Search: “model-releases”

Jury Duty: Calibration and Orientation Failures in MLLM-as-a-Judge Under Cultural Ambiguity

arXiv:2606.20676v1 Announce Type: new Abstract: MLLM-as-a-Judge is conventionally validated by agreement with human annotations, but this metric is undefined when the human pool is culturally heteroge

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Model Releases

Kamera: Unified Position-Invariant Multimodal KV Cache for Training-Free Reuse

DGX agent

arXiv:2606.23581v1 Announce Type: cross Abstract: Multimodal agents repeatedly re-examine the same video frames, UI screenshots, and rendered artifacts as their context window slides and reasoning ite

model-releasesarxiv-cs-cv

23 Jun 2026

L20-Edu-135M: An Auditable Single-GPU Study of Data-Efficient Small Language Modeling

arXiv:2606.22189v1 Announce Type: new Abstract: Small language models are cheap to serve and feasible on local hardware, but strong public 135M-class systems are commonly trained with hundreds of bill

Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset

arXiv:2606.22382v1 Announce Type: cross Abstract: Purpose: To evaluate whether large language model (LLM)-assisted label cleaning can identify label-report discordance in CT-RATE, a large-scale public

LAYUP: Asynchronous decentralized gradient descent with LAYer-wise UPdates

arXiv:2410.05985v4 Announce Type: replace Abstract: The increasing size of deep learning models has made distributed training across multiple devices essential. Synchronous, centralized methods incur

Learning a Normal World Model for Few-Shot Boundary-Calibrated Abnormality Detection

arXiv:2606.22261v1 Announce Type: new Abstract: Abnormality detection in complex systems faces two practical barriers: abnormal labels are scarce, and binary labels do not quantify how far an event ha

Learning-Augmented Algorithms for Online Vertex Cover

arXiv:2606.22831v1 Announce Type: cross Abstract: This paper studies learning-augmented online weighted vertex cover with advice and a parameter lambda in (0,1). We consider two graph cases: bipartite

Learning Bug Context for PyTorch-to-JAX Translation with LLMs

arXiv:2510.09898v2 Announce Type: replace Abstract: Large language models (LLMs) have shown strong performance on code translation between widely used programming languages. However, translation becom

Learning by Shifting: Temporal View Construction for Time Series Contrastive Learning

arXiv:2606.21957v1 Announce Type: new Abstract: Supervised learning demands large quantities of labeled data, a bottleneck that is expensive and reliant on domain-specific expertise. Self-supervised l

Leveraging LaBSE with Progressive Curriculum Learning for Multicultural Polarization

arXiv:2606.21718v1 Announce Type: cross Abstract: Detecting online polarization remains a critical challenge, particularly in multilingual and multicultural contexts where intergroup hostility is prev

LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2606.23686v1 Announce Type: new Abstract: Despite the impressive manipulation capabilities of Vision-Language-Action (VLA) models, their operational safety under strict constraints remains large

LLM-Based Generalizable Hierarchical Task Planning and Execution for Heterogeneous Robot Teams with Event-Driven Replanning

model-releasesarxiv-cs-ro

arXiv:2511.22354v2 Announce Type: replace Abstract: This paper introduces CoMuRoS (Collaborative Multi-Robot System), a generalizable hierarchical architecture for heterogeneous robot teams that unifi

Load Testing for Machine Learning Model Serving Systems at Scale

arXiv:2606.22013v1 Announce Type: new Abstract: Machine learning (ML) model serving has become a dominant consumer of GPU infrastructure, yet capacity planning in these systems remains largely ad hoc.

