Search: “framework”

RoadVGGT: Road-Structure-Aware Feed-Forward Road Surface Reconstruction

arXiv:2607.23758v1 Announce Type: new Abstract: Large-scale road surface reconstruction supports high-definition mapping, autonomous-driving perception, annotation, and simulation. Existing road-speci

agentsarxiv-cs-cv

Local Ai

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Robust Conformalized Selection with Noisy Responses

DGX agent

arXiv:2607.22985v1 Announce Type: cross Abstract: Conformalized selection has been widely applied to select high-quality candidates from large datasets with rigorous uncertainty quantification, such a

local-aiarxiv-cs-lg

28 Jul 2026

RODR: Riemannian Orthogonally Decoupled Regularization for Disentangled Manifold Representation

arXiv:2607.23958v1 Announce Type: new Abstract: Point cloud denoising is essentially a geometric recovery task that aims to reconstruct the intrinsic structure of a smooth 2D Riemannian manifold embed

SafeCRS: Personalized Safety Alignment for LLM-Based Conversational Recommender Systems

arXiv:2603.03536v2 Announce Type: replace-cross Abstract: Current LLM-based conversational recommender systems (CRS) primarily optimize recommendation accuracy and user satisfaction. We identify an un

SAGE: Safety-First Defense-in-Depth Guardrails for Verified Lifecycle Control of High-Impact Generative AI

arXiv:2607.22926v1 Announce Type: new Abstract: High-impact generative AI makes catastrophic misuse a lifecycle-control problem, not merely a prompt-filtering problem. SAGE is a safety-first, authoriz

Same Predictions, Different Reasons: The Effect of Quantization on Model Explanations

arXiv:2607.22872v1 Announce Type: cross Abstract: Post-training quantization (PTQ) has become a practical solution for deploying deep learning models on resource-constrained edge devices by compressin

SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models

arXiv:2607.23238v1 Announce Type: new Abstract: Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsettled.

SCAIR: Schema-Conditioned Agentic Iterative Reasoning for Enterprise Knowledge Graphs

arXiv:2607.22571v1 Announce Type: new Abstract: Knowledge Graph-based Retrieval-Augmented Generation (KG-RAG) enables natural language interaction with structured enterprise knowledge, yet existing ag

Applications

Scoping Review of AI, Metrology, and ESG in the Semiconductor Sector: Implications for Safe and Sustainable by Design (SSbD)

arXiv:2607.23082v1 Announce Type: cross Abstract: The semiconductor sector faces a dual transition: scaling manufacturing execution through Artificial Intelligence (AI) while satisfying stringent sust

applicationsarxiv-cs-ai

Seesaw: Accelerating Training by Balancing Learning Rate and Batch Size Scheduling

arXiv:2510.14717v2 Announce Type: replace-cross Abstract: Increasing the batch size during training -- a ''batch ramp'' -- is a promising strategy to accelerate large language model pretraining. While

SF-AMS: Strategic Forgetting for Structured Memory in LLM Agent

arXiv:2607.22562v1 Announce Type: new Abstract: Managing long-context dependencies remains a primary bottleneck in LLM agents, as redundant and irrelevant information can degrade multi-step reasoning.

agentsarxiv-cs-ai

SHReg: Strictly Rotation-Equivariant Point Cloud Registration via Spherical Harmonics

arXiv:2607.23096v1 Announce Type: new Abstract: Point cloud registration critically depends on local features that are both distinctive and robust to arbitrary 3D rotations. Existing learning-based me

SINT-Flow: Schema Integration using Large Language Model Workflows

model-releasesarxiv-cs-cl

arXiv:2607.24492v1 Announce Type: new Abstract: The goal of schema integration is, given a set of input schemata or tables, to derive a global, unified schema that is able to represent the concepts, a

Smooth Learning with Hard Constraints via Legendre-Regularized Policies

model-releasesarxiv-cs-lg

arXiv:2607.24007v1 Announce Type: cross Abstract: We revisit contextual optimization from the perspective of policy class design. A desirable policy class should be expressive enough to learn rich con

Hardware

Source-Aware Reranking for Retrieval-Augmented Generation: A Reliability Prior Approach

arXiv:2607.22584v1 Announce Type: new Abstract: Standard Retrieval-Augmented Generation pipelines rank retrieved documents by semantic similarity alone, without accounting for source provenance or cre

hardwarearxiv-cs-ai

Sparse Autoencoders Encode Both Concepts and Functions: The Downstream Geometry of Feature Effects

arXiv:2607.24645v1 Announce Type: cross Abstract: The wide-scale use of sparse autoencoders (SAEs) as interpretability tools is limited by inconsistent links between SAE features and model behavior. F

