Search: “deployment”

Comprehensive Assessment and Analysis for NSFW Content Erasure in Text-to-Image Diffusion Models

arXiv:2502.12527v2 Announce Type: replace Abstract: Text-to-image diffusion models have gained widespread application across various domains, demonstrating remarkable creative potential. However, the

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Applications

CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus

DGX agent

arXiv:2509.04859v4 Announce Type: replace Abstract: Fast and efficient photorealistic 3D reconstruction with (semantic) Gaussian Splatting (GS) is crucial for time-critical robotic perception and navi

applicationsarxiv-cs-cv

1 Sept 2026

Applications

Cross Lingual Transfer in Tulu Legal Comprehension: Script-Dependent Improvement and RAG-Induced Knowledge Conflict

arXiv:2608.28645v1 Announce Type: cross Abstract: Low-resource languages without an adequate training corpus often use a related, higher-resource language as a scaffold for comprehension. Still, there

applicationsarxiv-cs-ai

CrossAudit: A Git-Native, Cross-Vendor Audit Loop for Agentic Science

arXiv:2608.28631v1 Announce Type: new Abstract: An AI scientist should not grade its own homework. Yet in the systems we examined, the agent that reviews the work usually comes from the same model fam

D4orm: Multi-Robot Trajectories with Dynamics-aware Diffusion Denoised Deformations

arXiv:2503.12204v3 Announce Type: replace Abstract: This work presents an optimization method for generating kinodynamically feasible and collision-free multi-robot trajectories that exploits an incre

hardwarearxiv-cs-ro

Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents

arXiv:2606.19319v2 Announce Type: replace-cross Abstract: Production data integration is bottlenecked by repeated, lossy handoffs between data owners, engineers, and analysts who must collaboratively

Local Ai

Designing an Auditable LLM-Supported Workflow for Qualitative Thematic Analysis

arXiv:2608.30543v1 Announce Type: new Abstract: Large Language Models (LLMs) offer new possibilities for scaling qualitative analysis, but existing applications often provide limited methodological tr

local-aiarxiv-cs-ai

Designing for the Next Click: Bandits for Real-Time Page Layout

arXiv:2608.29850v1 Announce Type: new Abstract: E-commerce platforms increasingly personalize user experiences through machine learning, yet page layout decisions remain dominated by static rules and

Efficient GPU Retrieval for Semantic Search

arXiv:2608.28968v1 Announce Type: new Abstract: Semantic Search on LinkedIn must retrieve relevant profiles from a corpus of hundreds of millions in response to natural-language queries such as 'a fin

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Event-Driven Language Models with Sparse Neural Activity for Neuromorphic Hardware

arXiv:2608.30439v1 Announce Type: cross Abstract: Inference with transformer-based large language models (LLMs) is often limited by the memory-bound KV cache and quadratic attention cost. State-space

hardwarearxiv-cs-lg

Event-triggered Control and Online Learning for Networked Systems under Computational Delays

arXiv:2608.29576v1 Announce Type: new Abstract: Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve

researcharxiv-cs-lg

Faithfulness Is Not Free: Auditing Offline KV-Cache Quantization in Retrieval-Augmented Generation

arXiv:2608.30996v1 Announce Type: new Abstract: Retrieval-augmented generation systems can precompute and store key-value caches of retrieved documents to avoid re-encoding context at every query. Qua

researcharxiv-cs-cl

Applications

Federated Personalization of Early-Exit Networks

arXiv:2601.10015v2 Announce Type: replace Abstract: Personalized Federated Learning (PFL) excels at tailoring client-specific models, which is particularly critical for decentralized and heterogeneous

applicationsarxiv-cs-lg

Tutorials

From the Loss Landscape to Diverse Feature Learning in Neural Networks

arXiv:2608.28948v1 Announce Type: cross Abstract: Over the course of the last decade, neural networks have grown from an academic curiosity to moving the markets of nations. Despite this explosion in

tutorialsarxiv-cs-ai

Functional Degeneracy in Neural Networks: Measurement and Pruning

model-releasesarxiv-cs-lg

arXiv:2608.30741v1 Announce Type: new Abstract: A central question in modern machine learning is how much a trained model can be compressed without changing its behavior, to reduce the memory, compute

Generalizable Multi-Agent Planning from Signal Temporal Logic Specifications via Diffusion

arXiv:2608.29490v1 Announce Type: cross Abstract: Multi-agent systems in the real-world (e.g., drone swarms, autonomous cars, warehouse robots) must satisfy rich, temporal tasks while avoiding collisi

safetyarxiv-cs-ai

Generating Workflow DAGs from Natural Language with Non-Reasoning LLMs

arXiv:2608.30250v1 Announce Type: new Abstract: This paper addresses the problem of translating natural-language routing rules written by business administrators into executable workflow graphs for en

