Search: “models”

Evaluating Transformation Models for pCLE Mosaic Registration

arXiv:2609.24560v1 Announce Type: new Abstract: Confocal Laser Endomicroscopy (CLE) provides real-time, cellular-resolution optical biopsy but has a narrow field of view, which image mosaicing can ext

safetyarxiv-cs-cv

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ILRR: Inference-Time Steering Method for Masked Diffusion Language Models

DGX agent

arXiv:2601.21647v2 Announce Type: replace Abstract: Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for inferen

researcharxiv-cs-cl

22 Sept 2026

Layer-Aware Position Embeddings for Visual Token Pruning in Multimodal Large Language Models

arXiv:2609.23715v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) incur substantial computational overhead due to the reliance on hundreds of visual tokens to represent images.

Local Ai

LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models

arXiv:2609.24196v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) perform 'latent reasoning' by recursively refining internal latent representations with shared weights, offering a more

local-aiarxiv-cs-cl

Robot World Models Are Not Invariant to How the Actions Are Written

arXiv:2609.23252v1 Announce Type: cross Abstract: A robot policy is trained with one of two action parameterizations: absolute joint targets, or deltas relative to the current state. The choice is a l

safetyarxiv-cs-lg

Applications

See like a Robot: Robot-Centric Pointmaps for VLA Models

arXiv:2607.11498v2 Announce Type: replace Abstract: Vision-language-action (VLA) models require 3D spatial reasoning, yet RGB observations encode robot-object geometry only implicitly. Lifting depth w

applicationsarxiv-cs-ro

Applications

Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets

arXiv:2609.23995v1 Announce Type: new Abstract: Linear models with an L_1-norm penalty remain state-of-the-art for high-dimensional (d > 1,000,000) tasks, offering a straightforward method for solving

applicationsarxiv-cs-lg

Local Ai

Steering Vector Fields for Context-Aware Inference-Time Control in Large Language Models

arXiv:2602.01654v2 Announce Type: replace Abstract: Steering vectors (SVs) offer a lightweight way to control large language models (LLMs) at inference time by shifting hidden activations, providing a

local-aiarxiv-cs-cl

The Right Future for Action: Learning Action-Relevant Predictive States in World Action Models

arXiv:2609.23369v1 Announce Type: new Abstract: Generation-free world action models (WAMs) retain future-video prediction during training but act from internal video features at inference, leaving unc

Beyond Reference-Based Evaluation: Reward Models for Meta-Evaluation of Grammatical Error Correction

arXiv:2609.21231v1 Announce Type: new Abstract: Reference-based metrics for Grammatical Error Correction (GEC) such as M^2 and ERRANT assume that the reference set enumerates all valid edits, and ther

Complex Problem Solving in Large Language Models: A Statistical Control Survey and Diagnostic Framework

arXiv:2609.20973v1 Announce Type: cross Abstract: Complex problem solving (CPS) with large language models (LLMs) is often framed as a matter of stronger reasoning or longer generation. Yet early-step

safetyarxiv-cs-lg

Detecting Pretraining Data in Large Language Models from a Free-Energy Perspective

arXiv:2609.21888v1 Announce Type: cross Abstract: Detecting pretraining data in large language models is challenging because high likelihood can reflect either training exposure or strong generalizati

Applications

FAN: Foresight Action Normalization for Continual Adaptation of Vision-Language-Action Models

arXiv:2609.21358v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models pre-trained on large-scale, closed datasets have demonstrated remarkable success across diverse robotic manipulation

applicationsarxiv-cs-ro

From Retrieval to Recognition:How Vision--Language Models Become OCR Specialists

arXiv:2609.21543v1 Announce Type: new Abstract: Does a general vision--language model acquire specialized OCR ability by developing a new reading circuit or by reusing an existing mechanism? We addres

Prediction Dynamics in Depth-Recurrent Language Models

arXiv:2609.21383v1 Announce Type: new Abstract: Depth-recurrent language models refine predictions through repeated latent updates. Why can intermediate answers agree with the endpoint while their sco

CoMa: Contextual Massing Generation with Vision-Language Models

arXiv:2601.08464v2 Announce Type: replace-cross Abstract: Context-aware building massing is an important early-stage design task: given a site for buildings, a generated massing should not only fit th