Local Ai

SpecAHD: Localize to Specialize for Automated Heuristic Design in Large-Scale Routing Problems

arXiv:2607.23676v1 Announce Type: new Abstract: LLM-based automated heuristic design (AHD) typically scores executable programs on complete instances or within fixed solver components. In large-scale

local-aiarxiv-cs-ai

SpecBox: Speculative Sandbox Scheduling for Efficient LLM Agent Serving

arXiv:2607.23933v1 Announce Type: cross Abstract: As LLM agents increasingly rely on the Model Context Protocol (MCP) to invoke isolated external sandboxes, disaggregated sandbox deployment introduces

agentsarxiv-cs-ai

Spectral-Aware Analytic Class-Incremental Learning for Long-Tailed Distributions

arXiv:2607.22931v1 Announce Type: cross Abstract: Analytic Continual Learning (ACL) offers a computationally efficient alternative to gradient-based approaches. Recent ACL methods are based on Recursi

Stability of AI Governance Systems: A Coupled Dynamics Model of Public Trust and Social Disruptions

arXiv:2603.20248v2 Announce Type: replace-cross Abstract: AI systems are increasingly entrenched in public governance, yet scholarship lacks formal tools to determine when deviations of public trust i

StageGuard: Physiologically Constrained Sleep Staging

arXiv:2607.23284v1 Announce Type: new Abstract: Automated sleep staging is increasingly used in large-scale studies to derive sleep-architecture endpoints: total sleep time, REM latency, sleep efficie

safetyarxiv-cs-lg

STAIF: A Stage-wise Optimization for Complex Instruction Following

arXiv:2607.22649v1 Announce Type: new Abstract: Following complex instructions with multiple explicit constraints remains a fundamental challenge for large language models (LLMs). Existing alignment m

StAR: Segment Anything Reasoner

model-releasesarxiv-cs-cv

arXiv:2603.14382v2 Announce Type: replace Abstract: As AI systems are being integrated more rapidly into diverse and complex real-world environments, the ability to perform holistic reasoning over an

StepX-Edge: An On-Device UI Vision-Language Model via Architecture-Training-Deployment Co-Design

model-releasesarxiv-cs-cv

arXiv:2607.22708v1 Announce Type: new Abstract: Deploying a vision-language model with full UI understanding on end devices has long been trapped between accuracy and efficiency: on one side is the ac

Stochastic Counterdiabatic Driving via Biorthogonal Liouvillian Eigenmodes

arXiv:2607.24393v1 Announce Type: cross Abstract: Finite-time driving of stochastic systems generates excess dissipation, causing the evolving probability distribution to lag behind the instantaneous

researcharxiv-cs-lg

Stress-testing large language model agents in a robotic chemistry laboratory

arXiv:2607.23045v1 Announce Type: new Abstract: AI is evaluated through knowledge, reasoning and plan generation, yet scientific agency requires reliable physical action and adaptation to evidence. He

agentsarxiv-cs-ai

Structural Preservation Governs Data Augmentation in Deep Learning-Based Laser Speckle Material Classification

arXiv:2607.22725v1 Announce Type: cross Abstract: Data augmentation is routinely used to improve generalization in image classification, but the assumptions underlying standard policies are poorly mat

Structured Observation Language for Efficient and Generalizable Vision-Language Navigation

arXiv:2603.27577v2 Announce Type: replace Abstract: Vision-Language Navigation (VLN) requires an embodied agent to navigate complex environments by following natural language instructions, which typic

agentsarxiv-cs-cv

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

Synthetic Scenario Generation for Evaluation of Industry 4.0 Agents

arXiv:2607.22563v1 Announce Type: new Abstract: Industrial agent benchmarks require realistic evaluation scenarios that integrate telemetry, failure modes, maintenance records, and domain standards. H

SyRuP: Enhancing System-Prompt Following via Reward-Guided Prediction in LLM Decoding

arXiv:2607.23991v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly controlled through system prompts that specify roles, styles, formats, and safety requirements. However,

