Search: “research”

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

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Scalable Delphi: Large Language Models for Structured Risk Estimation

DGX agent

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

researcharxiv-cs-ai

2 Oct 2026

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

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

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

The Effect of Gait Stability Based on Two Types of Impact Strategies for Two-Link Walking and Brachiating Robots

arXiv:2610.01004v1 Announce Type: cross Abstract: In this paper, we explore the impulsive dynamics common to single-joint, two-link models of walking and brachiating gaits with respect to slope and sw

The Geometry of Time: Horizon-Independent Feasibility and Repair for STL

arXiv:2610.00199v1 Announce Type: cross Abstract: Signal Temporal Logic control synthesis frequently encounters physical infeasibility due to actuator limits or flawed task deadlines. Standard optimiz

The Life Cycle of a Massive Activation: Stochastic Birth, Weight-Decay-Driven Growth, and Competitive Consolidation

arXiv:2610.00423v1 Announce Type: cross Abstract: Massive activations, residual-stream coordinates with magnitudes far larger than typical activations, are associated with attention sinks in transform

Model Releases

The models are not trying to escape. They are trying to finish the task.

Something keeps happening in frontier AI, and the explanation we are given is always one size larger than the event. An OpenAI research model, running in a supposedly offline training environment, hit

model-releasesr-chatgpt

The Price of Correlated Tests: How Strict Should a Model Release Gate Be?

arXiv:2610.00993v1 Announce Type: cross Abstract: Before a machine learning model ships, it often has to pass a suite of automated tests. Requiring every test to pass looks safe, yet it can reject man

Tight Transition Time Bounds for Separable Logistic Regression at the Edge of Stability

arXiv:2610.01459v1 Announce Type: new Abstract: We study logistic regression on linearly separable data under gradient descent with a large constant stepsize eta. Such dynamics may exhibit a character

Toward Humanoid Robots in Construction: A Teleoperation Feasibility Study

arXiv:2610.00718v1 Announce Type: new Abstract: We present a teleoperation system that enables a single operator to perform construction tasks on a Unitree G1 humanoid, combining extended reality (XR)

Towards Physical Underwater Robotic Assistance for Scuba Diver Movement in Confined Spaces

arXiv:2610.01906v1 Announce Type: new Abstract: Scuba divers are taught to control their depth to avoid rapid ascents and descents, which could result in serious injuries such as gas embolisms and bar

TRACE: Privacy-Preserving Next-Best-View Selection over Distributed 3D Gaussian-Splat Maps

arXiv:2610.00822v1 Announce Type: new Abstract: Share the light, not the map. We study next-best-view selection for a team of robots, each of which builds its own 3D Gaussian Splatting map and keeps i

Training-Aware Target Coverage for Synthetic Data Selection

arXiv:2610.00814v1 Announce Type: cross Abstract: Synthetic data are increasingly used to scale LLM training, yet more synthetic data do not necessarily produce better models. Useful synthetic data mu

Trajectory Stitching for Solving Inverse Problems with Flow-Based Models

arXiv:2602.08538v2 Announce Type: replace-cross Abstract: Flow-based generative models have emerged as powerful priors for solving inverse problems. One option is to directly optimize the initial late