research

Rethinking Data Augmentation under Covariate Shift: Invariant-Guided Diffusion and Prototype Reweighting

arXiv:2610.00873v1 Announce Type: cross Abstract: In many industrial applications, 1) tabular data is scarce and imbalanced and thus requires synthetic expansion; 2) input distributions drift between

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

Reverse Item Response Theory for Sparsity-Robust Ranking in Fragmented Cancer Drug-Response Matrices

DGX agent

arXiv:2610.00002v1 Announce Type: new Abstract: We introduce reverse Item Response Theory (IRT) to pharmacogenomic drug-response analysis by treating cancer types as latent 'subjects' with resistance

researcharxiv-cs-lg

2 Oct 2026

Right In-Place (RiP) Convolution: A Simple, General, and Near-Optimal Strategy for Memory-Efficient CNN Inference

arXiv:2610.00586v1 Announce Type: cross Abstract: Activation memory, not compute, limits CNN inference on constrained hardware such as microcontrollers. Direct in-place convolution removes the dual-bu

RIQE: a NIQE-style reference model for Computed Tomography

arXiv:2610.00384v1 Announce Type: cross Abstract: The Natural Image Quality Evaluator (NIQE) scores an image by its statistical distance from a model fitted on pristine images, and its distributed mod

Robot Learning on Discrete Surfaces: Theory and Applications

arXiv:2610.01910v1 Announce Type: new Abstract: All the objects composing our world are enclosed within surfaces. Yet, most robot learning and motion generation frameworks treat surfaces as constraint

researcharxiv-cs-ro

Robust Evidential Learning Through Latent Consistency

arXiv:2610.01384v1 Announce Type: new Abstract: Reliable uncertainty quantification is essential for deploying deep learning models in high-stakes settings, where out-of-distribution and adversarial i

Robust Is Salient: An Informed Adversary Moves the Optimal Signal onto the Salience Pole

arXiv:2610.00233v1 Announce Type: new Abstract: When an informed adversary shares the audience of a constrained signalling channel, the signal that best protects the truth is the signal that best desc

Robust Non-Clairvoyant Scheduling with Classification Models

arXiv:2610.01343v1 Announce Type: new Abstract: We study the classical single-machine scheduling problem of minimizing the sum of completion times of jobs in a non-clairvoyant setting, where the proce

Roto-translated Local Coordinate Frames For Interacting Dynamical Systems

arXiv:2110.14961v4 Announce Type: replace Abstract: Modelling interactions is critical in learning complex dynamical systems, namely systems of interacting objects with highly non-linear and time-depe

SAGE: Similarity-Based Cleaning of Poisoned Training Data from Verified Examples

arXiv:2610.01788v1 Announce Type: new Abstract: As machine learning increasingly relies on public, untrusted data sources, data poisoning attacks, which inject malicious examples into training data to

SALD: Self-Referenced Advantage Learning for Diffusion Models

arXiv:2610.01496v1 Announce Type: new Abstract: Recent work on language-model adaptation has shown that single models can obtain informative training signals by evaluating their behavior in demonstrat

Scalable Delphi: Large Language Models for Structured Risk Estimation

arXiv:2602.08889v2 Announce Type: replace Abstract: Quantitative risk assessment relies on structured expert elicitation to estimate unobservable properties. The Delphi method produces calibrated, aud

Scaling Collider Event Generation with Residual-Quantized Tokens

arXiv:2610.00569v1 Announce Type: cross Abstract: Full detector simulation and reconstruction of collider events are projected to become major bottlenecks at the High-Luminosity Large Hadron Collider,

Selection-Based Structured Reasoning: Toward Efficient Multimodal Search Agents

arXiv:2610.01892v1 Announce Type: cross Abstract: Multimodal agents commonly generate free-form reasoning before each action. For small models, limited model capacity can result in lengthy reasoning t

Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos

arXiv:2610.01205v1 Announce Type: new Abstract: Objective assessment of microsurgical technical skill is essential for competency-based training and quality assurance, yet existing video-based approac

Sequential Capacity of Quantum Processes with Finite Memory

arXiv:2610.02068v1 Announce Type: cross Abstract: How complex can the responses of a quantum device become as it runs longer with a fixed internal memory? We quantify this complexity through sequentia

