open-thought/system-2-research

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OpenThought - System 2 Research Links

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.

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Cognitive Architectures

(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, 2015 Paper
• 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
• KIX (KIX: A Metacognitive Generalization Framework) by A. Kumar and Paul Schrater
• ACE (Autonomous Cognitive Entity) by David Shapiro et al., gh: daveshap/ACE_Framework
• Iterative Updating of Working Memory by Jared Reser, website, Video

Agent Papers

LLM Based

• ReAct: Synergizing Reasoning and Acting in Language Models
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
• The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, gh: SakanaAI/AI-Scientist
• OpenDevin: An Open Platform for AI Software Developers as Generalist Agents
• TextGrad: Automatic "Differentiation" via Text
• Trace is the New AutoDiff -- Unlocking Efficient Optimization of Computational Workflows
• 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
• Cognitive Architectures for Language Agents
• Large Language Models Can Self-Improve At Web Agent Tasks
• AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
• A Prefrontal Cortex-inspired Architecture for Planning in Large Language Models
• CLIN: A Continually Learning Language Agent for Rapid Task Adaptation and Generalization
• DeepSeek-Prover-V1.5: Harnessing Proof Assistant Feedback for Reinforcement Learning and Monte-Carlo Tree Search
• GPT-Swarm Language Agents as Optimizable Graphs
• Survey: Reasoning with Large Language Models, a Survey (Jul 2024)
• Survey: From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future (Aug 2024)
• ADAS: Automated Design of Agentic Systems
• IDEA:Enhancing the Rule Learning Ability of Language Agents through Induction, Deduction, and Abduction
• LAW: Language Models, Agent Models, and World Models: The LAW for Machine Reasoning and Planning
• GenRM: Generative Verifiers: Reward Modeling as Next-Token Prediction
• Perspective: Towards Building Specialized Generalist AI with System 1 and System 2 Fusion
• CodeAct: Executable Code Actions Elicit Better LLM Agents
• PLANSEARCH: Planning In Natural Language Improves LLM Search For Code Generation
• LLMs Still Can't Plan; Can LRMs? A Preliminary Evaluation of OpenAI's o1 on PlanBench
• Thinking LLMs: General Instruction Following with Thought Generation
• Agent S: An Open Agentic Framework that Uses Computers Like a Human

LLM Reasoning Improvements / Training on Synthetic Data

• Thinking LLMs: General Instruction Following with Thought Generation
• Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources
• Chain of Thought Imitation with Procedure Cloning
• Q*: Improving Multi-step Reasoning for LLMs with Deliberative Planning
• Self-Discover: Large Language Models Self-Compose Reasoning Structures
• TRICE: Training Chain-of-Thought via Latent-Variable Inference
• Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
• Self-Taught Reasoner: STaR: Bootstrapping Reasoning With Reasoning
• Self-Notes: Learning to Reason and Memorize with Self-Notes
• From Explicit CoT to Implicit CoT: Learning to Internalize CoT Step by Step

Direct o1 Replication Efforts

• OpenR: Technical Report, Project Page, code: openreasoner/openr
• GAIR-NLP/O1-Journey, O1 Replication Journey: Strategic Progress Report - Part 1
• OpenSource-O1/Open-O1
• bklieger-groq/g1: Using Llama-3.1 70b on Groq to create o1-like reasoning chains
• ack-sec/toyberry: Atlas Reasoning System (Toyberry)
• Blog: Reverse engineering OpenAI's o1 by Nathan Lambert

Reward Models (ORM/PRM)

• Rewarding Progress: Scaling Automated Process Verifiers for LLM Reasoning
• Solving math word problems with process- and outcome-based feedback
• Training Verifiers to Solve Math Word Problems
• RATIONALYST: Pre-training Process-Supervision for Improving Reasoning

RL

• RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
• Training Large Language Models for Reasoning through Reverse Curriculum Reinforcement Learning
• ReFT: Reasoning with Reinforced Fine-Tuning
• ARES: Alternating Reinforcement Learning and Supervised Fine-Tuning for Enhanced Multi-Modal Chain-of-Thought Reasoning Through Diverse AI Feedback

MCTS

• Agent Q: Advanced Reasoning and Learning for Autonomous AI Agents
• Improve Mathematical Reasoning in Language Models by Automated Process Supervision
• Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
• rStar: Mutual Reasoning Makes Smaller LLMs Stronger Problem-Solvers
• LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios, code: opendilab/LightZero
• MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model, open-source impl: werner-duvaud/muzero-general

Minecraft Agents

• Voyager: An Open-Ended Embodied Agent with Large Language Models
• JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models
• Optimus-1: Hybrid Multimodal Memory Empowered Agents Excel in Long-Horizon Tasks
• STEVE Series: Step-by-Step Construction of Agent Systems in Minecraft

Massive Sampling / Generate-and-Test

• Inference survey: From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• [Large Language Monkeys: Scaling Inference Compute
  with

Suggested labels

{'label-name': 'Cognitive Architectures', 'label-description': 'Approaches and theories for constructing artificial cognitive systems based on human cognition principles.', 'gh-repo': 'open-thought/system-2-research', 'confidence': 50.0}

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