Search: “model-releases”

Multimodal Action Diffusion for Robust End-to-End Autonomous Driving

arXiv:2606.02105v1 Announce Type: new Abstract: End-to-End Autonomous Driving (E2E-AD) systems have largely converged on predicting intermediate trajectory waypoints, delegating final control to hand-

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Model Releases

Multimodal Approaches for Visually-Rich Document Type Classification: A Comparative Analysis

DGX agent

arXiv:2606.02162v1 Announce Type: cross Abstract: Document type classification in visually rich documents remains challenging, as relevant information is distributed across textual, visual, and layout

model-releasesarxiv-cs-ai

2 Jun 2026

Multimodal Music Recommendation System using LLMs

arXiv:2606.00125v1 Announce Type: cross Abstract: Music recommendation systems typically treat songs as opaque tokens, relying on collaborative interaction histories which overlooks semantic or acoust

naPINN: Noise-Adaptive Physics-Informed Neural Networks for Recovering Physics from Corrupted Measurement

arXiv:2602.02547v2 Announce Type: replace-cross Abstract: Physics-Informed Neural Networks (PINNs) are effective methods for solving inverse problems and discovering governing equations from observati

Navigating the Reality Gap: On-Device Continual Adaptation of ASR for Clinical Telephony

arXiv:2512.16401v5 Announce Type: replace Abstract: Automatic Speech Recognition (ASR) can significantly reduce documentation burden in clinical workflows, but standard models degrade sharply in real-

Neural Network Compression by Approximate Differential Equivalence

arXiv:2606.01402v1 Announce Type: cross Abstract: Neural network compression is commonly achieved by pruning parameters based on local importance scores, e.g., magnitude-based pruning. We propose a co

NILC: Discovering New Intents with LLM-assisted Clustering

arXiv:2511.05913v2 Announce Type: replace-cross Abstract: New intent discovery (NID) seeks to recognize both new and known intents from unlabeled user utterances, which finds prevalent use in practica

Non-Learning Low-Light Stereo Vision

arXiv:2606.00379v1 Announce Type: new Abstract: We present a non-learning stereo framework for disparity estimation from severely noisy images. Using the Field of Junctions (FoJ), it retains coarse vi

NOS-Gate: Queue-Aware Streaming IDS for Consumer Gateways under Timing-Controlled Evasion

arXiv:2601.00389v2 Announce Type: replace-cross Abstract: Timing and burst patterns can leak through encryption, and an adaptive adversary can exploit them. This undermines metadata-only detection in

OARelatedWork: A Large-Scale Dataset of Related Work Sections with Full-texts from Open Access Sources

arXiv:2405.01930v2 Announce Type: replace Abstract: This paper introduces OARelatedWork: a dataset for related work generation from open-access sources. It is the first large-scale multi-document summ

Off-the-Shelf LLMs as Process Scorers: Training-Free Alternative to PRMs for Mathematical Reasoning

arXiv:2606.01682v1 Announce Type: cross Abstract: Selecting the best response from multiple small-model samples using a stronger scorer is a simple inference-time strategy, but fails when the small mo

OmniEEG-Bench: A Standardized Evaluation Benchmark for EEG Foundation Models

arXiv:2606.00815v1 Announce Type: new Abstract: Electroencephalography (EEG) supports a variety of brain-computer interface (BCI) tasks ranging from brain-state monitoring to human-LLM interactions. E

OmniOPD: Logit-Free On-Policy Distillation via Speculative Verification

arXiv:2606.01476v1 Announce Type: cross Abstract: On-Policy Distillation (OPD) trains a student model on its own generative trajectories under dense token-level feedback from a stronger teacher, mitig

On the Evaluation of Spiking Neural Network Configurations for Network Intrusion Detection

arXiv:2606.01442v1 Announce Type: cross Abstract: Network intrusion detection is a core component of modern cybersecurity infrastructure, yet the deep learning models that dominate the field are compu

On the Generalization Gap in Self-Evolving Language Model Reasoning

arXiv:2606.01075v1 Announce Type: new Abstract: Recent work suggests that large language models (LLMs) can improve through self-evolution (SE), using supervision signals generated by the model itself.

