Search: “framework”

SkillSentry: Reliable Skill Execution for LLM Agents via Runtime Assurance

arXiv:2608.09253v1 Announce Type: new Abstract: LLM agents are increasingly equipped with skills to perform complex tasks through multi-step reasoning and tool use. Although skills provide reusable pr

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Smart Compaction: Predicting Compaction Utility from Lakehouse Table Metadata

DGX agent

arXiv:2608.08639v1 Announce Type: new Abstract: Open lakehouse table formats accumulate small data files over time, which degrades query performance. Deciding when compaction is worthwhile remains thr

model-releasesarxiv-cs-ai

11 Aug 2026

SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing

arXiv:2601.19180v2 Announce Type: replace-cross Abstract: Inversion-free image editing using flow-based generative models challenges the prevailing inversion-based pipelines. However, existing approac

SoftMCC: An MCC-Brier Calibration Bridge for Threshold-Free Model Selection under Class Imbalance

arXiv:2608.08984v1 Announce Type: new Abstract: Model selection for imbalanced binary classification often uses the Matthews correlation coefficient (MCC), but thresholding makes validation rankings t

Space-Creating versus Dead Possession: An Off-Ball Possession-Quality Index for Broadcast Football

arXiv:2608.09887v1 Announce Type: new Abstract: Ball possession is the most-cited and most-misleading number in football: 60% recycled in one's own half is not 60% spent pinning the opponent back. Exi

Spatial Heterogeneity-Aware Multi-Hazard Susceptibility and Risk Mapping at Regional Scale

arXiv:2608.08321v1 Announce Type: new Abstract: Floods and landslides often co-occur, but their relationships with environmental controls vary spatially. This study develops a spatial heterogeneity-aw

Spatiotemporal Context-dependent Personalized Movement Compensation in Delayed Telemanipulation

arXiv:2608.08200v1 Announce Type: new Abstract: Communication delay remains a central challenge in telerobotics, where it disrupts visuomotor coordination and reduces task precision. Motion scaling is

safetyarxiv-cs-ro

SpeedTuning: Speeding Up Policy Execution with Lightweight Reinforcement Learning

arXiv:2608.09138v1 Announce Type: cross Abstract: While learned robotic policies hold promise for advancing generalizable manipulation, their practical deployment is often hindered by suboptimal execu

SplitGaussian: Reconstructing Dynamic Scenes via Visual Geometry Decomposition

arXiv:2508.04224v2 Announce Type: replace Abstract: Reconstructing dynamic 3D scenes from monocular video remains fundamentally challenging due to the need to jointly infer motion, structure, and appe

SportsGrounder: Proposal-Aided Interleaved Grounding for Dense Sports Video Reasoning

arXiv:2608.07932v1 Announce Type: new Abstract: Sports video analysis is crucial for athletic analytics and broadcasting enhancement. Dense sports video reasoning, however, demands a fine-grained unde

local-aiarxiv-cs-cv

SPRInG: Continual LLM Personalization via Selective Parametric Adaptation and Retrieval-Interpolated Generation

arXiv:2601.09974v2 Announce Type: replace Abstract: Personalizing Large Language Models typically relies on static retrieval or one-time adaptation, assuming user preferences remain invariant over tim

SRE-FER: Regional residual evidence learning for mitigating local evidence dilution in fine-grained facial expression recognition

arXiv:2608.08702v1 Announce Type: new Abstract: Fine-grained facial expression recognition (FER) hinges on capturing subtle muscular cues that distinguish adjacent emotions. Yet capturing these cues p

local-aiarxiv-cs-cv

Agents

Stochastic Physics-Informed Neural Networks on Lie Groups for Learning Underwater Vehicle Dynamics

arXiv:2608.08356v1 Announce Type: new Abstract: Accurate models of underwater vehicle motion are needed for autonomous execution of marine tasks like infrastructure inspection and scientific sampling.

agentsarxiv-cs-ro

StructReward: Efficient Structured Process Rewards for Self-Correcting Multimodal Reasoning

arXiv:2608.08326v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has emerged as an effective approach for improving multimodal reasoning. However, most existing me

Structure-Enhanced Features and Quality-Aware Dynamic Anchor Scoring for Robust Lane Detection

arXiv:2608.09610v1 Announce Type: cross Abstract: Lane detection requires recovering thin, elongated, and frequently occluded lane structures under challenging driving conditions. While anchor-based d

Structure-Preserving Uncertainty Propagation in First-Order Proof Search

arXiv:2608.09190v1 Announce Type: new Abstract: GK is a query-directed first-order prover that extends ordinary resolution-based proof search with explicit positive and negative claims, numerical conf

SuperLocalMemory 4.0: The Governed Memory Operating System for AI Agents

arXiv:2608.08253v1 Announce Type: new Abstract: AI agents are becoming shared infrastructure, yet durable memory is commonly assembled from separate retrieval, governance, and operational components.

