Tag
KV-PRM introduces a process reward model that leverages KV-cache transfer to avoid re-encoding, achieving up to 5000x FLOP reduction while maintaining or improving performance on reasoning benchmarks.
Introduces MARD, a 7B-parameter model for mechanism-level drug-drug interaction prediction using mirror-augmented reasoning distillation, achieving state-of-the-art accuracy at ~1% of frontier API cost and demonstrating genuine pharmacological reasoning over memorization.
This paper introduces Multimodal Multi-Dimensional Scalarization Process Reward Modeling (MMS-PRM), which enforces the worst dimension's robustness in multimodal reasoning to prevent failures like visual hallucinations from being masked by strong text logic.
SCI-PRM introduces a tool-aware Process Reward Model for scientific reasoning, trained on the SCIPRM70K dataset featuring 'Chain-of-Tool' trajectories that interleave reasoning with scientific tool execution. It enables effective test-time scaling and serves as a dense reward signal in reinforcement learning, outperforming proprietary models like GPT-5-Mini on tool-calling steps across scientific benchmarks.
This paper introduces a framework for time series forecasting that uses importance-aware news compression and process reward model-guided retrieval to incorporate long news articles within fixed context limits, improving prediction accuracy across finance, energy, traffic, and Bitcoin benchmarks.
This paper proposes MERIT, a dynamic multi-horizon memory retrieval framework for interactive text-to-SQL agents that uses episode-level and turn-level memory with learned retrieval policies optimized via reinforcement learning and a process reward model for dense rewards. Experiments on BIRD-Interact and Spider2-Snow show that MERIT outperforms static and single-horizon dynamic baselines in success rate while requiring fewer interaction turns.
BetaPRM is a process reward model that predicts both a step-level success probability and the reliability of that prediction using a Beta belief from Monte Carlo continuations, enabling adaptive computation allocation that reduces token usage by up to 33.57% while improving accuracy.