License=http://creativecommons.org/licenses/by/4.0/ - Custom.PTEX.Fullbanner=This is pdfTeX, Version 3.141592653-2.6-1.40.28 (TeX Live 2025) kpathsea version 6.4.1 - Custom.arXivID=https://arxiv.org/abs/2510.27484v2 - Producer=pikepdf 8.15.
saved by
related reading
- [2510.27484] Thought Branches: Interpreting LLM Reasoning Requires Resamplingarxiv.org
- the-illusion-of-thinking.pdfml-site.cdn-apple.com
- [2201.11903] Chain of Thought Prompting Elicits Reasoning in Large Language Modelsarxiv.org
- [2507.11473] Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safetyarxiv.org
- Reasoning Models Reason Well, Until They Don'tarxiv.org
- [2603.07267] How to Steal Reasoning Without Reasoning Tracesarxiv.org
- Thought Anchors: Which LLM Reasoning Steps Matter? — LessWronglesswrong.com
- Training Large Language Models to Reason in a Continuous Latent Spacearxiv.org
- Predicting LLM Safety Before Release by Simulating Deploymentcdn.openai.com
- Language Models can Solve Computer Tasksarxiv.org
- Base Models Know How to Reason, Thinking Models Learn Whenarxiv.org
- [2506.19143] Thought Anchors: Which LLM Reasoning Steps Matter?arxiv.org