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FIRSTPASS: A Multi-Domain, Multi-Round Peer Review Dataset Grounded in Real Editorial Outcomes

arXiv:2608.26129v1 Announce Type: cross Abstract: Scientific peer review datasets have trained AI systems exclusively on Computer Science and Machine Learning venues, producing models that critique ab

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Safety

Five Primitives for Governing Autonomous AI Agents at Runtime

DGX agent

arXiv:2608.26696v1 Announce Type: new Abstract: Enterprise deployments of autonomous AI agents inherit a control model built for human users and long-lived services, and the fit fails in three specifi

safetyarxiv-cs-ai

28 Aug 2026

Fixed-Haven Reservation for Online Multi-Agent Pickup and Delivery in Dense Warehouses

model-releasesarxiv-cs-ro

arXiv:2608.26759v1 Announce Type: cross Abstract: Dense warehouses often contain single-lane aisles, dead ends, and tree-like guidepaths that leave little room for idle agents to wait without blocking

FLARE: A Failure-Aware Framework for Autonomous Correction and Recovery in Visual-Language Robotic Manipulation

arXiv:2608.26645v1 Announce Type: new Abstract: Vision-Language-Action Models~(VLAs) have demonstrated significant promise in generalizing to complex, long-horizon robotic manipulation tasks. However,

safetyarxiv-cs-ro

Hardware

FlashVLA: Streaming Action Decoding for Fast and Asynchronous VLA Inference

arXiv:2608.27384v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models are increasingly promising for robotic manipulation, yet their real-world deployment remains bottlenecked by high in

hardwarearxiv-cs-ro

FLAT: FLow-Aligned Training of Unrolled Networks for MRI Reconstruction

arXiv:2512.03020v3 Announce Type: replace Abstract: Unrolled networks are widely used in Magnetic Resonance Imaging (MRI) reconstruction for their efficiency. Structured as a series of neural network

FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models

arXiv:2608.26676v1 Announce Type: cross Abstract: Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when

researcharxiv-cs-ai

FoldPipe: Bounded Remote Streaming of Native Molecular Shards with Asynchronous Prefetch

model-releasesarxiv-cs-lg

arXiv:2608.27029v1 Announce Type: cross Abstract: Training molecular machine-learning models on ephemeral or memory-constrained accelerator instances can require repeatedly retrieving preprocessed mol

Forward-Free Diffusion Language Models with BPTT-Free Looped Refinement

arXiv:2606.08357v2 Announce Type: replace Abstract: Diffusion language models generate text through iterative denoising, offering a powerful alternative to autoregressive generation. However, discrete

researcharxiv-cs-cl

From Accuracy to Robustness: A Study of Rule- and Model-based Verifiers in Mathematical Reasoning

arXiv:2505.22203v3 Announce Type: replace-cross Abstract: Trustworthy verifiers are essential for the success of reinforcement learning with verifiable reward (RLVR), which is the core methodology beh

From Atomic to Agentic: Towards Interpretable Evaluation of LLMs' Agentic Mathematical Capabilities

arXiv:2608.26950v1 Announce Type: new Abstract: Large Language Models (LLMs) are evolving from performing end-to-end mathematical reasoning to integrating agentic intelligence. However, most existing

From Reasoning to Pixels: Grounded Medical Multimodal LLMs for VQA and Segmentation

arXiv:2608.26856v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have demonstrated impressive performance in Medical Visual Question Answering (Med-VQA), their relia

Applications

From Sound to Symptom: Real-Time Respiratory Signal Understanding for Conversational Healthcare Agents

arXiv:2608.26163v1 Announce Type: cross Abstract: Cough events during live spoken conversations carry clinically valuable respiratory signals, yet existing dialogue systems treat them as acoustic nois

applicationsarxiv-cs-ai

Agents

From SQL to Knowledge Graphs: An LLM-Driven Multi-Agent Approach with Data Schema Improvement

arXiv:2608.26117v1 Announce Type: cross Abstract: RDBMS (Relational Database Management System) databases face several limitations, including slow execution with multi-hop queries and a lack of explai

agentsarxiv-cs-ai

From Static to Dynamic: Benchmarking Real-World Code Review with MCR-Bench

arXiv:2608.27442v1 Announce Type: cross Abstract: In real-world software development, code review typically involves iterative interactions between developers and reviewers to improve software quality

FU-Mamba: A Frequency-Enhanced Dynamic Scanning Framework for Oralscan Image Segmentation

arXiv:2608.26607v1 Announce Type: new Abstract: Oralscan image segmentation is essential for computer-aided diagnosis and treatment planning in digital dentistry. However, existing visual state space

