Search: “framework”

SafeRI: Recognition and Intervention for Token-Level Safety Intervention in Large Vision Language Models

arXiv:2609.03544v1 Announce Type: new Abstract: Existing safety alignment methods for vision-language models usually modify the model behavior globally: once the safety parameters are trained or loade

safetyarxiv-cs-cv

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SVG-Score: Human-Aligned Evaluation of Text-to-SVG Generation

DGX agent

arXiv:2609.03806v1 Announce Type: new Abstract: Scalable Vector Graphics (SVG) generation is attracting increasing attention as generative models improve in expressiveness and controllability. Progres

model-releasesarxiv-cs-ai

4 Sept 2026

TabScope: Question-Adaptive Scope Selection for Table Question Answering

arXiv:2609.03395v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown strong performance on table question answering, yet their accuracy often degrades as table size increases. We

Temporal Self-Distillation: Learning Visual State Tracking in Videos Without Supervision

arXiv:2609.04203v1 Announce Type: new Abstract: We introduce S^3T (Self-Supervised Self-Distillation over Time), which, to the best of our knowledge, is the first fully self-contained framework for co

Local Ai

The Natural Language Interaction Protocol and Standard for AI Agents

arXiv:2609.04135v1 Announce Type: new Abstract: AI agents are increasingly being developed and deployed across organizations using heterogeneous agent-development frameworks, AI models, tool interface

local-aiarxiv-cs-ai

Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review

arXiv:2312.09454v3 Announce Type: replace-cross Abstract: Purpose: Uncertainty Quantification (UQ) has gained traction in an attempt to improve the interpretability and robustness of machine learning

researcharxiv-cs-lg

AI Contextual Measurement for Recovering Individual and Group-Level Effects: Validation Against Survey Measures and an Occupational Application

arXiv:2609.02821v1 Announce Type: new Abstract: Researchers increasingly use artificial intelligence to construct measures of social, organizational, and occupational characteristics that are absent f

CAT-Flow: Curvature-Adaptive sTeps for Flow Matching

arXiv:2609.01746v1 Announce Type: new Abstract: Flow Matching has emerged as a leading framework for generative modeling, powering state-of-the-art systems such as FLUX and Stable Diffusion 3.5. Howev

researcharxiv-cs-lg

Do Large Language Models Capture the Diversity in their Training Data?

arXiv:2609.02275v1 Announce Type: cross Abstract: Large language models are trained to model conditional distributions over text, yet it remains inadequately understood whether they capture the full d

Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories

arXiv:2602.02028v3 Announce Type: replace Abstract: Enabling artificial intelligence systems, particularly large language models, to update knowledge and flexibly apply it during reasoning remains a c

Eliciting ESG Preferences for Reinforcement Learning-Based Portfolio Optimization

arXiv:2609.02677v1 Announce Type: cross Abstract: Modern portfolio management increasingly demands a balance between traditional risk-adjusted returns and strict Environmental, Social, and Governance

safetyarxiv-cs-lg

FairLens: Benchmarking Fairness in Vision-Language Models for High-Stakes Decision-Making

model-releasesarxiv-cs-cv

arXiv:2609.01691v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used to make decisions from visual inputs. We introduce FAIRLENS, a benchmark and evaluation framework fo

hLLM: Single Pass Decoding for Generative Reranking

arXiv:2609.01807v1 Announce Type: cross Abstract: Large language models (LLMs) achieve state-of-the-art generative ranking quality, but the ranking they produce must be decoded, and autoregressive dec

MMTryon: Multi-Modal Multi-Reference Control for High-Quality Fashion Generation

arXiv:2405.00448v5 Announce Type: replace Abstract: This paper introduces MMTryon, a multi-modal multi-reference VIrtual Try-ON (VITON) framework, which can generate high-quality compositional try-on

Agents

Multi-Agent Retrieval-Augmented Generation for Efficient Cloud Knowledge Base Search in Telecom SNOC Environment

arXiv:2609.01618v1 Announce Type: cross Abstract: Telecom Service and Network Operations Centers (SNOCs) rely on large collections of cloud documents, including Standard Operating Procedures (SOPs), v

agentsarxiv-cs-lg

Quantum MeanFlow: single-shot generative sampling on NISQ hardware

model-releasesarxiv-cs-lg

arXiv:2609.02186v1 Announce Type: cross Abstract: Quantum generative models offer a promising framework for exploring whether quantum computation can enhance generative machine learning. Flow matching

