New arXiv papers probe LLM efficiency and reasoning limits
A pruning paper reports a dense score with 0.906 split-half reliability predicted a 16.1% gain but its endpoint was 6.0% and 7.7% worse than controls. Other studies test entropy-based CoT step selection across models, context-length effects in long-context benchmarks, and early-exit/compression tradeoffs.
How this story unfolded
4 days · 10 reports · from Aug 3
- Aug 3
Demystifying Entropy-based Selection for Chain-of-Thought Compression in Large Reasoning Modelsarxiv.org
BLADE: Boundary-Expanded and Layer-Adaptive Dynamic Exit for Efficient LLM Reasoningarxiv.org
Reflection or Re-Generation? Why LLM Revision Fails Where Human Revision Succeedsarxiv.org
ThinkReset: Learnable Intermediate Interface Construction for Bounded-Context Long-Horizon Reasoningarxiv.org
How Hard Does It Think? Analyzing Step-Aware Reasoning Energy in LLM Chain-of-Thought Trajectoriesarxiv.org
- Aug 4
- Aug 6
- Aug 7
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