Search: “model-releases”

DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?

arXiv:2609.15122v1 Announce Type: cross Abstract: Generative CAD models are expected to remain behaviorally correct after parameter edits, so increasing the number of edit checks is often treated as a

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Designing UNICORN: a Unified Benchmark for Imaging in Computational Pathology, Radiology, and Natural Language

DGX agent

arXiv:2603.02790v2 Announce Type: replace Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data mo

model-releasesarxiv-cs-cv

15 Sept 2026

Diagnosing Faults in Reinforcement Learning Simulators and World Models with Canonical Polynomial Invariants

arXiv:2609.13194v1 Announce Type: cross Abstract: A large literature builds physical structure into learned dynamics on the premise that models respecting the underlying physics predict better. We tes

Differential Privacy of Gaussian Process Posterior Sampling

arXiv:2606.17995v2 Announce Type: replace-cross Abstract: We study the privacy of releasing functional posterior sample paths from a Gaussian process (GP) when the entire training set including covari

Diffusion-Based Multiple-Shooting Indirect Optimal Control for Fuel-Optimal Spacecraft Trajectory Generation

arXiv:2609.13990v1 Announce Type: cross Abstract: Diffusion-based generative models (DMs) have found applications in control problems, and in particular robotics, where the DMs enable exploration of p

DiffuTester: Accelerating Unit Test Generation for Diffusion LLMs via Mining Structural Pattern

arXiv:2509.24975v4 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) enable parallel generation and are promising for unit test generation (UTG), where efficient and large

Disentangling Topology and Diversity in Multi-Agent LLMs for Multilingual Low-Resource Emotion Detection

arXiv:2609.14570v1 Announce Type: cross Abstract: Multi-agent LLM systems combine multiple inference calls, but prior work often confounds how calls are connected with how they are diversified. We stu

Divide, Consult, Conquer: Capability Laundering Through Aligned LLMs

arXiv:2609.15383v1 Announce Type: cross Abstract: Language model safety is typically evaluated one interaction at a time. We show that a weaker, unaligned model can split a harmful task into benign-lo

DNF-SR: Dual-Input and Negative-Aware Feature Fine-Tuning for Real-World Image Super-Resolution

arXiv:2609.15120v1 Announce Type: new Abstract: Benefiting from the powerful generative priors of diffusion models, diffusion-based real-world image super-resolution (Real-ISR) methods have demonstrat

Do Tabular Foundation Models Still Need Feature Engineering?

arXiv:2609.13202v1 Announce Type: new Abstract: Feature engineering has long been a cornerstone of tabular machine learning. Tabular foundation models (TFMs) are pretrained on a wide range of tabular

Does Attention-Guided Masking Really Help Object Discovery in Object-Centric Learning?

arXiv:2609.15187v1 Announce Type: new Abstract: Object-Centric Learning (OCL) aims to decompose images into objects without human annotations. A major family of mainstream methods uses Slot Attention

Does Reasoning Improve Psychological Depth in Large Language Models? It Depends on Who's Judging

arXiv:2609.13773v1 Announce Type: cross Abstract: LLM-as-a-Judge evaluators are increasingly used to score open-ended generation, yet a judge's correlation with human ratings on its development set ma

Domain Generalization for Smartphone-Based Human Activity Recognition: A Systematic Analysis of Components and Interactions

arXiv:2609.14863v1 Announce Type: new Abstract: Smartphone-based Human Activity Recognition (HAR) models often degrade under distribution shifts caused by changes in users, devices, sensor placements,

Domain-Specific Jargon in Large Language Models: A Comparative Analysis between General-Purpose and Specialist Models

arXiv:2609.13556v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable proficiency on general-purpose tasks, yet their performance often degrades in highly-specialized te

Donors and Recipients: On Asymmetric Transfer Across Tasks and Languages with Parameter-Efficient Fine-Tuning

arXiv:2511.13368v3 Announce Type: replace-cross Abstract: Large language models (LLMs) perform strongly across tasks and languages, yet how improvements in one task or language affect other tasks and

Draining Fictitious Knots: Restoring Distance-Awareness Guarantees for High-Dimensional Spline Networks

arXiv:2609.15274v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) with spline activations have recently shown promise for interpretable function approximation. Distance-Aware Error for