G2D: Generative-to-Discriminative Collaborative Inference for Zero-Shot Image Classification

arXiv:2608.26744v1 Announce Type: new Abstract: Zero-shot classification needs efficient label retrieval and fine-grained visual reasoning, yet discriminative and generative vision-language models fai

GameWAM: A World Action Model for Video Games

arXiv:2608.26200v1 Announce Type: new Abstract: Modern video games combine first-person perception, rapid visual changes, persistent world state, and heterogeneous native controls. Existing game agent

researcharxiv-cs-ai

Gaussian Processes and Reproducing Kernel Hilbert Spaces: Connections and Equivalences

arXiv:2506.17366v2 Announce Type: replace-cross Abstract: This monograph studies the relations between two approaches using positive definite kernels: probabilistic methods using Gaussian processes, a

Gaze Authentication: Factors Influencing Authentication Performance

arXiv:2509.10969v3 Announce Type: replace Abstract: This paper examines the key factors that influence the performance of state-of-the-art gaze-based authentication. Experiments were conducted on a la

Generative Monte Carlo Sampling for Constant-Cost Particle Transport

arXiv:2512.13965v1 Announce Type: cross Abstract: We present Generative Monte Carlo (GMC), a novel paradigm for particle transport simulation that integrates generative artificial intelligence directl

Generative Semantic Scene Completion

arXiv:2608.26737v1 Announce Type: new Abstract: Outdoor LiDAR semantic scene completion (SSC) recovers a dense semantic voxel grid from a scan observing 1% of the target volume, under class imbalance

Geo-LoRA: Geometry-Aware Subspace Evolution for Low-Rank Adaptation in Continual Learning

model-releasesarxiv-cs-cv

arXiv:2608.26960v1 Announce Type: new Abstract: Rehearsal-free class-incremental learning (CIL) with LoRA adapters remains challenging because the low-rank subspaces updated across tasks evolve withou

GeoMAD: Geometry-Aware Multi-View Anomaly Detection via Deformable Fusion and Distributional Alignment

arXiv:2608.26724v1 Announce Type: new Abstract: Multi-view anomaly detection (MvAD) detects defects by exploiting complementary observations from multiple camera viewpoints. The central challenge is t

safetyarxiv-cs-cv

Glass Surface Detection Grounded in 3D Visual Geometry

arXiv:2608.26752v1 Announce Type: new Abstract: Glass surface detection (GSD) is critical for scene understanding and reconstruction, and yet remains challenging due to the transparency and reflectivi

Global universality via discrete-time signatures

model-releasesarxiv-cs-lg

arXiv:2603.09773v2 Announce Type: replace-cross Abstract: We establish global universal approximation theorems for non-anticipative and general path-dependent functionals on spaces of piecewise linear

GRAFT: Grounded and Efficient Online Reinforcement Adaptation for Fine-Grained Robot Manipulation

arXiv:2608.27079v1 Announce Type: new Abstract: Pretrained vision-language-action (VLA) policies provide strong priors for robot manipulation, yet adapting them online to fine-grained biomedical tasks

safetyarxiv-cs-ro

GRAIN: Bridging Name and Narrative Shifts in Real-World Graph Reasoning through Invariance-Rewarded Agentic RL

arXiv:2608.27142v1 Announce Type: new Abstract: Despite their potential in standardized graph tasks, Large Language Models (LLMs) remain brittle to real-world shifts in node identifiers and task formu

Applications

Graph-Based Modeling of Financial Volatility Dynamics

arXiv:2608.26127v1 Announce Type: cross Abstract: Accurate forecasting of realized volatility (RV) is crucial for risk management and derivatives pricing. Although the implied volatility (IV) surface

applicationsarxiv-cs-cl

Graph-Based Pseudo-multimodal Contrastive Learning for 12-Lead ECG Representations

arXiv:2608.26964v1 Announce Type: new Abstract: 12-lead electrocardiogram (ECG) is a standard, non-invasive examination widely used for diagnosing coronary artery disease, where clinical interpretatio

Graph-Guided Selective Unlearning for Language Models: Controlling Support Routes Beyond Forget Seeds

arXiv:2608.26743v1 Announce Type: new Abstract: Enterprises fine-tune language models on proprietary data that may later require removal due to privacy, contractual, or compliance obligations. Selecti

