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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

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Gromov-Monge Flow Matching for Equivariant Graph Generation

DGX agent

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

28 Aug 2026

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

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

researcharxiv-cs-cv

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

How Language Models Organize and Structure Moral Knowledge

arXiv:2608.27402v1 Announce Type: cross Abstract: How do large language models (LLMs) organize moral knowledge? Models detect moral content broadly, but detection is a low bar. We ask whether they go

How LLMs Distort Our Written Language

arXiv:2603.18161v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are used by over a billion people globally, most often to assist with writing. In this work, we demonstrate that

How Unlikely Is 'Unlikely'? Assessing Verbal Probability Perception Across Large Language Models

arXiv:2608.26327v1 Announce Type: cross Abstract: Large language models increasingly produce and interpret verbal probability expressions, yet whether these expressions carry consistent meaning across

HUG-VIS: A Multimodal Benchmark for Human-centered Understanding and Generation in Visual Intelligence

model-releasesarxiv-cs-cv

arXiv:2608.26517v1 Announce Type: new Abstract: Visual intelligence seeks to perceive, interpret, and synthesize the visual world and is central to modern computer vision. Human-centered visual intell

Hull First, Wake Second: Wake-Reliance Suppression for Robust Maritime Vessel Detection

model-releasesarxiv-cs-cv

arXiv:2608.26665v1 Announce Type: new Abstract: Maritime vessel detectors often face scenes where hulls are small, low-contrast, or blurred, while wakes are longer and easier to detect. This creates a

HybridProver: Augmenting Theorem Proving with LLM-Driven Proof Synthesis and Refinement

arXiv:2505.15740v2 Announce Type: replace-cross Abstract: Formal methods play a crucial role in ensuring the reliability of critical systems through rigorous mathematical verification. However, their

Applications

Hyperspectral Diffusion Equivariant Imaging (HyDiff-EI): A Self-supervised Framework for Hyperspectral Image Inpainting

arXiv:2608.26812v1 Announce Type: new Abstract: A novel Hyperspectral diffusion Equivariant Imaging (HyDiff-EI) framework for solving the hyperspectral image (HSI) inpainting problem has been presente

applicationsarxiv-cs-cv

Importance Scoring of Transformer Attention Heads in Learning Tabular Data

arXiv:2608.27241v1 Announce Type: new Abstract: Computationally demanding and opaque deep learning models can be better understood and optimized by analyzing how they transform data. While deep transf

Improving LLM Interpretability with User-Centric Chain-of-Thought Reasoning

arXiv:2608.26166v1 Announce Type: cross Abstract: Advancing reasoning capabilities allow large language models (LLMs) to tackle increasingly complex problems, while reasoning traces - intermediate ste

Incremental Recommendation via Causal Models

arXiv:2608.26804v1 Announce Type: cross Abstract: Recommendation impressions are a finite resource, hence delivering a recommendation to a user who would discover the content organically yields no inc

safetyarxiv-cs-lg

Inductive Correlation Clustering with Graph Neural Networks

arXiv:2608.27153v1 Announce Type: new Abstract: Correlation Clustering (CC) is a natural formulation of clustering in combinatorial optimization, which uses a graph representation of the input and doe

Local Ai

Information-Guided Frontier Decoding: Contextual Utility-Driven Commitment in dMLLMs

arXiv:2608.26641v1 Announce Type: new Abstract: Decoding quality in diffusion multimodal language models (dMLLMs) depends heavily on the order in which masked tokens are committed. Existing confidence

local-aiarxiv-cs-cl

Instruction Quality Matters: Refining Instructions for Effective Preference Learning

arXiv:2608.26779v1 Announce Type: new Abstract: Preference learning optimizes models using response pairs, yet the informativeness of these pairs is fundamentally shaped by the instructions from which

INTENT-AS-A-TOOL Makes it Easy to Track Agentic Misalignment

arXiv:2608.27348v1 Announce Type: new Abstract: As large language models (LLMs) are deployed as autonomous agents, safety failures increasingly involve consequential actions. We study agentic misalign

Interpretable, Fairly Evaluated Automated L2 Speaking Assessment that Beats the Single-Human Ceiling and Why Pause Encoding Does Not Change LLM Fluency Scores

arXiv:2608.26137v1 Announce Type: new Abstract: Second-language (L2) English learners can rarely rehearse speaking with a partner. Speaking is also the most anxiety-laden skill. These gaps drive a fas

Interpreting Latent Protein Language Model Features with Geometric Annotations

arXiv:2608.26419v1 Announce Type: cross Abstract: Protein language models (pLMs) encode information about protein sequences which enable downstream tasks such as structure prediction, but their intern