Dreaming in Code for Curriculum Learning in Open-Ended Worlds

arXiv:2602.08194v2 Announce Type: replace-cross Abstract: Open-ended learning frames intelligence as emerging from continual interaction with an ever-expanding space of environments. While recent adva

DreamSat-Bench: Development and Initial Testing of a Testbed for AI-Based Pose Estimation from 3D Reconstruction

model-releasesarxiv-cs-ro

arXiv:2609.14183v1 Announce Type: new Abstract: This paper presents the development and initial testing of DreamSat-Bench, a modular rendezvous and proximity operation testbed designed to benchmark AI

DynEoMT: Learning Object Dynamicity from Online Segmentation Queries

arXiv:2609.14466v1 Announce Type: new Abstract: Video segmentation models recognize and track objects over time, but they do not indicate whether each segmented region moves independently of the obser

E2A-Bench: Benchmarking Evidence-to-Action Reliability in Financial Chart Reasoning

arXiv:2609.14302v1 Announce Type: new Abstract: Can financial vision-language models (VLMs) turn chart evidence into reliable action recommendations? Existing hallucination evaluations are mostly clai

Editorial routing shapes how computational results are qualified in AI-assisted scientific writing

arXiv:2609.14288v1 Announce Type: new Abstract: Large language models increasingly analyze computational results and draft manuscripts, making reliable communication as important as correct analysis.

EEG-Xplain: Decoding Neural Black-Boxes of EEG Foundation Models

arXiv:2609.15687v1 Announce Type: new Abstract: EEG foundation models such as BIOT, LaBraM, and EEGMamba have achieved remarkable performance in neural signal decoding, but their black-box nature limi

Efficiency Hallucination: Formalizing and Measuring Behavioral Calibration in LLM-Based Code Optimization

arXiv:2609.14839v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) into automated code optimization introduces a critical reliability risk we term the Efficiency Halluci

Efficient On-Device Agents via Adaptive Context Management

arXiv:2511.03728v2 Announce Type: replace Abstract: On-device AI agents offer the potential for personalized, low-latency assistance, but their deployment is fundamentally constrained by limited memor

Eigenvalue-Decomposition Cost Denoising as an Alternative to Predict-then-Optimize for Shortest-Path Problems

arXiv:2609.15111v1 Announce Type: cross Abstract: Predict-then-optimize methods such as Smart 'Predict, then Optimize' (SPO+) of Elmachtoub and Grigas (2022) learn a mapping from contextual features t

EMCStereo: Attention-Enhanced Stereo Matching for Thin-Structure Depth Estimation with a Synthetic Tree-Branch Benchmark

arXiv:2609.13233v1 Announce Type: new Abstract: Thin structures such as tree branches are among the hardest cases for stereo matching: a branch is only a few pixels wide, the background is cluttered,

Empirical Evaluation of Open-Source Large Language Models for Retrieval-Augmented Generation in ESG Domain

arXiv:2609.15242v1 Announce Type: new Abstract: Environmental, Social, and Governance (ESG) reporting is critical for corporate accountability, with Large Language Models (LLMs) and Retrieval-Augmente

Empirical Evaluation of Task-Based Permission Scoping Architecture for AI Agents

arXiv:2609.15422v1 Announce Type: new Abstract: AI agents are provisioned the same as employee-owned hosts in many enterprise settings with a static credential set fixed at deployment which includes a

End-to-End Cell Detection via Instance-aware Graph Modeling

arXiv:2609.15354v1 Announce Type: cross Abstract: Accurate cell detection and classification are crucial for pathological analysis, directly affecting diagnostic accuracy and treatment planning. To ca

Enemray: Toward Capable Language Models for Hassaniya

arXiv:2609.14829v1 Announce Type: cross Abstract: We introduce Enemray, a Hassaniya-centric language model that enables general-purpose interaction in Hassaniya. Enemray is trained around a stability-

Enhancing Large Language Model-Based Systems for End-to-End Circuit Analysis Problem Solving

arXiv:2512.10159v3 Announce Type: replace-cross Abstract: LLMs have shown strong performance in data-rich domains such as programming, but their reliability in engineering tasks remains limited. Circu

ESG: Generating Physically Consistent Dynamic 3D Scenes from Text Descriptions

arXiv:2609.15392v1 Announce Type: cross Abstract: Recent progress in image and 3D scene generation has enabled increasingly realistic static environments, yet most methods remain confined to such stat

Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks

arXiv:2609.12839v2 Announce Type: replace-cross Abstract: The proliferation of highly capable open-weight Small Language Models (SLMs) democratizes access to advanced cybersecurity capabilities, posin

Evaluating Contextual Bias in CNN Image Classification: Evidence from Agricultural Benchmark Datasets

arXiv:2609.14654v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) are typically evaluated using held-out classification accuracy, an approach that presupposes predictions are based

Evaluating LLM-Generated Rules for Heart Disease Prediction

arXiv:2609.13192v1 Announce Type: new Abstract: This study compares traditional machine learning models and Large Language Model (LLM)-generated rule-based systems for heart disease prediction using t

Evaluation of optimisation and Bayesian inference methods for reaction rates in atmospheric chemical mechanisms

arXiv:2609.14569v1 Announce Type: cross Abstract: Constraining reaction rate coefficients is a central challenge in the development of explicit atmospheric chemical mechanisms, particularly for autoxi

Exact Finite Attention Responses From RoPE Derivatives

arXiv:2609.14127v1 Announce Type: cross Abstract: We derive exact local responses for attention interventions, allowing candidate edits to be scored from a cached baseline and one backward pass. The s

Exploring Automated Vulnerability Identification in JavaScript Code Using Large Language Models

arXiv:2609.13816v1 Announce Type: cross Abstract: JavaScript powers approximately 98.8% of all websites, making vulnerabilities in its code a significant security risk, yet existing detection approach

Exploring new directions in enhancing the ACTS parameter optimization suite

arXiv:2608.14714v1 Announce Type: cross Abstract: Track seeding strongly affects both the quality and computational cost of charged-particle reconstruction, yet its many configuration parameters are c

Fabrication After Tool Failure: Tool-Augmented Agents Assert Values Their Tools Did Not Return

arXiv:2609.14758v1 Announce Type: cross Abstract: Tool-augmented language models are evaluated on whether they reach the right answer, not on whether they report honestly when a tool fails to supply o

FaithfulBench: Does AI Counsel Uphold or Undermine the User's Professed Faith?

arXiv:2609.13634v1 Announce Type: cross Abstract: Do AI assistants help believers reason about moral dilemmas consistently with their faith? We present FaithfulBench, the first benchmark to score AI c

FedLTLib: A Comprehensive Benchmark for Federated Long-Tail Learning

arXiv:2609.15625v1 Announce Type: cross Abstract: Driven by the escalating demand for privacy-preserving computing, Federated Learning (FL) has witnessed remarkable progress, becoming a cornerstone te

FedV-KGQA in Practice: Design Lessons and an Interactive Prototype

arXiv:2609.13661v1 Announce Type: new Abstract: Knowledge graph question answering usually assumes that one system can reach the whole graph. In practice, facts are often held by organizations that sh

FFVO: A Feedforward Pose Decoder for Long-Horizon Visual Odometry

arXiv:2609.13733v1 Announce Type: new Abstract: Stable and reliable 4D spatial understanding is fundamental for autonomous driving systems. While feedforward reconstruction networks can estimate camer

Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations

arXiv:2609.13920v1 Announce Type: cross Abstract: Persistent Gaussian perturbations have been shown to prevent asymptotic oversmoothing in recurrent Graph Neural Networks (GNNs) by ensuring a positive

FLoKD: Adaptive Knowledge Distillation for Federated Low-Rank LLM over Wireless Networks

arXiv:2609.13580v1 Announce Type: new Abstract: Large language models (LLMs) have demonstrated strong capabilities across a wide range of natural language processing tasks. However, conventional fine-

Floquet Fibre Geometry and Higher-Order Reduced Coordinates for Off-Manifold Transients near Nonlinear Aeroelastic Flutter

arXiv:2609.14674v1 Announce Type: new Abstract: Assigning reduced coordinates to states near an attracting limit cycle requires the correct invariant-fibre geometry. The classical first-order phase-is

FlowTSFM: Turning Encoder Depth into Quantile Transport

arXiv:2609.13640v1 Announce Type: new Abstract: Encoder-based time series foundation models (TSFMs) typically rely on deep stacks of independently parameterized Transformer layers, where only the fina