Search: “models”

ReDiSC: A Reparameterized Masked Diffusion Model for Scalable Node Classification with Structured Predictions

arXiv:2507.14484v2 Announce Type: replace Abstract: In recent years, graph neural networks (GNN) have achieved unprecedented successes in node classification tasks. Although GNNs inherently encode spe

researcharxiv-cs-lg

30 Jul 2026

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RVC-NMPC: Nonlinear Model Predictive Control with Reciprocal Velocity Constraints for Mutual Collision Avoidance in Agile UAV Flight

DGX agent

arXiv:2512.08574v2 Announce Type: replace Abstract: This paper presents an approach to mutual collision avoidance based on Nonlinear Model Predictive Control (NMPC) with time-dependent Reciprocal Velo

applicationsarxiv-cs-ro

30 Jul 2026

ToxScreen: Detecting Whether an LLM Has Been Poisoned

model-releasesarxiv-cs-lg

arXiv:2607.26849v1 Announce Type: cross Abstract: As large language models (LLMs) are deployed in high-stakes domains, adversaries may poison training data to implant backdoors: hidden triggers that c

30 Jul 2026

Hardware

Visko Orbis 1.0: A Live Model for Real-Time Interactive Long Video Generation

arXiv:2607.26694v1 Announce Type: new Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long-video generation. Users can change the prompt at any moment during generation,

hardwarearxiv-cs-cv

30 Jul 2026

A Study of Crosslinguistic Influence in Language Models

arXiv:2601.21587v2 Announce Type: replace Abstract: The sequential acquisition of languages inevitably leads to Crosslinguistic Influence (CLI), where the syntactic properties of a first language (L1)

researcharxiv-cs-cl

Conformal Cascade: Distribution-Free Accuracy Guarantees for Multi-Tier LLM Inference

model-releasesarxiv-cs-lg

arXiv:2607.25018v1 Announce Type: new Abstract: Large language model (LLM) cascades reduce inference cost by routing easy queries to a small model and deferring hard queries to a larger one. Productio

Construction-Driven Injection: Linguistically-Grounded Edit-Based Code-Mixing Fingerprints for Large Language Models

arXiv:2607.25633v1 Announce Type: cross Abstract: Large language models (LLMs) are costly intellectual assets that remain exposed to unauthorized redistribution and commercial misuse. Injected fingerp

Local Ai

DC-WAM: Dynamic-Centric Visual Supervision and Reasoning for World-Action Models

arXiv:2607.25918v1 Announce Type: new Abstract: World-Action Models (WAMs) augment robot policies with future visual prediction, but it remains unclear what the visual modality should learn for contro

local-aiarxiv-cs-ro

Diff-ID: Identity Consistent Facial Image Generation and Morphing via Diffusion Models

arXiv:2607.25078v1 Announce Type: new Abstract: Generative diffusion models have revolutionized facial image synthesis, yet robust identity preservation in high resolution outputs remains a critical c

researcharxiv-cs-cv

Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation

arXiv:2607.25094v1 Announce Type: cross Abstract: Human language is driven by unspoken beliefs and belief updates, making these critical to model for successful communication between large language mo

Local Ai

INTACT: Isomorphic Intent-to-Action Learning for Search-Free World Models

arXiv:2607.26056v1 Announce Type: new Abstract: Forward latent world models predict how actions change a scene, but recover actions for a desired change only through expensive test-time search. We int

local-aiarxiv-cs-ro

Lantern: Conflict-Aware Gradient Blending for Physics-Guided Diffusion Models in Calorimeter Simulation

arXiv:2607.25060v1 Announce Type: cross Abstract: Monte Carlo simulation of calorimeter showers is a principal bottleneck for the High-Luminosity LHC, and diffusion models have emerged as fast, high-f

LLM as Forecasting Planner: Training-Free Text Conditioning for Time-Series Foundation Models

arXiv:2607.24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account

Real-Time Driver Safety Scoring Through Inverse Crash Probability Modeling

arXiv:2603.14841v3 Announce Type: replace-cross Abstract: Road crashes remain a leading cause of preventable fatalities. Existing prediction models predominantly produce binary outcomes, which offer l

Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn

arXiv:2607.24783v1 Announce Type: new Abstract: Job understanding is critical to LinkedIn's mission of connecting talent with opportunity. This task involves transforming unstructured and noisy job po

