research

Rare Gate Disagreements Can Limit Plasticity: When Gradient Flow Mispredicts Finite-Batch SGD

arXiv:2610.11475v1 Announce Type: new Abstract: Population gradient flow is a common tool for reasoning about how neural networks adapt, including after pretraining. We show that it can mispredict fin

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Reading the Whole Heart: Latent-Attention Masked Autoencoders for Multimodal Cardiac Representation Learning

DGX agent

arXiv:2609.12035v3 Announce Type: replace Abstract: Cardiovascular diagnosis and treatment rest on integrating complementary modalities, such as electrocardiogram, echocardiography, and chest X-rays,

researcharxiv-cs-ai

9 Oct 2026

Refinement as a Service: Algorithmic Predictor Refinement

arXiv:2610.11415v1 Announce Type: cross Abstract: Prediction aggregation aims to combine information from multiple predictors into a more informative one. We study this question in the setting of cali

Rehearse Everything, Remember Nothing: Attic-KV Rehearses What Will Be Read

arXiv:2610.12133v1 Announce Type: new Abstract: Many key-value (KV) caches are compressed before anyone knows what will be asked of them: a document cached for retrieval, a prompt prefix shared across

Residual spectral instabilities in representation learning

arXiv:2610.11257v1 Announce Type: new Abstract: Learned representations can lose latent degrees of freedom successively, suggesting a cascade of transitions whose underlying stability principle remain

Rethinking Contrastive Loss in CLIP Post-training: A Complementary Framework with Frozen Text Encoder

arXiv:2610.11374v1 Announce Type: cross Abstract: CLIP serves as a foundational vision-language model and the de facto vision encoder for downstream VLMs such as LLaVA. Post-training offers a lightwei

Rethinking Latency Denial-of-Service: Attacking the LLM Serving Framework, Not the Model

arXiv:2602.07878v2 Announce Type: replace-cross Abstract: LLM inference is inherently expensive, even a modest slowdown can translate into substantial operating costs and severe availability risks. Re

Rethinking the Tradeoff Between Temporal Encoding and Nonlinear Computation in Spiking Language Models

arXiv:2610.10933v1 Announce Type: cross Abstract: Spiking language models face a tradeoff between representing continuous semantic features over short temporal windows and retaining costly nonlinear a

Retrieval-Augmented Generation for Predicting Cellular Responses to Gene Perturbation

arXiv:2603.07233v2 Announce Type: replace Abstract: Predicting transcriptional responses to genetic perturbations is fundamental to functional genomics and therapeutic discovery. Recent deep learning

RGBD-to-3D Object Mesh Refinement via Depth Matching and Symmetry Propagation

arXiv:2610.11187v1 Announce Type: new Abstract: Single-view 3D reconstructors often produce plausible meshes that disagree with the input view, especially near depth discontinuities and self-occlusion

RL-ARC: Calibrating Large Reasoning Models via Reasoning-guided Uncertainty

arXiv:2610.11352v1 Announce Type: new Abstract: Language models (LMs) are commonly trained with Reinforcement Learning with Verifiable Rewards (RLVR) to enhance their reasoning capabilities. However,

RobustLDS: Learning linear dynamical systems under adversarial corruptions

arXiv:2610.11906v1 Announce Type: cross Abstract: We consider the problem of learning linear dynamical systems under adversarial contamination from a single trajectory of length T. While identificatio

Roundtables: A Conversation With the Creator of AI-Designed Viruses

Friday, October 16, 2026 Can AI design new life forms? In 2025, Stanford University PhD student Samuel King came up with a preliminary answer when he used a generative AI model to propose genetic blue

researchmit-tech-review

RouterInterp: Understanding Superposed Specialisation in Mixture of Experts Routing

arXiv:2610.11775v1 Announce Type: new Abstract: Sparse Mixture of Experts (MoE) models scale more efficiently than dense models by routing tokens to modular expert networks that are only active for pr

Sample-Efficient Generative Conformal Prediction

arXiv:2610.11349v1 Announce Type: new Abstract: Generative conformal prediction builds uncertainty sets from samples of a conditional generator, which are efficient only when the samples represent the

