ContractScrub: A benchmark for final review of legal contracts
ContractScrub is a new benchmark designed to evaluate how well Large Language Models (LLMs) perform "contract scrubbing"—the final, meticulous review of lega...
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ContractScrub is a new benchmark designed to evaluate how well Large Language Models (LLMs) perform "contract scrubbing"—the final, meticulous review of lega...
This paper introduces a control framework that combines spiking neural networks with Bayesian inference to enable adaptive control in uncertain environments....
MidTool: Mid-training Data Synthesis for Agentic Tool Use introduces a pipeline for training large language models (LLMs) to use external tools more effectiv...
ReguSim: Evaluating LLM Agent Rule Grounding in Financial Compliance introduces a framework to test how well AI agents follow financial regulations.
An Agentic Approach for Active Data Collection, Travel Behavior Modeling, and Weather-Sensitive Demand Prediction proposes a three-agent workflow to streamli...
ExPhy is a benchmark designed to evaluate how well AI models understand the physical properties—such as mass, friction, and restitution—that govern the movem...
DARS is a reinforcement learning framework designed to improve instruction-based image editing by addressing the "credit assignment" problem in two-stage sys...
What the paper is about The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls....
Rule-Compliant Visual Spatial Planning for Multimodal Large Language Models introduces a framework to improve how AI models navigate environments while follo...
Optimal Skill Selection for LLM Agents with Provable Bicriteria Guarantees introduces a formal framework to improve how LLM agents choose which external skil...
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