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Natural Human Motion Recovery by Aligning High-Order Temporal Dynamics from Monocular Videos

arXiv:2605.26879v1 Announce Type: new Abstract: Human motion recovered from monocular videos often appears overly smooth or dynamically inconsistent, even when joint positions are numerically accurate

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Clear filters

Natural Language Query to Configuration for Retrieval Agents

DGX agent

arXiv:2605.27361v1 Announce Type: new Abstract: Modern retrieval agents expose many configuration choices -- LLM, retriever, number of documents, number of hops, and synthesis strategy -- each shaping

researcharxiv-cs-ai

27 May 2026

Near-Optimal Regret in Adversarial Kernel Bandits

model-releasesarxiv-cs-lg

arXiv:2605.26585v1 Announce Type: new Abstract: We study the adversarial kernel bandit problem, in which the loss at each round is induced by an arbitrary bounded element of a reproducing kernel Hilbe

Negligible in Size, Significant in Effect: On Scale Vectors in Large Language Models

arXiv:2605.26895v1 Announce Type: cross Abstract: Normalization layers in modern large language models (LLMs) consist of a deterministic normalization operation and a learnable scale vector. While the

Applications

NeR-SC: Adapting Neural Video Representation to Screen Content

arXiv:2605.27024v1 Announce Type: new Abstract: Implicit neural representations have emerged as a promising paradigm for video compression, with recent methods achieving competitive performance on nat

applicationsarxiv-cs-cv

NestedKV: Nested Memory Routing for Long-Context KV Cache Compression

arXiv:2605.26678v1 Announce Type: new Abstract: Long-context language models are limited by the memory footprint of the key-value (KV) cache. Existing training-free KV compression methods usually rank

Neural Autoregressive Control Variates for the Quantum Monte Carlo Sign Problem

model-releasesarxiv-cs-lg

arXiv:2605.26814v1 Announce Type: cross Abstract: We train a pair of autoregressive models to construct zero-mean control variates to mitigate the sign problem in quantum Monte Carlo simulations. The

Agents

Neural Bayesian Sequential Routing

arXiv:2605.26147v1 Announce Type: new Abstract: Human decision-making is sequential and uncertainty-aware, yet standard neural networks often rely on static, dense forward computation with limited vis

agentsarxiv-cs-lg

Neural Scaling Laws of Deep ReLU and Deep Operator Network: A Theoretical Study

arXiv:2410.00357v2 Announce Type: replace Abstract: Neural scaling laws play a pivotal role in the performance of deep neural networks and have been observed in a wide range of tasks. However, a compl

Neuro-Symbolic Verification of LLM Outputs for Data-Sensitive Domains (extended preprint)

arXiv:2605.26942v1 Announce Type: new Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsistencies, and privacy vulnerabilities introduce una

Hardware

NightSight: Passive Computation for Navigation in Dark Using Events

arXiv:2605.26330v1 Announce Type: new Abstract: Small aerial robots are particularly well-suited for search and rescue in confined and hazardous environments due to their agility, low cost, and abilit

hardwarearxiv-cs-ro

Applications

No Data? No Problem: Robust Vision-Tabular Learning with Missing Values

arXiv:2512.19602v2 Announce Type: replace Abstract: Large-scale medical biobanks provide imaging data complemented by extensive tabular information, such as clinical measurements or demographics. Howe

applicationsarxiv-cs-cv

Nonlinear Data Integration via Kernel Methods for Data Collaboration Analysis

arXiv:2605.27219v1 Announce Type: new Abstract: Collaborative analysis of decentralized confidential datasets is important, but direct sharing of original datasets is often restricted by privacy and i

Normal Guidance is what Attention Needs

arXiv:2605.27306v1 Announce Type: new Abstract: We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such

tutorialsarxiv-cs-lg

Normalizing Flows on Quotient Manifolds via Boundary Quotients

arXiv:2511.22882v3 Announce Type: replace Abstract: We introduce boundary quotients and present a framework for learning densities on manifolds that arise as boundary quotients of simpler domains. We

Not All Disagreement Is Learnable: Token Teachability in On-Policy Distillation

model-releasesarxiv-cs-lg

arXiv:2605.26844v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts with token-level teacher supervision. Recent selective OPD methods exploit the non-uni

Not All Modalities Are Equal: Instruction-Aware Gating for Multimodal Videos

arXiv:2605.26232v1 Announce Type: new Abstract: Pre-trained video large language models excel at visual reasoning. However, they struggle when videos arrive with auxiliary streams, such as audio, dept

Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation

arXiv:2605.27194v1 Announce Type: new Abstract: Distilling demonstration effects into hidden-space interventions offers a lightweight alternative to full finetuning. However, existing multimodal varia

researcharxiv-cs-cl

NSF-SciFy: Mining the NSF Awards Database for Scientific Claims

arXiv:2503.08600v3 Announce Type: replace Abstract: We introduce NSF-SciFy, a comprehensive dataset of scientific claims and investigation proposals extracted from National Science Foundation award ab

researcharxiv-cs-cl

O-MARC: Omni Memory-Augmented Compression Distillation for Efficient Video Understanding

arXiv:2605.26584v1 Announce Type: new Abstract: Omnimodal large language models enable unified audio video understanding, but long joint token sequences make inference costly, and existing benchmarks

Object Pose and Shape Estimation for Grasping: Does it Work?

arXiv:2605.26944v1 Announce Type: cross Abstract: The problem of object pose and shape estimation has seen key advancements lately. Encoder-decoder (e.g., SAM3D, LRM, CRISP) and diffusion-based models

OCR-Reasoning Benchmark: Unveiling the True Capabilities of MLLMs in Complex Text-Rich Image Reasoning

arXiv:2505.17163v2 Announce Type: replace-cross Abstract: Recent advancements in multimodal slow-thinking systems have demonstrated remarkable performance across various visual reasoning tasks. Howeve

ODOV: Benchmark the Open-Domain Open-Vocabulary Object Detection

arXiv:2508.01253v2 Announce Type: replace Abstract: Existing studies typically investigate domain shift and category shift as independent problems, however, in real-world scenarios, the two types of s

Olaf-World: Orienting Latent Actions for Video World Modeling

arXiv:2602.10104v2 Announce Type: replace-cross Abstract: Scaling action-controllable world models is limited by the scarcity of action labels. While latent action learning promises to extract control

Omanic: Towards Step-wise Evaluation of Multi-hop Reasoning in Large Language Models

arXiv:2603.16654v2 Announce Type: replace-cross Abstract: Evaluating the reasoning abilities of large language models (LLMs) solely from final answers can obscure failures in intermediate steps, espec

OMD-GraphRAG: Enhancing GraphRAG with Ontology-Guided Extraction, Multi-Dimensional Clustering and Dual-Channel Fusion

arXiv:2603.25152v3 Announce Type: replace Abstract: Retrieval-Augmented Generation (RAG) systems face significant challenges in complex reasoning, multi-hop queries, and domain-specific QA. While exis

Local Ai

OmniGF: A Dual-Branch Vision-Language Framework for Unified Gaze Following

arXiv:2605.26399v1 Announce Type: new Abstract: Understanding human gaze behavior is essential for complex scene comprehension and human-computer interaction. Traditional gaze following models are typ

local-aiarxiv-cs-cv

OmniInteract: Benchmarking Real-World Streaming Interaction for Real-Time Omnimodal Assistants

arXiv:2605.26485v1 Announce Type: cross Abstract: We introduce OmniInteract, a streaming benchmark for real-time omnimodal large language models evaluated through native online inference over audio-vi

OmniRetriever: Any-to-Any Audio-Video-Text Retrieval via Fusion-as-Teacher Distillation

arXiv:2605.26641v1 Announce Type: new Abstract: Unified multimodal embedding spaces have become the standard interface for cross-modal retrieval and multimodal RAG, and recent audio-video-text (AVT) e

OmniToM: Benchmarking Theory of Mind in LLMs via Explicit Belief Modeling

arXiv:2605.26322v1 Announce Type: new Abstract: Theory of Mind (ToM), the ability to infer others' knowledge, intentions, and emotions, is commonly evaluated in large language models (LLMs) using end-

On the Detection of Commutative Factors in Factor Graphs: Necessary and Sufficient Conditions

arXiv:2605.26908v1 Announce Type: new Abstract: Exploiting the indistinguishability of objects in a probabilistic graphical model such as a factor graph is key to lifted probabilistic inference algori

researcharxiv-cs-ai

On the Error-Correcting Effects of Stochasticity in Discrete Diffusion

arXiv:2605.26582v1 Announce Type: cross Abstract: Discrete diffusion models achieve strong performance in text and image generation, but their inference remains slow and must inherently balance sampli

researcharxiv-cs-ai

On the Generalization Capabilities, Design Choices and Limitations of Keypoint Imitation Learning

arXiv:2605.26649v1 Announce Type: new Abstract: RGB-based imitation learning requires many demonstrations to generalize to unseen objects or scenes, motivating research into intermediate representatio

tutorialsarxiv-cs-ro

On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning

arXiv:2605.27083v1 Announce Type: new Abstract: Counterfactual tuning (CFT) has emerged as a promising paradigm for Large Language Model (LLM) unlearning by training models to generate alternative fic