Sequential Functional Structured Tucker Compression for Large Language Model Attentions

arXiv:2610.00717v1 Announce Type: cross Abstract: Post-training compression of LLM attention is often formulated as independent matrix approximation, ignoring both the shared structure among attention

Sharp Non-Asymptotic Analysis of the Penalized Challenger in eta-EB-TCI for Bernoulli Bandits

arXiv:2610.01951v1 Announce Type: new Abstract: Top-two algorithms are simple and effective for fixed-confidence best-arm identification, but their sharp non-asymptotic behavior is still not well unde

Sharp Oracle-Regret Tradeoffs for Projection-Free Online Convex Optimization

arXiv:2610.00254v1 Announce Type: new Abstract: We characterize the regret attainable in online convex optimization when access to the feasible set is limited to an exact linear optimization oracle. T

ShatterQuant: Breaking Uniform Precision with Block-Wise Mixed-Precision on a Systolic Transformer Hardware Accelerator

arXiv:2610.00207v1 Announce Type: cross Abstract: Due to limited support for intra-tensor heterogeneous precision in conventional accelerators, neural network quantization remains largely restricted t

SIEVE: Selective attention-value Suppression for Vision-Language Models Unlearning

arXiv:2610.01962v1 Announce Type: cross Abstract: The ability of vision-language models (VLMs) to associate visual identities with biographical information creates a need for selective unlearning of p

Signal-Noise Factorization Isolates Nuisance Variation into Removable Subspaces

arXiv:2610.00751v1 Announce Type: cross Abstract: Recent theoretical work identified fundamental properties of representation geometry that shape inference ability of deep neural networks. These inclu

Sim+Real: Joint Simulation - Experiment Training Improves Balanced Prediction in Physical Systems

arXiv:2610.01974v1 Announce Type: new Abstract: Simulation and experimental measurements provide complementary data for learning spatiotemporal physical systems, but standard simulation-to-experiment

Soundwich: Video Generation with Layered and Controllable Audio

arXiv:2610.00691v1 Announce Type: new Abstract: Recent joint audio-video generative models can synthesize realistic videos with synchronized sound, but typically generate audio as a single mixed track

Source Identification Is Not Fitness Testing: Measuring the Limits of Synthetic-Data Attribution

arXiv:2610.00417v1 Announce Type: new Abstract: Repeated training on model-generated data can degrade later models. One possible response is to use provenance when deciding which generated examples to

Spatially Gated Diffusion for Localized Counterfactual Chest Radiograph Editing

arXiv:2610.00805v1 Announce Type: cross Abstract: Editing a chest radiograph requires completing the requested change while preserving unrelated content. We study a latent diffusion editor with an ins

Specificity-Aware Diffusion Steering via Variance-Reduced Sequential Monte Carlo

arXiv:2610.00395v1 Announce Type: new Abstract: Inference-time steering enables pretrained diffusion models to satisfy new constraints without full retraining. However, specificity-aware generation is

SpikeMoE: Brain-Inspired Competitive Routing for Flexible Spiking Mixture-of-Experts

arXiv:2610.01418v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) enable event-driven computation through biologically inspired dynamics at the neuronal scale, while Mixture-of-Experts (M

SPIRAL: Learning to Search and Aggregate

arXiv:2606.23595v2 Announce Type: replace Abstract: Language model reasoning can be substantially improved at test time via scaffolds that scale inference compute across different primitives -- sequen

StabilityArc: Decoding Protein Sequence Embeddings into Generalizable Stability Landscapes

arXiv:2610.00742v1 Announce Type: cross Abstract: Every protein has a unique stability landscape, but the physical consequences of mutation are governed by recurring biochemical constraints. We test w

STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models

arXiv:2610.01302v1 Announce Type: new Abstract: Concept erasure suppresses a target concept while preserving behavior on unrelated inputs. Existing closed-form methods were designed for 2D image diffu

STEER: Reducing Inference Cost in Relational Foundation Models through Semantically Informed Sampling

arXiv:2610.00907v1 Announce Type: cross Abstract: Relational foundation models (RFMs) are pretrained once on a collection of relational databases and prediction tasks, and then applied zero-shot to pr