Scalable LEO Conjunction Screening using Adaptive Synthetic-Covariance Thresholds

arXiv:2610.11645v1 Announce Type: new Abstract: As orbital populations grow, conjunction screening must search more object pairs for potential collision risks. Existing pipelines use early-stage filte

researcharxiv-cs-ro

SCORE: Spectral Correlation Estimation for Multivariate Gaussians

arXiv:2610.12096v1 Announce Type: new Abstract: Neural network-based predictive modeling with high-dimensional structured Gaussian targets requires an efficient and numerically stable, yet expressive

Selective Listening: Mechanism-Guided Control of Audio Influence in Large Audio-Language Models

arXiv:2610.11196v1 Announce Type: cross Abstract: Large audio-language models (LALMs) exploit multimodal evidence, yet task-irrelevant audio can alter text-reasoning decisions when listening is unnece

Self-Organization from Constrained Geometric Radiation

arXiv:2610.10621v1 Announce Type: new Abstract: How does dynamic order emerge spontaneously in closed systems without external driving? Existing paradigms all require external energy flows, temperatur

Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy

arXiv:2610.11825v1 Announce Type: new Abstract: Detecting intra-operative speech impairment during awake craniotomy is essential for preserving language function. However, automated detection remains

SFT-as-Context Mitigates Forgetting in Supervised Fine-Tuning

arXiv:2610.11132v1 Announce Type: cross Abstract: Supervised fine-tuning (SFT) equips large language models (LLMs) with specialized capabilities, but often comes at the cost of forgetting the general

sigmaTransfer: Uncertainty Transfer from Small to Large Networks under muP

arXiv:2610.11668v1 Announce Type: cross Abstract: Reliable predictive uncertainty in Laplace approximations depends critically on the prior precision, yet selecting it requires a posterior sweep that

Similar Predictive Fit but Different Latent Dynamics: Characterizing Learned Dynamical Structure in Personalized Models of Brain Disorders

arXiv:2610.10850v1 Announce Type: new Abstract: As AI models move toward clinical decision-making and personalized treatment, understanding what a model learns is important beyond predictive accuracy

Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning

arXiv:2602.18867v3 Announce Type: replace Abstract: Active Learning (AL) reduces annotation costs in medical imaging by selecting only the most informative samples for labeling, but suffers from cold-

Slot3R: Set-Associative Spatial Memory for Streaming 3D Reconstruction

arXiv:2610.12282v1 Announce Type: new Abstract: Streaming 3D reconstruction must preserve evidence from each frame while processing an expanding scene online. Spatial memory is a natural fit because i

SLVR: Structured Latent Visual Reasoning via Human-like Reasoning Flows

arXiv:2610.10563v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) often answer visual reasoning questions by relying on linguistic priors rather than task-relevant visual evid

Social Pain Disrupts Emotion-Action Brain-State Dynamics in Adolescents with Non-Suicidal Self-Injury

arXiv:2610.11155v1 Announce Type: new Abstract: Non-suicidal self-injury (NSSI) is prevalent among adolescents with depression, but the rapid brain-state dynamics linking social distress to maladaptiv

SP-DocReader: Difference-Aware Self-Play for Precise Document OCR

arXiv:2610.11148v1 Announce Type: cross Abstract: Accurate page transcription remains difficult for vision language models under limited input and training budgets. We present SP-DocReader, a self-pla

SpaceFlow: Locally Controllable 3D Generation

arXiv:2610.12399v1 Announce Type: cross Abstract: Current 3D generation methods lack explicit local control: geometric adherence is often defined by a global control strength, and appearance cannot be

Sparse Attention Is Matrix Approximation, Not Choosing from a Bag of Values

arXiv:2610.10871v1 Announce Type: new Abstract: Large Language Models (LLMs) achieve strong performance across many domains, but their efficiency is limited by the quadratic cost of attention with res

SPD-MetaFormer is what you need for small-data brain decoding

arXiv:2610.10952v1 Announce Type: new Abstract: Brain signal decoding is challenging because neural recordings are noisy and vary across individuals, while labeled data are often limited. Recent atten

Specialized Decision Models vs. General-Purpose LLMs: Benchmarking Jev Across Knowledge, Reasoning, and Multilingual Tasks

arXiv:2610.11978v1 Announce Type: new Abstract: Jev is a 'System One' model that returns a choice among given options instead of generating text. We study how such a specialized decision model compare

