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SHRED: Retain-Set-Free Unlearning via Self-Distillation with Logit Demotion

arXiv:2605.07482v1 Announce Type: cross Abstract: Machine unlearning for large language models (LLMs) aims to selectively remove memorized content such as private data, copyrighted text, or hazardous

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Sign-Based Optimizers Are Effective Under Heavy-Tailed Noise

DGX agent

arXiv:2602.07425v2 Announce Type: replace-cross Abstract: While adaptive gradient methods are the workhorse of modern machine learning, sign-based optimization algorithms such as Lion and Muon have re

researcharxiv-cs-cl

11 May 2026

Skip-It? Theoretical Conditions for Layer Skipping in Vision-Language Models

arXiv:2509.25584v2 Announce Type: replace Abstract: Vision-language models achieve incredible performance across a wide range of tasks, but their large size makes inference costly. Recent work has sho

SMT-Based Active Learning of Weighted Automata

arXiv:2605.07758v1 Announce Type: cross Abstract: We present an SMT-based active learning algorithm for nondeterministic weighted automata (WFAs) as a practical and robust alternative to Hankel/L*-sty

SocialReasoning-Bench: Measuring whether AI agents act in users’ best interests

researchmicrosoft-research

Using SocialReasoning Bench, we observed a stable pattern across models—agents execute competently, but fail to consistently improve the user’s position, even with explicit instructions to optimize fo

SoLAR: Error-Resilient Streamable Long-Horizon Free-Viewpoint Video Reconstruction with Anchor Activation and Latent Recalibration

arXiv:2605.07346v1 Announce Type: new Abstract: Free-Viewpoint Video (FVV) has emerged as a cornerstone of next-generation immersive media systems and attracted widespread attention. Previous methods

Solving Max-Cut to Global Optimality via Feasibility-Preserving Graph Neural Networks

arXiv:2605.07113v1 Announce Type: new Abstract: Exact solution of hard combinatorial optimization problems often relies on strong convex relaxations, but solving these relaxations repeatedly inside a

Sparse Attention as Compact Kernel Regression

arXiv:2601.22766v3 Announce Type: replace Abstract: Recent work has revealed a link between self-attention mechanisms in transformers and test-time kernel regression via the Nadaraya-Watson estimator,

SpecBlock: Block-Iterative Speculative Decoding with Dynamic Tree Drafting

arXiv:2605.07243v1 Announce Type: new Abstract: Speculative decoding accelerates LLM inference by drafting a tree of candidate continuations and verifying it in one target forward. Existing drafters f

Spectral Dynamics in Deep Networks: Feature Learning, Outlier Escape, and Learning Rate Transfer

arXiv:2605.07870v1 Announce Type: cross Abstract: We study the evolution of hidden-weight spectra in wide neural networks trained by (stochastic) gradient descent. We develop a two-level dynamical mea

Spectral Filtering for Complex Linear Dynamical Systems

arXiv:2601.22400v2 Announce Type: replace-cross Abstract: We study the problem of learning complex-valued linear dynamical systems (CLDS) with sector-bounded spectrum. This class captures oscillatory

Spectral Surgery: Class-Targeted Post-Hoc Rebalancing via Hessian Spike Perturbation

arXiv:2605.07790v1 Announce Type: cross Abstract: The Hessian spectrum of trained deep networks exhibits a characteristic structure: a continuous bulk of near-zero eigenvalues and a small number of la

Spectrum-Adaptive Generalization Bounds for Trained Deep Transformers

arXiv:2605.07297v1 Announce Type: cross Abstract: Understanding why trained Transformers generalize well is a fundamental problem in modern machine learning theory, and complexity-based generalization

SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere

arXiv:2605.08003v1 Announce Type: new Abstract: Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods

SSP-based construction of evaluation-annotated data for fine-grained aspect-based sentiment analysis

arXiv:2605.07446v1 Announce Type: new Abstract: We report the construction of a Korean evaluation-annotated corpus, hereafter called 'Evaluation Annotated Dataset (EVAD)', and its use in Aspect-Based

STARFlow2: Bridging Language Models and Normalizing Flows for Unified Multimodal Generation

arXiv:2605.08029v1 Announce Type: new Abstract: Deep generative models have advanced rapidly across text and vision, motivating unified multimodal systems that can understand, reason over, and generat

