NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability

arXiv:2607.28942 · cs.AI · Submitted 2026-07-31 · Read on arXiv

cs.AI

Submitted: 2026-07-31

Updated: 2026-09-27

Code: https://github.com/ysymyth/ReAct

Terminology

• Benchmark — 13× in this paper · explained in 2 episodes
• Sequential Monte Carlo — 13× in this paper
• Knowledge Graph — 10× in this paper · explained in 7 episodes
• Twisted Sequential Monte Carlo — 8× in this paper
• Reinforcement Learning — 4× in this paper · explained in 4 episodes
• Computational overhead — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 3× in this paper · explained in 6 episodes
• World Model — 3× in this paper · explained in 7 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Agentic Task — 2× in this paper · explained in 1 episode
• Agentic Tasks — 2× in this paper · explained in 1 episode
• Alignment — 2× in this paper · explained in 1 episode
• Effective Reliability — 2× in this paper
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Reward Function — 2× in this paper · explained in 2 episodes
• Self-consistency — 2× in this paper · explained in 2 episodes
• Action Space — 1× in this paper · explained in 1 episode
• Agentic Approaches — 1× in this paper · explained in 1 episode
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought (CoT) Reasoning — 1× in this paper · explained in 2 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Hallucination — 1× in this paper · explained in 4 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Monte Carlo Tree Search — 1× in this paper
• RAG — 1× in this paper · explained in 3 episodes
• Sequential Monte Carlo (SMC) — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• IterResearch: Rethinking Long-Horizon Agents with Interaction Scaling
• Group-in-Group Policy Optimization for LLM Agent Training
• Retrieval-Augmented Generation for Large Language Models: A Survey
• The Llama 3 Herd of Models
• From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future
• ACON: Optimizing Context Compression for Long-horizon LLM Agents
• Reason for Future, Act for Now: A Principled Framework for Autonomous LLM Agents with Provable Sample Efficiency
• Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
• ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
• Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents
• Self-Reflection in LLM Agents: Effects on Problem-Solving Performance
• ALFWorld: Aligning Text and Embodied Environments for Interactive Learning
• OpenAI GPT-5 System Card
• Self-Consistency Improves Chain of Thought Reasoning in Language Models
• AgentGym: Evolving Large Language Model-based Agents across Diverse Environments
• The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
• AgentOhana: Design Unified Data and Training Pipeline for Effective Agent Learning
• A Survey of Process Reward Models: From Outcome Signals to Process Supervisions for Large Language Models

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