On the Generalization in Topology Optimization via Sensitivity-Conditioned Bernoulli Flow Matching

arXiv:2606.02179v1 Announce Type: cross Abstract: Surrogate models for topology optimization (TO) exhibit highly variable out-of-distribution (OOD) generalization under distribution shifts such as cha

On the Limits of Token Reduction for Efficient Unified Vision Language Training

arXiv:2606.01503v1 Announce Type: cross Abstract: Unified vision-language models (VLMs) integrate visual understanding and visual generation within a single autoregressive backbone, but their joint tr

On the Scaling of PEFT: Towards Million Personal Models of Trillion Parameters

arXiv:2606.02437v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) is usually treated as a cheaper alternative to full fine-tuning. We study a broader role: small trainable adapt

On the Theoretical Limitations of Embedding-based Link Prediction

arXiv:2506.22271v3 Announce Type: replace Abstract: Neural networks often map low-dimensional embeddings to high-dimensional output spaces. Usually, the output layer is linear, which can create a 'ran

On Wednesdays, We Ask Questions: Optimizing 'Active Listening' in Automated Legal Triage and Referral

arXiv:2606.00272v1 Announce Type: new Abstract: The FETCH classifier generates follow-up questions to help refine the best match for the applicant's legal problem, using a low-cost ensemble of LLMs. I

OncoReason: Structuring Clinical Reasoning in LLMs for Robust and Interpretable Survival Prediction

arXiv:2510.17532v2 Announce Type: replace Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinica

OneVLA: A Unified Framework for Embodied Tasks

model-releasesarxiv-cs-ro

arXiv:2606.01241v1 Announce Type: new Abstract: Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact p

Ontology-Guided Reasoning for Affordance-Based Explanations of Robot Navigation

model-releasesarxiv-cs-ro

arXiv:2606.00117v1 Announce Type: new Abstract: This paper proposes ontology-guided reasoning for affordance-based explanations of robot navigation. In human environments, it is not sufficient for a r

OpenDPR: Open-Vocabulary Change Detection via Vision-Centric Diffusion-Guided Prototype Retrieval for Remote Sensing Imagery

arXiv:2603.27645v2 Announce Type: replace Abstract: Open-vocabulary change detection (OVCD) seeks to recognize arbitrary changes of interest by enabling generalization beyond a fixed set of predefined

OpenWebRL: Demystifying Online Multi-turn Reinforcement Learning for Visual Web Agents

arXiv:2606.02031v1 Announce Type: cross Abstract: Building capable visual web agents requires long-horizon reasoning, precise grounding, and robust interaction with dynamic real-world websites. Despit

Optimal Regularization for Performative Learning

arXiv:2510.12249v2 Announce Type: replace Abstract: In performative learning, the data distribution reacts to the deployed model - for example, because strategic users adapt their features to game it

Order within Chaos: Capturing Intrinsic Energy Anomalies for AI-Manipulated Image Forgery Localization

arXiv:2606.02178v1 Announce Type: cross Abstract: Recent advancements in generative AI have led to image editing models capable of producing realistic forgeries that evade traditional image forgery lo

PaintBench: Deterministic Evaluation of Precise Visual Editing

arXiv:2606.00188v1 Announce Type: cross Abstract: While current multimodal models are proficient at open-ended visual editing, executing precise single-answer edits remains an important obstacle. To p

PaperVoyager : Building Interactive Web with Visual Language Models

arXiv:2603.22999v3 Announce Type: replace Abstract: Recent advances in visual language models have enabled autonomous agents for complex reasoning, tool use, and document understanding. However, exist

Parameter Alignment Mitigates Catastrophic Forgetting in Multilingual Expert Language Models

arXiv:2606.00284v1 Announce Type: new Abstract: While continual pretraining~(CPT) is a practical way to extend large language models to new languages, naive finetuning on targeted data erodes existing

Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification

arXiv:2606.02341v1 Announce Type: cross Abstract: Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment.

