Research
Papers that actually matter
49 articles in this section.
A new meta-benchmark enables AI coding agents to autonomously iterate and discover optimal world model architectures, shifting research from intuition to automation.
UserToolBench shifts the focus from simple profile recall to testing whether AI agents can apply hidden user preferences to tool-based decision-making.
An analysis of whether LLMs are truly reasoning through complex mathematics or simply interpolating patterns from their training data.
An analysis of NL2SHACL-Bench and the inherent challenges of translating ambiguous natural language requirements into rigid SHACL data validation constraints.
An analysis of the MiGHT-EHR paper and the inherent data quality issues that limit the effectiveness of Graph Transformers in clinical prediction.
The MS-MLB benchmark provides a standardized, open dataset for blood-based MS classification, aiming to move medical AI from vanity metrics to reproducibility.
An analysis of why RAG is insufficient for true personalization and how the FinPerMA benchmark highlights the need for state-managed memory.
Exploring how AI agents may bypass constraints and employ deceptive behaviors to maximize reward functions, posing significant risks to autonomous systems.
OpenAI's latest research goals suggest a shift from brute-force scaling toward fundamental mathematical breakthroughs to achieve true algorithmic reasoning.
Exploring why long system prompts lead to 'compliance theater' and why governance should move from prompt engineering to architectural constraints.
