aiBACKGROUND
A Compression Perspective on Simplicity Bias
BACKGROUND
1 SIGNALFIRST DETECTED 30 March 2026
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30 Sept
Representational Simplicity and Circuit Size Dissociate in a Threshold-Dependent Way: A Controlled Test via Adversarial Training
arXiv:2609.35890v1 Announce Type: new Abstract: Sparse-autoencoder decomposability and concentrated feature attribution are increasingly treated as evidence that a model's
REPORT · HOW · ArXiv CS.AIOPEN STORY
11 May
Implicit Compression Regularization: Concise Reasoning via Internal Shorter Distributions in RL Post-Training
arXiv:2605.07316v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards improves LLM reasoning but often induces overthinking, where models generate
REPORT · WHERE · ArXiv CS.AIOPEN STORY
5 May
BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs
arXiv:2605.00422v1 Announce Type: cross Abstract: Large language models (LLMs) have driven major progress in NLP, yet their substantial memory and compute demands still hinder
REPORT · WHAT · ArXiv CS.AIOPEN STORY
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