AI Papers of the Week
Every paper worth reading in AI, hand-picked one week at a time.
Discover and explore top AI papers with Claude Code or Codex
npx @dair-ai/mcp setup
LLMs for Data Annotation
A survey that maps the rapidly growing literature on using LLMs to generate, evaluate, and learn from data annotations.
Chain-of-Thought Reasoning Without Prompting
DeepMind shows that LLMs often *already* emit chain-of-thought reasoning in alternative decoding paths, and that selecting those paths via confidence lifts reasoning accuracy with no prompt engineering.
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.
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 Mathematical Reasoning
A survey of the fast-growing literature on using LLMs for mathematical reasoning, from arithmetic word problems to theorem proving.
AlphaGeometry
DeepMind's AlphaGeometry is a theorem prover that solves Olympiad-level geometry problems at near gold-medallist performance, and crucially, without needing any human demonstrations.
ReFT (Reinforced Fine-Tuning)
ByteDance's ReFT enhances LLM reasoning by combining supervised fine-tuning with online RL that samples alternative reasoning paths, without a learned reward model.
Chain-of-Table
Google's Chain-of-Table prompts LLMs to iteratively transform a complex table step-by-step to answer questions reliably, extending CoT reasoning to tabular data.
Generative AI for Math (OpenWebMath / MathPile)
Releases a diverse, high-quality math-centric corpus of ~9.5B tokens designed for training math-capable foundation models.
Survey of Reasoning with Foundation Models
A comprehensive survey of reasoning with foundation models, covering tasks, methods, benchmarks, and future directions.
ReST Meets ReAct
Proposes a ReAct-style agent that improves itself via reinforced self-training on its own reasoning traces.
Mathematical LLMs Survey
A survey on the progress of LLMs on mathematical reasoning tasks, covering methods, benchmarks, and open problems.
Gemini 1.0
Google launches Gemini 1.0, a multimodal family natively designed to reason across text, images, video, audio, and code from the ground up.
LLMs on Graphs
A comprehensive overview of the many ways LLMs can be applied to graph-structured data and when each pattern is useful.
Chain of Code
DeepMind's Chain of Code extends CoT by encouraging LMs to write pseudocode that mixes real code with LM-simulated sub-routines.
Medprompt
Microsoft researchers show that careful prompt engineering can push general-purpose GPT-4 to state-of-the-art on medical benchmarks, no domain fine-tuning required.
System 2 Attention (S2A)
Meta's S2A uses the LLM's own reasoning to decide what context actually matters, regenerating a clean prompt before the final response step.
Teaching Small LMs to Reason
An approach that teaches smaller language models to explicitly select among reasoning techniques for each problem.
GPQA
A graduate-level Google-proof QA benchmark designed to stress-test reasoning in systems that might exceed human expertise.
Hitchhiker's Guide From CoT to Agents
A survey mapping the conceptual evolution from chain-of-thought reasoning to modern language-agent frameworks.
GAIA
Meta's GAIA is a benchmark for general AI assistants that requires reasoning, multimodal handling, web browsing, and tool use to solve real-world questions.
MedAgents
A collaborative multi-round framework for medical reasoning that uses role-playing LLM agents to improve accuracy and reasoning depth.
Contrastive Chain-of-Thought
Proposes contrastive CoT prompting where models see both valid *and* invalid reasoning demonstrations to reduce reasoning errors.