Search: “inference”

arXiv:2608.12721v1 Announce Type: new Abstract: Complex video object segmentation requires robust target propagation under severe occlusion, disappearance and reappearance. Although SAM3 provides stro

agentsarxiv-cs-cv

14 Aug 2026

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Safety

When Should Multi-Round RAG Stop? Structured Stopping Judgments and Retrieval Reduction in Search-R1

DGX agent

arXiv:2608.13237v1 Announce Type: cross Abstract: Multi-round retrieval-augmented generation (RAG) must decide when to stop searching as evidence accumulates. Because the deployed policy is determined

safetyarxiv-cs-cl

14 Aug 2026

Applications

A Theoretical Framework for Modular Learning of Robust Generative Models

arXiv:2602.17554v3 Announce Type: replace Abstract: Training large-scale generative models is resource-intensive and relies heavily on heuristic dataset weighting. We address two fundamental questions

applicationsarxiv-cs-lg

Air Quality Station Simulation via LSTM and Attention-Based Modelling

model-releasesarxiv-cs-lg

arXiv:2608.11839v1 Announce Type: new Abstract: Poor air quality in urban areas is driven by a complex chain of processes and presents a significant public health concern. To better understand and con

AWARe: Mitigating Catastrophic Forgetting via Activation-Weighted Adaptive REtention

model-releasesarxiv-cs-cl

arXiv:2608.11758v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) exhibit strong generalization and reasoning abilities due to large-scale multimodal pre-training. However, fine

Agents

Better, Faster, Stronger: Programmatic Skill Learning Best Reduces Agent Cost

arXiv:2608.11338v1 Announce Type: new Abstract: Recently, the practice of augmenting LLM agent capability with skills has gained prevalence. We explore the cost effective adaptation of agents to novel

agentsarxiv-cs-cl

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning

model-releasesarxiv-cs-lg

arXiv:2608.12108v1 Announce Type: new Abstract: Federated learning (FL) enables collaborative model training across distributed clients while keeping data local. A central challenge is determining whi

BoltNet: An Ultra-Lightweight Convolutional Network for On-Device Plant Species Identification

arXiv:2608.11844v1 Announce Type: new Abstract: Automated plant species identification from citizen-science imagery is an established, demanding fine-grained recognition problem: large taxonomic label

local-aiarxiv-cs-cv

CLEAR: Class-wise Expert Aggregation with Structured Sampling for Long-Tailed Classification

arXiv:2608.11287v1 Announce Type: cross Abstract: Long-tailed classification poses a reliability challenge because models trained on imbalanced data are unevenly reliable across frequent and underrepr

researcharxiv-cs-ai

COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification

arXiv:2505.18315v3 Announce Type: replace-cross Abstract: We introduce extbf{CoLoRA} (Convolutional Low-Rank Adaptation), a parameter-efficient fine-tuning method for convolutional neural networks (CN

Confucius4-TTS: Transcript-Free Cross-Lingual Zero-Shot TTS with a Learnable Speaker Encoder

model-releasesarxiv-cs-cl

arXiv:2608.11650v1 Announce Type: cross Abstract: Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS s

Detecting a Route Flip Is Easier Than Knowing Whether to Fix It: Causal Route-Mediated Damage in Quantized Mixture-of-Experts

arXiv:2608.11212v1 Announce Type: new Abstract: Top-k Mixture-of-Experts (MoE) routing is discontinuous, so a deployment-motivated numerical disturbance -- simulated 4-bit KV-cache quantization read b

local-aiarxiv-cs-ai

Diffuse to Compress: Leveraging Diffusion LMs for Lossless Compression

arXiv:2608.11249v1 Announce Type: cross Abstract: We study the problem of lossless text compression, motivated by the rapid growth in the collection and storage of digital textual data - including pla

Distillation of Foundation Models for Time-dependent PDEs

arXiv:2608.11937v1 Announce Type: new Abstract: Foundation models for time-dependent partial differential equations (PDEs) are trained on large and diverse collections of physical systems and can gene

local-aiarxiv-cs-lg

dots-studio/dots3-note-prev · Hugging Face

dots3-note preview is the first open-weight model in the dots3 family. It is a Mixture-of-Experts model with 280B total parameters, 16B activated parameters, and support for a context length of up to

local-air-localllama

End-to-end Differentiable Calibration and Reconstruction for Optical Particle Detectors

arXiv:2602.24129v3 Announce Type: replace-cross Abstract: Large-scale homogeneous detectors with optical readouts are widely used in particle detection, with Cherenkov and scintillator neutrino detect

researcharxiv-cs-lg

EnterpriseRAG: Benchmarking LLM Instruction Adherence and Robustness under Non-Ideal Enterprise Retrieval

arXiv:2608.11584v1 Announce Type: new Abstract: Enterprise RAG deployments face a critical reliability gap: while LLMs satisfy 80% of individual constraints, only 26.8% of responses meet all requireme

