Search: “models”

When Do Models Admit They Are Wrong? Failure Disclosure Is Unstable Under Reinforcement Learning

arXiv:2609.33220v1 Announce Type: new Abstract: Outcome-based reinforcement learning can produce models with similar task performance but very different ways of communicating about their mistakes. We

safetyarxiv-cs-lg

29 Sept 2026

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When Does Domain Adaptation Help on Physical Vibration Sensors? A Held-Out-Bearing Study of Neural-Operator and Convolutional Models

DGX agent

arXiv:2609.31639v1 Announce Type: new Abstract: Diagnosing rolling-element bearing faults from vibration is a canonical physical-sensing task and a widely used benchmark for domain adaptation under op

model-releasesarxiv-cs-lg

29 Sept 2026

ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Models

arXiv:2609.34457v1 Announce Type: new Abstract: Transformer-based models are widely used for reasoning, coding, and multimodal agentic tasks. To provide formal assurance of desirable behaviors, such a

safetyarxiv-cs-lg

29 Sept 2026

Achieving Tokenizer Flexibility in Language Models through Heuristic Adaptation and Supertoken Learning

arXiv:2505.09738v2 Announce Type: replace-cross Abstract: Pretrained language models (LLMs) are often constrained by their fixed tokenization schemes, leading to inefficiencies and performance limitat

researcharxiv-cs-ai

Exploring the Benefits of Vision Foundation Models for Unsupervised Domain Adaptation

arXiv:2406.09896v3 Announce Type: replace Abstract: Achieving robust generalization across diverse data domains remains a significant challenge in computer vision. This challenge is important in safet

safetyarxiv-cs-cv

FLAME: Flow Enhanced Legendre Memory Models for General Time Series Forecasting

arXiv:2512.14253v4 Announce Type: replace Abstract: In this work, we introduce FLAME, a family of extremely lightweight and capable Time Series Foundation Models, which support versatile forecasting t

safetyarxiv-cs-lg

Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization

arXiv:2609.31383v1 Announce Type: cross Abstract: End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on

DAWN: Noise-Robust Quadruped Parkour via Depth-Denoising World Models

arXiv:2609.29092v1 Announce Type: cross Abstract: Vision-based legged locomotion methods assume clean depth at training time and rely on hand-tuned post-processing filters at deployment. However, filt

Foundations of Large Language Models

arXiv:2501.09223v3 Announce Type: replace-cross Abstract: This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive

HelloWorld: Towards Practical Applications of Generative Driving World Models

arXiv:2609.28931v1 Announce Type: new Abstract: Driving world models provide a promising route toward scalable counterfactual data generation and interactive simulation beyond recorded driving logs. R

Seeing Is Not Measuring: Tool-Augmented Metric Spatial Reasoning for Vision-Language Models

arXiv:2609.29073v1 Announce Type: new Abstract: Vision-Language Models (VLMs) describe scenes well but reason poorly about metric 3D structure such as absolute distances, physical sizes, or egocentric

Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models

model-releasesarxiv-cs-cl

arXiv:2609.27359v1 Announce Type: new Abstract: Vietnam's Personal Data Protection Law (Law No. 91/2025/QH15) and Decree No. 356/2025/ND-CP, effective January 1, 2026, require organizations to establi

24 Sept 2026

Damnatio Memoriae: Adversarially and Selectively Forgetting Identities in the Embedding Space of Face Recognition Models

arXiv:2609.27115v1 Announce Type: new Abstract: A face recognition model links two images of a person recorded on separate occasions when their embedding similarity exceeds an operating threshold. We

24 Sept 2026

Generalizable Robotic Insertion with World Models

arXiv:2609.28258v1 Announce Type: cross Abstract: Robotic assembly in high-mixture settings requires adaptable systems that can handle diverse parts, yet current approaches typically rely on policies

24 Sept 2026

ChemVTS-Bench: Evaluating Visual-Textual-Symbolic Reasoning of Multimodal Large Language Models in Chemistry

arXiv:2511.17909v2 Announce Type: replace Abstract: Chemical reasoning inherently integrates visual, textual, and symbolic modalities, yet existing benchmarks rarely capture this complexity, often rel

