Search: “inference”

Improving Temporal Action Segmentation via Constraint-Aware Decoding

arXiv:2605.10149v1 Announce Type: new Abstract: Temporal action segmentation (TAS) divides untrimmed videos into labeled action segments. While fully supervised methods have advanced the field, challe

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Information Filtering via Variational Regularization for Robot Manipulation

DGX agent

arXiv:2601.21926v4 Announce Type: replace Abstract: Diffusion-based visuomotor policies built on 3D visual representations have achieved strong performance in learning complex robotic skills. However,

applicationsarxiv-cs-ro

12 May 2026

Interactive Inverse Reinforcement Learning of Interaction Scenarios via Bi-level Optimization

arXiv:2605.08131v1 Announce Type: new Abstract: Inverse reinforcement learning (IRL) learns a reward function and a corresponding policy that best fit the demonstration data of an expert. However, in

safetyarxiv-cs-lg

Inverse Design for Conditional Distribution Matching

arXiv:2605.09439v1 Announce Type: new Abstract: Generative models are powerful tools for sampling from a learned distribution P(Y mid X), and inverse-design methods invert this map to find an input x

Local Ai

Kintsugi: Learning Policies by Repairing Executable Knowledge Bases

arXiv:2605.09487v1 Announce Type: new Abstract: Modern embodied agents achieve impressive performance, but their task knowledge is often stored in neural weights, latent state, or prompt-bound memory,

local-aiarxiv-cs-lg

Language-Conditioned Visual Grounding with CLIP Multilingual

arXiv:2605.09060v1 Announce Type: new Abstract: Multilingual vision-language models exhibit systematic performance gaps across languages, but the mechanism remains ambiguous: cross-language divergence

researcharxiv-cs-cl

Latent Geometry Beyond Search: Amortizing Planning in World Models

arXiv:2605.08732v1 Announce Type: cross Abstract: Modern vision-based world models can represent observations as compact yet expressive latent manifolds, but fast goal-oriented planning in these space

LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling

arXiv:2605.10292v1 Announce Type: cross Abstract: Time series forecasting serves as an essential tool for many real-world applications, supporting tasks such as resource optimization and decision-maki

applicationsarxiv-cs-ai

Learning stochastic multiscale models through normalizing flows

arXiv:2605.09718v1 Announce Type: cross Abstract: Many systems in physics, engineering, and biology exhibit multiscale stochastic dynamics, where low-dimensional slow variables evolve under the influe

Learning to Bid with Unknown Private Values in Budget-Constrained First-Price Auctions

arXiv:2605.09448v1 Announce Type: new Abstract: The transition to First-Price Auctions (FPA) in digital advertising has spurred significant research, yet existing work typically assumes access to a va

Learning to Explore: Scaling Agentic Reasoning via Exploration-Aware Policy Optimization

arXiv:2605.08978v1 Announce Type: new Abstract: Recent advancements in agentic test-time scaling allow models to gather environmental feedback before committing to final actions. A key limitation of e

safetyarxiv-cs-ai

Learning to Stay Safe: Adaptive Regularization Against Safety Degradation during Fine-Tuning

arXiv:2602.17546v2 Announce Type: replace Abstract: Instruction-following language models are trained to be helpful and safe, yet their safety behavior can deteriorate under benign fine-tuning and wor

safetyarxiv-cs-cl

Likelihood scoring for continuations of mathematical text: a self-supervised benchmark with tests for shortcut vulnerabilities

arXiv:2605.10810v1 Announce Type: new Abstract: We introduce an automatically generated benchmark for predicting hidden text in technical papers. A paper supplies visible context X and a hidden contin

LiteMedCoT-VL: Parameter-Efficient Adaptation for Medical Visual Question Answering

arXiv:2605.09384v1 Announce Type: cross Abstract: The reasoning gap between large and compact vision-language models (VLMs) limits the deployment of medical AI on portable clinical devices. Compact VL

LLM Jaggedness Unlocks Scientific Creativity

arXiv:2605.10574v1 Announce Type: new Abstract: As artificial intelligence advances, models are not improving uniformly. Instead, progress unfolds in a jagged fashion, with capabilities growing uneven

Locking Pretrained Weights via Deep Low-Rank Residual Distillation

arXiv:2605.10777v1 Announce Type: new Abstract: The quality of open-weight language models has dramatically improved in recent years. Sharing weights greatly facilitates model adoption by enabling the

Loom: Hybrid Retrieval-Scoring Outfit Recommendation with Semantic Material Compatibility and Occasion-Aware Embedding Priors

arXiv:2605.09830v1 Announce Type: cross Abstract: We present Loom, an outfit recommendation system that combines neural embedding retrieval with structured domain scoring to generate complete, coheren

