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Understand new AI research with readable explanations and links to the original papers.
This research provides a practical framework for choosing guidance methods to improve protein structure predictions when using expensive external oracles.
GUIDE is a governed multi-agent framework designed to automate the conversion of complex, multimodal enterprise guideline documents into structured, executab...
Mechanist is an agentic framework designed to automate the discovery of the internal mechanisms that drive AI behavior.
This paper introduces a framework to automate the creation of Dynamic Master Logic (DML) models by converting technical documentation into Knowledge Graphs (...
This paper introduces an agentic workflow designed to modernize massive, legacy High-Performance Computing (HPC) codebases by automating routine code transfo...
Who Thinks Best Depends on How Long You Let Them: Budget-Dependent Rankings in LLM Evaluation investigates whether the ranking of Large Language Models (LLMs...
CTBench is a public benchmark designed to evaluate how well AI agents perform as telecom troubleshooting engineers.
Claim-Level Reliability Assessment (CLR) is a training-free framework designed to improve the reasoning accuracy of large language models (LLMs) by reallocat...
Graph-Structured Rubrics (GSR) is a framework designed to improve the reliability of LLM-based evaluation by replacing implicit, prompt-based rubric interpre...
VAKRA (eValuating API and Knowledge Retrieval Agents) is a benchmark designed to measure how well AI agents perform multi-step reasoning when they must inter...
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