Explaining spatial information flow in short-term traffic forecasting models using a gated graph attention network

arXiv:2609.20217v1 Announce Type: new Abstract: Short-term traffic forecasting supports real-time monitoring and control of road networks, and graph attention networks (GAT) are the standard means of

FASA: Feedback-Aware Sampling Adaptation for Efficient Diffusion-Based VLA Models

arXiv:2609.19475v1 Announce Type: new Abstract: Diffusion-based Vision-Language-Action (VLA) models achieve strong performance in embodied tasks, but their iterative sampling imposes heavy computation

researcharxiv-cs-ro

Generalization through Lexical Abstraction in Transformer Models: The Case of Functional Words

arXiv:2609.19887v1 Announce Type: new Abstract: Pronouns, adverbs and other functional words (such as they, her, somewhere, there) are often used in language to replace concrete nouns or phrases, when

HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface

arXiv:2609.20659v1 Announce Type: cross Abstract: Large-scale vision-language-action (VLA) models provide powerful priors for robot manipulation, yet adapting them to a specific deployment remains cha

Agents

QEncodeBench: Can Large Language Models Encode Classical Problems into Verified Quantum Oracles?

arXiv:2609.20319v1 Announce Type: cross Abstract: Grover search, amplitude amplification, and quantum counting all rely on the same reusable subroutine, a phase oracle, whose construction the algorith

agentsarxiv-cs-lg

TAP Accuracy Below the Fluctuation Scale and Universal Posterior Geometry in Spherical Linear Models

arXiv:2609.20577v1 Announce Type: cross Abstract: We study the Bayes-optimal spherical linear model as the ambient dimension and sample size grow proportionally, under a quantitative Marchenko--Pastur

A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models

arXiv:2509.19276v2 Announce Type: replace-cross Abstract: Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this ta

17 Sept 2026

Agents

CLEAR: Cross-Source Evidence Adjudication for Large Language Models in Medicine

arXiv:2609.16301v1 Announce Type: new Abstract: Medical knowledge evolves continuously, whereas the parametric knowledge encoded in large language models (LLMs) is fixed at training time. External ret

agentsarxiv-cs-ai

Agents

Distilling Foundation Models for Agentic What-If Reasoning:Cost, Latency, and Governance in a Hybrid LLM+SLM Architecture

arXiv:2609.16091v1 Announce Type: new Abstract: Tabular foundation models deliver strong zero-training predictive performance via in-context learning, but their high inference latency makes them impra

agentsarxiv-cs-lg

Layers, Sinks, and Scaling: Adaptive Evidence Selection for Multimodal Large Language Models

arXiv:2609.16795v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can answer knowledge-intensive visual questions by combining visual evidence from images with facts retrieved f

Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation

arXiv:2512.02323v2 Announce Type: replace Abstract: Efficient sampling from Boltzmann distributions over discrete variables is a fundamental operation in a wide range of applications. While fast non-M

DenMark: Robust Semantic Watermarking for Diffusion Language Models

arXiv:2609.14257v1 Announce Type: new Abstract: Semantic text watermarks encode signals in meaning rather than surface token choices, offering robustness to paraphrasing and other semantic-preserving

15 Sept 2026

Tutorials

Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds

arXiv:2603.25165v3 Announce Type: replace Abstract: Recent advances in self-supervised learning (SSL) for point clouds have substantially improved 3D scene understanding without human annotations. Exi

tutorialsarxiv-cs-cv

15 Sept 2026

Beyond Similarity: Foundation Models as an Efficient Backbone for Training-Free Composed Video Retrieval

arXiv:2609.10008v1 Announce Type: new Abstract: Composed video retrieval (CoVR) searches a gallery for the target video that realizes a natural-language modification of a source clip. However, at gall

10 Sept 2026

Frequency-Conditioned Flow Matching for Vision-Language-Action Models

arXiv:2609.10405v1 Announce Type: new Abstract: Robot actions are temporally correlated trajectories whose frequency components encode motion at different scales with highly non-uniform energy distrib

researcharxiv-cs-ro

10 Sept 2026

Hardware

Ordinary, Reasonable Chatbots: Do AI Models Track Human Legal Judgments?

