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3,686 papers
Inference-time steering combines pretrained diffusion experts or rewards without retraining by changing the dynamics that transport noise to data. Feynman-Kac correction compensates for proposal mismatch through importance-weighted sequential Monte Carlo (SMC), whose finite-particle behavior depends on the proposal. Va...
Recent studies on reinforcement learning (RL) report seemingly conflicting evidence about large language model (LLM) reasoning. Training on mathematics can improve performance in other domains, yet gains in Pass@1 can coincide with lower Pass@$N$ than the base model. This raises a fundamental question: does RL expand a...
Localizing behavior to individual components of a language model is a central goal of mechanistic interpretability. However, scoring components one at a time misses context-dependent effects: a primary component can inhibit the activation of a backup, leading to issues with ranking components. Actual causality studies...
Self-evolving large language model (LLM) systems repeatedly propose, evaluate, and incorporate updates to prompts, skills, or other persistent artifacts. Despite their growing effectiveness, these systems typically operate under a predetermined iteration or compute budget, without a principled criterion to determine wh...
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Large language models (LLMs) are increasingly used to generate, complete, and transform information in settings where their outputs can shape consequential decisions, raising concerns about their impact on demographic disparities. In this context, causal inference provides a principled basis for assessing fairness, bec...
Cloud resources already exist that enable doing science on massive datasets and computationally large problems. There is also the pressure for collaboration and replicability, which clouds are already strong with. It requires some adjustment by scientists to learn cloud tools. Early adopters are willing to self-start,...
Cloud resources already exist that enable doing science on massive datasets and computationally large problems. There is also the pressure for collaboration and replicability, which clouds are already strong with. It requires some adjustment by scientists to learn cloud tools. Early adopters are willing to self-start,...
Objective To map the existing evidence on CVD risk among individuals with complex and disabling health conditions including traumatic brain injury (TBI), multiple sclerosis (MS), spinal cord injury (SCI), cerebral palsy (CP), spina bifida (SB), and poliomyelitis and identify gaps to guide future research.Design A scopi...
Machine learning reproducibility requires documentation of interdependent research components and the decisions made throughout a study. This poster presents ongoing work to develop an RO-Crate profile for representing reproducibility metadata in machine learning–based bioinformatics studies, guided by PRIMAD-LID and i...
Machine learning reproducibility requires documentation of interdependent research components and the decisions made throughout a study. This poster presents ongoing work to develop an RO-Crate profile for representing reproducibility metadata in machine learning–based bioinformatics studies, guided by PRIMAD-LID and i...
Aim: To evaluate the effectiveness of psychological therapy in the management of temperomandibular disorders compared to other treatment modalities. Methodology: Studies were selected according to PICO criteria, considering the RDC/TMD as a reference, improvement of pain and psychological parameters were the primary ou...
This study examines how Informal governance is associated with Organizational decoupling among Chinese university students, with Academic discipline proposed as an explanatory mechanism. Drawing on self-determination and self-regulated learning perspectives, a conditional process model was specified and evaluated using...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Health & Bioscience
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Data Mining & Modeling
GPT-Lab
· gpt-lab.eu
Nov 3, 2026
Is your company’s AI sovereign? A practical guide for CIOs on AI vendor lock-in, EU Data Act switching, cloud sovereignty, and testing your exit plan before you need it. The post Sovereignty Is the Option to Leave appeared first on GPT-Lab.
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