Stochastic Rounding in Low-Precision Transformer Inference: A Variable-Precision Emulation Study of a Small GPT-2

arXiv:2610.01889v1 Announce Type: new Abstract: Should low-precision transformer inference use stochastic rounding (SR) or round-to-nearest (RN)? The answer depends on where in the network you look. W

Streaming algorithms for robust max-min diversification

arXiv:2610.01456v1 Announce Type: new Abstract: Given a set of n points X in a metric space and an integer k, max-min diversification aims to select k points of X maximizing their minimum pairwise dis

Structure-agnostic Causal Representation Learning

arXiv:2610.00968v1 Announce Type: cross Abstract: Causal representation learning aims to discover robust features by exploiting the causal structure underlying data generation. Existing methods requir

Structured-Noise Masked Modeling for Video, Audio and Beyond

arXiv:2503.16311v2 Announce Type: replace-cross Abstract: Masked modeling has emerged as a robust self-supervised learning framework. However, most methods rely on random masking, which disregards the

Supervise What Decides Success: Criterion-Aligned Auxiliary Losses for Latent World-Model Planning

arXiv:2610.01224v1 Announce Type: new Abstract: Latent world models plan by scoring candidate action sequences with distances in latent space. However, task success is judged by physical quantities, w

Surface-volume self-supervised representation learning of brain MRI for genetic discovery

arXiv:2610.02114v1 Announce Type: new Abstract: Existing genome-wide association studies (GWAS) of brain imaging provide predefined or deep-learning-derived imaging phenotypes, yet these phenotypes co

Synthetic training for long-tail haemorrhagic lesion segmentation in data-scarce settings

arXiv:2610.01542v1 Announce Type: new Abstract: Cerebral microbleeds (CMBs) and cortical superficial siderosis (cSS) are imaging markers of cerebral small vessel disease, but their automated segmentat

TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model

arXiv:2610.00638v1 Announce Type: new Abstract: World action models improve robotic manipulation by conditioning actions on predicted futures, yet existing tactile variants largely inherit video-gener

researcharxiv-cs-ro

Target-Dependent Limits of Causal Repair: A Leading-Log Frontier in a Gaussian Model

arXiv:2610.00424v1 Announce Type: cross Abstract: Knowing how much a causal predictor could improve need not reveal the gain of the repair actually learned. We quantify this gap in a scalar Gaussian c

Task-Adaptive Grounded 3D-Programmers Using 2D VLMs

arXiv:2610.02021v1 Announce Type: cross Abstract: Recent vision-language models (VLMs) exhibit remarkable generalization and reasoning abilities, yet 3D understanding in these models is limited by dat

Temporal-Difference Learning for Dragonchess

arXiv:2610.01845v1 Announce Type: new Abstract: Our research investigates how two adaptive AI methods, evolutionary transfer learning and TD(lambda), perform in the three-dimensional chess environment

Temporally-Resolved Token Attribution Reveals the Generation Dynamics of Diffusion Language Models

arXiv:2610.01177v1 Announce Type: cross Abstract: This work presents Diffusion Layer Integrated Gradients (DLIG), a token attribution method for diffusion language models (DLMs) that extends Integrate

Ten years ago, AlphaGo’s Move 37 shocked the world. It wasn’t intuition alone that produced it. AlphaGo could search possible futures, test …

Ten years ago, AlphaGo’s Move 37 shocked the world. It wasn’t intuition alone that produced it. AlphaGo could search possible futures, test its instincts and reason about what would happen next. In a

researchyann-lecun--x

The AI Assessment Sandbox Configurator: A Framework to Support Technical Assessment in AI Regulatory Sandboxes

arXiv:2610.01539v1 Announce Type: new Abstract: The EU's Artificial Intelligence Act requires all Member States to establish AI Regulatory Sandboxes (AIRS) by August 2027: supervised environments brin

The COTe score: A decomposable framework for evaluating Document Layout Analysis models

arXiv:2603.12718v3 Announce Type: replace Abstract: Document Layout Analysis (DLA) is the process by which a page is parsed into meaningful elements, often using machine learning models. Typically, th

The Curvature of Regret in Contextual Linear Optimization

arXiv:2610.01980v1 Announce Type: new Abstract: Decision-focused learning for linear optimization is complicated by the discontinuity of the optimizer, where small cost errors may leave the decision u