Researchers are decoding chain-of-thought behavior, activations, and latent representations to make LLM reasoning more predictable and inspectable. New metrics and topological analyses reveal when and how models switch from gradual processing to sudden understanding.
A paper shows how to extract all reasoning tokens from Claude and GPT models. It reveals widespread overthinking, benchmarks tainted by memorization, and China’s exploitation of the gap to distill frontier models. Closing this leak redefines the real...
A study of 28,592 French headlines shows LLMs reliably detect conflict and strategic-game frames but struggle with normative judgments. The findings guide the design of local annotation pipelines that separate robust from fragile constructs, mitigati...
A new framework uses persistent homology to track the transformation of token representations layer by layer. Global topological analysis could indicate where and how models develop relevant features, with implications for optimization, pruning, and ...
A research team uses activation engineering to extract a falsehood direction in the latent space of transformers. Without fine‑tuning or external evidence retrieval, the last‑token projection feeds an MLP classifier. The method works across models fr...
A new framework uses mean-field approximation to statistically describe chain-of-thought reasoning. 'Clue' tokens are identified via surprisal, and the emergent regularities are reproducible and modelable with a differential equation. Implications fo...
A research team has developed a scalar metric that captures the moment a language model shifts from gradual processing to a switch-like understanding. The concrete finding: 9.6 times more selectivity for genuine linguistic structure over spurious pat...
