Automated reasoning

Process Reward Models (PRMs) have recently emerged as a powerful framework for enhancing the reasoning capabilities of large reasoning models (LRMs), particularly in the context of test-time scaling (TTS). However, their potential for supervising LRMs on tabular reasoning domains remains underexplored. Through detailed empirical analyses, we identify that existing PRMs, though widely adopted for supervising

Reasoning about failures is crucial for building reliable and trustworthy robotic systems. Prior approaches either treat failure reasoning as a closed-set classification problem or assume access to ample human annotations. Failures in the real world are typically subtle, combinatorial, and difficult to enumerate, whereas rich reasoning labels are expensive to acquire. We address this problem by introducing

Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where irrelevant or redundant passages dominate. Current methods-fixed top-k retrieval, cross-encoder reranking, or policybased iteration-depend on static heuristics or costly reinforcement learning, failing to assess evidence sufficiency, detect subtle mismatches, or reduce redundancy, leading to hallucinations and poor grounding

E-commerce search faces challenges such as sparse data and poor generalization from issues like multi-attribute resolution, multihop reasoning, and implicit intent. We propose iterative reranking as a compute-scaling strategy for LLM-based rankers, repeatedly applying listwise rankers to refine results by exploiting LLM non-determinism. Evaluated on three open datasets with three open-source LLMs, the method

Cloud computing providers employ sophisticated authorization engines to decide when a request to access a resource should be allowed or denied. Several approaches have formalized the behavior of individual authorization policies, but authorization engines employ multiple types of policies that can interact in different ways. This paper presents a modular formalization of the Amazon Web Services (AWS) authorization