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Prediction Is Not a Sufficient Memory State
A memory that reproduces every current prediction may still have forgotten how to learn from the next observation. We characterize this distinction for exact cumulative learning and attention. For smooth convex residual losses wit...
Inference-Time Steering Across Scaffolds and Epitopes for Sample-Efficient De Novo Nanobody Design
Generative molecular design typically relies on over-generation and post-hoc filtering. Inference-time steering methods like Feynman-Kac (FK) steering improve efficiency by biasing trajectories toward high-reward candidates, yet t...
Specialists Learn Faster: Knowledge Organization in Pre-training Shapes Knowledge Update Efficiency
Language models must continually update their knowledge to remain current. Most existing work focuses on making these post-training updates efficient, but this paper investigates a more foundational question: how the organization...
TabMF: Learning Dataset Representations with Tabular Foundation Models
Representations of supervised datasets serve as a foundation for higher-level machine-learning tasks such as dataset retrieval and distribution-shift monitoring. In practice, practitioners have needed reliable ways to characterize...
Spanning Diffusion and Flow-based Posterior Sampling via Exact Gaussian Conditioning
Flow-matching and diffusion models have become priors for linear inverse problems, yet most methods remain bespoke to a single class of trajectory prior. Plug-and-play alternatives exist but implicitly decorrelate the guided traje...
Show HN: Aporium – A UMAP galaxy of 5800 philosophy excerpts in conversation
Aporium Releases Starfield Interface for Exploring Machine Learning Research Aporium has launched a new starfield visualization that maps 5,868 machine learning papers and concepts into an interactive 3D space. The interface lets...
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...