Search: “models”

Neurosymbolic Repo-level Code Localization

arXiv:2604.16021v1 Announce Type: cross Abstract: Code localization is a cornerstone of autonomous software engineering. Recent advancements have achieved impressive performance on real-world issue be

Phase Transitions as the Breakdown of Statistical Indistinguishability

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DGX agent

arXiv:2604.15773v1 Announce Type: cross Abstract: We introduce a novel characterization of phase transitions based on hypothesis testing. In our formulation, a phase transition is defined as the break

model-releasesarxiv-cs-ai

20 Apr 2026

PIIBench: A Unified Multi-Source Benchmark Corpus for Personally Identifiable Information Detection

arXiv:2604.15776v1 Announce Type: cross Abstract: We present PIIBench, a unified benchmark corpus for Personally Identifiable Information (PII) detection in natural language text. Existing resources f

Resource-efficient equivariant quantum convolutional neural networks

arXiv:2410.01252v2 Announce Type: replace-cross Abstract: Equivariant quantum neural networks (QNNs) are promising variational models that exploit symmetries to improve machine learning capabilities.

Robust Multispectral Semantic Segmentation under Missing or Full Modalities via Structured Latent Projection

arXiv:2604.15856v1 Announce Type: cross Abstract: Multimodal remote sensing data provide complementary information for semantic segmentation, but in real-world deployments, some modalities may be unav

safetyarxiv-cs-ai

SCHK-HTC: Sibling Contrastive Learning with Hierarchical Knowledge-Aware Prompt Tuning for Hierarchical Text Classification

model-releasesarxiv-cs-cl

arXiv:2604.15998v1 Announce Type: new Abstract: Few-shot Hierarchical Text Classification (few-shot HTC) is a challenging task that involves mapping texts to a predefined tree-structured label hierarc

Seeing the imagined: a latent functional alignment in visual imagery decoding from fMRI data

arXiv:2604.15374v1 Announce Type: cross Abstract: Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diff

Sequential KV Cache Compression via Probabilistic Language Tries: Beyond the Per-Vector Shannon Limit

arXiv:2604.15356v1 Announce Type: cross Abstract: Recent work on KV cache quantization, culminating in TurboQuant, has approached the Shannon entropy limit for per-vector compression of transformer ke

researcharxiv-cs-ai

SIMMER: Cross-Modal Food Image--Recipe Retrieval via MLLM-Based Embedding

arXiv:2604.15628v1 Announce Type: cross Abstract: Cross-modal retrieval between food images and recipe texts is an important task with applications in nutritional management, dietary logging, and cook

safetyarxiv-cs-cl

Sketch and Text Synergy: Fusing Structural Contours and Descriptive Attributes for Fine-Grained Image Retrieval

arXiv:2604.15735v1 Announce Type: cross Abstract: Fine-grained image retrieval via hand-drawn sketches or textual descriptions remains a critical challenge due to inherent modality gaps. While hand-dr

Softpick: No Attention Sink, No Massive Activations with Rectified Softmax

arXiv:2504.20966v4 Announce Type: replace Abstract: We introduce softpick, a rectified, not sum-to-one, drop-in replacement for softmax in transformer attention mechanisms that eliminates attention si

SSFT: A Lightweight Spectral-Spatial Fusion Transformer for Generic Hyperspectral Classification

arXiv:2604.15828v1 Announce Type: new Abstract: Hyperspectral imaging enables fine-grained recognition of materials by capturing rich spectral signatures, but learning robust classifiers is challengin

Target-Oriented Pretraining Data Selection via Neuron-Activated Graph

arXiv:2604.15706v1 Announce Type: new Abstract: Everyday tasks come with a target, and pretraining models around this target is what turns them into experts. In this paper, we study target-oriented la

researcharxiv-cs-cl

The Illusion of Equivalence: Systematic FP16 Divergence in KV-Cached Autoregressive Inference

arXiv:2604.15409v1 Announce Type: cross Abstract: KV caching is a ubiquitous optimization in autoregressive transformer inference, long presumed to be numerically equivalent to cache-free computation.

