GIM: Evaluating models via tasks that integrate multiple cognitive domains
The Grounded Integration Measure (GIM) is a new benchmark designed to evaluate Large Language Models (LLMs) by focusing on how well they coordinate multiple...
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The Grounded Integration Measure (GIM) is a new benchmark designed to evaluate Large Language Models (LLMs) by focusing on how well they coordinate multiple...
Democratizing Large-Scale Re-Optimization with LLM-Guided Model Patches Optimization models are essential tools for industrial decision-making, but they ofte...
Position: A Three-Layer Probabilistic Assume-Guarantee Architecture Is Structurally Required for Safe LLM Agent Deployment This paper argues that the current...
What Does the AI Doctor Value? Auditing Pluralism in the Clinical Ethics of Language Models Medicine is a field where there is rarely one "correct" answer to...
AI for Auto-Research: Roadmap & User Guide provides a comprehensive analysis of how artificial intelligence is transforming the academic research lifecycle.
SkillGenBench is a new benchmark designed to evaluate how well AI models can turn raw information—such as software code repositories or long technical docume...
Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning This research addresses the challenge of creating "gene...
Actionable World Representation The research community is increasingly focused on building "physical world models"—systems that act as internal simulators fo...
Large Language Models (LLMs) are increasingly used as scientific assistants, but most existing benchmarks assume that a scientific problem is already perfect...
Context, Reasoning, and Hierarchy: A Cost-Performance Study of Compound LLM Agent Design in an Adversarial POMDP This research investigates how to effectivel...
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