Entropy and Attention Dynamics in Small Language Models: A Trace-Level Structura

<!-- SC_OFF --><div class="md"><p>&#x200b;</p> <p>## Abstract</p> <p>Post-training makes language models more decisive without necessarily making them more accurate — and we find

<!-- SC_OFF --><div class="md"><p><strong>Noam Brown</strong> — OpenAI Research Scientist and co-creator of o1, Libratus, and Cicero — sat down with Dwarkesh Patel for a 1h 20m

<!-- SC_OFF --><div class="md"><p>We’ve been working on a probabilistic interpretation of causal self-attention where token embeddings are treated as latent variables. In that

arXiv:2603.22300v1 Announce Type: cross Abstract: Scaling Transformers to ultra-long contexts is bottlenecked by the $O(n^2 d)$ cost of self-attention. Existing methods reduce

<!-- SC_OFF --><div class="md"><p><em>Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory

<p>The quadratic attention problem has quietly shaped everything you've built with LLMs. RAG pipelines, agentic decomposition, hybrid architectures — these aren't the natural

<table> <tr><td> <a href="https://www.reddit.com/r/LocalLLaMA/comments/1s59rct/rys_part_3_llms_think_in_geometry_not_language/"> <img alt="RYS Part 3: LLMs think in geometry, not

arXiv:2605.19376v1 Announce Type: new Abstract: How should future neural reasoning systems implement extended computation? Recursive Reasoning Models (RRMs) offer a promising