The Relic Condition: When Published Scholarship Becomes Material for Its Own Replacement

arXiv:2604.16116v1 Announce Type: cross Abstract: We extracted the scholarly reasoning systems of two internationally prominent humanities and social science scholars from their published corpora alon

TwinTrack: Post-hoc Multi-Rater Calibration for Medical Image Segmentation

arXiv:2604.15950v1 Announce Type: new Abstract: Pancreatic ductal adenocarcinoma (PDAC) segmentation on contrast-enhanced CT is inherently ambiguous: inter-rater disagreement among experts reflects ge

UA-Net: Uncertainty-Aware Network for TRISO Image Semantic Segmentation

arXiv:2604.15542v1 Announce Type: new Abstract: Tristructural isotropic (TRISO)-coated particle fuels undergo dimensional changes and chemical reactions during high-temperature neutron irradiation. Po

researcharxiv-cs-cv

Understanding New-Knowledge-Induced Factual Hallucinations in LLMs: Analysis and Interpretation

arXiv:2511.02626v3 Announce Type: replace Abstract: Prior works have shown that fine-tuning on new knowledge can induce factual hallucinations in large language models (LLMs), leading to incorrect out

researcharxiv-cs-cl

Agents

VeriGraph: Scene Graphs for Execution Verifiable Robot Planning

arXiv:2411.10446v3 Announce Type: replace-cross Abstract: Recent progress in vision-language models (VLMs) has opened new possibilities for robot task planning, but these models often produce incorrec

agentsarxiv-cs-ai

Local Ai

VeRVE: Versatile Retrieval for Videos via Unified Embeddings

arXiv:2601.12193v3 Announce Type: replace Abstract: Modern video retrieval systems are expected to handle diverse tasks ranging from corpus-level retrieval, fine-grained moment localization to flexibl

local-aiarxiv-cs-cv

Video-STAR: Reinforcing Open-Vocabulary Action Recognition with Tools

arXiv:2510.08480v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have demonstrated remarkable potential in bridging visual and textual reasoning, yet their reliance on text

researcharxiv-cs-cv

A Mechanistic Account of Attention Sinks in GPT-2: One Circuit, Broader Implications for Mitigation

arXiv:2604.14722v1 Announce Type: new Abstract: Transformers commonly exhibit an attention sink: disproportionately high attention to the first position. We study this behavior in GPT-2-style models w

safetyarxiv-cs-lg

A multi-platform LiDAR dataset for standardized forest inventory measurement at long term ecological monitoring sites

model-releasesarxiv-cs-ro

arXiv:2604.14635v1 Announce Type: new Abstract: We present a curated multi-platform LiDAR reference dataset from an instrumented ICOS forest plot, explicitly designed to support calibration, benchmark

A Nonlinear Separation Principle: Applications to Neural Networks, Control and Learning

arXiv:2604.15238v1 Announce Type: cross Abstract: This paper investigates continuous-time and discrete-time firing-rate and Hopfield recurrent neural networks (RNNs), with applications in nonlinear co

ADAPT: Benchmarking Commonsense Planning under Unspecified Affordance Constraints

model-releasesarxiv-cs-cl

arXiv:2604.14902v1 Announce Type: cross Abstract: Intelligent embodied agents should not simply follow instructions, as real-world environments often involve unexpected conditions and exceptions. Howe

AgentGA: Evolving Code Solutions in Agent-Seed Space

arXiv:2604.14655v1 Announce Type: cross Abstract: We present AgentGA, a framework that evolves autonomous code-generation runs by optimizing the agent seed: the task prompt plus optional parent archiv

AgentIAD: Agentic Industrial Anomaly Detection via Adaptive Memory Augmentation

arXiv:2512.13671v2 Announce Type: replace Abstract: Industrial anomaly detection (IAD) is challenging due to the subtle and highly localized nature of many defects, which single-pass vision--language

AIM: Asymmetric Information Masking for Visual Question Answering Continual Learning

arXiv:2604.14779v1 Announce Type: cross Abstract: In continual visual question answering (VQA), existing Continual Learning (CL) methods are mostly built for symmetric, unimodal architectures. However

researcharxiv-cs-cl

Awakening Dormant Experts:Counterfactual Routing to Mitigate MoE Hallucinations

arXiv:2604.14246v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) models have achieved remarkable scalability, yet they remain vulnerable to hallucinations, particularly when processing

Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios

model-releasesarxiv-cs-ro

arXiv:2604.15202v1 Announce Type: new Abstract: Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical

Bird-SR: Bidirectional Reward-Guided Diffusion for Real-World Image Super-Resolution

arXiv:2602.07069v2 Announce Type: replace Abstract: Powered by multimodal text-to-image priors, diffusion-based super-resolution excels at synthesizing intricate details; however, models trained on sy

safetyarxiv-cs-cv

Tutorials

CausalEmbed: Auto-Regressive Multi-Vector Generation in Latent Space for Visual Document Embedding

arXiv:2601.21262v3 Announce Type: replace Abstract: Although Multimodal Large Language Models (MLLMs) have shown remarkable potential in Visual Document Retrieval (VDR) through generating high-quality

tutorialsarxiv-cs-cl

CLion: Efficient Cautious Lion Optimizer with Enhanced Generalization

arXiv:2604.14587v1 Announce Type: new Abstract: Lion optimizer is a popular learning-based optimization algorithm in machine learning, which shows impressive performance in training many deep learning