Localizing and Editing Knowledge in Large Audio-Language Models

arXiv:2603.14343v2 Announce Type: replace Abstract: Large Audio-Language Models (LALMs) have shown strong performance in speech understanding, making speech a natural interface for accessing factual i

LoCC: Detection and Localization of Lip-Syncing Deepfakes via Counterfactual Frame Consistency

arXiv:2606.22772v1 Announce Type: new Abstract: Lip-syncing deepfakes are among the most challenging forms of manipulated media because their artifacts are localized almost exclusively to the mouth re

LOGOS: LiDAR-Only Gaussian Elevation Splatting for Unified Tiny Obstacle Segmentation

arXiv:2606.21527v1 Announce Type: cross Abstract: Robust obstacle segmentation is essential for the safety of intelligent robots, where LiDAR-based perception systems play a fundamental role in the ro

Low-variance estimators overcome the phase-gradient bottleneck in complex-valued neural quantum states

arXiv:2606.13912v2 Announce Type: replace-cross Abstract: Complex neural quantum states are difficult to optimize when their wavefunction phase carries gauge, chiral, fermionic, or topological structu

LUMINA-26: Low-Light Understanding for Modeling and Interpreting Night-time Actions

arXiv:2606.23118v1 Announce Type: new Abstract: Low-light human action recognition remains a challenging problem due to poor illumination, amplified noise, motion ambiguity, and diverse real-world sce

LUQ: Layerwise Ultra-Low Bit Quantization for Multimodal Large Language Models

arXiv:2509.23729v3 Announce Type: replace Abstract: Large Language Models (LLMs) with multimodal capabilities have revolutionized vision-language tasks, but their deployment often requires huge memory

MammoExpert: Benchmarking Chain-of-Thought Reasoning in Mammography Diagnosis

arXiv:2606.21119v1 Announce Type: new Abstract: Mammography is an essential tool for breast cancer detection, with millions of examinations conducted annually. However, publicly available high-quality

MapReason-OSM: Can Vision-Language Models Make Graph-Verifiable Mobility Decisions from Street Maps ?

arXiv:2606.22597v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to read maps for logistics, delivery, and accessible navigation, where the output is an actionable d

Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials

arXiv:2606.21830v1 Announce Type: new Abstract: Reinforcement learning from verifiable rewards (RLVR) has driven rapid progress in mathematical and code reasoning, but when extended to science, existi

Measuring Intent Comprehension in LLMs

arXiv:2506.16584v3 Announce Type: replace-cross Abstract: People judge interactions with large language models (LLMs) as successful when outputs match what they want, not what they type. Yet LLMs are

MEDLAYXPLAIN: Benchmarking the Expert-Lay Gap in Medical Vision-Language Models

arXiv:2606.21194v1 Announce Type: new Abstract: Medical Vision-Language Models (Med-VLMs) achieve strong expert-level performance, yet their ability to generate patient-accessible descriptions remains

MedTS-TTT: Test-Time Training for Medical Time Series Classification

arXiv:2606.21329v1 Announce Type: new Abstract: Medical time series (MedTS) signals such as electroencephalography (EEG) and electrocardiography (ECG) support many clinical applications. However, subs

Mesh2GS: White-Box 3DGS Construction via Plenoptic Sampling

arXiv:2606.21898v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) has emerged as a promising method for high-quality, real-time 3D reconstruction. To associate 3DGS with mesh representati

Mirage: a Clean-Label Backdoor against LiDAR 3D Object Detection

arXiv:2606.20752v1 Announce Type: new Abstract: Deep neural network-based LiDAR 3D object detection serves as a critical perception component in safety-critical autonomous systems. However, recent stu

Mitigating Measurement-Induced Training Instability in Hybrid Quantum Neural Networks for Protein Classification

arXiv:2606.22551v1 Announce Type: cross Abstract: Hybrid Quantum Neural Network (QNN) classifiers produce logits as expectation values of quantum measurement operators. For standard Pauli measurements

MMGist: A Comprehensive Multimodal Benchmark for 2027

arXiv:2606.22437v1 Announce Type: new Abstract: We conduct a systematic study of 18 widely used vision-language benchmarks and identify three major issues: 1) many items do not rely on visual cues and

MMOU: A Massive Multi-Task Omni Understanding and Reasoning Benchmark for Long and Complex Real-World Videos

arXiv:2603.14145v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have shown strong performance in visual and audio understanding when evaluated in isolation. However,

Model Merging in the Essential Subspace

arXiv:2602.20208v2 Announce Type: replace Abstract: Model merging aims to integrate multiple task-specific fine-tuned models derived from a shared pre-trained checkpoint into a single multi-task model