Investigating the Influence of Prompt and Response Languages on LLM Content Generation

arXiv:2608.26186v1 Announce Type: cross Abstract: This study examines how prompt and response language influence the behavior of large language models. Using five models, we evaluated answers to 68 no

Invocation-Level Reliability of Tool-Using Agents

arXiv:2608.26189v1 Announce Type: new Abstract: Tool-using agents fail two ways: choosing the wrong tool, or forming wrong arguments, and an early failure of either kind can silently corrupt everythin

Is Your Neighborhood Safe? Place-based Stigma in Large Language Models' Urban Safety Judgments

arXiv:2608.26188v1 Announce Type: new Abstract: Large language models are increasingly used to inform safety decisions in cities, such as where it is safe to walk, rent, or travel. We ask whether such

safetyarxiv-cs-ai

ITL: Interpretable Document Alignment with Structured Reference Frameworks

arXiv:2608.27031v1 Announce Type: new Abstract: Measuring alignment between documents and structured reference frameworks requires identifying conceptual evidence distributed throughout the text and r

J-Zero: Unified Challenger--Solver--Judge Co-Evolution from Zero Data

arXiv:2608.26582v1 Announce Type: cross Abstract: Self-evolving language models have recently emerged as a promising path toward superintelligence, with the advantage of reducing the cost of human sup

JudgeStealer: Extracting LLM Judging Capabilities across Evaluation Protocols

arXiv:2608.26982v1 Announce Type: new Abstract: Large language model (LLM) judges are increasingly used across various evaluation scenarios, making their judgment capabilities valuable intellectual pr

researcharxiv-cs-cl

KinyaEmbed: Contrastive Sentence Embeddings for Kinyarwanda via Multi-Stage Curriculum Training

model-releasesarxiv-cs-cl

arXiv:2608.26941v1 Announce Type: new Abstract: We present KinyaEmbed, the first dedicated sentence embedding model for Kinyarwanda, a morphologically rich Bantu language spoken by over 12 million peo

Applications

KISS-GS: 3D Gaussian Splatting Compression Kept Simple

arXiv:2608.26948v1 Announce Type: new Abstract: Scene reconstruction with 3D Gaussian Splatting (3DGS) has become common, however deployment remains painful as the uncompressed file sizes can be massi

applicationsarxiv-cs-cv

KnockGS:interaction-Grounded Calibrationof Physical Gaussian Representations

arXiv:2608.27365v1 Announce Type: cross Abstract: Physics-integrated 3D Gaussian representations now allow reconstructed deformable objects to be simulated and rendered under explicit material models.

Agents

Knowing When Not to Reuse: Conditional Experience Transfer in Autonomous LLM Post-Training

arXiv:2608.26730v1 Announce Type: new Abstract: Large language models offer broad capabilities, but adapting them to evolving domains, tools, and requirements often entails repeated post-training. Aut

agentsarxiv-cs-ai

Knowledge Cards: Structured Knowledge for AI Systems

arXiv:2608.26176v1 Announce Type: new Abstract: AI systems whose outputs inform real decisions, and increasingly consequential ones, require something that current documentation practice does not prov

Knowledge Distillation Driven Semantic NOMA with GAN Refinement for 6G Robotic Vehicle Networks

arXiv:2608.27198v1 Announce Type: cross Abstract: To achieve sustainable intelligent mobility, 6G-empowered robotic vehicles (RVs) require high-fidelity visual perception under stringent bandwidth and

researcharxiv-cs-cv

Knowledge-Verified Emergent Deception in LLM Agents Under Conflicting Incentives

arXiv:2608.26372v1 Announce Type: cross Abstract: Large language models are increasingly deployed as autonomous agents serving users on behalf of companies, placing them in settings where user and dep

LAAF: A Layered Accountability Architecture Framework for LLM Applications

arXiv:2608.27102v1 Announce Type: new Abstract: Large Language Models (LLMs) operate in hospitals, courtrooms, banks, and public service desks, where fluent, confident outputs are treated as authorita

safetyarxiv-cs-ai

Local Ai

Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap

arXiv:2608.26111v1 Announce Type: new Abstract: Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric

local-aiarxiv-cs-ai

Leakage-Free Evaluation and Distribution-Robust Spatio-Temporal Graph Learning for Inductive Kriging

arXiv:2509.23631v2 Announce Type: replace Abstract: Inductive kriging estimates values at unobserved locations from sparse sensor data, enabling continuous field reconstruction when dense deployment i