Search: “research”

Noise-Induced Navigation in Non-convex Domains and Compact Manifolds

arXiv:2610.10949v1 Announce Type: cross Abstract: In this note, we study the problem of designing a feedback law that globally steers a system to a prescribed target configuration. Even if the system

researcharxiv-cs-ro

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

NP-Hardness of Minimizing Neurons in Two-Hidden-Layer ReLU Neural Networks

DGX agent

arXiv:2610.11313v1 Announce Type: new Abstract: A fundamental question in neural network architecture optimization is whether the minimum hidden-neuron count required to approximate a target function

researcharxiv-cs-lg

9 Oct 2026

On the estimation and validity of AI time horizons---a statistical look at the METR plot

arXiv:2610.12466v1 Announce Type: new Abstract: METR's 50% time horizon measures the human completion time of software tasks that an AI solves with 50% probability, allowing AI capabilities to be expr

Onboard Marine Anomaly Detection on Phisat-2: From Simulation-Based Development to In-Orbit Demonstration

arXiv:2610.11735v1 Announce Type: new Abstract: Onboard Artificial Intelligence can improve responsiveness and bandwidth efficiency of Earth Observation systems by processing data directly on the sate

Optimal random quantisers for spherically symmetric distributions

arXiv:2610.11772v1 Announce Type: cross Abstract: Zador's celebrated theorem is a cornerstone of optimal quantisation: it establishes both the weak limit of the empirical distribution of an optimal n-

Optimal Transport Depth Up-Scaling

arXiv:2508.08011v2 Announce Type: replace Abstract: Pre-training Large Language Models (LLMs) from scratch at larger scales yields remarkable performance but incurs substantially high training costs.

Optimally Pacing Budget Spending and Learning

arXiv:2610.11074v1 Announce Type: new Abstract: We establish near-optimal regret bounds for budget-constrained online learning against arbitrary classes of budget-pacing experts in the adversarial set

Optimizing Large Language Models with Chained LMOs

arXiv:2610.10975v1 Announce Type: cross Abstract: Muon has motivated a growing family of optimizers that compose multiple matrix normalizations, but these methods remain fragmented and lack a unified

Overcoming Prior Barriers: Supervised Fine-Tuning under Long-Tail Distribution

arXiv:2610.12345v1 Announce Type: new Abstract: Supervised fine-tuning (SFT) adapts pretrained large language models (LLMs) to downstream tasks, but the required concepts can receive substantially dif

OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation

arXiv:2610.11547v1 Announce Type: new Abstract: NeRF and Gaussian splatting methods have been successfully applied on X-ray scenes where the views are too sparse for 3D reconstruction via classical me

PathTime-VLA: Path-Time Decoupling for Factorized Post-Training of Vision-Language-Action Policies

arXiv:2610.11771v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies typically predict actions at fixed time intervals, coupling the route a robot follows with its execution pace. Thi

researcharxiv-cs-ro

Phase-aware video generation for physics-grounded dynamics and interactions

arXiv:2610.11791v1 Announce Type: new Abstract: Generating physically plausible videos for solid-gas dynamics is challenging as different phases exhibit distinct dynamics yet remain coupled through ph

Phonologically Informed Tokenization for German Speech Recognition: A Cross-Domain Study

arXiv:2610.11646v1 Announce Type: new Abstract: German is a morphologically rich language whose syllable structure is exceptionally well-predicted by the Knuth--Liang hyphenation algorithm. We ask whe

Pose-Free Feed-Forward 3D Inpainting via Learnable Mask Attention and Support Token Refinement

arXiv:2610.11857v1 Announce Type: new Abstract: 3D scene inpainting aims to recover missing or occluded regions in edited 3D scenes, while ensuring geometric and textural consistency. Existing approac

PRAXIS: Learning Dynamics of Self-Improving Models with Symbolic Archives

arXiv:2610.11803v1 Announce Type: new Abstract: Self-improving learning systems adapt data selection, optimization, and auxiliary symbolic components, inducing nonstationary objectives outside standar

Predictive Multiplicity in Cell-Fate Assignment: Label-Free Rashomon Sets and the Limits of Per-Cell Certification

arXiv:2610.11185v1 Announce Type: new Abstract: Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can ass

Probing for Long-Horizon Deductive Reasoning Capabilities in Language Models with Prolog

arXiv:2610.11592v1 Announce Type: new Abstract: Current frontier LLMs can theoretically process long contexts with 1M tokens or more. But to what extent can they go beyond simple retrieval and perform

Prospective Prediction of OOD Degradation from Source-Side Training Dynamics

arXiv:2610.12397v1 Announce Type: new Abstract: We study whether persistent out-of-distribution (OOD) degradation can be predicted before it is directly observed using only source-side training dynami

Puffin: Probabilistic Learning of Spatial Detail From Coarse Observations

arXiv:2610.11914v1 Announce Type: new Abstract: High-resolution socioeconomic variables are important for applications such as urban planning, public health, disaster response, and resource allocation

PulseBound: Future-Beat State Forecasting Under an Explicit Information Boundary

arXiv:2610.12010v1 Announce Type: new Abstract: Predictive representation learning from photoplethysmography (PPG) can violate causal information access even with causal attention, as normalization, n

Quickest Change Detection with Diffusion-Integrated Scores

arXiv:2610.12200v1 Announce Type: cross Abstract: Classical CUSUM relies on the log-likelihood ratio of the underlying distributions, which cannot generally be computed from finite pre- and post-chang

RAG-Stress: Probing the Limits of Evidence Reliance in Retrieval-Augmented Generation

arXiv:2610.11183v1 Announce Type: cross Abstract: Following retrieved evidence does not guarantee factual correctness: misleading evidence can induce a model to replace an answer it previously gave co

RAGenome: Scaling Retrieval-Based Genomic Language Models to Long Contexts

arXiv:2610.11761v1 Announce Type: new Abstract: The genome holds the blueprint that governs the biological properties of the cell. Consequently, advancing our knowledge of genomic function is crucial

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

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

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

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 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

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