Test-Time Adaptation via Dual Distillation for Videos Under Severe Distribution Shifts

arXiv:2607.24611v1 Announce Type: new Abstract: Deep learning models have achieved state-of-the-art performance in several computer vision tasks. However, they experience severe performance degradatio

safetyarxiv-cs-cv

The Curse of Precision: A Data Scaling Law for High-Precision Robotic Manipulation

arXiv:2607.23108v1 Announce Type: new Abstract: While scaling laws for imitation learning have primarily focused on generalization in open-world settings, the relationship between data and precision i

safetyarxiv-cs-ro

The Equalizer: Introducing Shape-Gain Decomposition in Neural Audio Codecs

arXiv:2602.15491v2 Announce Type: replace-cross Abstract: Neural audio codecs (NACs) typically encode the short-term energy (gain) and normalized structure (shape) of speech/audio signals jointly with

The Few-shot Dilemma: Over-prompting Large Language Models

model-releasesarxiv-cs-cl

arXiv:2509.13196v2 Announce Type: replace Abstract: Over-prompting, a phenomenon where excessive examples in prompts lead to diminished performance in Large Language Models (LLMs), challenges the conv

Applications

The Illusion of Secure LLM Code: Closing the Security Gap via Iterative Reprompting

arXiv:2607.23710v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authen

applicationsarxiv-cs-ai

To Erase, or Not to Erase: Robust Training-Free Concept Erasure with Preservation aware Adaptive Ranked Subspace Expansion

arXiv:2607.23492v1 Announce Type: new Abstract: Concept erasure techniques (CETs) edit text-to-image diffusion models to erase undesired targets such as NSFW content or copyrighted styles, while prese

Token-Region Guided Cross-Attention Fusion for Multimodal Affect Interpretation

arXiv:2607.23493v1 Announce Type: cross Abstract: Automated analysis of multimodal content on social networks has become a critical task for understanding public sentiment and information diffusion in

Tokengeist: Multi-Turn Attribution Tracing in Agentic Conversations

arXiv:2607.22610v1 Announce Type: new Abstract: When a language model produces a response in a multi-turn conversation, which tokens from prior turns shaped that answer, and how did those dependencies

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

Towards Dual-Brain Minimal Sufficient Representation for Vision-Language Navigation

arXiv:2607.23181v1 Announce Type: new Abstract: Vision-and-Language Navigation in continuous environments (VLN-CE) requires an agent to ground language in egocentric observations and plan in unseen sc

safetyarxiv-cs-cv

Tutorials

Towards Ultrafast Depth Sensing Via Active Event-based Stereo Vision

arXiv:2607.23684v1 Announce Type: new Abstract: Conventional frame-based imaging for active stereo systems has encountered major challenges in fast-motion scenarios. However, how to design a novel par

tutorialsarxiv-cs-ro

Applications

Traceable LLM Reasoning for Fake-Order Fraud Detection

arXiv:2607.23075v1 Announce Type: cross Abstract: Detecting fake-order fraud at scale remains a critical challenge for large online-to-offline (O2O) service platforms, as existing approaches often rel

applicationsarxiv-cs-ai

Local Ai

Training Language Models to Cooperate with Inference-Time Controllers

arXiv:2607.23771v1 Announce Type: new Abstract: Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reaso

local-aiarxiv-cs-ai

TreeAdapter: Hierarchical Taxonomy-Guided Adapter Composition for Fine-Grained Species Image Generation

arXiv:2607.24215v1 Announce Type: new Abstract: Although general text-to-image models excel in open-domain generation, their performance degrades significantly in specialized downstream domains, parti

Trustworthy Medical Segmentation: Uncertainty-Aware U-Net Evaluation Under Clinical Image Degradation

model-releasesarxiv-cs-cv

arXiv:2607.22727v1 Announce Type: new Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can

Try Once, Then Optimal: De-Redundified Procedure Memory for Cross-Episode Exploration Amortization

model-releasesarxiv-cs-ro

arXiv:2607.23702v1 Announce Type: new Abstract: Manipulating objects with hidden internal state, such as a latched microwave, forces a robot to probe before it can act. Yet a robot that has solved an

Unequal Trips, Unequal Places: Diagnosing and Mitigating Delay Inequity in Autonomous Vehicle Fleet Coordination

arXiv:2607.24336v1 Announce Type: new Abstract: City-scale autonomous vehicle fleet coordinators are typically optimized for aggregate travel time, yet fleet averages conceal how delay is distributed