Parameter-Efficient Fine-Tuning of Large Pretrained Models for Instance Segmentation Tasks

arXiv:2606.01947v1 Announce Type: cross Abstract: Research and applications in artificial intelligence have recently shifted with the rise of large pretrained models, which deliver state-of-the-art re

Parameter-Free and Group Conditional Online Conformal Prediction

arXiv:2606.00419v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is critical for the deployment of machine learning predictors in real-world scenarios where the data distribution may

PaSBench-Video: A Streaming Video Benchmark for Proactive Safety Warning

arXiv:2606.02443v1 Announce Type: cross Abstract: Between the first visible sign of danger and the moment an accident occurs, there is often a window where intervention remains possible. Video-capable

Pause and Think: A Dataset and Benchmark for Video-Grounded Assistive Action Suggestion

arXiv:2606.00616v1 Announce Type: cross Abstract: Recent Vision-Language Models (VLMs) struggle with grounded reasoning, temporal consistency, and context aware planning in videos. We introduce pause-

Perception First: A Frontier Native-Video Model with Self-Consistency for Implicit Video Question Answering

arXiv:2606.01485v1 Announce Type: new Abstract: We describe our submission to the VRR Challenge @ CVPR 2026, built on the ImplicitQA / VRR-QA benchmark~ite{implicitqa}: multiple-choice video question

Permissive Safety Through Trusted Inference: Verifiable Belief-Space Neural Safety Filters for Assured Interactive Robotics

arXiv:2606.02562v1 Announce Type: cross Abstract: Autonomous robots that interact with people must make safe and efficient decisions under human-induced uncertainty, such as their preferences, goals,

Persona Attack: Incremental Memory Injection Jailbreak Attack against Large Language Models

arXiv:2606.00150v1 Announce Type: cross Abstract: As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety tr

Personalized 3D Myocardial Infarct Geometry Reconstruction from Cine MRI for Cardiac Digital Twins

arXiv:2606.01808v1 Announce Type: new Abstract: Accurate 3D geometric characterization of myocardial infarction (MI) is essential for building cardiac digital twins (CDTs) to precisely simulate infarc

Perturbative methods for non-parametric instrumental variable

arXiv:2606.00322v1 Announce Type: new Abstract: We introduce a perturbative approach for nonparametric instrumental variable (NPIV) estimation. By drawing inspiration from perturbation theory in physi

PFT: Phonon Fine-tuning for Machine Learned Interatomic Potentials

arXiv:2601.07742v4 Announce Type: replace-cross Abstract: Many materials properties depend on higher-order derivatives of the potential energy surface, yet machine learned interatomic potentials (MLIP

Physics-Encoded Inverse Modeling for Arctic Snow Depth Prediction

arXiv:2601.17074v4 Announce Type: replace-cross Abstract: Accurate estimation in time-varying inverse problems under limited and sparse observations remains a fundamental challenge across scientific d

Physics from Video: Identifiability of Time-Invariant Second-Order ODEs under Minimal Trajectory Conditions

arXiv:2606.00115v1 Announce Type: new Abstract: Bridging the gap between visual realism and physical understanding is a core challenge for video-based world models. We study the structural identifiabi

Physics-Guided Attention in a Lightweight TCN for Efficient WiFi CSI-Based Human Activity Recognition

arXiv:2606.01834v1 Announce Type: cross Abstract: Human Action Recognition (HAR) using WiFi Channel State Information (CSI) has gained increasing attention due to its non-contact, low-cost, and privac

Plan-R1: Safe and Feasible Trajectory Planning as Language Modeling

arXiv:2505.17659v4 Announce Type: replace-cross Abstract: Safe and feasible trajectory planning is critical for real-world autonomous driving systems. However, existing learning-based planners rely he

POIROT: Interrogating Agents for Failure Detection in Multi-Agent Systems

arXiv:2606.02282v1 Announce Type: new Abstract: Orchestrating Large Language Models into Multi-Agent Systems (LLM-MAS) has unlocked remarkable reasoning capabilities, yet emergent failures and halluci

PolySpeech-100: A Large-Scale Benchmark for Speech Understanding Across 100+ Languages and Dialects

arXiv:2606.01016v1 Announce Type: cross Abstract: While End-to-End (E2E) Speech-Large Language Models (Speech-LLMs) are rapidly evolving, their evaluation methodologies remain limited to the era of si

Position Paper: Post-Solve Robustness in Decision Engines: Feasible Regions and Smoothness Under Perturbations

arXiv:2606.00002v1 Announce Type: new Abstract: Mixed-Integer Linear Programming (MILP) decision engines routinely output nominally optimal plans for high-stakes industrial systems. Yet deployment rar