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Forgetting-Resistant and Lesion-Aware Source-Free Domain Adaptive Fundus Image Analysis with Vision-Language Model

model-releasesarxiv-cs-cv

arXiv:2602.19471v2 Announce Type: replace Abstract: Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled ta

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Safety

Forgotten History or Test-of-Time? Retrospect and Prospect on RAG from an IR Perspective

DGX agent

arXiv:2608.08445v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) is widely regarded as a novel paradigm born from the limitations of large language models (LLMs)--a mechanism to gr

safetyarxiv-cs-ai

11 Aug 2026

Foundation Models are Implicit Deepfake Detectors

arXiv:2608.09427v1 Announce Type: new Abstract: Pretrained self-supervised representations have emerged as a core component of current deepfake detection methods, yet it remains unclear which of their

Fourier Self-Supervision for Fine-Grained Generalized Category Discovery

arXiv:2608.08963v1 Announce Type: cross Abstract: Generalized Category Discovery aims to recognize known categories while identifying novel ones within unlabeled data. Existing methods, typically base

FreCast: Refining Radar Echo Intensity via Phase-Preserving Amplitude Residual Diffusion for Precipitation Nowcasting

arXiv:2608.08436v1 Announce Type: new Abstract: Precipitation nowcasting predicts the spatiotemporal evolution of future radar echoes from historical radar echo sequences, thereby estimating the occur

tutorialsarxiv-cs-cv

Frequency-Domain Dual-Branch Fusion for Medical Visual Question Answering

arXiv:2608.08307v1 Announce Type: cross Abstract: Medical Visual Question Answering (VQA) requires aligning subtle visual evidence, including lesion texture, boundary sharpness, and diffuse density ch

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification

arXiv:2608.08207v1 Announce Type: cross Abstract: Multivariate Time Series Classification (MTSC) demands models that can effectively capture complex temporal patterns across multiple scales while rema

From Alignment to Synthesis Contrastive Volumetric Grounding for Text-to-CT Generation

arXiv:2506.00633v3 Announce Type: replace-cross Abstract: Generating semantically controllable 3D CT volumes from radiology reports requires more than a rich text encoder, it requires vision-language

Hardware

From Approachability Residuals to Anytime-Valid Evidence: The Online Convex Geometry of Testing by Betting

arXiv:2608.09450v1 Announce Type: new Abstract: Betting-based sequential tests and Blackwell approachability are linked by a rate-explicit reduction through support-function residuals. For a compact c

hardwarearxiv-cs-lg

From Benchmark Performance to Tool Deployment: Human-in-the-Loop Anomaly Detection

model-releasesarxiv-cs-cv

arXiv:2608.07770v1 Announce Type: cross Abstract: Automated anomaly detection methods often report strong performance on curated academic benchmarks, but their behavior under real-world industrial con

From Chains to DAGs: Probing the Graph Structure of Reasoning in LLMs

arXiv:2601.17593v3 Announce Type: replace Abstract: Recent progress in large language models has renewed interest in how multi-step reasoning is represented internally. While prior work often treats r

researcharxiv-cs-cl

From Diagnosis to Correction: Benchmarking and Improving Real-World Table Parsing

model-releasesarxiv-cs-cv

arXiv:2608.09842v1 Announce Type: new Abstract: Recent document parsers achieve table TEDS scores above 93 on OmniDocBench v1.6, yet community feedback and our audit reveal persistent failures on comp

From Evaluated Models to Evaluation Aids: A Multi-Evidence Study of LLM-Based Difficulty Calibration for Programming Examinations

arXiv:2608.07523v1 Announce Type: cross Abstract: Difficulty differences across parallel-class programming examinations affect the fairness of course assessment. This study repositions large language

From Fake to Real: Pretraining on Balanced Synthetic Images to Prevent Spurious Correlations in Image Recognition

arXiv:2308.04553v4 Announce Type: replace Abstract: Visual recognition models are prone to learning spurious correlations induced by a biased training set where certain conditions B (eg, Indoors) are

safetyarxiv-cs-cv

From Inaudible Inputs to Model Failures: Low-Frequency Safety Risks in LALMs

arXiv:2608.09158v1 Announce Type: cross Abstract: Large audio-language models (LALMs) have demonstrated strong capabilities in understanding diverse audio inputs. This diversity includes low-frequency

