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Rethinking Text-Based Image Retrieval in Specific Domain

arXiv:2608.10524v1 Announce Type: cross Abstract: Driven by the rapid advancement of vision-language representation learning, Text-based Image Retrieval (TBIR) has made notable progress. However, exis

Applications

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Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets

DGX agent

arXiv:2608.10657v1 Announce Type: cross Abstract: Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing sin

applicationsarxiv-cs-cv

12 Aug 2026

Retrieval-Corrected Conformal Prediction for Time Series

arXiv:2608.10553v1 Announce Type: cross Abstract: Conformal prediction (CP) provides distribution-free prediction intervals for fixed forecasters, but its standard calibration procedure is often ineff

local-aiarxiv-cs-ai

Riemann GeoResolver: A Non-Euclidean Attention Framework from Euclidean Resolver to Hyperbolic-Spherical Geometry

arXiv:2608.10416v1 Announce Type: cross Abstract: We present a theoretical foundation for inverse-distance attention, from its Euclidean prototype (Resolver) to its non-Euclidean realization (Riemann

Risk-Averse Wasserstein Distributionally Robust Online Learning

arXiv:2602.20403v2 Announce Type: replace Abstract: We study distributionally robust online learning, where a risk-averse learner updates decisions sequentially to guard against worst-case distributio

researcharxiv-cs-lg

Risk-Aware Kinodynamic Motion Planning Under Uncertainty For Safe Navigation on Planetary Environments

arXiv:2608.11175v1 Announce Type: new Abstract: For autonomous space exploration, robotic agents need to perform motion planning in which environmental interactions may be unknown. Learning these inte

agentsarxiv-cs-ro

RLMOpt: Adaptive Prompt Optimization via Recursive Language Models

arXiv:2608.10471v1 Announce Type: new Abstract: Prompt optimizers automate the search for prompts that improve language-model performance, but existing methods rely on a predefined optimization proced

Robust and Secure Code Watermarking for Large Language Models via ML/Crypto Codesign

arXiv:2502.02068v3 Announce Type: replace-cross Abstract: This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid int

researcharxiv-cs-cl

Robust Multi-Agent Bandits with Heavy-Tailed Rewards and Information Asymmetry

arXiv:2608.10529v1 Announce Type: cross Abstract: The multi-armed bandit problem is a central framework in sequential decision-making, extensively studied under sub-Gaussian reward assumptions. Howeve

agentsarxiv-cs-ai

Robust Safety Filtering for Input-Constrained Underactuated Linear Systems

arXiv:2608.10872v1 Announce Type: cross Abstract: We present a robust safety-filtering framework for input-constrained underactuated linear systems subject to unknown disturbances. A baseline H-infty

safetyarxiv-cs-ro

Robust Sliding Mode and Admittance Control of Underactuated Aerial Manipulators for Contact-Based Inspection

arXiv:2608.10656v1 Announce Type: new Abstract: Contact-based industrial inspection requires aerial platforms to maintain stable interaction while rejecting disturbances. Underactuated aerial manipula

safetyarxiv-cs-ro

Robustness of transferability estimation metrics for medical imaging

arXiv:2608.09999v1 Announce Type: cross Abstract: In transfer learning, the choice of source model largely influences the performance on a target dataset. Still, selecting a fitting source remains a c

researcharxiv-cs-cv

RTSKG: Building a Rail Transit Station Knowledge Graph Dataset

arXiv:2608.11080v1 Announce Type: new Abstract: Rail transit systems play a vital role in urban mobility and economic development. As key components of such systems, rail transit stations function as

Rule of Thumb: Explaining Artificial Intelligence Systems using Partial Information

arXiv:2608.10766v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) seeks to explain how an Artificial Intelligence (AI) system arrived at a particular decision. We propose ''Rul

SafeCA: Safe Cross-Attention Localization and Regulation for Text-to-Video Jailbreak Defense

arXiv:2608.10933v1 Announce Type: new Abstract: Text-to-Video (T2V) generative models are vulnerable to jailbreak attacks in real-world deployment, leading them to produce harmful or inappropriate con

local-aiarxiv-cs-cv

SafeCap: Improving LVLM Safety with Image Captioning Reinforcement Learning

arXiv:2608.10513v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) remain vulnerable to jailbreak attacks that exploit visual inputs to bypass safety alignment inherited from their

SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception

arXiv:2608.10497v1 Announce Type: new Abstract: While foundation models have significantly advanced human recognition across diverse modalities, they predominantly rely on static, geometric feature ex

safetyarxiv-cs-cv

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

model-releasesarxiv-cs-cv

arXiv:2608.11142v1 Announce Type: new Abstract: Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them