Tutorials

Generative multi-domain transfer learning for fault detection in data-scarce wind turbines

arXiv:2608.30323v1 Announce Type: new Abstract: Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require su

tutorialsarxiv-cs-lg

Generative vs. Encoder Models for Multilingual NER: A Comprehensive Empirical Study on Naamapadam

model-releasesarxiv-cs-cl

arXiv:2608.29959v1 Announce Type: new Abstract: Language is humanity's most consequential technology, yet for over a billion speakers across India's twenty-two constitutionally recognised languages, i

Geometric Attractor Monitoring: A Robust and Frugal Framework for Multi-modal Industrial Robotic Cycles

arXiv:2608.30804v1 Announce Type: new Abstract: Monitoring the health of heterogeneous industrial robot fleets is severely challenged by the multi-modal nature of their operational cycles and a persis

GreenBench: Benchmarking Energy Efficiency and Carbon Footprint of Open-Source LLM Inference on Apple Silicon

arXiv:2608.28667v1 Announce Type: cross Abstract: The rapid proliferation of Large Language Models (LLMs) has raised concerns about their environmental impact during inference. While Green AI research

Gurukul AI: An Interactive AI-Driven Educational Platform for Indian Education System

arXiv:2608.28611v1 Announce Type: cross Abstract: Recent advances in large language models (LLMs) like ChatGPT and LLaMA have transformed AI-driven education, but these systems are predominantly train

Hallucination Mitigation for Large Vision-Language Models via Implicit Feature Stabilization

arXiv:2608.29924v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) are prone to hallucinations: they fluently describe objects, attributes, and scenes that are not in the image. We

researcharxiv-cs-ai

How to Size GPUs for AI Inference and TCO Without Overspending

Inference workloads fall into four use‑case buckets (Chatbots/Copilots, Agents, Content Generation, Translation), each with distinct token patterns that dictate the required GPU memory and compute. Si

hardwarenvidia-developer

Local Ai

INTERVenE: Temporal-Abstraction-Interval Based Transformers for Short-Horizon Medical Event Prediction

arXiv:2608.29901v1 Announce Type: cross Abstract: Electronic Health Record (EHR) prediction models in the intensive care unit must learn from sparse and irregular measurements while preserving the cli

local-aiarxiv-cs-ai

Investigating Social Bias Changes in Quantized Language Models

model-releasesarxiv-cs-cl

arXiv:2602.06181v2 Announce Type: replace Abstract: Post-training quantization reduces the memory needed to run large language models but alters their social biases in ways that aggregate metrics fail

JudgePanel: A Compact Judge with Panel Deliberation via Adaptive Multi-Reward Reinforcement Learning

arXiv:2608.29168v1 Announce Type: new Abstract: The LLM-as-a-Judge paradigm has emerged as a scalable alternative to human evaluation. However, single-model judges are limited by their inherent model

Kascade: A Practical Sparse Attention Method for Long-Context LLM Inference

arXiv:2512.16391v2 Announce Type: replace-cross Abstract: Attention is the dominant source of latency during long-context LLM inference, an increasingly popular workload with reasoning models and RAG.

hardwarearxiv-cs-ai

Learning Simple Test-Time Environments for LLM Web Agents

arXiv:2608.29305v1 Announce Type: cross Abstract: Large language model (LLM) agents have demonstrated remarkable proficiency in manually constructed environments, yet their performance frequently coll

LLP: LLM-Based Product Pricing in E-commerce

arXiv:2510.09347v2 Announce Type: replace Abstract: Unlike Business-to-Consumer e-commerce platforms (e.g., Amazon), inexperienced individual sellers on Consumer-to-Consumer platforms (e.g., eBay) oft

safetyarxiv-cs-cl

Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic

arXiv:2608.30406v1 Announce Type: new Abstract: Goal-conditioned reinforcement learning struggles with long horizons when rewards are sparse. While a planner can provide subgoals to guide a low-level