Applications

Domain Fine-Tuning FinBERT on Finnish Histopathological Reports: Train-Time Signals and Downstream Correlations

arXiv:2604.14815v1 Announce Type: new Abstract: In NLP classification tasks where little labeled data exists, domain fine-tuning of transformer models on unlabeled data is an established approach. In

applicationsarxiv-cs-cl

ECM Contracts: Contract-Aware, Versioned, and Governable Capability Interfaces for Embodied Agents

arXiv:2604.13097v1 Announce Type: cross Abstract: Embodied agents increasingly rely on modular capabilities that can be installed, upgraded, composed, and governed at runtime. Prior work has introduce

Applications

Energy-based Regularization for Learning Residual Dynamics in Neural MPC for Omnidirectional Aerial Robots

arXiv:2604.14678v1 Announce Type: cross Abstract: Data-driven Model Predictive Control (MPC) has lately been the core research subject in the field of control theory. The combination of an optimal con

applicationsarxiv-cs-ro

Exploiting Correlations in Federated Learning: Opportunities and Practical Limitations

arXiv:2604.14751v1 Announce Type: cross Abstract: The communication bottleneck in federated learning (FL) has spurred extensive research into techniques to reduce the volume of data exchanged between

FAIR Universe Weak Lensing ML Uncertainty Challenge: Handling Uncertainties and Distribution Shifts for Precision Cosmology

arXiv:2604.14451v1 Announce Type: cross Abstract: Weak gravitational lensing, the correlated distortion of background galaxy shapes by foreground structures, is a powerful probe of the matter distribu

FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching

arXiv:2604.15115v1 Announce Type: new Abstract: Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proporti

FreqTrack: Frequency Learning based Vision Transformer for RGB-Event Object Tracking

arXiv:2604.14526v1 Announce Type: new Abstract: Existing single-modal RGB trackers often face performance bottlenecks in complex dynamic scenes, while the introduction of event sensors offers new pote

Generalization in LLM Problem Solving: The Case of the Shortest Path

arXiv:2604.15306v1 Announce Type: cross Abstract: Whether language models can systematically generalize remains actively debated. Yet empirical performance is jointly shaped by multiple factors such a

H2VLR: Heterogeneous Hypergraph Vision-Language Reasoning for Few-Shot Anomaly Detection

arXiv:2604.14507v1 Announce Type: new Abstract: As a classic vision task, anomaly detection has been widely applied in industrial inspection and medical imaging. In this task, data scarcity is often a

Hardware

HERMES: KV Cache as Hierarchical Memory for Efficient Streaming Video Understanding

arXiv:2601.14724v3 Announce Type: replace-cross Abstract: Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated significant improvement in offline video understanding. Howe

hardwarearxiv-cs-cl

Hybrid Latents -- Geometry-Appearance-Aware Surfel Splatting

arXiv:2604.14928v1 Announce Type: new Abstract: We introduce a hybrid Gaussian-hash-grid radiance representation for reconstructing 2D Gaussian scene models from multi-view images. Similar to NeST spl

safetyarxiv-cs-cv

IROSA: Interactive Robot Skill Adaptation using Natural Language

arXiv:2603.03897v3 Announce Type: replace-cross Abstract: Foundation models have demonstrated impressive capabilities across diverse domains, while imitation learning provides principled methods for r

safetyarxiv-cs-cl

Learning to Think Like a Cartoon Captionist: Incongruity-Resolution Supervision for Multimodal Humor Understanding

arXiv:2604.15210v1 Announce Type: cross Abstract: Humor is one of the few cognitive tasks where getting the reasoning right matters as much as getting the answer right. While recent work evaluates hum

safetyarxiv-cs-cl

MIOFlow 2.0: A unified framework for inferring cellular stochastic dynamics from single cell and spatial transcriptomics data

arXiv:2603.22564v2 Announce Type: replace Abstract: Understanding cellular trajectories via time-resolved single-cell transcriptomics is vital for studying development, regeneration, and disease. A ke

MixAtlas: Uncertainty-aware Data Mixture Optimization for Multimodal LLM Midtraining

model-releasesarxiv-cs-cl

arXiv:2604.14198v1 Announce Type: cross Abstract: Domain reweighting can improve sample efficiency and downstream generalization, but data-mixture optimization for multimodal midtraining remains large