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

arXiv:2605.09948v1 Announce Type: new Abstract: Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action p

safetyarxiv-cs-ai

Agents

MAGE: Multi-Agent Self-Evolution with Co-Evolutionary Knowledge Graphs

arXiv:2605.10064v1 Announce Type: new Abstract: Self-evolving language-model agents must decide what to learn next and how to preserve what they have learned across iterations. Existing systems typica

agentsarxiv-cs-ai

Medical Model Synthesis Architectures: A Case Study

arXiv:2605.09716v1 Announce Type: new Abstract: Medicine is rife with high-stakes uncertainty. Doctors routinely make clinical judgments and decisions that juggle many fundamental unknowns, like predi

applicationsarxiv-cs-ai

MemPrivacy: Privacy-Preserving Personalized Memory Management for Edge-Cloud Agents

model-releasesarxiv-cs-cl

arXiv:2605.09530v1 Announce Type: cross Abstract: As LLM-powered agents are increasingly deployed in edge-cloud environments, personalized memory has become a key enabler of long-term adaptation and u

Meow-Omni 1: A Multimodal Large Language Model for Feline Ethology

model-releasesarxiv-cs-cl

arXiv:2605.09152v1 Announce Type: new Abstract: Deciphering animal intent is a fundamental challenge in computational ethology, largely because of semantic aliasing, the phenomenon where identical ext

Mirror, Mirror on the Wall: Can VLM Agents Tell Who They Are at All?

arXiv:2605.08816v1 Announce Type: new Abstract: In the animal kingdom, mirror self-recognition is a canonical probe of higher-order cognition, emerging only in some species. We ask whether an analogou

Mitigating Many-shot Jailbreak Attacks with One Single Demonstration

arXiv:2605.08277v1 Announce Type: cross Abstract: Many-shot jailbreaking (MSJ) causes safety-aligned language models to answer harmful queries by preceding them with many harmful question-answer demon

safetyarxiv-cs-ai

Model-Free Neural Filtering: A Comparison with Classical Filters in Nonlinear Systems

arXiv:2601.21266v3 Announce Type: replace Abstract: Neural network models are increasingly used for state estimation in control and decision-making, yet it remains unclear to what extent they behave a

Modular Retrieval-Augmented Generalization for Human Action Recognition

arXiv:2605.08117v1 Announce Type: cross Abstract: Inertial Measurement Unit (IMU)-based Human Activity Recognition (HAR) aims to interpret and classify user behaviors from temporal motion signals. Rec

applicationsarxiv-cs-cv

MOTOR-Bench: A Real-world Dataset and Multi-agent Framework for Zero-shot Human Mental State Understanding

model-releasesarxiv-cs-cv

arXiv:2605.09703v1 Announce Type: new Abstract: Understanding human mental states from natural behavior is crucial for intelligent systems in the real world. However, most current research focuses on

Tutorials

MUR: Momentum Uncertainty guided Reasoning

arXiv:2507.14958v2 Announce Type: replace Abstract: Current models have achieved impressive performance on reasoning-intensive tasks, yet optimizing their reasoning efficiency remains an open challeng

tutorialsarxiv-cs-cl

NaiAD: Initiate Data-Driven Research for LLM Advertising

arXiv:2605.09918v1 Announce Type: cross Abstract: Reconciling platform revenue with user experience in LLM advertising motivates a data-centric foundation. We introduce NaiAD, the first comprehensive

researcharxiv-cs-ai

Agents

Nano-U: Efficient Terrain Segmentation for Tiny Robot Navigation

arXiv:2605.10210v1 Announce Type: cross Abstract: Terrain segmentation is a fundamental capability for autonomous mobile robots operating in unstructured outdoor environments. However, state-of-the-ar

agentsarxiv-cs-cv

NCO: A Versatile Plug-in for Handling Negative Constraints in Decoding

arXiv:2605.10065v1 Announce Type: cross Abstract: Controlling Large Language Models (LLMs) to prevent the generation of undesirable content, such as profanity and personally identifiable information (

researcharxiv-cs-ai

Nectar: Neural Estimation of Cached-Token Attention via Regression

arXiv:2605.09778v1 Announce Type: cross Abstract: Evaluating softmax attention over a fixed long context requires reading every cached key-value pair for each new query token. For a given context (a b

applicationsarxiv-cs-cl

Neural at ArchEHR-QA 2026: One Method Fits All: Unified Prompt Optimization for Clinical QA over EHRs

arXiv:2605.10877v1 Announce Type: new Abstract: Automated question answering (QA) over electronic health records (EHRs) demands precise evidence retrieval, faithful answer generation, and explicit gro

safetyarxiv-cs-cl

Neural Posterior Estimation of Terrain Parameters from Radar Sounder Data

arXiv:2605.08179v1 Announce Type: cross Abstract: Radar sounders are electromagnetic instruments that can probe deep into the subsurface of Earth and other planetary bodies by processing the echo of t

