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GRAIL: Autonomous Concept Grounding for Neuro-Symbolic Reinforcement Learning

arXiv:2604.16871v1 Announce Type: cross Abstract: Neuro-symbolic Reinforcement Learning (NeSy-RL) combines symbolic reasoning with gradient-based optimization to achieve interpretable and generalizabl

agentsarxiv-cs-lg

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Graph neural network for colliding particles with an application to sea ice floe modeling

DGX agent

arXiv:2602.16213v2 Announce Type: replace-cross Abstract: This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ic

tutorialsarxiv-cs-cv

21 Apr 2026

Graph Neural Networks for Graphs with Heterophily: A Survey

arXiv:2202.07082v4 Announce Type: replace Abstract: Recent years have witnessed fast developments of graph neural networks (GNNs) that have benefited myriad graph analytic tasks and applications. Most

applicationsarxiv-cs-lg

Graph Transformer-Based Pathway Embedding for Cancer Prognosis

arXiv:2604.16685v1 Announce Type: new Abstract: Accurate prediction of cancer progression remains a challenge due to the high heterogeneity of molecular omics data across patients. While biologically

tutorialsarxiv-cs-lg

GraSP: Graph-Structured Skill Compositions for LLM Agents

arXiv:2604.17870v1 Announce Type: new Abstract: Skill ecosystems for LLM agents have matured rapidly, yet recent benchmarks show that providing agents with more skills does not monotonically improve p

local-aiarxiv-cs-cl

Greedy Kalman-Swarm: Improving State Estimation in Robot Swarms in Harsh Environments

arXiv:2604.16868v1 Announce Type: new Abstract: State estimation is a fundamental requirement in robotics, where the accurate determination of a robot's state is essential for stable operation despite

agentsarxiv-cs-ro

Grokking of Diffusion Models: Case Study on Modular Addition

arXiv:2604.17673v1 Announce Type: new Abstract: Despite their empirical success, how diffusion models generalize remains poorly understood from a mechanistic perspective. We demonstrate that diffusion

applicationsarxiv-cs-lg

GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting

arXiv:2604.18047v1 Announce Type: new Abstract: Continuous Spatio-Temporal Video Super-Resolution (C-STVSR) aims to simultaneously enhance the spatial resolution and frame rate of videos by arbitrary

safetyarxiv-cs-cv

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

arXiv:2604.18556v1 Announce Type: new Abstract: Weight quantization has become a standard tool for efficient LLM deployment, especially for local inference, where models are now routinely served at 2-

Guardrails in Logit Space: Safety Token Regularization for LLM Alignment

model-releasesarxiv-cs-lg

arXiv:2604.17210v1 Announce Type: new Abstract: Fine-tuning well-aligned large language models (LLMs) on new domains often degrades their safety alignment, even when using benign datasets. Existing sa

HABIT: Chrono-Synergia Robust Progressive Learning Framework for Composed Image Retrieval

arXiv:2604.18037v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is a flexible image retrieval paradigm that enables users to accurately locate the target image through a multimodal quer

researcharxiv-cs-cv

HalluSAE: Detecting Hallucinations in Large Language Models via Sparse Auto-Encoders

arXiv:2604.16430v1 Announce Type: new Abstract: Large Language Models (LLMs) are powerful and widely adopted, but their practical impact is limited by the well-known hallucination phenomenon. While re

Hard to Be Heard: Phoneme-Level ASR Analysis of Phonologically Complex, Low-Resource Endangered Languages

arXiv:2604.18204v1 Announce Type: new Abstract: We present a phoneme-level analysis of automatic speech recognition (ASR) for two low-resourced and phonologically complex East Caucasian languages, Arc

Harness as an Asset: Enforcing Determinism via the Convergent AI Agent Framework (CAAF)

model-releasesarxiv-cs-lg

arXiv:2604.17025v1 Announce Type: cross Abstract: Large Language Models (LLMs) produce a controllability gap in safety-critical engineering: even low rates of undetected constraint violations render a