arXiv:2609.06769v1 Announce Type: cross Abstract: As people increasingly rely on artificial intelligence (AI) for guidance in their own lives, scholars, lawyers, and even judges have begun to consider

hardwarearxiv-cs-ai

10 Sept 2026

DXPR: Depth-Based Vision-LiDAR Cross-Modal Place Recognition Using Vision Foundation Models

arXiv:2609.09005v1 Announce Type: new Abstract: We present DXPR, a depth-based cross-modal place recognition (CMPR) framework that uses vision foundation models (VFMs) to match monocular camera querie

safetyarxiv-cs-cv

How to Learn from What a Human Would Avoid? Intervention-Aware World Models with Real-World RL for Dexterous Manipulation

arXiv:2609.06009v1 Announce Type: new Abstract: Multi-fingered dexterous manipulation remains a frontier for real-world reinforcement learning (RL) due to the high-dimensional action space and the pro

safetyarxiv-cs-ro

ID-Align: RoPE-Conscious Position Remapping for Dynamic High-Resolution Adaptation in Vision-Language Models

arXiv:2505.21465v2 Announce Type: replace Abstract: Currently, a prevalent approach for enhancing Vision-Language Models (VLMs) performance is to encode both the high-resolution version and the thumbn

MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data

arXiv:2601.06847v2 Announce Type: replace Abstract: Vision-Language Models (VLMs) can generate convincing clinical narratives, yet frequently struggle to visually ground their statements. We posit tha

MoEMB: Scaling Universal Multimodal Embeddings with Efficient Mixture-of-Experts Models

arXiv:2609.08663v1 Announce Type: new Abstract: Universal multimodal embedding (UME) increasingly demands encoder's capacity for handling a broad range of tasks and modalities with increased complexit

Sharp Structure-Agnostic Minimax Risk for Partial Linear Models

arXiv:2609.07997v1 Announce Type: new Abstract: We characterize the sharp structure-agnostic minimax risk for coefficient estimation in the partial linear model when the outcome and treatment nuisance

Strategic Doctrine Language Models (sdLM): A Learning-System Framework for Doctrinal Consistency and Geopolitical Forecasting

arXiv:2601.14862v2 Announce Type: replace Abstract: We introduce Strategic Doctrine Language Models (sdLM), a learning-system framework for multi-document strategic reasoning with doctrinal consistenc

Streaming Hierarchical Inference with Tabular Foundation Models

arXiv:2609.07956v1 Announce Type: new Abstract: Tabular Foundation Models (TFMs) have recently demonstrated strong predictive performance through in-context learning, but their deployment in high-thro

safetyarxiv-cs-lg

Where Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models

arXiv:2609.06114v1 Announce Type: new Abstract: Vision-language-action (VLA) models adapted through supervised fine-tuning (SFT) inherit a structural asymmetry: expert demonstrations teach the policy

safetyarxiv-cs-ro

Adaptation Interfaces for In-Context Tabular Foundation Models in Time-to-Event Prediction

arXiv:2609.04901v1 Announce Type: cross Abstract: Tabular foundation models (TabFMs) achieve strong performance on structured data, particularly for standard classification and regression problems. Ye

Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction

arXiv:2609.05404v1 Announce Type: cross Abstract: Diffusion TV is an interactive AI art installation that offers a tangible and embodied experience of diffusion models through a modified CRT TV. By ph

Graph Foundation Models for Recommendation: A Comprehensive Survey

arXiv:2502.08346v4 Announce Type: replace-cross Abstract: Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements pl

Harness-agnostic detection and immunization of reward hacking in self-evolving language models

arXiv:2609.04665v1 Announce Type: new Abstract: Self-evolving language models improve by proposing candidate updates and keeping whatever raises a visible score. When that score is an imperfect proxy

Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models

arXiv:2503.24377v2 Announce Type: replace-cross Abstract: Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to perform complex reasoning tasks, transitionin

Language models judge war differently when tested for alignment

arXiv:2609.05009v1 Announce Type: new Abstract: Safety evaluations can mischaracterize deployed behaviour if artificial-intelligence systems respond to being evaluated. We test this possibility in a f

Large Language Models for HVAC Operations in Building Energy Systems: A Critical Review of Methods, Applications, and Deployment Readiness

arXiv:2609.05314v1 Announce Type: new Abstract: Building automation systems generate rich sensor data yet remain insight-poor because heterogeneous point naming, missing metadata, and fragmented docum