SuperNeuroMAT: An Efficient Matrix-based Simulator for Spiking Neural Networks

arXiv:2608.08479v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer a promising pathway to energy-efficient AI and brain-inspired computing. However, their widespread adoption is hi

SurakshaEval: An Indic Safety Benchmark for Multilingual LLMs

model-releasesarxiv-cs-cl

arXiv:2608.07862v1 Announce Type: new Abstract: Existing safety evaluation datasets for large language models (LLMs) predominantly focus on English and Western contexts, often overlooking the linguist

SurgWMBench: A Vision-Based Benchmark for World-Modeling Surgical Instrument Motion Planning

model-releasesarxiv-cs-ro

arXiv:2608.08070v1 Announce Type: new Abstract: Reliable surgical planning requires models that move beyond recognizing the current surgical step or imitating expert demonstrations, and instead antici

SymboUQ: Symbolic Uncertainty Quantification for Spatial Reasoning in LLMs

arXiv:2608.00417v2 Announce Type: replace Abstract: Although large language models (LLMs) can produce fluent spatial reasoning traces, their intermediate relations may fail to support the final conclu

SymDiag: Explainable Diagnosis for LLM Reasoning via Neuro-Symbolic Verification

arXiv:2608.08786v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought (CoT) can be unfaithful even when final answers ar

SynVAR: Synergizing Spatial and Semantic Alignment in Visual Autoregressive Model

arXiv:2608.07948v1 Announce Type: new Abstract: VAR has gained widespread popularity due to its next-scale prediction paradigm. However, it faces substantial performance bottlenecks when handling comp

safetyarxiv-cs-cv

Targeted Counterfactual Fingerprinting for Black-Box LLM Ownership Verification

arXiv:2608.08195v1 Announce Type: cross Abstract: Large language models (LLMs) are high-value assets that can be derived through redeployment, fine-tuning, quantization, or further alignment. Because

The Evolution of Mixture-of-Experts Architectures in Large Language Models: Routing, Topology, Load Balancing, and Expert Parallelism

model-releasesarxiv-cs-cl

arXiv:2608.08650v1 Announce Type: new Abstract: Mixture-of-Experts models increase parameter capacity while keeping the computation activated by each token bounded, but their architectural evolution c

The No-Meaning Falsity: The Structural Impossibility of the Arbitrary Sign in Classical Arabic

arXiv:2608.07737v1 Announce Type: new Abstract: This paper investigates whether the postmodern claim of unrestricted semantic indeterminacy, and its foundational Saussurean axiom of the arbitrary sign

researcharxiv-cs-cl

The Voiceprint Fallacy: Why Voices Are Not Unique Biometric Imprints

arXiv:2608.07980v1 Announce Type: cross Abstract: In recent years, the term voiceprint has regained attention, particularly in technological applications and policy-making contexts, often carrying the

safetyarxiv-cs-cl

Tutorials

Thinking Hard, Not Smart: Reasoning Models Fail to Ration Test-Time Compute Across Questions

arXiv:2608.07968v1 Announce Type: cross Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one questio

tutorialsarxiv-cs-ai

Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation

arXiv:2608.08919v1 Announce Type: cross Abstract: During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resu

Toward Mask Annotation-Free Surgical Instrument Segmentation from Endoscopic Images Using Text-Prompted Segment Anything Model 3 (SAM3)

model-releasesarxiv-cs-cv

arXiv:2608.08844v1 Announce Type: new Abstract: Surgical instrument segmentation is a fundamental task for computer-assisted interventions, yet most existing methods rely on pixel-level annotations or