From Independent to Correlated Diffusion: Generalized Generative Modeling with Probabilistic Computers

arXiv:2603.27996v2 Announce Type: replace Abstract: Diffusion models have emerged as a powerful framework for generative tasks in deep learning. They decompose generative modeling into two computation

researcharxiv-cs-lg

From Manuals to Maintenance: Fine-Tuning MedGemma for Multi-Modal Imaging System Support in Low-Resource Settings

arXiv:2608.08896v1 Announce Type: new Abstract: Imaging device downtime is a major barrier to healthcare delivery in low- and middle-income countries (LMICs), often driven by limited access to special

From Mimicry to True Intelligence (TI) -- A New Paradigm for Artificial General Intelligence

arXiv:2509.14474v3 Announce Type: replace Abstract: The debate around Artificial General Intelligence (AGI) remains open due to two fundamentally different goals: replicating human-level performance v

From Noise to Meaning: Meaningful Secret Sharing with Tamper Detection for Facial Recognition

arXiv:2608.08924v1 Announce Type: new Abstract: Popularity of AI-based face recognition system directly demands protection of sensitive biometric data used for training. Visual secret sharing is an in

From Objectives to What Models Learn: A Landau Theory of Invariant Learning

arXiv:2608.09396v1 Announce Type: new Abstract: Invariant learning seeks representations that remain predictive across environments, yet the behavior of its objectives along the regularization path is

tutorialsarxiv-cs-lg

From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving

arXiv:2608.08941v1 Announce Type: cross Abstract: Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe wha

From Product Search to Preference Articulation: The Economics of Agentic Commerce

arXiv:2608.08395v1 Announce Type: cross Abstract: Generative AI is shifting digital commerce from browsing toward agentic search, in which consumers delegate product discovery to AI agents. We compare

Agents

From Prompt to Harness: Coderlet from Scratch

arXiv:2608.09480v1 Announce Type: new Abstract: A model alone does not determine how a programming agent acts. What the model sees, how actions enter the environment, how feedback returns, and how one

agentsarxiv-cs-ai

From Rebound to Remedy: Understanding and Mitigating Reward Hacking via Representation Engineering

arXiv:2604.01476v2 Announce Type: replace-cross Abstract: Reinforcement learning for LLMs is vulnerable to reward hacking, where models exploit shortcuts to maximize reward without solving the intende

safetyarxiv-cs-cl

From Recovery to Drop-off: How Action Post-training Reduces a VLM's Late-Layer Depth Decodability

arXiv:2608.08904v1 Announce Type: cross Abstract: How much of a vision-language model's (VLM) spatial understanding remains after the action post-training process of building a vision-language-action

Agents

From Relevance to Execution Utility: Reward-Aware Dynamic Execution Gating for Skill-Based LLM Agents

arXiv:2608.09168v1 Announce Type: new Abstract: Agent skills are increasingly used to equip large language model (LLM) agents with reusable procedural knowledge. Although recent work has substantially

agentsarxiv-cs-ai

From Semantic Grounding to Decision Optimization: A Unified Framework for Long-Horizon UAV Vision-Language Navigation

arXiv:2608.09564v1 Announce Type: cross Abstract: UAV vision-language navigation (UAV-VLN) focuses on enabling an aerial agent to follow natural-language instructions in open 3D environments from egoc

From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems

arXiv:2608.07627v1 Announce Type: new Abstract: Hospitals are racing to embed AI, while coping with the surge in adaptation of the technology in other industries, into the triage management, documenta

From Speech to Interaction: Analyzing Multimodal Systems in Cocktail-Party Scenarios

arXiv:2608.08510v1 Announce Type: new Abstract: Humans have the remarkable ability to engage in spontaneous informal conversations and selectively attend to individual speakers while filtering out com

researcharxiv-cs-cl

From Sweep to Seam: Interleaved Cross-Block Post-Training Quantization

arXiv:2608.09595v1 Announce Type: new Abstract: Compressing large language models to two bits or fewer is increasingly feasible through block-wise post-training quantization; cross-block variants reco