Benchmarking the Domain Gap: Model Selection Instability Under Domain Shift in Video Capsule Endoscopy

arXiv:2607.22736v1 Announce Type: new Abstract: Video capsule endoscopy (VCE) classification is typically evaluated within a single dataset, yet clinical deployment demands robustness across acquisiti

researcharxiv-cs-cv

CAPT: A Multi-task Continuous Autoregressive Transformer enabling Cross-dataset and Cross-species Transfer for Calcium Population Dynamics

arXiv:2607.23258v1 Announce Type: new Abstract: Large-scale calcium imaging has created an opportunity to build foundation-style models for neural population dynamics, but a central question remains u

DSTFView: Multi-View Cloud-Edge Workload Forecasting with Dual-Input Spatio-Temporal-Frequency Modeling

arXiv:2607.22565v1 Announce Type: new Abstract: With the widespread deployment of edge-side AI inference, edge platforms are increasingly required to support latency-sensitive, highly concurrent, and

ESF-Bench: Benchmarking Challenging Slot-Filling Scenarios for Real-World Enterprise Applications

arXiv:2607.23326v1 Announce Type: new Abstract: The rapid rise of large language models (LLMs) has driven transformative adoption across enterprises. However, deploying these models in real-world sett

FlowCTS: On-policy Continuous Trajectory Supervision of Flow Models

arXiv:2607.24522v1 Announce Type: cross Abstract: While on-policy distillation (OPD) effectively addresses sparse rewards and exposure bias in large language model post-training, its extension to flow

safetyarxiv-cs-cv

Local Ai

Let AI Agents Translate Networks, Not Reason About Them

arXiv:2607.22947v1 Announce Type: new Abstract: A formal model enables verifying reachability, localizing an outage, or anticipating the blast radius of a change. Yet, virtually no production network

local-aiarxiv-cs-ai

Agents

Lexical discovery in unknown environments orchestrated by Large Language Models

arXiv:2607.22591v1 Announce Type: new Abstract: Populations of autonomous agents deployed in unknown environments (e.g. planetary or deep-sea exploration) must develop shared vocabularies to refer to

agentsarxiv-cs-ai

Logit-Coordinate Generative Models for Mixed Continuous-Categorical Tabular Data

arXiv:2607.23348v1 Announce Type: cross Abstract: Mixed continuous--categorical data pose a representation problem for continuous generative models. Flow Matching and Gaussian diffusion operate in Euc

researcharxiv-cs-lg

MAViE: A Multi-scale Adaptive Vision Encoder for Fine-grained Visual Perception and Efficient Multimodal Reasoning

model-releasesarxiv-cs-cv

arXiv:2607.24424v1 Announce Type: new Abstract: Vision-language models commonly project all tokens produced by a pretrained vision encoder into a large language model. However, final-layer features ca

MoLGE: Mixture of Language Group Experts for Efficient Scaling of Massively Multilingual Speech Recognition

model-releasesarxiv-cs-cl

arXiv:2607.24030v1 Announce Type: new Abstract: Massively multilingual automatic speech recognition (ASR) models covering hundreds of languages must maintain robust performance across diverse linguist

Applications

Music-Source-Separation-Training (MSST): A Unified Framework for Training and Evaluating Music Demixing Models

arXiv:2607.23395v1 Announce Type: cross Abstract: Music Source Separation (MSS), the task of recovering individual sound components (stems) from a polyphonic mixture, is central to applications rangin

applicationsarxiv-cs-lg

N_0-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens

arXiv:2607.23782v1 Announce Type: new Abstract: We present N_0-VTLA, a vision-tactile-language-action (VTLA) foundation model capable of (1) fine-grained contact-rich manipulation with tactile percept

safetyarxiv-cs-ro

Reason-Mediated Behavioral Models for Auditing LLM Social Simulators

arXiv:2607.24649v1 Announce Type: new Abstract: Large language models are increasingly used as social simulators, including as synthetic survey respondents. Most evaluations ask whether simulated outc

Structured Redundancy Modeling for Efficient Visual Token Pruning in High-Resolution MLLMs

arXiv:2607.23046v1 Announce Type: new Abstract: Recent high-resolution Multimodal Large Language Models (MLLMs) generate thousands of visual tokens per input, leading to a visual token explosion that

safetyarxiv-cs-cv

TLA^{+}-Bench: An Execution-Grounded Benchmark and Dataset for Natural-Language to TLA+ Specification Generation

arXiv:2607.23425v1 Announce Type: cross Abstract: Large language models increasingly write TLA^{+} formal specifications from natural-language descriptions, but progress is hard to measure: existing r

