Tag: research
7 discussions across 1 post tagged "research".
AI Signal - January 13, 2026
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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.
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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.
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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.
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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.
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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.
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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.
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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.