SBCO: Self-Supervised, Verifier-Grounded Harness Optimization For Planning Agents

arXiv:2608.10157v1 Announce Type: new Abstract: Self-improving agents seek to reduce the human engineering effort behind AI systems by enabling them to evolve and self-improve their performance over t

Tutorials

SceneNAT: Masked Generative Modeling for Language-Guided Indoor Scene Synthesis

arXiv:2601.07218v2 Announce Type: replace Abstract: We present SceneNAT, a masked non-autoregressive Transformer for 3D indoor scene synthesis from natural language instructions. It generates complete

tutorialsarxiv-cs-cv

Scheduling Mixed RL Rollouts Beyond Prefix Locality

arXiv:2608.11152v1 Announce Type: cross Abstract: Modern reinforcement learning (RL) post-training pipelines for large language models (LLMs) increasingly combine rollout workloads across multiple dom

safetyarxiv-cs-lg

SCOUT: Symmetric Consensus Outlier Detection for Failure Localization in LLM Pre-Training

arXiv:2608.11034v1 Announce Type: cross Abstract: In LLM pre-training, synchronization propagates rank-local stalls, slowdowns, and numerical errors into job-wide symptoms, obscuring their origin. Exi

local-aiarxiv-cs-lg

Seeds Before Objectives: Rethinking Evaluation for Low-Resource Garhwali ASR

model-releasesarxiv-cs-cl

arXiv:2608.10670v1 Announce Type: new Abstract: At corpus sizes typical of low-resource dialects, single-run comparisons can yield gains that do not replicate. We show this for Garhwali, an under-reso

Seeing above the waves: A modular sensing framework for data acquisition at sea

arXiv:2608.10997v1 Announce Type: new Abstract: Advancing autonomy for surface vessels requires systematic evaluation of their sensing and perception subsystems. Yet, maritime environments impose uniq

agentsarxiv-cs-ro

SeFaR: Semantic Feature-aware Robustness Testing of Deep Neural Networks

arXiv:2608.10289v1 Announce Type: new Abstract: Deep neural networks are increasingly deployed in safety-critical domains as perception modules, where failures are often caused due to rare and under-r

safetyarxiv-cs-cv

SeFoRA: Sketch-Aggregated Federated Low-Rank Adaptation with Heterogeneous Client Ranks

model-releasesarxiv-cs-lg

arXiv:2608.10144v1 Announce Type: new Abstract: We consider federated parameter efficient fine-tuning of large neural networks with low-rank adaptation (LoRA,~Hu et al. 2022). Combining LoRA with fede

Selective Prediction Reduces the Negative Effects of Automation Bias Overall but Increases False Negatives

arXiv:2508.07617v2 Announce Type: replace-cross Abstract: AI has the potential to augment human decision making. However, even high-performing models can produce inaccurate predictions when deployed.

Self-Correcting Long-Horizon Search Agents via Tree-Structured Memory

arXiv:2608.10676v1 Announce Type: new Abstract: Large language model (LLM)-based search agents answer questions through multi-step interactions with external environments. However, providing complete

Self-evolving Agentic Customer Support System at LinkedIn

arXiv:2608.10224v1 Announce Type: new Abstract: Enterprise support agents operate in rapidly changing environments where policies, product capabilities, and knowledge bases evolve continuously, making

agentsarxiv-cs-ai

Self-Geometry: GT-Free and Plug-and-Play Test-Time Adaptation for Geometrically Consistent 3D Vision Foundation Models

arXiv:2608.10708v1 Announce Type: new Abstract: Recent Vision Foundation Models (VFMs) predict depth, camera pose, and pointmap in a single forward pass without per-scene optimization, achieving stron

researcharxiv-cs-cv

Applications

Self-Knowledge Retrieval Augmented Generation Framework for Patent Matching

arXiv:2608.11030v1 Announce Type: cross Abstract: Patent retrieval and matching based on large language models (LLMs) play a vital role in intellectual property protection. However, due to the complex

applicationsarxiv-cs-cl

Self-Normalized Inference for Constant-Stepsize Temporal-Difference Learning under Markovian Sampling

arXiv:2608.10896v1 Announce Type: cross Abstract: Constant-stepsize temporal-difference (TD) learning is attractive for policy evaluation, but inference from a single Markov trajectory must account fo

safetyarxiv-cs-lg

Sensor-Informed Per-Point Covariance for Structured-Light 3D Imaging

arXiv:2608.10888v1 Announce Type: new Abstract: Per-point uncertainty models are important in structured-light 3D reconstruction for probabilistic registration, fusion, and quality assessment. In prac

local-aiarxiv-cs-cv

Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data

model-releasesarxiv-cs-lg

arXiv:2607.15606v2 Announce Type: replace Abstract: Synthetic sequential tabular data are increasingly used for privacy-preserving data sharing and data-driven research, but evaluating their fidelity

