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Hidden Heroes and Gradient Bloats: Layer-Wise Redundancy Inverts Attribution in Transformers

arXiv:2602.01442v3 Announce Type: replace-cross Abstract: Gradient-based attribution is the workhorse of mechanistic interpretability, yet whether it reliably tracks causal importance at the component

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HiDrive: A Closed-Loop Benchmark for High-Level Autonomous Driving

DGX agent

arXiv:2605.09972v1 Announce Type: cross Abstract: End-to-end autonomous driving has witnessed rapid progress, yet existing benchmarks are increasingly saturated, with state-of-the-art models achieving

model-releasesarxiv-cs-cv

12 May 2026

Hierarchical Attention-based Graph Neural Network with Relevance-driven Pruning

arXiv:2605.09308v1 Announce Type: cross Abstract: Graph Neural Networks (GNNs) excel at relational reasoning but face two persistent challenges: the lack of interpretable attribution for heterogeneous

Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control

arXiv:2605.10624v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used to operate safety-critical infrastructure by predicting future trajectories and optimizing control actions

Hierarchical End-to-End Taylor Bounds for Complete Neural Network Verification

arXiv:2605.10621v1 Announce Type: new Abstract: Reachability analysis of neural networks, which seeks to compute or bound the set of outputs attainable over a given input domain, is central to certify

safetyarxiv-cs-lg

Hierarchical Mixture-of-Experts with Two-Stage Optimization

arXiv:2605.08292v1 Announce Type: cross Abstract: Sparse Mixture-of-Experts (MoE) models scale capacity by routing each token to a small subset of experts. However, their routers exhibit a fundamental

Tutorials

Hierarchical Multi-Fidelity Learning for Predicting Three-Dimensional Flame Wrinkling and Turbulent Burning Velocity

arXiv:2605.08232v1 Announce Type: new Abstract: High-fidelity experimental characterization of turbulent premixed flames remains limited by the cost and complexity of advanced diagnostics, particularl

tutorialsarxiv-cs-lg

Agents

Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning

arXiv:2605.08330v1 Announce Type: new Abstract: We present a hierarchical language-driven framework for robotic task and motion planning to improve natural, intuitive human-robot interaction in servic

agentsarxiv-cs-ro

Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution

arXiv:2410.14927v2 Announce Type: replace-cross Abstract: Automated equity trading requires converting noisy market and news signals into executable portfolio decisions under risk, turnover, and trans

High-Entropy Tokens as Multimodal Failure Points in Vision-Language Models

arXiv:2512.21815v2 Announce Type: replace Abstract: Vision-language models (VLMs) achieve remarkable performance but remain vulnerable to adversarial attacks. Entropy, as a measure of model uncertaint

researcharxiv-cs-cv

High Precision Hydraulic Excavator Control for Heavy-Duty Grading

model-releasesarxiv-cs-ro

arXiv:2605.09465v1 Announce Type: new Abstract: High-precision heavy-duty grading is a common step in earthworks, traditionally carried out manually by skilled operators. Removing a significant amount

High-probability zeroth-order online convex optimisation beyond Euclidean geometry

arXiv:2509.21484v3 Announce Type: replace Abstract: We study online convex optimisation with ell_q-Lipschitz losses, ell_p-regularised FTRL, and randomised two-point finite-difference gradient estimat

researcharxiv-cs-lg

Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation

arXiv:2605.08864v1 Announce Type: new Abstract: We study online estimation in latent-variable models by recasting the problem as tracking a moving empirical equilibrium. Standard online EM and stochas

Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning

arXiv:2605.10546v1 Announce Type: new Abstract: Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early bench

Hint Tuning: Less Data Makes Better Reasoners

model-releasesarxiv-cs-cl

arXiv:2605.08665v1 Announce Type: new Abstract: Large reasoning models achieve high accuracy through extended chain-of-thought but generate 5--8 more tokens than necessary, applying verbose reasoning

HLGFA: High-Low Resolution Guided Feature Alignment for Unsupervised Anomaly Detection

arXiv:2602.09524v3 Announce Type: replace Abstract: Unsupervised industrial anomaly detection (UAD) is essential for modern manufacturing inspection, where defect samples are scarce and reliable detec

safetyarxiv-cs-cv

Holmes: A Benchmark to Assess the Linguistic Competence of Language Models

model-releasesarxiv-cs-cl

arXiv:2404.18923v5 Announce Type: replace Abstract: We introduce Holmes, a new benchmark designed to assess language models (LMs) linguistic competence - their unconscious understanding of linguistic