Toward Automated Detection of Documentation Inconsistencies in Electronic Health Records

model-releasesarxiv-cs-cl

arXiv:2607.22954v1 Announce Type: new Abstract: Objective: To characterize the kinds of internal documentation inconsistencies a general-domain large language model (LLM) can surface from real-world d

Training with (Swap) Regret Loss in a Single-Layer Self-Attention Model: A Case Study on the Probability Simplex

arXiv:2607.23333v1 Announce Type: cross Abstract: We revisit the regret loss framework introduced in Park et al. (2025), which uses decision-theoretic regret as a direct loss function for training mod

Understanding Machine Unlearning Through the Lens of Mode Connectivity

arXiv:2607.23970v1 Announce Type: cross Abstract: Machine Unlearning aims to remove undesired information from trained models without full retraining from scratch. Despite recent progress, the loss la

Applications

Visual Information Extraction from Documents via Classification-Guided Large Vision-Language Models

arXiv:2607.22723v1 Announce Type: new Abstract: Visual information extraction (VIE) from visually rich documents remains challenging due to high layout variability and real-world impairments. Existing

applicationsarxiv-cs-cv

Zing: Social Mind for LLMs

model-releasesarxiv-cs-cl

arXiv:2607.23740v1 Announce Type: new Abstract: As large language models move from isolated task solving toward long-term service in human environments, they require social intelligence: the ability t

Be Consistent! Enhancing Robust Visual Reasoning in LVLMs with Consistency Constraints

model-releasesarxiv-cs-cv

arXiv:2607.21722v1 Announce Type: new Abstract: While Large Vision-Language Models (LVLMs) exhibit strong perceptual capabilities, they remain vulnerable in visual reasoning tasks. Existing benchmarks

27 Jul 2026

Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

arXiv:2607.21904v1 Announce Type: cross Abstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. I

researcharxiv-cs-cl

27 Jul 2026

Pixels for Programs? A Cross-Provider Case Study of Input-Token Accounting for Source Code as Text and Images

model-releasesarxiv-cs-cv

arXiv:2607.21672v1 Announce Type: cross Abstract: Long source-code contexts consume many text tokens, motivating the proposal to render code as images for vision-language models. Recent work asks whet

27 Jul 2026

Applications

Attention-based Experience Replay Framework for Continual Learning of Agnostic Time Series Forecasting Models

arXiv:2607.20493v1 Announce Type: new Abstract: Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing. However, a major limita

applicationsarxiv-cs-ai

Break Through the Compression Bottleneck: From Theory to Practice

arXiv:2607.20434v1 Announce Type: cross Abstract: As the parameter size of language models continues to grow, effective model compression is required to reduce their computational and memory overhead.

Euclid-MCP: A Model Context Protocol Server for Deterministic Logical Reasoning via Prolog

arXiv:2607.21412v1 Announce Type: new Abstract: Large Language Models (LLMs) excel at natural language understanding and generation but remain unreliable for multi-step logical reasoning, especially i

Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction

arXiv:2410.00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally a

researcharxiv-cs-cv

Graph Learning on Ensembles of Cyclic Peptides: An Investigation of Molecular Ensemble Modeling

arXiv:2607.21561v1 Announce Type: new Abstract: Molecular property prediction from structure often uses a single representative conformation, even though many molecules exist as conformational ensembl

researcharxiv-cs-lg

Grounding Investor Views: Neural Predicates in the Black-Litterman Model

arXiv:2607.20533v1 Announce Type: cross Abstract: Portfolio construction under the Black-Litterman model requires investors to specify views on asset returns alongside explicit uncertainty estimates -

Phonetic forced alignment for low-resource language varieties: Model training and evaluation on Chengdu Mandarin

arXiv:2607.21332v1 Announce Type: cross Abstract: Phonetic forced alignment is a key technique in phonetic research, yet existing alignment systems lack specialized models for low-resource language va

Simple Policy Gradients for Reasoning with Diffusion Language Models

arXiv:2510.04019v3 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) represent a promising alternative to autoregressive LLMs; however, the lack of effective post-training

Spatially Grounded Concept Bottleneck Models for Trustworthy Breast Ultrasound Diagnosis

arXiv:2607.20691v1 Announce Type: cross Abstract: Concept Bottleneck Models provide interpretable-by-design predictions by mediating diagnosis through human-understandable concepts, but in medical ima

Vision-Language-Policy Model for Dynamic Robot Task Planning

arXiv:2512.19178v2 Announce Type: replace-cross Abstract: Bridging the gap between natural language commands and autonomous execution in unstructured environments remains an open challenge for robotic