Papersby GlyphCarnegie Mellon University research papers
A university publishing mostly on agents and evaluation, best known here for Mamba.11 papers · 10,211 citations · latest Oct 2026
Random Is Almost Enough? Stable and Low-Cost Benchmark Compression via BestRandBestRand is a method for selecting small benchmark subsets that reliably estimate full evaluation scores at lower computational costs.
Carnegie Mellon University51 upvotesRead →Eat Your Precision: Precision Objective Mitigates Reward Hacking in Rubric-based RLFORK jointly optimizes recall and precision via rubric-driven necessity credits and factual grading to improve response quality and reduce verbosity in reinforcement learning.Carnegie Mellon University34 upvotesRead →Into the Unknown: Benchmarking AI agents on open scientific research problemsThis benchmark evaluates how well AI agents can write follow-up scientific research papers based on unpublished directions from domain experts.Carnegie Mellon University62 upvotesRead →X-Ragent: Tool-Augmented Reinforcement Learning for Evidence-Grounded Chest X-Ray ReasoningThis paper introduces a reinforcement learning framework for medical agents that selectively uses external evidence to improve diagnostic reasoning in chest X-rays.Carnegie Mellon University44 upvotesRead →Selection-Based Structured Reasoning: Toward Efficient Multimodal Search AgentsSSR improves multimodal agent efficiency by replacing free-form reasoning generation with a selection process from pre-specified reasoning candidates.Carnegie Mellon University37 upvotesRead →Predictive Credit: Measuring What Scientific Explanations Add to Experimental ForecastsThis paper introduces a protocol to measure how much scientific explanations actually improve the accuracy of experimental forecasts.Carnegie Mellon University103 upvotesRead →Training Object Permanence in World ModelsThis paper introduces a dataset and training stack to improve object permanence and physical reasoning in video world models.Carnegie Mellon University242 upvotesRead →JEV-as-a-Judge: Accept When Confident, Escalate When UnsureJEV-as-a-Judge is a cost-effective evaluation framework that uses a simple decision-only judge for easy cases and escalates complex ones.Carnegie Mellon University57 citations42 upvotesRead →Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and EvaluationBenchmark Radar is a searchable living database and discovery engine for AI evaluation benchmarks that aggregates sources, score histories, and evidence to support benchmark selection and comparison.Carnegie Mellon University171 upvotesRead →K-BrowseComp: A Web Browsing Agent Benchmark Grounded in Korean ContextsKorean web-browsing agent benchmark K-BrowseComp evaluates frontier LLMs' capabilities with 400 problems, showing significant performance gaps compared to English benchmarks and highlighting the need for more robust Korean AI development.Carnegie Mellon University3 citations57 upvotesRead →Mamba: Linear-Time Sequence Modeling with Selective State SpacesA selective state space model that scales linearly with sequence length and matches Transformers on language.Carnegie Mellon University10.2k citations152 upvotesRead →
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Topics: Agents and evaluation 5, Applied AI 2, Foundation models 1, Reasoning 1, Multimodal and robotics 1, Architectures 1
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