Evaluating and Calibrating Diffusion Model-derived Uncertainty for Quantitative MRI Mapping

model-releasesarxiv-cs-cv

arXiv:2608.11942v1 Announce Type: new Abstract: Quantitative MRI (qMRI) provides standardised tissue parameter maps, but the reliability of deep learning-based qMRI mapping methods is often not explic

From Synthesis to Removal: Physics-Grounded Reflection Simulation and Diffusion-Based Video Dereflection

arXiv:2608.11562v1 Announce Type: cross Abstract: Videos captured through glass often contain reflections that degrade visual quality and interfere with downstream vision tasks. Although single-image

Generative Semantic Segmentation via an Observable Semantic-Image Interface and Hierarchical Generator Evidence Alignment

arXiv:2608.11537v1 Announce Type: cross Abstract: Generative semantic segmentation exposes structured predictions as images, but direct color decoding is susceptible to color drift and boundary mixing

safetyarxiv-cs-ai

Glance, Scrutinize, and Think: Advancing Video Anomaly Detection from Training-Free to Agentic Reasoning

arXiv:2608.11260v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) aims to identify anomalous events and localize their temporal intervals. Existing approaches exhibit a 'when-what' dissoci

Grounding Large Language Models as Generalizable Policies in Network Control

arXiv:2512.11839v2 Announce Type: replace Abstract: Designing generalizable control policies that operate reliably under changing conditions is essential for robust network services in modern digital

safetyarxiv-cs-lg

HarmoniDPO: Video-guided Audio Generation via Preference-Optimized Diffusion

arXiv:2608.11913v1 Announce Type: new Abstract: Video-to-audio (V2A) generation faces significant challenges in achieving precise temporal synchronization and high perceptual quality due to the comple

safetyarxiv-cs-cv

Information Abundance Paradox: Long-Context Training Undermines Parametric Knowledge

arXiv:2608.12218v1 Announce Type: cross Abstract: Large language models are increasingly trained and deployed with long contexts that span documents, code repositories, and interaction histories. This

KANResDiff: Learning Local Residual Diffusion via Kolmogorov-Arnold Network for Ambiguous Medical Image Segmentation

arXiv:2608.11617v1 Announce Type: new Abstract: Ambiguous medical image segmentation aims to provide a series of diverse but plausible segmentation hypotheses. However, existing methods introduce stoc

local-aiarxiv-cs-cv

Language-Structured Relational Q-Learning for Threat-Aware Control in Safety-Critical Driving

arXiv:2608.11498v1 Announce Type: new Abstract: Natural-language-based scenario generation offers an intuitive means of describing rare and complex driving interactions, yet it is still uncertain whet

safetyarxiv-cs-cv

Latent variable models for simultaneous EOV identification and removal in population-based SHM

model-releasesarxiv-cs-lg

arXiv:2608.11995v1 Announce Type: cross Abstract: The robust treatment of environmental and operational variability (EOV) is an open challenge in population-based structural health monitoring (PBSHM).

LEMUR: Latent Entropy-aware Multimodal Unlearning via Visual-anchored Reasoning Redirection

arXiv:2608.11691v1 Announce Type: cross Abstract: Reinforcement-learning (RL) post-training equips multimodal large reasoning models (MLRMs) with exploratory chains of thought (CoT), substantially imp

LiveAnimate: Stable Long-Form Streaming Human Animation in Real-Time

model-releasesarxiv-cs-cv

arXiv:2608.11745v1 Announce Type: new Abstract: Pose-driven human animation synthesizes a video of a target person from a single reference image and a driving pose stream. Real-time generation is esse

Measure, Don't Optimize: Forecasting Recovery in LLM Unlearning

arXiv:2608.11408v1 Announce Type: new Abstract: Prior white-box studies show that large language models can retain latent traces of target knowledge after unlearning, even when the knowledge is no lon

Mechanist: AI as a Scientific Instrument for Discovering the Mechanisms of Intelligence

arXiv:2608.12036v1 Announce Type: new Abstract: AI models have achieved remarkable success across diverse domains, yet the mechanisms underlying their capabilities and the risks they may pose remain p

MicroAUNet: Boundary-Enhanced Multi-scale Fusion with Knowledge Distillation for Colonoscopy Polyp Image Segmentation

arXiv:2511.01143v2 Announce Type: replace-cross Abstract: Early and accurate segmentation of colorectal polyps is critical for reducing colorectal cancer mortality, which has been extensively explored