Statistical Patterns in the Equations of Physics and the Emergence of a Meta-Law of Nature

arXiv:2408.11065v2 Announce Type: replace-cross Abstract: Physics seeks to uncover the laws of Nature and express them through mathematical equations. Despite the vast diversity of natural phenomena,

STEPS: A Temporal Smooth Error Propagation Solver on the Manifolds for Test-Time Adaptation in Time Series Forecasting

arXiv:2605.08005v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) aims to improve time series forecasting under distribution shifts by using limited observations revealed during inference. Ho

Stochastic Transition-Map Distillation for Fast Probabilistic Inference

arXiv:2605.07661v1 Announce Type: cross Abstract: Diffusion models achieve strong generation quality, diversity, and distribution coverage, but their performance often comes with expensive inference.

Streaming Adversarial Robustness in Fuzzy ARTMAP: Mechanism-Aligned Evaluation, Progressive Training, and Interpretable Diagnostics

arXiv:2605.06902v1 Announce Type: new Abstract: Adversarial robustness has been studied extensively for offline deep networks, but less is known about strict single-pass streaming neural learners. Thi

StreamPhy: Streaming Inference of High-Dimensional Physical Dynamics via State Space Models

arXiv:2605.07384v1 Announce Type: new Abstract: Inferring the evolution of high-dimensional and multi-modal (e.g., spatio-temporal) physical fields from irregular sparse measurements in real time is a

Structural Rationale Distillation via Reasoning Space Compression

arXiv:2605.07139v1 Announce Type: cross Abstract: When distilling reasoning from large language models (LLMs) into smaller ones, teacher rationales for similar problems often vary wildly in structure

Surgical Visual Understanding (SurgVU) Dataset

arXiv:2501.09209v2 Announce Type: replace Abstract: Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data scien

SWaRL: Safeguard Code Watermarking via Reinforcement Learning

arXiv:2601.02602v2 Announce Type: replace-cross Abstract: We present SWaRL, a robust and fidelity-preserving watermarking framework designed to protect the intellectual property of code LLMs by embedd

Synergistic Benefits of Joint Molecule Generation and Property Prediction

arXiv:2504.16559v3 Announce Type: replace Abstract: Modeling the joint distribution of data samples and their properties allows to construct a single model for both data generation and property predic

Task-Oriented Communication for Human Action Understanding via Edge-Cloud Co-Inference

arXiv:2605.07354v1 Announce Type: cross Abstract: The expanding application of smart sensing has created a growing demand for the accurate understanding of human action at the network edge. Traditiona

Teacher-Feature Drifting: One-Step Diffusion Distillation with Pretrained Diffusion Representations

arXiv:2605.07327v1 Announce Type: new Abstract: Sampling from pretrained diffusion and flow-matching models typically requires many forward passes to generate diverse and high-fidelity images. Existin

Tessellations of Semi-Discrete Flow Matching

arXiv:2605.07513v1 Announce Type: new Abstract: We study Flow Matching in a semi-discrete setting where a Gaussian source is transported toward a discrete target supported on finitely many points. Thi

Testing Noise Assumptions of Learning Algorithms

arXiv:2501.09189v3 Announce Type: replace Abstract: We pose a fundamental question in computational learning theory: can we efficiently test whether a training set satisfies the assumptions of a given

The Download: the hantavirus outbreak and Musk v. Altman week 2

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Here’s what you need to know about the cruise ship hantavirus

researchmit-tech-review

The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs

arXiv:2602.13298v3 Announce Type: replace-cross Abstract: This paper investigates the relationship between convolutional neural network (CNN) topology and image recognition performance through a compa

The Minimax Rate of Second-Order Calibration

arXiv:2605.07808v1 Announce Type: new Abstract: We characterize the minimax rate of estimating the second-order calibration error for binary classification, which quantifies whether a higher-order pre

Thinky's secret plan: 1: Increase Human<->AI bandwidth 2: Raise ceiling of human+AI intelligence 3: Help humans continue as main-characters …

researchsoumith-chintala--x

Thinky's secret plan: 1: Increase Human<->AI bandwidth 2: Raise ceiling of human+AI intelligence 3: Help humans continue as main-characters in the new world We are at Step 1. Interaction Models are gr

