New tools and studies are probing latent space, attention and hidden reasoning tokens in transformer models. This line of work reveals overthinking, truth directions and layer-wise representation changes, extending introspection beyond static benchmarks.
A controlled six-condition ablation challenges a common assumption: in LLM self-reflection for armed conflict forecasting, structured diagnostic questions and uncertainty taxonomies add no measurable value. Typed action routing drives consistent gain...
A recurrent retrofit on Qwen2.5-0.5B-Instruct enables iterative latent reasoning at small parameter budgets. It matches the full block, extrapolates supervised depth, and answers 7.6 times faster than a scratchpad model. The inverse task reveals cata...
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 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...
The fruit of two years of development: a hybrid code/GUI environment inside notebooks for inspecting models, managing prompts, analyzing logits, attention, and hidden states, removing boilerplate code and automatically managing GPU memory. Open to re...
