News
AI, cybersecurity, machine learning, scraping & programming news.
105 results on this page · clear filters
Distilling Graph Geometry: Knowledge Gap from GNNs to MLPs
GNN-to-MLP distillation aims to retain the predictive accuracy of a message-passing teacher while deploying a graph-free MLP at inference. Existing methods mainly transfer node-wise predictions or use confidence-based reweighting,...
Why Forget-Only Unlearning Needs Memorization
Reinforcement learning with verifiable rewards has become the dominant recipe for improving the reasoning abilities of language models. RLVR samples solutions to problems, scores each solution with a programmatic verifier, and shi...
Your Prompt Should Do More: Effects of Retrieval Instructions in Embedding Models
Prompted embedding models have recently received increasing attention, particularly for retrieval, where detailed retrieval instructions are provided as part of the retrieval prompt. Several new datasets and studies have examined...
Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective
Conformal prediction has become a standard tool for uncertainty quantification because it produces prediction sets with finite-sample coverage guarantees under minimal assumptions. Practitioners routinely treat the size of these s...
Base Models Can Reason By Taking a Cue From Training Data
The study of reasoning in base models has taken a surprising turn. Rather than relying on reinforcement learning to shape behavior, researchers have found that specific starting token cues can unlock reasoning capabilities already...
Deep Learning for Sleep Heart Rate Estimation from Accelerometers: Toward Population-Scale Cardiac Insight Without Optical Sensors
Large longitudinal cohorts often contain wrist accelerometry without optical heart-rate sensing, motivating recovery of cardiac information from motion signals already collected during sleep. We present SeqSmoother, a transformer-...
What Should World Models Forget? Stratified Retention for Continual Adaptation
Stationary targets versus drifting environments Continual learning has long treated performance loss on previously observed data as a signal of failure. This convention originates from prediction settings where a correct label rem...
RNADyn: A Benchmark for Generating and Understanding RNA Dynamics
RNA conformational dynamics and the limitations of static structures RNA molecules adopt multiple three-dimensional shapes to perform cellular tasks. These conformational changes are not captured by static structures obtained from...
LESSER: Post-Training Data Selection with Output-Layer Gradients
Why Post-Training Data Selection Matters for LLM Adaptation The choice of post-training data substantially affects large language model downstream performance. This makes data curation a high-leverage activity for teams that fine-...
Pivot-SD: Efficient Self-Distillation for Masked Diffusion Language Models
Masked diffusion language models (dLMs) present a parallel alternative to autoregressive models for complex reasoning, yet they encounter a credit-assignment problem: a few commitments during denoising sharply reduce uncertainty o...