Noise-Started One-Step Real-World Super-Resolution via LR-Conditioned SplitMeanFlow and GAN Refinement

arXiv:2605.09328v1 Announce Type: new Abstract: Pre-trained text-to-image (T2I) diffusion models have shown strong potential for real-world image super-resolution (Real-ISR), owing to their noise-star

applicationsarxiv-cs-cv

Nonlinear GENERIC Informed Neural Networks (N-GINNs): learning GENERIC dynamics with non-quadratic dissipation potentials

arXiv:2605.09058v1 Announce Type: cross Abstract: We introduce Nonlinear GENERIC Informed Neural Networks (N-GINNs), a deep learning framework for discovering evolution equations of systems governed b

Not Blind but Silenced: Rebalancing Vision and Language via Adversarial Counter-Commonsense Equilibrium

arXiv:2605.10676v1 Announce Type: new Abstract: During MLLM decoding, attention often abnormally concentrates on irrelevant image tokens. While existing research dismisses this as invalid noise and fo

Hardware

Octopus Protocol: One-Shot Hardware Discovery and Control for AI Agents via Infrastructure-as-Prompts

arXiv:2605.09055v1 Announce Type: cross Abstract: Recent agentic-robotics systems, from Code-asPolicies to modern vision-language-action (VLA) foundation models, presuppose that drivers, SDKs, or ROS-

hardwarearxiv-cs-ai

On Observation Time for Recovering Latent Hawkes Networks

arXiv:2605.08400v1 Announce Type: cross Abstract: Dynamics of interacting systems in engineering, society, and nature often evolve over latent networks that govern which entities can interact. We stud

applicationsarxiv-cs-lg

One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory

arXiv:2505.23617v3 Announce Type: replace-cross Abstract: Effective video tokenization is critical for scaling transformer models for long videos. Current approaches tokenize videos using space-time p

researcharxiv-cs-ai

Optimal Attention Temperature Improves the Robustness of In-Context Learning under Distribution Shift in High Dimensions

arXiv:2511.01292v2 Announce Type: replace-cross Abstract: Pretrained Transformers can perform in-context learning (ICL) from a few demonstrations, but this ability can fail sharply when the test distr

Optimized Culprit Identification Using Mobilenet and Attention Mechanisms

arXiv:2605.08169v1 Announce Type: cross Abstract: Automated culprit identification in surveillance systems is a critical task that requires high accuracy along with computational efficiency for real-t

PaceVGGT: Pre-Alternating-Attention Token Pruning for Visual Geometry Transformers

arXiv:2605.08371v1 Announce Type: new Abstract: Visual Geometry Transformer (VGGT) is a strong feed-forward model for multiple 3D tasks, but its Alternating-Attention (AA) stack scales quadratically i

PAINET: A Principled Efficient Transformer for 3D Dynamics Modeling

arXiv:2510.04233v2 Announce Type: replace-cross Abstract: Modeling 3D dynamics is a fundamental problem in multi-body systems across scientific and engineering domains and has important practical impl

applicationsarxiv-cs-ai

Local Ai

Path-Dependent Denoising: A Non-Conservative Field Perspective on Order Collapse in Diffusion Language Models

arXiv:2605.09303v1 Announce Type: new Abstract: Diffusion language models (DLMs) offer a structural alternative to autoregressive generation: denoising can update tokens in arbitrary orders or in para

local-aiarxiv-cs-lg

Personal Visual Context Learning in Large Multimodal Models

model-releasesarxiv-cs-cv

arXiv:2605.10936v1 Announce Type: new Abstract: As wearable devices like smart glasses integrate Large Multimodal Models (LMMs) into the continuous first-person visual streams of individual users, the

Phoenix-VL 1.5 Medium Technical Report

arXiv:2605.10391v1 Announce Type: cross Abstract: We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Sing

PhysEDA: Physics-Aware Learning Framework for Efficient EDA With Manhattan Distance Decay

arXiv:2605.10547v1 Announce Type: new Abstract: Electronic design automation (EDA) addresses placement, routing, timing analysis, and power-integrity verification for integrated circuits. Learning met

safetyarxiv-cs-lg