HAVEN: Hierarchical Adversary-aware Visibility-Enabled Navigation with Cover Utilization using Deep Transformer Q-Networks

arXiv:2512.00592v2 Announce Type: replace Abstract: Autonomous navigation in partially observable environments requires agents to reason beyond immediate sensor input, exploit occlusion, and ensure sa

safetyarxiv-cs-ro

HEALing Entropy Collapse: Enhancing Exploration in Few-Shot RLVR via Hybrid-Domain Entropy Dynamics Alignment

arXiv:2604.17928v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Reward (RLVR) has proven effective for training reasoning-oriented large language models, but existing methods la

safetyarxiv-cs-lg

HeLa-Mem: Hebbian Learning and Associative Memory for LLM Agents

arXiv:2604.16839v1 Announce Type: new Abstract: Long-term memory is a critical challenge for Large Language Model agents, as fixed context windows cannot preserve coherence across extended interaction

agentsarxiv-cs-cl

HeteroCache: A Dynamic Retrieval Approach to Heterogeneous KV Cache Compression for Long-Context LLM Inference

arXiv:2601.13684v2 Announce Type: replace Abstract: The linear memory growth of the KV cache poses a significant bottleneck for LLM inference in long-context tasks. Existing static compression methods

Heterogeneity in Formal Linguistic Competence of Language Models: Is Data the Real Bottleneck?

arXiv:2604.17930v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit a puzzling disparity in their formal linguistic competence: while they learn some linguistic phenomena with near-pe

tutorialsarxiv-cs-cl

Heterogeneous Self-Play for Realistic Highway Traffic Simulation

arXiv:2604.16406v1 Announce Type: cross Abstract: Realistic highway simulation is critical for scalable safety evaluation of autonomous vehicles, particularly for interactions that are too rare to stu

safetyarxiv-cs-lg

Hierarchical Retrieval with Out-Of-Vocabulary Queries: A Case Study on SNOMED CT

arXiv:2511.16698v2 Announce Type: replace Abstract: SNOMED CT is a biomedical ontology with a hierarchical representation, modelling terminological concepts at a large scale. Knowledge retrieval in SN

Hierarchical Vision Transformer Enhanced by Graph Convolutional Network for Image Classification

arXiv:2604.16823v1 Announce Type: new Abstract: Vision Transformer (ViT) has brought new breakthroughs to the field of image classification by introducing the self-attention mechanism and Graph Convol

local-aiarxiv-cs-cv

High-Resolution Visual Reasoning via Multi-Turn Grounding-Based Reinforcement Learning

arXiv:2507.05920v2 Announce Type: replace Abstract: State-of-the-art large multi-modal models (LMMs) face challenges when processing high-resolution images, as these inputs are converted into enormous

safetyarxiv-cs-cv

HiGMem: A Hierarchical and LLM-Guided Memory System for Long-Term Conversational Agents

arXiv:2604.18349v1 Announce Type: new Abstract: Long-term conversational large language model (LLM) agents require memory systems that can recover relevant evidence from historical interactions withou

HiP-LoRA: Budgeted Spectral Plasticity for Robust Low-Rank Adaptation

arXiv:2604.17751v1 Announce Type: cross Abstract: Adapting foundation models under resource budgets relies heavily on Parameter-Efficient Fine-Tuning (PEFT), with LoRA being a standard modular solutio

HiPrune: Hierarchical Attention for Efficient Token Pruning in Vision-Language Models

arXiv:2508.00553v3 Announce Type: replace Abstract: Vision-Language Models (VLMs) encode images and videos into abundant tokens, which contain substantial redundancy and computation cost. While visual

researcharxiv-cs-cv

HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution

arXiv:2604.17745v1 Announce Type: new Abstract: Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental resu

HopRank: Self-Supervised LLM Preference-Tuning on Graphs for Few-Shot Node Classification

arXiv:2604.17271v1 Announce Type: new Abstract: Node classification on text-attributed graphs (TAGs) is a fundamental task with broad applications in citation analysis, social networks, and recommenda

HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions

arXiv:2505.15087v3 Announce Type: replace Abstract: Multi-Hop Question Answering (MHQA) is crucial for evaluating the model's capability to integrate information from diverse sources. However, creatin

HORIZON: A Benchmark for In-the-wild User Behaviour Modeling

arXiv:2604.17259v1 Announce Type: cross Abstract: User behavior in the real world is diverse, cross-domain, and spans long time horizons. Existing user modeling benchmarks however remain narrow, focus

Horizon-Aware Forecasting of Passenger Assistance Demand for Rail Station Workforce Planning

arXiv:2604.16464v1 Announce Type: cross Abstract: Passenger assistance services are essential for accessible rail travel, yet demand varies substantially across stations and over time, creating challe

applicationsarxiv-cs-lg

HorizonBench: Long-Horizon Personalization with Evolving Preferences

arXiv:2604.17283v1 Announce Type: new Abstract: User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent lif