Agents

GraphMemix: Query-Aware Evidence Forests for Long-Term Multimodal Agent Memory

arXiv:2608.26983v1 Announce Type: new Abstract: Organizing long-term memory for multimodal agents remains challenging because existing methods either suffer from expensive question-agnostic offline su

agentsarxiv-cs-ai

GRAS: Guided Reduced-Variance Proposals and Adaptive Selection for Training-Free Reward Alignment in Discrete Diffusion

arXiv:2608.26585v1 Announce Type: new Abstract: Discrete diffusion models have become a strong, widely adopted class of generators for sequence data, and steering them toward a downstream reward at in

safetyarxiv-cs-lg

Gromov-Monge Flow Matching for Equivariant Graph Generation

arXiv:2608.26961v1 Announce Type: new Abstract: Graphs are invariant under node permutations, motivating the use of permutation-equivariant architectures in generative models. In flow matching, howeve

safetyarxiv-cs-lg

GROUND: Reducing Hallucinations in LLM-Based Enterprise Analytics Through Governed Semantic Definitions

arXiv:2608.26157v1 Announce Type: new Abstract: Natural-language analytics over enterprise data warehouses is increasingly important, but production use is limited by hallucinated metrics, invalid joi

Tutorials

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

arXiv:2604.02324v2 Announce Type: replace-cross Abstract: Language models (LMs) are increasingly extended with new learnable vocabulary tokens for domain-specific tasks, such as Semantic-ID tokens in

tutorialsarxiv-cs-ai

GSM8K-V: Can Vision Language Models Solve Grade School Math Word Problems in Visual Contexts

arXiv:2509.25160v2 Announce Type: replace-cross Abstract: Mathematical reasoning is a key capability for vision-language models (VLMs), yet current benchmarks mainly evaluate text-based or explicitly

Guided Data Generation for Understanding Model Behavior

model-releasesarxiv-cs-lg

arXiv:2502.06658v4 Announce Type: replace Abstract: We propose a method for generating distributions over the input space as an inspection tool for understanding trained models. Our framework poses qu

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

arXiv:2608.26552v1 Announce Type: cross Abstract: Randomized sketch-and-solve algorithms accelerate overconstrained ell_2 regression by replacing the input with a smaller problem. Standard subspace em

Hallucinations in LLMs: A Lifecycle-Based Survey of Causes, Detection, Mitigation, and Prevention

arXiv:2608.26168v1 Announce Type: cross Abstract: The lifecycle of hallucination in LLMs is a concept that enables building solid frameworks on the control and reliability of LLMs in high-stakes envir

Applications

HALO: A Heterogeneity-Aware Language-Aligned IMU Foundation Model for Open-Set Human Activity Recognition

arXiv:2608.27233v1 Announce Type: new Abstract: Human Activity Recognition (HAR) using inertial measurement units (IMUs) enables a wide range of applications, yet the field still lacks a unified model

applicationsarxiv-cs-lg

Hierarchical Channel Stacking: A Structured Decision Framework for AI-Generated Image Detection

model-releasesarxiv-cs-cv

arXiv:2608.26648v1 Announce Type: new Abstract: Many synthetic-image detectors produce accurate predictions but offer limited insight into how those decisions are formed. This paper introduces Hierarc

High-Fidelity Face Content Recovery via Tamper-Resilient Versatile Watermarking

arXiv:2603.23940v2 Announce Type: replace-cross Abstract: The proliferation of AIGC-driven face manipulation and deepfakes poses severe threats to media provenance, integrity, and copyright protection

researcharxiv-cs-ai

Local Ai

High-Frequency First: A Two-Stage Approach for Improving Image INR

arXiv:2508.15582v3 Announce Type: replace Abstract: Implicit Neural Representations (INRs) have emerged as a powerful alternative to traditional pixel-based formats by modeling images as continuous fu

local-aiarxiv-cs-cv

High Probability Derivative Bounds for Random tanh Neural Networks on a Hypercube

arXiv:2608.26526v1 Announce Type: new Abstract: We establish high-probability bounds for mixed input derivatives of wide random neural networks whose activation derivatives satisfy a factorial growth

hoBIT: A Profile-Aware Retrieval-Augmented Chatbot for University Academic Advising

arXiv:2608.26604v1 Announce Type: cross Abstract: In university academic advising, identical questions can require different answers depending on a student's department, admission cohort, and degree p

How AI Experiences Art: Emergent Aesthetic Structure in a Self-Supervised Multimodal Embedding Space

arXiv:2608.27121v1 Announce Type: cross Abstract: Aesthetics are an important part of the symbolism of artistic works. Although subjective, humans categorize art based on the emotion evoked regardless

Agents

How Do LLM Agents Actually Get the Flag? Trace-Level Provenance for Agentic Offensive Security Evaluation

arXiv:2608.26237v1 Announce Type: cross Abstract: Capture-the-Flag (CTF) benchmarks are widely used to assess the offensive security capabilities of autonomous language-model agents. Evaluations rely

agentsarxiv-cs-ai