Neuro-symbolic PRM: Enhancing Scientific Reasoning via Structured Traces and Symbolic Verification

arXiv:2608.26329 · cs.CL · Submitted 2026-08-26 · Read on arXiv

cs.CL

Submitted: 2026-08-26

Updated: 2026-08-26

Terminology

• Verifier — 76× in this paper · explained in 2 episodes
• Verification — 23× in this paper · explained in 2 episodes
• Trace — 20× in this paper · explained in 2 episodes
• Benchmark — 14× in this paper · explained in 2 episodes
• Grounding — 8× in this paper · explained in 3 episodes
• Process Reward Model — 8× in this paper
• Counterfactual Symbolic Perturbation — 7× in this paper
• Accuracy — 5× in this paper · explained in 1 episode
• Structured Reasoning — 5× in this paper · explained in 3 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Decoupling — 4× in this paper · explained in 2 episodes
• Robustness — 4× in this paper · explained in 5 episodes
• Abstention — 3× in this paper · explained in 1 episode
• Generative Model — 3× in this paper · explained in 2 episodes
• Pruning — 3× in this paper · explained in 4 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Core — 2× in this paper · explained in 2 episodes
• F1 Score — 2× in this paper · explained in 3 episodes
• Fine-Tuning — 2× in this paper · explained in 5 episodes
• Formal Verification — 2× in this paper · explained in 2 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Monte Carlo Tree Search — 2× in this paper
• Self-consistency — 2× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Chain-of-Thought (CoT) — 1× in this paper · explained in 4 episodes
• Chain-of-Thought Prompting — 1× in this paper · explained in 2 episodes
• Cognitive Load — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Direct Preference Optimization — 1× in this paper
• Direct Preference Optimization (DPO) — 1× in this paper · explained in 9 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Domain Adaptation — 1× in this paper · explained in 5 episodes
• Failure Modes — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Graceful Degradation — 1× in this paper · explained in 2 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Process Reward Model (PRM) — 1× in this paper · explained in 3 episodes
• Process Reward Models (PRMs) — 1× in this paper · explained in 3 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Structural Constraints — 1× in this paper · explained in 2 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Supervised Fine-Tuning — 1× in this paper
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes

Sources

• Llemma: An Open Language Model For Mathematics
• Training Verifiers to Solve Math Word Problems
• RLHF Workflow: From Reward Modeling to Online RLHF
• rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
• Measuring Mathematical Problem Solving With the MATH Dataset
• Skywork-Reward: Bag of Tricks for Reward Modeling in LLMs
• Let's reward step by step: Step-Level reward model as the Navigators for Reasoning
• GPQA: A Graduate-Level Google-Proof Q&A Benchmark
• SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
• Evaluating Mathematical Reasoning Beyond Accuracy
• Qwen2 Technical Report

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