MuseCritic: Learning Multi-Aspect Song Rewards through Natural-Language Aesthetic Critiques

model-releasesarxiv-cs-cl

arXiv:2608.11755v1 Announce Type: cross Abstract: Long-form song generation models continue to improve in duration, structural integrity, and acoustic complexity, making reliable aesthetic rewards inc

Applications

MuseVLA: An Adaptive Multimodal Sensing Vision-Language-Action Model for Robotic Manipulation

arXiv:2606.17598v2 Announce Type: replace-cross Abstract: Humans naturally leverage diverse sensing modalities to interact with the physical world, while most Vision-Language-Action (VLA) models for r

applicationsarxiv-cs-cv

Agents

OpenAg: Democratizing Agricultural Intelligence

arXiv:2506.04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies

agentsarxiv-cs-ai

PolarSym: Polar Geometry-aware Attention for CAD Floorplan Parsing

arXiv:2608.11793v1 Announce Type: new Abstract: CAD plan parsing is a fundamental task in Building Information Modeling (BIM), aiming to automatically extract architectural elements including walls, d

researcharxiv-cs-cv

Policy-as-logic for robust reasoning over rules

arXiv:2608.11905v1 Announce Type: new Abstract: In many practical applications of generative AI systems, from tax rules to airline baggage allowance, responses to natural language queries must respect

safetyarxiv-cs-ai

Predicting Functions, Not Features: KANs with Function-Space Joint-Embedding Predictive Learning for Medical Image Segmentation

arXiv:2608.12050v1 Announce Type: new Abstract: Kolmogorov--Arnold Networks (KANs) introduce explicit functional representations by parameterizing each network edge as a learnable univariate function.

local-aiarxiv-cs-cv

Qdrant and Minima Deliver 2.92x More Agentic RAG Tasks per GPU-Hour

Reducing Retrieval and Calls When a retrieval-augmented generation (RAG) agent runs, it often has to plan a search, check the evidence it gets back, and try again when that evidence falls short. Those

model-releasesqdrant

Quantifying the Relationship Between Clinical Safety and Environmental Impact in Therapeutic LLMs

arXiv:2608.11830v1 Announce Type: cross Abstract: The deployment of large language models (LLMs) in mental health contexts raises questions about the relationship between clinical safety and environme

safetyarxiv-cs-cl

Industry

Ramp's July AI index: Anthropic's market share hit 43.5%, widening its lead over OpenAI; Fable 5 is only 6% of tokens businesses bought, likely due to high cost (Ara Kharazian/Ramp)

Ara Kharazian / Ramp: Ramp's July AI index: Anthropic's market share hit 43.5%, widening its lead over OpenAI; Fable 5 is only 6% of tokens businesses bought, likely due to high cost — Dear Colleagues

industrytechmeme

Retry, Switch, or Abstain? Learning Strategy-Aware Tool-Use Policies via Controlled Error Injection

arXiv:2608.11977v1 Announce Type: new Abstract: Tool-using LLM agents are commonly trained and evaluated in environments where tool calls succeed reliably, yet deployed tools can fail transiently, per

Ripple-Pivot Search: Active Parallel Decoding for Diffusion Large Language Models

arXiv:2608.11742v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have emerged as a competitive alternative to autoregressive language models, offering the potential for substant

RLinf-VLA: A Unified and Efficient Framework for Reinforcement Learning of Vision-Language-Action Models

arXiv:2510.06710v3 Announce Type: replace Abstract: Recent studies have demonstrated the potential of reinforcement learning (RL) to improve the task performance of vision-language-action (VLA) models

researcharxiv-cs-ro

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

arXiv:2608.11587v1 Announce Type: cross Abstract: Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalist

safetyarxiv-cs-cl

RT-SEMamba: Real-Time Speech Enhancement Mamba via Progressive Knowledge Distillation

arXiv:2608.12099v1 Announce Type: cross Abstract: We present RT-SEMamba, a fully causal speech enhancement (SE) model built upon causal time-frequency Mamba blocks. Unlike Transformer-based architectu

StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models

model-releasesarxiv-cs-ro

arXiv:2608.11671v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models can follow instructions and manipulate objects, but their performance often collapses out of distribution (OOD), whe

TD-VAD: Breaking Visual Dependence in Video Anomaly Detection with Text-Driven Learning

arXiv:2608.11820v1 Announce Type: new Abstract: Visual data is typically a prerequisite for training existing video anomaly detection (VAD) methods. However, obtaining sufficient annotated anomaly dat

researcharxiv-cs-cv