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NVIDIA and MIT researchers published model weights for Open-H3-Regenerate-2K on Hugging Face, a video enhancement project built on MiniMax's open-source H3 video enhancement approach. The release targets high-resolu...
A new paper proposes using value of information, estimated from archived rollouts with a learned world model, to address both memory retrieval and credit assignment in reinforcement learning. The authors derive a lower b...
Researchers introduce HEMO, a framework that adapts the harness used in LLM-based automatic operator design for multi-objective combinatorial optimization. It updates modules governing experience retrieval, operator impr...
This paper introduces Assur-KB, a representation for planar mechanisms that exposes reusable constraint subassemblies, and shows it improves prediction from limited data. Theoretical results characterize transfer through...
CovFedAvg is a new federated learning method that retains local models and curve controls on clients to fit a common endpoint via aggregated gradient contributions, enabling model selection without centralized calibratio...
The MedCBR research framework combines boundary-aware preference optimization with reinforcement learning guided by clinical rubrics. It focuses training on cases near a model’s capability limit and uses prediction entro...
The InferD paper introduces a low-interaction honeypot framework for studying threats targeting AI inference infrastructure, including model-serving stacks, inference APIs, vector databases and agent gateways. It emulate...
A new paper examines why safety-aligned language models reject harmless prompts, reporting increasing overlap between harmful and over-refusal representations in deeper layers and gradient interactions that favor refusal...
Researchers fine-tuned five open-weight base models on declarative statements defining what each model identifies as itself, without explicit preferences or value judgments. Across tested scenarios, models became more li...
Researchers propose an LSTM-based variational autoencoder to generate training sequences representing under-observed transitions toward unstable anaerobic digestion operations. Using one year of full-scale plant data, th...
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