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PRISM: Pushing the Frontier of Deep Think via Process Reward Model-Guided Inference

arXiv – CS AI|Rituraj Sharma, Weiyuan Chen, Noah Provenzano, Tu Vu|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce PRISM, a new AI inference algorithm that uses Process Reward Models to guide deep reasoning systems. The method significantly improves performance on mathematical and scientific benchmarks by treating candidate solutions as particles in an energy landscape and using score-guided refinement to concentrate on higher-quality reasoning paths.

Key Takeaways

→PRISM uses step-level verification to guide both population refinement and solution aggregation in deep reasoning systems.
→The algorithm achieved 90.0% on AIME25, 75.4% on HMMT25, and 71.4% on GPQA Diamond benchmarks with gpt-oss-20b.
→PRISM matches or exceeds the performance of much larger models like gpt-oss-120b while using less compute.
→The method addresses the population-enhancement bottleneck where deeper deliberation can amplify errors in existing frameworks.
→PRISM produces consistent net-directional correction during refinement and remains reliable even with few initially correct candidates.

#ai-research #deep-learning #reasoning #process-reward-models #mathematical-reasoning #inference-algorithms #prism #deepthink #arxiv #benchmark-performance

Read Original →via arXiv – CS AI

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