From token probabilities to calibrated confidence: An empirical study of mathematical question answering

arXiv:2608.07827v1 Announce Type: cross Abstract: Confidence estimation for large language models (LLMs) aims to estimate the probability that a generated answer is correct, while calibration aligns t

researcharxiv-cs-cl

From Trajectories to Evidence: Auditable Experimental Records for Industrial Research Agents

arXiv:2608.05235v1 Announce Type: cross Abstract: Research agents increasingly conduct multi-round machine-learning experiments in industrial recommendation settings and retain the resulting trajector

tutorialsarxiv-cs-ai

From Uncertainty to Failure Attribution: Self-Diagnosing Models for Failure Attribution under Distribution Shift

arXiv:2608.07953v1 Announce Type: new Abstract: Distribution shift poses a significant challenge to the robustness of machine learning models, but the current solutions only aim to detect out-of-distr

researcharxiv-cs-lg

From Values to Benchmarks: Evaluating Large Language Models for Governmental Use in Dutch

arXiv:2608.09925v1 Announce Type: cross Abstract: Large language models are increasingly being deployed in governmental settings, yet few existing evaluation frameworks jointly reflect the values of p

From Visual to Multimodal: Systematic Ablation of Encoders and Fusion Strategies in Animal Identification

arXiv:2603.02270v2 Announce Type: replace Abstract: Automated animal identification is a practical task for reuniting lost pets with their owners, yet current systems often struggle due to limited dat

FSTC-Encoder: Feature--Spatial--Temporal Correlation Learning for Generalizable RF Sensing

arXiv:2608.08439v1 Announce Type: cross Abstract: Heterogeneous RF sensing differs substantially in feature structure, spatial layout, and temporal scale, making existing models difficult to reuse acr

Full-bandwidth transformer

arXiv:2608.08888v1 Announce Type: new Abstract: Autoregressive transformers compute along two axes: horizontally across generated tokens, and vertically through model depth. Dense attention gives each

Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints

arXiv:2608.09255v1 Announce Type: new Abstract: Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data ar

researcharxiv-cs-lg

Fusion Training for Mathematical Generalization in Large Language Models

arXiv:2608.09893v1 Announce Type: cross Abstract: Thinking Mode Fusion (TMF) enables large language models to support both concise responses and long-form reasoning by unifying a non-thinking mode and

Local Ai

GALA: Graph-Augmented LLM Agents for Root Cause Analysis and Incident Response in Microservices

arXiv:2608.08968v1 Announce Type: cross Abstract: Microservice root cause analysis (RCA) requires correlating failures across heterogeneous telemetry within complex service dependency graphs. Existing

local-aiarxiv-cs-ai

Gaming Without an Attacker: Benchmark Fingerprinting in LLM-Driven Search Under Selection Pressure

arXiv:2608.08722v1 Announce Type: cross Abstract: Benchmarks for systems that are optimized against the evaluation signal measure something different from what they claim. We document this concretely

Gated Spatial Redundancy Projection for Pathology Transformer Attentions

arXiv:2608.08374v1 Announce Type: new Abstract: Transformer models are increasingly used for whole-slide image analysis in computational pathology. Yet, WSIs differ fundamentally from natural images:

GENCO - A Unified Neural Solver Embedded in a Development Framework for Steady-State Grid Analysis

arXiv:2608.09921v1 Announce Type: new Abstract: Foundation models are transforming business workflows and boosting productivity, yet they remain largely absent from engineering domains such as power s

Local Ai

General OOD Detection via Model-aware and Subspace-aware Variable Priority

arXiv:2512.13003v2 Announce Type: replace-cross Abstract: Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its t

local-aiarxiv-cs-lg

Generalized Convexity and Smoothness via Conjugate Duality: Optimization Theory for Deep Neural Networks

arXiv:2608.09523v1 Announce Type: new Abstract: Deep neural network (DNN) training with stochastic gradient descent (SGD) and its variants achieves strong empirical performance, yet classical optimiza

safetyarxiv-cs-lg

Generalizing deep reinforcement learning across cable-driven parallel robot configurations with actuator-level policies

arXiv:2608.07546v1 Announce Type: cross Abstract: Cable-driven parallel robots (CDPRs) present diverse configurations and complex control challenges, which can be addressed by deep reinforcement learn

Generative Models: Principles, Architectures, and Applications

arXiv:2608.08101v1 Announce Type: new Abstract: Generative AI has emerged as one of the most transformative forces in modern artificial intelligence, reshaping how we create, imagine, and interact wit

tutorialsarxiv-cs-ai

GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association

arXiv:2608.09581v1 Announce Type: new Abstract: Multi-object tracking (MOT) involves maintaining consistent target identities as objects dynamically enter and leave a scene. Deterministic approaches,