HOME-KGQA: A Benchmark Dataset for Multimodal Knowledge Graph Question Answering on Household Daily Activities

arXiv:2605.09348v1 Announce Type: cross Abstract: Large Language Models (LLMs) provide flexible natural language processing capabilities, while knowledge graphs (KGs) offer explicit and structured kno

HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing

arXiv:2605.08143v1 Announce Type: cross Abstract: Large language models encode vast factual knowledge that inevitably becomes outdated or incorrect after deployment, yet retraining is costly prohibiti

How Instruction and Reasoning Data shape Post-Training: Data Quality through the Lens of Layer-wise Gradients

arXiv:2504.10766v2 Announce Type: replace-cross Abstract: As the post-training of large language models (LLMs) advances from instruction-following to complex reasoning tasks, understanding how differe

How LLMs Are Persuaded: A Few Attention Heads, Rerouted

arXiv:2605.09314v1 Announce Type: new Abstract: Language models can be persuaded to abandon factual knowledge. This vulnerability is central to AI safety, but its internal mechanism remains poorly und

How Mobile World Model Guides GUI Agents?

arXiv:2605.10347v1 Announce Type: new Abstract: Recent advances in vision-language models have enabled mobile GUI agents to perceive visual interfaces and execute user instructions, but reliable predi

How Much Do Circuits Tell Us? Measuring the Consistency and Specificity of Language Model Circuits

arXiv:2605.08348v1 Announce Type: new Abstract: The circuits framework in mechanistic interpretability aims to identify causally important sparse subgraphs of model components, typically evaluated by

researcharxiv-cs-cl

How Much is Brain Data Worth for Machine Learning?

arXiv:2605.09243v1 Announce Type: new Abstract: If a person can solve a task, can measuring their brain make it easier to train a model to solve that task too? Recent NeuroAI work suggests that supple

How Should LLMs Listen While Speaking? A Study of User-Stream Routing in Full-Duplex Spoken Dialogue

arXiv:2605.10199v1 Announce Type: new Abstract: Full-duplex spoken dialogue requires a model to keep listening while generating its own spoken response. This is challenging for large language models (

researcharxiv-cs-cl

How You Begin is How You Reason: Driving Exploration in RLVR via Prefix-Tuned Priors

arXiv:2605.08817v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) recently thrives in large language model (LLM) reasoning tasks. However, the reward sparsity and t

Tutorials

HPGN: Hybrid Priors-Guided Network for Compressed Low-Light Image Enhancement

arXiv:2504.02373v3 Announce Type: replace-cross Abstract: In practical applications, low-light images are often compressed for efficient storage and transmission. Most existing methods disregard compr

tutorialsarxiv-cs-cv

HS-FNO: History-Space Fourier Neural Operator for Non-Markovian Partial Differential Equations

arXiv:2605.09523v1 Announce Type: new Abstract: Neural operators provide fast surrogate models for time-dependent partial differential equations, but their standard autoregressive use usually assumes

HTPO: Towards Exploration-Exploitation Balanced Policy Optimization via Hierarchical Token-level Objective Control

arXiv:2605.08283v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a pivotal technique for enhancing the reasoning capabilities of Large Language Mo

Agents

HULK: Large-scale Hierarchical Coordination under Continual and Uncertain Temporal Tasks

arXiv:2605.08722v1 Announce Type: new Abstract: Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for delivery, surveillance, search and rescue. Coord

agentsarxiv-cs-ro

Human-Inspired Memory Architecture for LLM Agents

arXiv:2605.08538v1 Announce Type: new Abstract: Current LLM agents lack principled mechanisms for managing persistent memory across long interaction horizons. We present a biologically-grounded memory

Human-LLM Dialogue Improves Diagnostic Accuracy in Emergency Care

arXiv:2605.08533v1 Announce Type: new Abstract: Clinical decision-making in emergency medicine demands rapid, accurate diagnoses under uncertainty. Despite benchmark progress, evidence for LLMs as int

Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation

model-releasesarxiv-cs-cv

arXiv:2501.12202v4 Announce Type: replace Abstract: We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two fo