Tutorials

Continuous Optimization for p-adic Models

arXiv:2609.25501v1 Announce Type: new Abstract: We present the first method for native, continuous gradient descent for machine learning models with p-adic parameters. Existing native optimizers are d

tutorialsarxiv-cs-lg

Local Ai

Not All 4-bit Quantizers Are Equal: Deployment-Time Mitigation of PII Leakage in Fine-Tuned Small Language Models

arXiv:2609.25014v1 Announce Type: cross Abstract: Organizations fine-tune small language models on private data and then compress them to 4 bits for resource-efficient deployment. We show that the com

local-aiarxiv-cs-lg

Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems

arXiv:2609.25945v1 Announce Type: new Abstract: The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI technique

safetyarxiv-cs-cv

Vision Foundation Models with Synthetic-Only Training for Monocular Spacecraft Pose Estimation

arXiv:2609.26561v1 Announce Type: cross Abstract: We present an improvement on previous spacecraft pose estimation architectures that results in the lowest published mean rotation errors we know of on

researcharxiv-cs-ro

Algebraic Consistency Alone Does Not Certify Temporal Structure in Latent Action Models

arXiv:2609.23478v1 Announce Type: new Abstract: Latent action models infer a code for the transition between two frames of action-free video. Recent methods regularise this code to compose additively

AURA: Uncertainty-Routed Activation Editing for Acoustic Grounding in Speech Foundation Models

arXiv:2609.23979v1 Announce Type: cross Abstract: Attention encoder-decoder (AED) Speech Foundation Models achieve strong ASR performance but can generate acoustically unsupported text when inputs con

From Outliers to Topics in Language Models: Anticipating Trends in News Corpora

arXiv:2509.22030v2 Announce Type: replace Abstract: This paper examines how outliers, often dismissed as noise in topic modeling, can act as weak signals of emerging topics in dynamic news corpora. Us

On the Efficiency-Safety Dilemma in Large Reasoning Models

arXiv:2609.23587v1 Announce Type: new Abstract: Large reasoning models (LRMs) incur high inference costs, often mitigated by efficiency techniques like quantization and pruning. However, the impact of

safetyarxiv-cs-cl

Read-Best Is Not Steer-Best: A Probing--Steering Layer Dissociation in Omni-Modal Large Language Models

arXiv:2609.22135v1 Announce Type: new Abstract: Omni-modal large language models integrate text, audio, and image signals into a shared residual stream, where concepts such as emotion can be linearly

Agents

Relationally Grounded Latent World Models for Autonomous Driving

arXiv:2609.24626v1 Announce Type: new Abstract: Latent world models learn predictive representations for autonomous driving, but the relational semantics these states preserve often remain implicit. W

agentsarxiv-cs-cv

Seeing Through Conflicts: Improving Instruction Hierarchy Alignment in Vision-Language Models

arXiv:2609.22234v1 Announce Type: new Abstract: Instruction hierarchy (IH) alignment teaches language models to prioritize higher-level instructions when inputs conflict. While studied primarily in te

safetyarxiv-cs-cl

Sim2Real Diffusion: Leveraging Foundation Vision Language Models for Adaptive Automated Driving

model-releasesarxiv-cs-ro

arXiv:2507.00236v4 Announce Type: replace Abstract: Simulation-based design, optimization, and validation of autonomous vehicles have proven to be crucial for their improvement over the years. Neverth

SKstars at SHROOM: Visions Agreement-Guided Ensembling of Zero-Shot and LoRA-Adapted Vision--Language Models

arXiv:2609.24198v1 Announce Type: cross Abstract: This paper describes the SKstars submission to SHROOM-Visions 2026, a shared task on fine-grained hallucination detection in large vision-language mod

Towards Robust Classroom Attendance: A Comprehensive Evaluation of Face Detection and Recognition Models

model-releasesarxiv-cs-cv

arXiv:2609.22750v1 Announce Type: new Abstract: Manual attendance methods, such as paper or register-based systems, take a lot of time, can lead to errors, and are easy to falsify. Face recognition is

Tutorials

GUARD: Natural Forgetting in Large Reasoning Models via Guided Answer-Reasoning Distillation

arXiv:2609.21677v1 Announce Type: new Abstract: Recent advances in large reasoning models (LRMs) have made machine unlearning more challenging, as protected facts or unsafe rationales may surface in i

tutorialsarxiv-cs-ai

21 Sept 2026

VLA-Scope: Shift-Aware Failure Prediction for Vision-Language-Action Models

arXiv:2609.21246v1 Announce Type: cross Abstract: Vision-language-action (VLA) models map visual observations and natural-language instructions to robotic actions, but distribution shifts can compromi

researcharxiv-cs-ai

21 Sept 2026

Agents

A frontend-backend architecture for tool calls in full-duplex speech models

arXiv:2609.19334v1 Announce Type: new Abstract: Full-duplex speech-to-speech (S2S) models provide natural, low-latency conversational interaction and would benefit from the ability to use external too

agentsarxiv-cs-cl

Cross-Modal Attention Acts as a Frequency Filter: Why Verbose Prompts Improve Robustness in Vision-Language Models

arXiv:2609.20139v1 Announce Type: cross Abstract: Vision-language models (VLMs) are fragile under image corruption. We find that the wording of the question affects VLMs in two opposite ways. Verbose

researcharxiv-cs-ai

DART: Distillation-Aware Reparameterization for Training-Free LoRA Reuse in Few-Step Video Diffusion Models

arXiv:2609.20051v1 Announce Type: new Abstract: Step distillation reduces the cost of video generation, but reusing a LoRA trained for a longer trajectory can alter its functional effect or degrade ta