Applications

Towards Adaptive Super-Resolution and Quality Assessment via Test-Time Adaptation

arXiv:2608.08508v1 Announce Type: new Abstract: This paper presents doctoral research on adaptive video super-resolution and perceptual quality modeling under real-world conditions. Existing video sup

applicationsarxiv-cs-cv

TRACE-Memory: Public-Conditioned Retrieval and Utility-Aware Evidence Admission for Personalized Generation

arXiv:2608.08446v1 Announce Type: new Abstract: Personalized generation systems retrieve user history by request--memory relevance and inject it into the model context. Yet relevant history may concer

TRACE: TRajectory Attribution for Automated Context Engineering

arXiv:2608.09153v1 Announce Type: new Abstract: Production AI agents fail when their context sources -- system prompts, knowledge bases, tool descriptions, and procedural skills -- contain errors or g

Trajectory Design and Budgeted Querying for Digital Twin Calibration

model-releasesarxiv-cs-lg

arXiv:2608.08631v1 Announce Type: new Abstract: Digital-twin calibration requires interaction data that is expensive to collect. We study two acquisition decisions: which trajectories to generate, and

Trajectory Divergence Horizon Decision for Reliable Dual-Arm Surgical Subtask Manipulation

model-releasesarxiv-cs-ro

arXiv:2608.09125v1 Announce Type: new Abstract: Surgical robotic systems are increasingly being adopted as clinical workload rises, motivating autonomous solutions for repetitive manipulation subtasks

TransNRank: Towards Accurate Neoantigen Ranking with Transformer

arXiv:2608.01924v2 Announce Type: replace-cross Abstract: Personalized neoantigen prediction is challenging due to the scarcity of positive samples, the noise of the experimental data, the severe clas

Tree-of-Experience: Hierarchical Experience Management for Self-Evolving Agents

arXiv:2608.09044v1 Announce Type: new Abstract: Continual self-evolution requires LLM agents to transform environmental interactions into reliable and reusable experience. Existing methods typically r

researcharxiv-cs-cl

TreeHop: Efficient Embedding-Level Query Rewriter

arXiv:2504.20114v3 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) systems face significant challenges in multi-hop question answering (MHQA), where complex queries require

Triple Expert Learning from Noisy Labels for Semi-Supervised Vision Foundation Model Adaptation

arXiv:2608.09052v1 Announce Type: cross Abstract: Semi-supervised adaptation of vision foundation models (VFMs) commonly freezes the pretrained backbone and updates lightweight modules such as LoRA. H

TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity

arXiv:2608.08119v1 Announce Type: new Abstract: The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, class

Twin Rollouts: Noise-Coupled Counterfactual Branching in Interactive Video World Models

arXiv:2608.08982v1 Announce Type: new Abstract: Interactive video world models generate rollouts autoregressively under an action stream, yet they are trained and evaluated almost exclusively on factu

Uncertainty-Aware 4D Gaussian Splatting for Monocular Occluded Human Rendering

arXiv:2602.06343v3 Announce Type: replace Abstract: High-fidelity rendering of dynamic humans from monocular videos typically degrades catastrophically under occlusions. Existing solutions incorporate

Uncertainty-Aware Variational Reward Factorization via Probabilistic Preference Bases for LLM Personalization

arXiv:2604.00997v2 Announce Type: replace Abstract: Reward factorization personalizes large language models (LLMs) by decomposing rewards into shared basis functions and user-specific weights. Yet, ex

safetyarxiv-cs-cl

Understanding Reasoning from Pretraining to Post-Training

arXiv:2607.16097v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is l

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models

arXiv:2608.08627v1 Announce Type: new Abstract: Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes

Tutorials

UniScale: Arbitrary-Scale Industrial Anomaly Generation

arXiv:2608.07864v1 Announce Type: new Abstract: Industrial anomaly inspection faces a major challenge due to the lack of real-world anomaly samples. While generative models are used to create anomaly

tutorialsarxiv-cs-cv

UNSPECIFIC: General Constraint Synthesis for Breaking Copy-and-Paste Shortcut in LLM Instruction Following

model-releasesarxiv-cs-cl

arXiv:2608.09154v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly expected to follow long lists of constraints in complex instructions, and synthesizing instructions from a

Unsupervised Domain Adaptation for Multitask Image Analysis in Realistic Context with Extreme Label Shift; Application to the CTAO first Large Sized Telescope

arXiv:2608.09630v1 Announce Type: cross Abstract: Unsupervised domain adaptation is a widespread set of methods that leverages the knowledge of a labeled source domain to train a model to perform well