This works really well btw, at the end of your query ask your LLM to 'structure your response as HTML', then view the generated file in your…

This works really well btw, at the end of your query ask your LLM to 'structure your response as HTML', then view the generated file in your browser. I've also had some success asking the LLM to prese

researchkarpathy--x

Three-in-One World Model: Energy-Based Consistency, Prediction, and Counterfactual Inference for Marketing Intervention

arXiv:2605.07199v1 Announce Type: new Abstract: Marketing decisions reflect the interaction of latent consumer heterogeneity, time-varying internal states, and explicit interventions, a structure that

TimeLesSeg: Unified Contrast-Agnostic Cross-Sectional and Longitudinal MS Lesion Segmentation via a Stochastic Generative Model

arXiv:2605.07955v1 Announce Type: cross Abstract: Multiple sclerosis (MS) expresses substantial clinical and radiological heterogeneity, which poses significant challenges for automatic lesion segment

To be clear, I meant 'idea' in the context of science and engineering, in particular AI, as in 'theory' or 'invention', like a new theory of…

researchfrancois-chollet--x

To be clear, I meant 'idea' in the context of science and engineering, in particular AI, as in 'theory' or 'invention', like a new theory of cognition or an idea for a new ML approach. I thought that

Today we're sharing our work on interaction models. A new class of model trained from scratch to handle real-time interaction natively, inst…

researchsoumith-chintala--x

Today we're sharing our work on interaction models. A new class of model trained from scratch to handle real-time interaction natively, instead of gluing it onto a turn-based one. https://youtu.be/A12

TopoPrune: Robust Data Pruning via Unified Latent Space Topology

arXiv:2602.02739v2 Announce Type: replace-cross Abstract: Geometric data pruning methods, while practical for leveraging pretrained models, are fundamentally unstable. Their reliance on extrinsic geom

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning

arXiv:2605.07799v1 Announce Type: cross Abstract: Training foundation models is computationally intensive and often slow to converge.We introduce PIQL,Privileged Information for Quick and Quality Lear

Towards an Inferentialist Account of Information Through Proof-theoretic Semantics

arXiv:2605.05368v2 Announce Type: replace-cross Abstract: Information is one of the most widely-discussed concepts of the current era. However, a great deal of insightful work notwithstanding, it is y

Towards Explainable Industrial Anomaly Detection via Knowledge-Guided Latent Reasoning

arXiv:2602.09850v2 Announce Type: replace Abstract: Industrial anomaly detection demands precise reasoning over fine-grained defect patterns. However, existing multimodal large language models (MLLMs)

Towards Highly-Constrained Human Motion Generation with Retrieval-Guided Diffusion Noise Optimization

arXiv:2605.08054v1 Announce Type: new Abstract: Generating human motion that satisfies customized zero-shot goal functions, enabling applications such as controllable character animation and behavior

TRACE: Tourism Recommendation with Accountable Citation Evidence

arXiv:2605.07677v1 Announce Type: cross Abstract: Tourism is a high-stakes setting for conversational recommender systems (CRS): a plausible-sounding suggestion can waste real money and trip time once

Tracing the Arrow of Time: Diagnosing Temporal Information Flow in Video-LLMs

arXiv:2605.07568v1 Announce Type: cross Abstract: The Arrow-of-Time (AoT) task, determining whether a video plays forward or backward by recognizing temporal irreversibility, is one humans solve with

Transformer-Based Wildlife Species Classification from Daily Movement Trajectories

arXiv:2605.06726v1 Announce Type: new Abstract: Inferring the identity of wildlife species from daily movement data alone is a challenging task. We train sequence models on large-scale, 7-species GPS

TRAS: An Interactive Software for Tracing Tree Ring Cross Sections

arXiv:2605.08025v1 Announce Type: new Abstract: Tree ring marking remains a key step in dendrometry and dendrochronology, but it is often performed manually, making the process time-consuming, subject

TriP: A Triangle Puzzle Approach to Robust Translation Averaging

arXiv:2605.07143v1 Announce Type: new Abstract: Translation averaging aims to recover camera locations from pairwise relative translation directions and is a fundamental component of global Structure-