Full-Duplex Speech Models Take the Floor When Asked, Not When Needed

arXiv:2609.19596v1 Announce Type: new Abstract: Full-duplex speech models listen and speak at once, promising always-on assistants. Yet they must also decide when they should speak. Human listeners sp

Mammography Foundation Models for Opportunistic Prediction of Major Adverse Cardiovascular Events

arXiv:2609.19385v1 Announce Type: cross Abstract: Cardiovascular disease (CVD) remains the leading cause of death among women, yet cardiovascular risk assessment often relies on clinical variables tha

Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer

arXiv:2609.19203v1 Announce Type: new Abstract: AI applications have shifted from single, monolithic foundation models (FM) to compound agentic systems. Yet today's stacks remain fragmented: even as p

Butterfly Effect and the Kinetic Energy Cascade in Probabilistic Machine Learning Weather Prediction Models

arXiv:2609.18489v1 Announce Type: cross Abstract: This study analyses kinetic energy (KE) spectra, difference kinetic energy (DKE) spectra, and signatures of KE transfer across spatial scales in four

researcharxiv-cs-lg

Applications

CADSplat: Sparse-View 3D Gaussian Splatting Aided by CAD Models for Robust, Photorealistic Digital-Twin Reconstruction

arXiv:2609.18473v1 Announce Type: new Abstract: We present CADSplat, a framework that reconstructs photorealistic, geometrically accurate digital twins from sparse (<15 views), wide-baseline posed ima

applicationsarxiv-cs-cv

Calibrated Probabilistic Obstruction Reasoning with Vision-Language Models for Grasping in Clutter

model-releasesarxiv-cs-ro

arXiv:2609.18718v1 Announce Type: new Abstract: Retrieving a target from clutter requires deciding whether to grasp the target, remove a blocker, or defer. Existing methods typically commit to a singl

Democratizing Clinical Tumor Whole Genome Sequencing: 18-hour End-to-end Analysis via Trillion-parameter Large Language Models Locally Deployed on Consumer-grade Hardware

model-releasesarxiv-cs-lg

arXiv:2609.17620v1 Announce Type: cross Abstract: Whole genome sequencing (WGS) is essential for precision oncology, yet its clinical adoption remains limited by prohibitive computational costs and mu

Local Ai

First Token Matters: Understanding Safety Collapse in Large Reasoning Models

arXiv:2609.18471v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) exhibit strong problem-solving abilities, yet their safety alignment often degrades when handling harmful queries. Existin

local-aiarxiv-cs-ai

Improving Reduced-Order Rotating Detonation Engine Models with Data Assimilation and Machine Learning

arXiv:2609.16237v1 Announce Type: cross Abstract: Rotating detonation engines (RDEs) exhibit strongly nonlinear, multiscale wave dynamics that set the observed thermal field. High-fidelity simulations

researcharxiv-cs-lg

Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making

arXiv:2609.16779v1 Announce Type: new Abstract: LLMs are increasingly employed in a wide range of decision-making tasks. However, the opacity of their internal reasoning makes it difficult to validate

RegRet: Enhancing Region-Level Retrieval in Large Multimodal Models

arXiv:2609.16847v1 Announce Type: cross Abstract: Region-level retrieval aims to align user-specified image regions with relevant regions or textual descriptions, playing a crucial role in realworld a

SAVLA: Symmetry-Aware Vision-Language-Action Models for Robotic Manipulation

arXiv:2609.16641v1 Announce Type: new Abstract: Vision-language-action (VLA) models have become the dominant paradigm for language-conditioned robot manipulation. However, although images and language

safetyarxiv-cs-ro

There Is More to Refusal in Large Language Models than a Single Direction

arXiv:2602.02132v2 Announce Type: replace Abstract: Prior work argues that refusal in large language models is mediated by a single direction, enabling steering and abliteration. We show that this acc

Agents

A Building as a Repository: KIR, a Typed Intermediate Representation for Agent-Authored Building Information Models