Sequential Modality Dropout for Robust Multi-Modal Sequential Recommendation

arXiv:2608.10240v1 Announce Type: cross Abstract: Multi-modal sequential recommenders assume every item carries every modality, but real product catalogs often miss images or text, and a model trained

researcharxiv-cs-lg

Share First, Route What Remains: A Unified Framework for Token-Adaptive MoE Computation

arXiv:2608.10392v1 Announce Type: cross Abstract: Mixture-of-experts (MoE) models have recently moved beyond routing a fixed number of complete experts. Shared-expert designs preserve reusable knowled

researcharxiv-cs-cl

Sheaf-Based Federated Representation Learning

arXiv:2608.10016v1 Announce Type: cross Abstract: Heterogeneous federated systems require agents to learn and exchange informative representations despite differences in data distributions, sensing mo

local-aiarxiv-cs-ai

Significance and Stability Analysis of Gene-Environment Interaction using GxEStat

model-releasesarxiv-cs-cv

arXiv:2604.03337v2 Announce Type: replace Abstract: Genotype-environment (GxE) interactions can influence the performance of genotypes across diverse environments, limiting the reliability of genotype

Signpost Watermarking: Joint Optimization for Visual Watermark Coexistence

arXiv:2608.10091v1 Announce Type: new Abstract: We present a method for training imperceptible visual watermarks to coexist with other such watermarks. Recent work has shown that independently trained

researcharxiv-cs-cv

Similarity Gates Approve Reversals: A Validity Audit of Embedding-Cosine Thresholds in Agent Systems

arXiv:2608.10216v1 Announce Type: cross Abstract: Agent frameworks ship quality gates that compare text blocks by embedding-cosine similarity and decide at a fixed cutoff. Deduplication filters, seman

Simplex Relaxation for Discrete Diffusion

arXiv:2608.10615v1 Announce Type: new Abstract: Discrete diffusion models for categorical generation are defined by a corruption kernel, which determines the intermediate state space and the associate

researcharxiv-cs-cl

SinD 2.0: A Multi-City UAV Dataset with Semantic Risk Annotations for SOTIF-Oriented Safety Validation at Signalized Intersections

model-releasesarxiv-cs-ro

arXiv:2607.16943v2 Announce Type: replace Abstract: Safety validation at signalized intersections remains a critical bottleneck for the deployment of autonomous driving systems (ADS), as these scenari

Situation Graph Prediction for User Perspective Modeling

arXiv:2602.13319v2 Announce Type: replace Abstract: Perspective-aware AI requires modeling evolving internal states---goals, emotions, contexts---not merely preferences. Progress is limited by a data

SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models

arXiv:2608.10538v1 Announce Type: new Abstract: Agent skills represent a standardized format for packaging procedural knowledge and domain expertise, serving within agent harness systems as an essenti

agentsarxiv-cs-ai

SkillLens: Visual Skill Cards for Retrieval-Augmented GUI Action Prediction and On-Policy Distillation

arXiv:2608.10775v1 Announce Type: new Abstract: Computer-using agents can perceive rich software interfaces, yet their decisions often lack visual procedural memory: they may recognize individual cont

SkillZip: Evaluation-Free Skill Compression for Self-Evolving Agents by Discovering Reusable Structure

arXiv:2608.11079v1 Announce Type: new Abstract: Self-evolving agents accumulate reusable skills by appending successful procedures and failure fixes. Over time, the same requirement is often restated

sLTN: Structural Logic Tensor Networks

arXiv:2608.11136v1 Announce Type: new Abstract: Logic Tensor Networks (LTN) provide a neurosymbolic framework in which first-order logic is interpreted through tensor operations, enabling logical cons

Smart Enough to Go Extinct? An Evolutionary Challenge to the Value of General Intelligence and Its Ethical Implications for AGI

arXiv:2608.10730v1 Announce Type: cross Abstract: The pursuit of artificial general intelligence (AGI) rests on a seemingly self-evident premise: that general intelligence, the kind of flexible, domai