Tag: research

AI Signal - January 13, 2026

• GPT-5.2 Solves Another Erdős Problem, [#729](/tags/729/)

  Following the first-ever LLM resolution of Erdős problem [#728](/tags/728/), GPT-5.2 adapted that proof to resolve #729—a similar combinatorial problem. The team used iterations between GPT-5.2 Thinking, GPT-5.2 Pro, and Harmonic's Aristotle to produce a complete Lean-verified proof. This marks the second unsolved mathematical problem resolved by LLMs.

  #llm #research
• DeepSeek introduces Engram: Memory lookup module for LLMs

  DeepSeek's new research paper introduces Engram, a deterministic O(1) lookup memory using modernized hashed N-gram embeddings that offloads early-layer pattern reconstruction from neural computation. Under iso-parameter and iso-FLOPs conditions, Engram models show consistent gains across knowledge, reasoning, code, and math tasks—suggesting memory retrieval is a new axis for model improvement beyond scale.

  #llm #research
• Geoffrey Hinton on agent knowledge sharing at scale

  Geoffrey Hinton describes how AI agents can share knowledge at unprecedented scales: 10,000 agents studying different topics can sync learnings instantly, with each agent gaining the knowledge of all 10,000. This parallelized learning represents a fundamental advantage over human knowledge transfer, which relies on slow communication bottlenecks.

  #agentic-ai #research
• GPT 5.2 Pro Agent Achieves new record on MIT professor's library

  A GPT-5.2-pro research agent achieved a new best-known spherical packing for n=11, N=432, verified against MIT's benchmark library. The agent escaped a numerically "jammed" configuration that had resisted prior optimization. The team is extending the framework to computational physics.

  #agentic-ai #research
• [R] Extending the Context of Pretrained LLMs by Dropping Positional Embeddings

  Sakana AI's DroPE method challenges fundamental Transformer assumptions: positional embeddings like RoPE are critical for training convergence but eventually become the primary bottleneck preventing generalization to longer sequences. By dropping positional embeddings post-training, they extend context length without massive fine-tuning compute costs.

  #research #llm
• NVIDIA and Lilly collaborate on AI lab for drug discovery

  NVIDIA and Eli Lilly announce a multidisciplinary AI lab combining scientists, AI researchers, and engineers to tackle hard problems in drug discovery. The lab features robotics and physical AI, suggesting they're building closed-loop experimental systems where AI designs experiments and robots execute them.

  #research #robotics
• [R] Why doubly stochastic matrix idea only made popular in DeepSeek's mHC paper

  Discussion of why the Sinkhorn-Knopp algorithm for creating doubly stochastic matrices (preventing gradient vanishing/explosion) only gained attention with DeepSeek's mHC paper despite being known for decades. The technique helps maintain gradient stability across layers but wasn't emphasized in earlier RNN work.

  #research #machine-learning