Category
20,463 papers
OLIVIA, a protocol for point-in-time inputs with company identities masked, selection on validation data only, an analysis plan frozen by cryptographic hash, and a test period unblinded once through a logged lock is proposed.
FITTER, a multi-agent framework that integrates clinical and wearable data with condition-specific guidance to generate personalized FITT-VP prescriptions and applies deterministic safety audit and repair to correct rule-checkable violations, is developed.
Across six LLMs, committed personas are intrinsically stable: in each of the five models tested without peer input, every agent keeps its assigned side for 20 rounds, making the LLM choice a first-class validity variable for social simulation.
This work proposes Human-AI-Human collaboration: an interaction topology in which AI connects researchers' contextss and supports inspectable collective evaluation and decision-making, prioritizing shared context and team-visible evaluation checkpoints for controlled follow-up.
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A public, versioned Conditional Memory Utility Dataset of paired measurements and costs is provided to evaluate memory compression by the task utility it preserves alongside its costs.
ProofPaths, an AI agentic framework that combines rubric-based grading with individualized and interactive interviews, is introduced, an AI agentic framework that combines rubric-based grading with individualized and interactive interviews.
It is conjecture that reliably acquiring and maintaining interventional competence when causal commitments fail requires autonomous participation in proposing alternatives, testing their consequences, and revising them in response to error.
This work developed and compared an RPA solution (Playwright) and an APA solution (browser-use, LangGraph and Gemini 2.5 Flash-lite) to retrieve mammography and breast ultrasound reports from a laboratory portal, treating reports solely as informational artifacts, and found RPA was superior for a stable interface.
LaplaceDiag is introduced, a data-driven method that uses changes in actuator effects to narrow the search for faulty equipment by separating nonlinear prediction based on measured history from an explicit matrix that describes how actuator values affect sensor values.
Experiments show that LHM outperforms state-of-the-art methods such as ACM-TIES and ORCA, achieving over 4% accuracy gain and up to 93.1% reduction in output length relative to the long-CoT model.
El Agente F\'armaco, an AI scientist that uses modern large language model (LLM) technology to turn discovery goals expressed in everyday language into complex, scalable computational screening workflows, provides a foundation for flexible, scalable drug discovery workflows that connect scientific reasoning with an end...
This work identifies a novel phenomenon termed Ambient Context Illusion, where agents fail to execute tasks correctly when non-essential contextual details are omitted, despite unchanged task semantics, and demonstrates that this phenomenon persists in real-world robotic systems, where it can lead to incorrect decision...
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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