LongGuard: Mechanistic Analysis and Training-Free Mitigation of Long-Context Failure in Safety Guardrails

arXiv:2608.27580v2 Announce Type: replace Abstract: Safety guardrails serve as the last line of defense against harmful inputs and outputs of large language models (LLMs), yet they are trained and eva

safetyarxiv-cs-ai

MaCTG: Multi-Agent Collaborative Thought Graph for Automatic Programming

arXiv:2410.19245v3 Announce Type: replace-cross Abstract: With the rapid advancement of Large Language Models (LLMs), LLM-based approaches have demonstrated strong problem-solving capabilities across

agentsarxiv-cs-cv

MAS-on-the-Fly: In-Context Structural Adaptation of LLM-Based Multi-Agent Systems

arXiv:2602.13671v2 Announce Type: replace-cross Abstract: Large Language Model (LLM)-based multi-agent systems (MAS) have emerged as a promising paradigm for complex tasks. However, existing works oft

MMLongEmbed: Benchmarking Multimodal Embedding Models in Long-Context Scenarios

arXiv:2606.14747v2 Announce Type: replace-cross Abstract: Recent advancements have significantly expanded the theoretical context windows of Multimodal Embedding Models (MEMs). However, larger context

MobileDreamer: Generative Sketch World Model for GUI Agent

arXiv:2601.04035v2 Announce Type: replace Abstract: Mobile GUI agents have shown strong potential in real-world automation and practical applications. However, most existing agents remain reactive, ma

Multiclass Linear Perceptrons with Multiplicative Margins

arXiv:2608.30028v1 Announce Type: new Abstract: This paper introduces a family of multiclass linear Perceptron classifiers with a multiplicative margin mechanism (MMPerc), as an alternative to standar

NFAD: Nuisance-Filtered Anomaly Detection Under Distribution Shift

model-releasesarxiv-cs-cv

arXiv:2608.29112v1 Announce Type: new Abstract: Recent advances in anomaly detection (AD) for industrial inspection have pushed performance on standard benchmarks toward saturation. However, strong be

No Detectable Change in Side-Level WER from Prompt-Level Context: A Preregistered Ablation on a Production Oral-History Corpus

arXiv:2608.28875v1 Announce Type: cross Abstract: Supplying context at inference time to a large multimodal model is an inexpensive lever for adapting speech transcription to a domain, and earlier res

On the Recoverability of Private Information Unlearning in Large Language Models

arXiv:2608.29943v1 Announce Type: cross Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to r

researcharxiv-cs-cl

On the Resilience of Text-to-Video Diffusion Models to Hardware Faults

model-releasesarxiv-cs-lg

arXiv:2608.29598v1 Announce Type: new Abstract: We present the first systematic study of the resilience of text-to-video (T2V) diffusion models under random hardware-level faults. While T2V models are

OntoAligner-Ensemble: Voting-Based Fusion across Heterogeneous Ontology Alignment Techniques

arXiv:2608.31137v1 Announce Type: new Abstract: Ontology alignment (OA) has evolved through several methodological paradigms, ranging from lexical and structural aligners to knowledge graph embedding

Overreliance on AI in Information-seeking from Video Content

arXiv:2603.19843v2 Announce Type: replace-cross Abstract: The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search

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Paper Pilot: A Human-in-the-Loop Expert System for Evidence-Traceable Scientific Manuscript Generation in Applied Sciences

arXiv:2608.28596v1 Announce Type: new Abstract: Large language model (LLM) agents are increasingly embedded in scientific workflows for literature analysis, drafting, and review. Existing systems adva

Applications

PixelIR: Fidelity-Perception Decoupling via Pixel-Space Image-Residual Flow Matching for Efficient One-Step Real-World Super-Resolution

arXiv:2608.30782v1 Announce Type: new Abstract: Real-world image super-resolution (Real-ISR) aims to preserve structures supported by the degraded observation while reconstructing perceptually realist

applicationsarxiv-cs-cv

Pro-Router: Token-Aware Progressive Model Routing with Adaptive Edge-Cloud Collaboration for Efficient Multimodal LLM Inference

arXiv:2608.28726v1 Announce Type: new Abstract: The remarkable performance of multimodal large language models (MLLMs) comes at the cost of substantial computational overhead, posing significant chall

ProGS: Towards Progressive Coding for 3D Gaussian Splatting

arXiv:2603.09703v2 Announce Type: replace Abstract: Progressive transmission of 3D Gaussian Splatting (3DGS) requires each completed transmission stage to be decodable from received data and directly

researcharxiv-cs-cv

PUFFER: Incremental Fuzzy Deduplication for Continuously Evolving Corpora

arXiv:2608.28622v1 Announce Type: cross Abstract: Large language model training corpora grow through successive, often redundant releases, so each release must be deduplicated against both itself and

researcharxiv-cs-ai