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20,515 papers
Retrospective Self-Distillation (RSD), a multi-round framework that turns such experience into dense supervision using privileged retrospective context, and frontier-guided context-policy evolution, where a stronger frontier model proposes constrained edits to how privileged context is constructed.
Mitigating catastrophic risks from large language models requires excising hazardous operational capabilities, such as cyberwarfare exploitation and pathogen enhancement, without degrading benign scientific utility. In these dual-use domains, legitimate defensive science and malicious attacks share vocabulary, syntax,...
EmbGA is presented, an embedding-space genetic algorithm that recovers concepts present in a set of images but absent from the prompt that generated them, and runs much faster than language-model-guided search.
This work first proposes a model inversion attack on FL systems using Deep Convolutional Generative Adversarial Networks (DCGANs) in the context of large-scale medical imaging, and examines the effect of client-side differential privacy (DP) in mitigating privacy leakage and incorporate personalized FL to improve model...
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It is argued that continual prompt evolution is a practical, training-free alternative to retraining for multimodal forecasting.
On-policy learning is central to post-training: training on a model's own generations limits distribution shift and helps preserve existing capabilities. On hard problems, however, on-policy RL provides no reward-based learning signal when all attempts fail. On-policy distillation provides next-token supervision from a...
Vector-Borda Empirical-Gap Elimination (VB-EGE) is presented, a fixed-confidence adaptive sampling algorithm for efficiently identifying models on the Pareto frontier in arena-style evaluation and established a worst-case lower bound showing that VB-EGE is minimax-optimal up to logarithmic factors.
Solving reasoning tasks supported on graphs typically requires providing a Language Reasoning Model (LRM) with a serialized description of the graph. This approach results in the input length growing with the number of edges, which limits the size of the problems that can be solved. Furthermore, even when graphs fit in...
This work introduces $MLIPS: a training-free method that shares candidate search while retaining each consumer's own scoring and selection, and characterize this shared working-set structure and use it to motivate Top-$G, an exploratory bounded pool construction for prefill.
Mixture-of-experts (MoE) models already make a sparse choice for every token: which experts should process it. We ask whether a sparse dictionary should reuse that choice. NR-SAE gives each routed expert a small dictionary and opens only those the frozen router selects. At block 15 of Gemma 4 26B-A4B, after ten million...
This work formalizes the interaction between AI systems and users as a game with three players: a defender and an attacker that are computationally or informationally bounded, and an AI that provides public advice and is less constrained.
We study trail-weighted pruning of evidence for Bayesian-network prediction. A traversal enumerates the active trails from each evidence node to the outcome, scores each trail by the product of its edge strengths, and drops evidence whose score falls below a threshold. For collider-free networks (chains and forks) over...
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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