Horospherical Depth and Busemann Median on Hadamard Manifolds

arXiv:2604.18242v1 Announce Type: cross Abstract: We introduce the horospherical depth, an intrinsic notion of statistical depth on Hadamard manifolds, and define the Busemann median as the set of its

researcharxiv-cs-lg

How Creative Are Large Language Models in Generating Molecules?

arXiv:2604.18031v1 Announce Type: new Abstract: Molecule generation requires satisfying multiple chemical and biological constraints while searching a large and structured chemical space. This makes i

local-aiarxiv-cs-cl

How Language Models Conflate Logical Validity with Plausibility: A Representational Analysis of Content Effects

arXiv:2510.06700v3 Announce Type: replace Abstract: Both humans and large language models (LLMs) exhibit content effects: biases in which the plausibility of the semantic content of a reasoning proble

safetyarxiv-cs-cl

How Much Cache Does Reasoning Need? Depth-Cache Tradeoffs in KV-Compressed Transformers

arXiv:2604.17935v1 Announce Type: new Abstract: The key-value (KV) cache is the dominant memory bottleneck during Transformer inference, yet little is known theoretically about how aggressively it can

researcharxiv-cs-lg

How Much Data is Enough? The Zeta Law of Discoverability in Biomedical Data, featuring the enigmatic Riemann zeta function

arXiv:2604.17581v1 Announce Type: new Abstract: How much data is enough to make a scientific discovery? As biomedical datasets scale to millions of samples and AI models grow in capacity, progress inc

researcharxiv-cs-lg

How Non-Linguistic Is the Indus Sign System? A Synthetic-Baseline Scorecard

arXiv:2604.17828v1 Announce Type: new Abstract: Whether the Indus Valley sign system (c. 2600-1900 BCE) encodes spoken language has been debated for decades. This paper introduces a multi-metric discr

How Robustly do LLMs Understand Execution Semantics?

model-releasesarxiv-cs-lg

arXiv:2604.16320v1 Announce Type: cross Abstract: LLMs demonstrate remarkable reasoning capabilities, yet whether they utilize internal world models or rely on sophisticated pattern matching remains o

How Should We Enhance the Safety of Large Reasoning Models: An Empirical Study

arXiv:2505.15404v2 Announce Type: replace Abstract: Large Reasoning Models (LRMs) have achieved remarkable success on reasoning-intensive tasks such as mathematics and programming. However, their enha

How to Approximate Inference with Subtractive Mixture Models

arXiv:2604.16714v1 Announce Type: new Abstract: Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance

tutorialsarxiv-cs-lg

How Tokenization Limits Phonological Knowledge Representation in Language Models and How to Improve Them

arXiv:2604.17105v1 Announce Type: new Abstract: Tokenization is the first step in every language model (LM), yet it never takes the sounds of words into account. We investigate how tokenization influe

local-aiarxiv-cs-cl

How Training Data Shapes the Use of Parametric and In-Context Knowledge in Language Models

arXiv:2510.02370v3 Announce Type: replace Abstract: Large language models leverage both parametric knowledge acquired during pretraining and in-context knowledge provided at inference time. Crucially,

HPLT 3.0: Very Large-Scale Multilingual Resources for LLMs and MT. Mono- and Bi-lingual Data, Multilingual Evaluation, and Pre-Trained Models

arXiv:2511.01066v3 Announce Type: replace Abstract: We present an ongoing initiative to provide open, very large, high-quality, and richly annotated textual datasets for almost 200 languages. At 30 tr

HQA-VLAttack: Towards High Quality Adversarial Attack on Vision-Language Pre-Trained Models

model-releasesarxiv-cs-cv

arXiv:2604.16499v1 Announce Type: new Abstract: Black-box adversarial attack on vision-language pre-trained models is a practical and challenging task, as text and image perturbations need to be consi

HSG: Hyperbolic Scene Graph

arXiv:2604.17454v1 Announce Type: new Abstract: Scene graph representations enable structured visual understanding by modeling objects and their relationships, and have been widely used for multiview

tutorialsarxiv-cs-cv

Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy

arXiv:2508.01486v3 Announce Type: replace Abstract: Sentiment analysis for low-resource languages remains challenging in an era where interpretability, human alignment, and fairness are increasingly n

safetyarxiv-cs-cl

Human Cognition in Machines: A Unified Perspective of World Models

arXiv:2604.16592v1 Announce Type: cross Abstract: This comprehensive report distinguishes prior works by the cognitive functions they innovate. Many works claim an almost 'human-like' cognitive capabi

agentsarxiv-cs-cv