Query Rewriting for Complex Object Segmentation in 4D Gaussian Representations

arXiv:2609.02664v1 Announce Type: new Abstract: Recent 4D Gaussian representation frameworks have demonstrated strong performance in language-guided dynamic scene understanding. However, these methods

safetyarxiv-cs-cv

READY or Not: Reliable Enterprise Agent Deployment

arXiv:2609.02095v1 Announce Type: new Abstract: An AI agent can perform well on benchmarks and still be unsuitable for deployment. Existing AI-agent benchmarks measure whether an agent can complete re

safetyarxiv-cs-ai

Retrosynthesis of Synthetic Media for Explainable AI Provenance Forensics

arXiv:2609.02268v1 Announce Type: cross Abstract: With the rapid proliferation of generative models on Machine Learning as a Service (MLaaS) platforms, reliably tracing the provenance of synthetic med

Reviewing the Reviewer: LLM-Assisted Reviewer Feedback Generation for Guideline Compliance

arXiv:2602.10118v2 Announce Type: replace Abstract: Peer review is central to scientific quality, yet reliance on simple heuristics, namely lazy thinking and non-specific critiques, has threatened rev

researcharxiv-cs-cl

RINSE: Robust Target-Time Normality Estimation for Zero-Shot Graph Anomaly Detection

arXiv:2609.02497v1 Announce Type: cross Abstract: Zero-shot graph anomaly detection seeks to deploy a detector trained on source graphs to unseen, unlabeled targets, yet domain shift can make source-d

Agents

ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

arXiv:2609.02780v1 Announce Type: cross Abstract: Streaming video understanding is a critical capability for real-world applications, including embodied intelligence, autonomous driving, industrial mo

agentsarxiv-cs-cl

SocialBuddy: Tailoring Search Agent for Social Scenarios

model-releasesarxiv-cs-lg

arXiv:2609.01641v1 Announce Type: cross Abstract: In the era of digital social interaction, searching friends' posts from massive social streams has become a fundamental user need. However, while mode

Local Ai

Subcellularly Resolved Single-Cell Embedding Learning with Transcriptomic data, Protein Structure and Localization Information

arXiv:2609.02344v1 Announce Type: cross Abstract: Existing cell embedding methods predominantly rely on transcriptomic or proteomic measurements and represent each cell as a holistic entity, thereby o

local-aiarxiv-cs-ai

Hardware

A Composable Evaluation System for Reproducible Omni-Modal Foundation Model Evaluation

arXiv:2609.01315v1 Announce Type: new Abstract: Building an omni-modal foundation model means evaluating it across text, image, video, and audio. Excellent evaluation toolkits exist for each modality,

hardwarearxiv-cs-ai

A Machine Learning-Driven Solution for Denoising Inertial Confinement Fusion Images

arXiv:2511.16717v3 Announce Type: replace-cross Abstract: Neutron imaging is essential for diagnosing and optimizing inertial confinement fusion implosions at the National Ignition Facility. Due to th

A Mathematical Theory of Reusable Neural Bases for Network Compression

arXiv:2609.01550v1 Announce Type: cross Abstract: As large AI models become increasingly prevalent across a wide range of applications, memory cost has become a critical bottleneck in both training an

Anamnesis: An Open-Source Platform for Large-Scale Backstory-Conditioned Survey Simulation

arXiv:2607.10628v2 Announce Type: replace-cross Abstract: We present Anamnesis, an interactive system for demographically controllable survey simulation using large language models. Open-source and de

Autoresearch for Marketplace Catalogs: From Legacy Forms to AI-Native Matching

arXiv:2609.00274v1 Announce Type: new Abstract: Two-sided service marketplaces are moving from deterministic request-form intake to AI-native probabilistic matching, enabled by large language models (

Breaking the Structural Identity: Personalized Federated LoRA Fine-tuning under Rank Heterogeneity

arXiv:2609.00632v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved remarkable success across diverse domains, but their adaptation to privacy-sensitive, distributed datasets

C-DiffSET: Leveraging Latent Diffusion for SAR-to-EO Image Translation with Confidence-Guided Reliable Object Generation

arXiv:2411.10788v4 Announce Type: replace Abstract: Synthetic Aperture Radar (SAR) imagery provides robust environmental and temporal coverage (e.g., during clouds, seasons, day-night cycles), yet its

CADKnitter: Compositional CAD Generation from Text and Geometry Guidance

arXiv:2512.11199v2 Announce Type: replace Abstract: Computer-aided design (CAD) defines 3D models as compact, precise, and editable representations, making it directly useful for several fields. Recen

applicationsarxiv-cs-cv

CATeye: Coupled Attribute-Topology Invariance Learning for Voucher Abuse Detection

model-releasesarxiv-cs-lg

arXiv:2609.01425v1 Announce Type: new Abstract: Voucher abuse poses a major challenge in e-commerce, where malicious users exploit promotional vouchers for profit. Unfortunately, fraud patterns evolve