Speedbumps: Rejection Attacks on Speculative Decoding

arXiv:2610.10929v1 Announce Type: cross Abstract: Speculative decoding is a popular technique for increasing the speed and reducing the costs of large language model (LLM) inference by verifying multi

SR-TTA: Spatial-Redundancy Test-Time Adaptation for Interference-Robust Respiration Sensing

arXiv:2610.11755v1 Announce Type: new Abstract: Future 6G networks aim to expose sensing as a native service by reusing communication infrastructure. We study respiration sensing on a cell-free massiv

SRUG: A Fusion-Driven Generator Network for Medical Image Translation

arXiv:2601.04785v2 Announce Type: replace-cross Abstract: MRI sequence synthesis aims to recover missing image contrast while preserving patient-specific anatomy. The choice of generation mechanism af

SteerCast: Retrieval-Based Latent Steering for Decoder-Only Time Series Forecasting

arXiv:2610.11229v1 Announce Type: new Abstract: Time series forecasting aims to predict future values from historical observations and auxiliary features. We propose extbf{SteerCast}, a retrieval-base

Streaming-Aware Diffusion for Real-Time Video Super-Resolution via Cross-Step Attention

arXiv:2610.11746v1 Announce Type: new Abstract: Real-time video super-resolution requires high spatio-temporal fidelity under strict latency constraints, challenging diffusion models due to their iter

Stride Independent Patching for Deep Learning

arXiv:2610.12216v1 Announce Type: new Abstract: This paper presents semi-automatic stride-independent patching (SSP) as an alternative to automatic stride-dependent patching techniques. SSP uses user

Structured Sentiment Analysis Using Sequence Labeling as Dependency Graph Parsing

arXiv:2610.11695v1 Announce Type: new Abstract: This study addresses the problem of structured sentiment analysis, whose goal is to obtain a fine-grained sentiment graph where the nodes represent span

Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube

arXiv:2610.12358v1 Announce Type: cross Abstract: We study Gaussian regression under squared population L_2 loss in a known m-dimensional subspace of degree-at-most-k functions on the d-dimensional Bo

Tabula Rasa: Monte Carlo estimation of unit-variance noise with controlled spatio-temporal correlation

arXiv:2610.11653v1 Announce Type: cross Abstract: We suggest a method to generate time-varying Gaussian noise with controlled variance and controlled temporal correlation. This noise is used in severa

TAM: Task-Aware Memory Distillation for Efficient Spatiotemporal Prediction

arXiv:2610.11617v1 Announce Type: cross Abstract: Knowledge distillation enables efficient spatiotemporal prediction by transferring knowledge from an accurate teacher to a compact student. However, m

TAP3D: Thermal-Assisted 3D Human Point Clouds

arXiv:2610.11241v1 Announce Type: new Abstract: Human body point clouds are a versatile representation for AI-enabled human sensing. However, existing methods using LiDAR, radar, and depth cameras suf

TAPNAV: Humanoid Navigation through Tactile Active Perception

arXiv:2610.10748v1 Announce Type: new Abstract: Navigation in vision-denied environments is challenging for humanoid robots because proprioceptive odometry drifts and localization uncertainty accumula

researcharxiv-cs-ro

Task-Centric Personalized Federated Fine-Tuning of Language Models

arXiv:2604.00050v3 Announce Type: replace-cross Abstract: Federated Learning (FL) has emerged as a promising technique for training language models on distributed and private datasets of diverse tasks

Temporal transformer CAN encoder with federated lightweight heads for anomaly detection

arXiv:2610.10613v1 Announce Type: new Abstract: Modern vehicles rely on large numbers of Electronic Control Units (ECUs) that constantly exchange information over the Controller Area Network (CAN) bus

Test-Time Scaling with Diffusion Language Models via Reward-Guided Stitching

arXiv:2602.22871v2 Announce Type: replace-cross Abstract: Reasoning with large language models often benefits from generating multiple chains-of-thought, but existing aggregation strategies are typica

The Ball and the Box: Two Geometries of Computation in Superposition

arXiv:2610.11744v1 Announce Type: new Abstract: Neural representations can encode more features than they have dimensions, a phenomenon known as superposition. We study the dimension needed to compute