Here you find a collection of material (books, papers, blog-posts etc.) related to reasoning and cognition in AI systems. Specifically we want to cover agents, cognitive architectures, general problem solving strategies and self-improvement.
The term "System 2" in the page title refers to the slower, more deliberative, and more logical mode of thought as described by Daniel Kahneman in his book Thinking, Fast and Slow.
You know a great resource we should add? Please see How to contribute.
(looking for additional links & articles and summaries)
- SOAR (State, Operator, And Result) by John Laird, Allen Newell, and Paul Rosenbloom
- ACT-R (Adaptive Control of Thought-Rational) by John Anderson at CMU
- SPAUN (Semantic Pointer Architecture Unified Network) by Chris Eliasmith at Waterloo, SPAUN 2.0 by Feng-Xuan Choo
- ART (Adaptive resonance theory) by Stephen Grossberg and Gail Carpenter
- CLARION (Connectionist Learning with Adaptive Rule Induction ON-line) by Ron Sun
- EPIC (Executive Process/Interactive Control) by David Kieras and David Meyer
- LIDA (Learning Intelligent Distribution Agent) by Stan Franklin
- Sigma by Paul Rosenbloom
- OpenCog by Ben Goertzel
- NARS (Non-Axiomatic Reasoning System) by Pei Wang
- Icarus by Pat Langley
- MicroPsi by Joscha Bach
- Thousand Brains Theory & HTM (Hierarchical Temporal Memory) by Jeff Hawkins
- SPH (Sparse Predictive Hierarchie) by Eric Laukien
- Leabra (Local, Error-driven and Associative, Biologically Realistic Algorithm), 2016 Paper by Randall O'Reilly
- CogNGen (COGnitive Neural GENerative system) by Alexander Ororbia and Mary Alexandria Kelly, see also here and here
- OpenDevin: An Open Platform for AI Software Developers as Generalist Agents
- SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
- TextGrad: Automatic "Differentiation" via Text
- ReAct: Synergizing Reasoning and Acting in Language Models
- Agentless: Demystifying LLM-based Software Engineering Agents
- Competition-Level Code Generation with AlphaCode
- AI Agents That Matter
- Sibyl: Simple yet Effective Agent Framework for Complex Real-world Reasoning
- Trial and Error: Exploration-Based Trajectory Optimization for LLM Agents
- Self-Rewarding Language Models
- ArchCode: Incorporating Software Requirements in Code Generation with Large Language Models
- MedAgent-Zero: Agent Hospital: A Simulacrum of Hospital with Evolvable Medical Agents
- Self-Discover: Large Language Models Self-Compose Reasoning Structures
- Cognitive Architectures for Language Agents
- Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
- Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
- Large Language Monkeys: Scaling Inference Compute with Repeated Sampling
- AlphaCode 2 Technical Report
- A Path Towards Autonomous Machine Intelligence
- GAIA-1: A Generative World Model for Autonomous Driving
- Latent space world-models: Dreamer, V2, V3, DayDreamer
- World Models, web: project page
- HYSYNTH: Context-Free LLM Approximation for Guiding Program Synthesis
- SymbolicAI: A framework for logic-based approaches combining generative models and solvers
- DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning
- Surveys:
- (Feb 2024) A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
- Prompt Engineering Guide Prompting Techniques
- Chain-of-Thoughts (COT): Paper
- Tree-of-Thoughts (ToT): Paper
- Graph-of-Thoughts (GoT): Paper, code
- Algorithm of Thoughts (AoT): Paper
- Chain-of-Verification (CoVe/CoV): Paper
- Mixture-of-Agents (MoA): Paper
- Tool-Integrated Reasoning (ToRA / TIR): Paper
- Program of Thoughts (PoT): Paper
- DeepMind AlphaProof and AlphaGeometry 2
- Getting 50% (SoTA) on ARC-AGI with GPT-4o
- Schmidhuer: Artificial Curiosity & Creativity
- synthesis.ai: Do Androids Dream? World Models in Modern AI
Introductions and Courses
- Distill A Gentle Introduction to Graph Neural Networks (2021)
- Geometric Deep Learning - Grids, Groups, Graphs, Geodesics, and Gauges
- Stanford CS224W: Machine Learning with Graphs
Papers
- How Powerful are Graph Neural Networks: Paper
- Graph Convolutional Networks (GCN): Paper
- Design Space for Graph Neural Networks: Paper
Weak methods are general but don't use knowledge (heuristics) to guide the search process.
- depth-first-search (DFS)
- breadth-first-search (BFS)
- depth-limited-search, iterative-deepening-depth-first-search (IDDFS)
- generate-and-test
- hill-climbing (borderline case between weak and strong methods)
- The Soar Cognitive Architecture, John E. Laird, MIT Press, 2019
- How to Build a Brain: A Neural Architecture for Biological Cognition Chris Eliasmith, Oxford Series on Cognitive Models and Architectures, 2013
- Active Inference: The Free Energy Principle in Mind, Brain, and Behavior, Thomas Parr, Giovanni Pezzulo, Karl J. Friston, MIT Press, 2022, MLST Interview with Thomas Parr
- Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition, Joscha Bach, Oxford Series on Cognitive Models and Architectures Book 4, 2009
- Conscious Mind, Resonant Brain: How Each Brain Makes a Mind, Stephen Grossberg, Oxford University Press, 2021
- The Society of Mind, Marvin Minsky, Simon & Schuster, 1986
- Ogma Sparse Predictive Hierarchies (SPH): whitepaper
- The Tolman-Eichenbaum Machine: Unifying space and relational memory through generalisation in the hippocampal formation (TEM), TEM-t
- paul-gauthier/aider
- OpenDevin
- princeton-nlp/SWE-agent, documentation
- meta-llama/llama-agentic-system
- stanfordnlp/dspy
- InternLM/lagent - lightweight framework for building LLM-based agents
- Software Engineering
- WebArena: A Realistic Web Environment for Building Autonomous Agents, web: project page, Leaderboard
- ARC-AGI: Leaderboad, On the Measure of Intelligence
- PlanBench: Paper, gh: karthikv792/LLMs-Planning
- GAIA: a benchmark for General AI Assistants: Leaderboard
- Nous Research Open Reasoning Tasks, a list of reasoning tasks, gh: NousResearch/Open-Reasoning-Tasks
- MIT AGI: Cognitive Architecture (Nate Derbinsky)
- Consciousness as a coherence-inducing operator Talk by Josha Bach at the Models of Consciousness Conferences
- Channel: David Shapiro
- Channel: Thinking About Thinking (Mathematics of Neuroscience and AI)
To share a link related to reasoning in AI systems that is missing here please create a pull request for this file. See editing files in the github documentation.