MoECodec: Image Compression for joint human and machine perception via Mixture-of-Experts

arXiv:2606.21033v1 Announce Type: cross Abstract: Image compression for machines calls for a unified codec that serves multiple downstream vision tasks. Existing approaches either adopt task-specific

MOOZY: A Patient-First Foundation Model for Computational Pathology

arXiv:2603.27048v3 Announce Type: replace Abstract: Computational pathology needs whole-slide image (WSI) foundation models that transfer across diverse clinical tasks, yet current approaches remain l

MORL-A2C: Multi-Objective Reinforcement Learning Reranker for Optimizing Healthiness in MOPI-HFRS

arXiv:2606.23603v1 Announce Type: new Abstract: Unhealthy dietary behavior continues to be a persistent public health issue in the United States, exacerbated by recommendation systems that prioritize

Morphology-Aware Multimodal Representation Learning for Insect Phylogenetic Reconstruction

arXiv:2606.22077v1 Announce Type: new Abstract: Morphological traits provide important evidence for phylogenetic reconstruction and evolutionary relationship analysis. Recent image-based approaches ha

MotionHalluc: Diagnosing Kinematic Hallucinations in Fine-Grained Motion Reasoning

arXiv:2606.23061v1 Announce Type: new Abstract: Motion instruction generation in cross-video comparison aims to produce corrective feedback that describes the differences between a query and a referen

Multigrid Training for Molecular Generation using Graph Neural Networks

arXiv:2606.22377v1 Announce Type: new Abstract: Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D gri

Muown Implicitly Performs Angular Step-size Decay

arXiv:2606.23637v1 Announce Type: new Abstract: Matrix-aware optimizers such as Muon and Muown have recently shown strong empirical performance for pre-training Transformers. In particular, Muown sepa

Neural Parameter Calibration for Finite-State Mean Field Games

arXiv:2606.23155v1 Announce Type: cross Abstract: Mean field games efficiently approximate a very large population of strategic agents. While these games can aid the understanding of complex systems,

Next-Gen CAPTCHAs: Leveraging the Cognitive Gap for Scalable and Diverse GUI-Agent Defense

arXiv:2602.09012v2 Announce Type: replace Abstract: The rapid evolution of GUI-enabled agents has rendered traditional CAPTCHAs obsolete. While previous benchmarks like OpenCaptchaWorld established a

NNiT: Width-Agnostic Neural Network Generation with Structurally Aligned Weight Spaces

arXiv:2603.00180v2 Announce Type: replace Abstract: Generative modeling of neural network parameters is often tied to architectures because standard parameter representations rely on known weight-matr

Non-asymptotic estimates of the minimal risk in statistical learning

arXiv:2606.23295v1 Announce Type: new Abstract: In this paper we prove some concentration inequalities for two types of error probabilities in the Empirical Risk Principle (ERP) in statistical learnin

Nous: A Predictive World Model for Long-Term Agent Memory

arXiv:2606.22030v1 Announce Type: cross Abstract: We present Nous, a novel agent memory architecture grounded in the principle that knowledge is prediction, not storage. Rather than persisting facts a

OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

arXiv:2602.00012v3 Announce Type: replace Abstract: We present OGD4All, a transparent, auditable, and reproducible framework based on Large Language Models (LLMs) to enhance citizens' interaction with

Open Annotations and Synthetic Data for Field Localisation in Indian Bank Cheques

arXiv:2606.20682v1 Announce Type: new Abstract: Automated cheque processing requires localising key fields (date, legal amount, IFSC code, account number, signature, and payee name) before any recogni

Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?

arXiv:2606.23676v1 Announce Type: new Abstract: AdamW is the de facto optimizer for training large language models (LLMs), yet the theory behind it still lives mostly in finite-variance regimes. This

Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models using Reinforcement Learning from Ranking Feedback

arXiv:2510.02561v2 Announce Type: replace Abstract: Recent advances in large video-language models (VLMs) rely on extensive fine-tuning techniques that strengthen alignment between textual and visual

ORBIT: Training-Free Multi-Attribute Behavioral Steering via Orthogonal Subspace Rotation

arXiv:2606.22357v1 Announce Type: cross Abstract: Language models are widely used in assistant settings, where controlling behavioral attributes is often essential. Activation steering modifies hidden