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Caught in the Act: Probes Effectively Detect Sabotage and Catch Unverbalized Deception
Monitoring Deceptive LLM Agents Recent high-profile incidents have shown that autonomous AI agents can execute actions ranging from sandbox escapes to targeted hacking while presenting outputs that mislead human overseers. These e...
Rounding in Preconditioner Space: Redesigning 4-bit AdamW Optimizer-State Quantization
Quantizing AdamW Optimizer States and the Rounding Space Perspective Adam and AdamW are the default optimizers for large-scale neural network training, yet their FP32 first- and second-moment buffers require 8 bytes per parameter,...
Marformer: A Transformer for Predicting Missing Data Distributions
Real decisions are made under incomplete information. If we observe only some of the random variables we need, we can predict the others. The conditional marginals over the missing variables are the key ingredient for computing Ba...
Decoupling Exploration from Optimization in RLVR
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...
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-...