Grandmaster-Level Chess Without Search
DeepMind shows that a 270M-parameter transformer trained purely with supervised learning on Stockfish-generated data reaches grandmaster-level chess without any search at inference time.
AnyTool
AnyTool is a training-free LLM agent that scales tool-use to 16K+ Rapid APIs through a hierarchical retriever and a self-reflective solver.
Phase Transition in Dot-Product Attention
A theoretical paper that analyzes a solvable low-rank tied-QK attention model and uncovers a data-driven phase transition between positional and semantic attention regimes.
Indirect Reasoning with LLMs (DIR)
Direct-Indirect Reasoning augments standard CoT with contrapositive and proof-by-contradiction templates, giving LLMs an explicit way to attack problems they can't solve forward.
ALOHA 2
ALOHA 2 is a refreshed low-cost bimanual teleoperation platform from Stanford/DeepMind, designed for large-scale robot-learning data collection.
More Agents Is All You Need
The paper shows that simply running more independent LLM agents and voting produces reliable scaling gains across tasks, without any method changes.
Self-Discover
Google's Self-Discover lets LLMs compose their own task-specific reasoning strategies from a small library of atomic reasoning modules, at dramatically lower inference cost than self-consistency.
DeepSeekMath
DeepSeek releases DeepSeekMath 7B, a math-specialized LLM that closes much of the gap to GPT-4 and Gemini-Ultra on MATH by combining better data and a new RL objective.
LLMs for Table Processing: A Survey
A survey covering how LLMs and VLMs are used across the full spectrum of table-processing tasks, from classic TableQA to spreadsheet manipulation.
LLM-based Multi-Agent Systems Survey
A survey of the fast-growing LLM-based multi-agent systems space, covering both problem-solving applications and "world simulation" research.
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