HY-Himmel Technical Report: Hierarchical Interleaved Multi-stream Motion Encoding for Long Video Understanding

arXiv:2605.08158v1 Announce Type: cross Abstract: Long-video understanding with multimodal language models suffers from three compounding bottlenecks: heavy decode cost to obtain dense RGB frames, qua

Local Ai

HyDRA Scorpion: A Cost-effective and Modular ROV for Real-Time Underwater Inspection, Intervention, and Object Detection

arXiv:2605.09093v1 Announce Type: new Abstract: A Remotely Operated Vehicle (ROV) is a tethered underwater robot used for tasks like inspection and intervention. While essential tools for underwater s

local-aiarxiv-cs-ro

HyNeuralMap: Hyperbolic Mapping of Visual Semantics to Neural Hierarchies

arXiv:2605.09392v1 Announce Type: new Abstract: Understanding the intricate mappings between visual stimuli and neural responses is a fundamental challenge in cognitive neuroscience. While current app

safetyarxiv-cs-cv

HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction

arXiv:2602.06527v2 Announce Type: replace Abstract: Scaling test-time compute with multi-path chain-of-thought improves reasoning accuracy, but its effectiveness depends critically on the exploration-

Hyperbolic Distillation: Geometry-Guided Cross-Modal Transfer for Robust 3D Object Detection

arXiv:2605.09899v1 Announce Type: cross Abstract: Cross-modal knowledge distillation has emerged as an effective strategy for integrating point cloud and image features in 3D perception tasks. However

Hypergraph-Enhanced Training-Free and Language-Free Few-Shot Anomaly Detection

arXiv:2605.10628v1 Announce Type: new Abstract: Few-shot anomaly detection (FSAD) has made significant strides, yet existing methods still face critical challenges: (i) dependence on task- or dataset-

researcharxiv-cs-cv

Hyperparameter Transfer for Dense Associative Memories

arXiv:2605.10164v1 Announce Type: new Abstract: Dense Associative Memory (DenseAM) is a promising family of AI architectures that is represented by a neural network performing temporal dynamics on an

researcharxiv-cs-lg

HYPERPOSE: Hyperbolic Kinematic Phase-Space Attention for 3D Human Pose Estimation

arXiv:2605.10100v1 Announce Type: cross Abstract: We introduce HYPERPOSE, a novel 3D human pose estimation framework that performs spatio-temporal reasoning entirely within the Lorentz model of hyperb

HyperSpace: A Generalized Framework for Spatial Encoding in Hyperdimensional Representations

arXiv:2604.15113v2 Announce Type: replace Abstract: Vector Symbolic Architectures (VSAs) provide a well-defined algebraic framework for compositional representations in hyperdimensional spaces. We int

Hyperspherical Autoencoder for High-Fidelity Image Reconstruction and Generation

arXiv:2601.22904v2 Announce Type: replace-cross Abstract: Recent studies have explored using pretrained Vision Foundation Models (VFMs) such as DINO for generative autoencoders, showing strong generat

HyperTransport: Amortized Conditioning of T2I Generative Models

arXiv:2605.08254v1 Announce Type: cross Abstract: As foundation models grow in capability, the ability to efficiently and reliably control their behavior becomes critical. Fine-tuning these models can

Hypothesis-Driven Deep Research with Large Language Models: A Structured Methodology for Automated Knowledge Discovery

arXiv:2605.10224v1 Announce Type: new Abstract: Current AI-powered research systems adopt a direct search-then-summarize paradigm that treats hypotheses as end products of scientific discovery. We arg

Hystar: Hypernetwork-driven Style-adaptive Retrieval via Dynamic SVD Modulation

model-releasesarxiv-cs-cv

arXiv:2605.10009v1 Announce Type: new Abstract: Query-based image retrieval (QBIR) requires retrieving relevant images given diverse and often stylistically heterogeneous queries, such as sketches, ar

ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression

arXiv:2605.10560v1 Announce Type: new Abstract: This paper describes our system to SemEval-2026 Task 3 Track A Subtask 1 on Dimensional Aspect Sentiment Regression (DimASR). We propose a lightweight a

researcharxiv-cs-cl

Identified-Set Geometry of Distributional Model Extraction under Top-K Censored API Access

arXiv:2605.10407v1 Announce Type: new Abstract: Modern LLM APIs often reveal only top-K logit scores and censor the remaining vocabulary. We study the per-position distribution-recovery limits of this

researcharxiv-cs-lg