CoVer: Conflict-Aware Claim Verification

arXiv:2609.00508v1 Announce Type: new Abstract: Social media fact-checking has long been challenged by evidence-level and aggregation-level conflicts, where erroneous evidence mimics authoritative new

Data-Driven Persona-Conditioned Agents for A/B Test Simulation

arXiv:2609.01038v1 Announce Type: new Abstract: A/B testing is the gold standard for evaluating product changes, but each experiment requires real user traffic, engineering effort, and weeks of measur

DiverseVAR: Balancing Diversity and Quality of Next-Scale Visual Autoregressive Models

arXiv:2511.21415v2 Announce Type: replace Abstract: We introduce DiverseVAR, a framework that enhances the diversity of text-conditioned visual autoregressive models (VAR) at test time without requiri

Does Reasoning Mitigate Backdoor Attacks? A Neuro-Symbolic Perspective

arXiv:2609.00464v1 Announce Type: cross Abstract: Neuro-Symbolic (NeSy) AI has recently emerged as a novel paradigm to enable trustworthy AI, aiming at integrating sub-symbolic neural perception with

GazeRefine: Expert Gaze as a Test-Time Prompt for Training-Free Medical Image Segmentation

arXiv:2609.01310v1 Announce Type: cross Abstract: Medical image segmentation remains difficult to scale because high-performing methods typically rely on dense expert annotations and task-specific tra

Generativism: Toward a Learning Theory for the Age of Generative Artificial Intelligence

arXiv:2606.12441v2 Announce Type: replace-cross Abstract: The four dominant learning theories of behaviorism, cognitivism, constructivism, and connectivism show significant conceptual limitations as g

Local Ai

GeoPAR: Large-Scale Multi-Agent Combinatorial Optimization with Geometry-Guided Parallel Autoregressive Learning

arXiv:2609.00577v1 Announce Type: cross Abstract: Multi-agent combinatorial optimization problems are notoriously challenging due to their NP-hard nature. Recent parallel autoregressive neural solvers

local-aiarxiv-cs-ai

GRRM: Group Relative Reward Modeling for Machine Translation

arXiv:2602.14028v2 Announce Type: replace Abstract: While Group Relative Policy Optimization (GRPO) offers a powerful framework for LLM post-training, its effectiveness in open-ended domains like Mach

safetyarxiv-cs-cl

H3-World: Turning Language Understanding into World Control

arXiv:2609.01560v1 Announce Type: cross Abstract: We present H3-World, an efficient framework that turns the 33B MiniMax-H3 video generator into an interactive world model. Our key finding is that, as

HiLRP: Toward One Trustworthy Explanation for Vision Transformer: Conservation-Valid Attribution via Attention Primitives

arXiv:2609.01282v1 Announce Type: cross Abstract: Vision Transformer (ViT) design has become increasingly diverse, with backbones combining convolutional stems, windowed, linear, or multi-axis attenti

Knowing When to Stop: Adaptive Action Chunking via Internal Cross-Attention Dynamics in VLAs

arXiv:2609.00908v1 Announce Type: new Abstract: Action chunking is a standard execution strategy in modern Vision-Language-Action (VLA) frameworks, but fixed execution horizons impose a trade-off betw

safetyarxiv-cs-ro

LEAP: Likelihood Elicitation and Aggregation for LLM-based Probabilistic Forecasting

arXiv:2609.01337v1 Announce Type: new Abstract: LLM-based forecasting systems have improved on real-world tasks such as financial markets and sports outcomes, largely through stronger search and tool

Agents

Learning What to Retain: Gated-Memory Routing for Efficient Collaboration in Multi-Agent LLM Systems

arXiv:2609.00237v1 Announce Type: new Abstract: Large language model (LLM)-based multi-agent systems tackle complex reasoning by orchestrating how multiple agents are configured and how they collabora

agentsarxiv-cs-ai

Neuro-Symbolic Geometric Abstraction (NeuSOGA): From Observations to Symbolic Mathematical Representations

arXiv:2609.01408v1 Announce Type: new Abstract: A fundamental challenge in artificial intelligence is the transformation of observations into explicit symbolic representations suitable for abstraction

On the Reliability of Generative Augmentation: A Wasserstein-Based Theoretical and Empirical Study

arXiv:2609.01410v1 Announce Type: cross Abstract: Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstream generalization remains poorly unders

researcharxiv-cs-lg