On the Push-Based Asynchronous Federated Learning: A Bias-Correction Aggregation Approach

arXiv:2605.26162v1 Announce Type: cross Abstract: Asynchronous decentralized federated learning (ADFL) eliminates central coordination and global synchronization, making it attractive for large-scale

On the Robustness of Machine Unlearning for Vision-Language Models

arXiv:2605.26992v1 Announce Type: new Abstract: Vision-language models (VLMs) may memorize undesirable information from training data, motivating growing interest in machine unlearning. In this work,

On the Role of Inductive Bias in Time-Series Pretraining: A Case Study in Learning Generalizable Representations for Clinical Time Series

arXiv:2605.26194v1 Announce Type: new Abstract: Clinical time-series learning is routinely constrained by small, heterogeneous cohorts and protocol drift, while its downstream use spans both classific

safetyarxiv-cs-lg

On the Sensitivity of Instruction-tuned LLMs to Harmful Sentences in Long Inputs

arXiv:2510.05864v2 Announce Type: replace Abstract: Large language models (LLMs) increasingly operate on long inputs, yet their behavior when harmful sentences are sparsely embedded within such inputs

Once-For-All: A Train-Once and Select-Anytime Framework for Multimodal Instruction Tuning

arXiv:2605.26761v1 Announce Type: new Abstract: Multimodal instruction tuning is the de facto recipe for adapting vision language models (VLMs), yet instruction data are highly redundant, making data

Online Learning on Hidden-Convex Losses via Algorithmic Equivalence: Optimal Regret, Geometric Barrier, and Bandit Feedback

arXiv:2605.26373v1 Announce Type: new Abstract: We study adversarial online learning with hidden-convex losses, i.e., nonconvex losses that become convex after a nonlinear reparameterization. Ghai, Lu

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks

arXiv:2605.26526v1 Announce Type: new Abstract: Recent defenses for safeguarding open-weight large language models (LLMs) are intended to prevent adversarial usage. Underlying these defenses is an ass

safetyarxiv-cs-lg

Agents

Optimal Rates for Feasible Payoff Set Estimation in Games

arXiv:2602.04397v2 Announce Type: replace-cross Abstract: We study a setting in which two players play a (possibly approximate) Nash equilibrium of a bimatrix game, while a learner observes only their

agentsarxiv-cs-lg

Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics

arXiv:2605.26840v1 Announce Type: new Abstract: Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as

tutorialsarxiv-cs-cl

ORCA: An End-to-End Interactive Copilot for Optimized Root Cause Analysis

arXiv:2605.27022v1 Announce Type: new Abstract: Causal analysis is a crucial task in many domains, including manufacturing, social science, and medicine. However, despite recent progress, the conceptu

tutorialsarxiv-cs-ai

ORLoopBench: Solver-in-the-Loop Benchmarks for Self-Correction and Behavioral Rationality in Operations Research

arXiv:2601.21008v3 Announce Type: replace-cross Abstract: Operations Research practitioners debug infeasible models through an iterative process: inspecting Irreducible Infeasible Subsystems ( IIS), i

OSMa-Bench++: Toward Open-Ended Benchmarking of Semantic Mapping for Manipulation with Prompt-Generated Synthetic Scenes

arXiv:2605.26831v1 Announce Type: new Abstract: Semantic mapping methods are increasingly used as intermediate scene representations for downstream robotic reasoning and manipulation, yet their evalua

Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization

arXiv:2602.00827v2 Announce Type: replace Abstract: Feature learning strength (FLS), i.e., the inverse of the effective output scaling of a model, plays a critical role in shaping the optimization dyn

safetyarxiv-cs-lg

Pair-In, Pair-Out: Latent Multi-Token Prediction for Efficient LLMs

arXiv:2605.27255v1 Announce Type: cross Abstract: Long chain-of-thought reasoning has made autoregressive decoding the dominant inference cost of modern large language models. Existing methods target