Search: “inference”

EMCompress: Video-LLMs with Endomorphic Multimodal Compression

arXiv:2508.21094v3 Announce Type: replace Abstract: Video-LLMs face a fundamental tension in long-video reasoning: static, sparse frame sampling either dilutes evidence across task-irrelevant segments

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Model Releases

EPM-RL: Reinforcement Learning for On-Premise Product Mapping in E-Commerce

DGX agent

arXiv:2604.23993v1 Announce Type: cross Abstract: Product mapping, the task of deciding whether two e-commerce listings refer to the same product, is a core problem for price monitoring and channel vi

model-releasesarxiv-cs-ai

28 Apr 2026

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604.23466v1 Announce Type: cross Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while ret

Evaluating Temporal Consistency in Multi-Turn Language Models

model-releasesarxiv-cs-cl

arXiv:2604.23051v1 Announce Type: new Abstract: Language models are increasingly deployed in interactive settings where users reason about facts over time rather than in isolation. In such scenarios,

Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models

arXiv:2510.02629v3 Announce Type: replace Abstract: Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, r

researcharxiv-cs-cl

Expert Evaluation of LLM's Open-Ended Legal Reasoning on the Japanese Bar Exam Writing Task

arXiv:2604.23730v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on legal benchmarks, including multiple-choice components of bar exams. However, their capaci

applicationsarxiv-cs-ai

Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review

arXiv:2504.10527v2 Announce Type: replace Abstract: Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numero

Exploring Audio Hallucination in Egocentric Video Understanding

arXiv:2604.23860v1 Announce Type: cross Abstract: Egocentric videos provide a distinctive setting in which sound serves as crucial cues to understand user activities and surroundings, particularly whe

FlashOverlap: Minimizing Tail Latency in Communication Overlap for Distributed LLM Training

arXiv:2604.24013v1 Announce Type: cross Abstract: The rapid growth in the size of large language models has necessitated the partitioning of computational workloads across accelerators such as GPUs, T

researcharxiv-cs-cv

From Skeletons to Pixels: Few-Shot Precise Event Spotting via Representation and Prediction Distillation

arXiv:2604.22839v1 Announce Type: cross Abstract: Precise Event Spotting (PES) is essential in fast-paced sports such as tennis, where fine-grained events occur within very short temporal windows. Acc

Agents

From Static to Interactive: Authoring Interactive Visualizations via Natural Language

arXiv:2601.17736v2 Announce Type: replace-cross Abstract: Interactivity is crucial for effective data visualizations. However, it is often challenging to implement interactions for existing static vis

agentsarxiv-cs-ai

GAMMAF: A Common Framework for Graph-Based Anomaly Monitoring Benchmarking in LLM Multi-Agent Systems

arXiv:2604.24477v1 Announce Type: cross Abstract: The rapid integration of Large Language Models (LLMs) into Multi-Agent Systems (MAS) has significantly enhanced their collaborative problem-solving ca

Gated Tree Cross-Attention for Checkpoint-Compatible Syntax Injection in Decoder-Only LLMs

arXiv:2602.15846v2 Announce Type: replace Abstract: Decoder-only large language models achieve strong broad performance but are brittle to minor grammatical perturbations, undermining reliability for

researcharxiv-cs-cl

Generating Place-Based Compromises Between Two Points of View

model-releasesarxiv-cs-cl

arXiv:2604.24536v1 Announce Type: new Abstract: Large Language Models (LLMs) excel academically but struggle with social intelligence tasks, such as creating good compromises. In this paper, we presen

Global Context or Local Detail? Adaptive Visual Grounding for Hallucination Mitigation

arXiv:2604.24396v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination--generating content that contradicts visual reality--due to an over-re

GoClick: Lightweight Element Grounding Model for Autonomous GUI Interaction

arXiv:2604.23941v1 Announce Type: new Abstract: Graphical User Interface (GUI) element grounding (precisely locating elements on screenshots based on natural language instructions) is fundamental for

Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong

arXiv:2604.23523v1 Announce Type: cross Abstract: Safety specifications in cyber-physical systems (CPS) capture the operational conditions the system must satisfy to operate safely within its intended

safetyarxiv-cs-ai

GSAR: Typed Grounding for Hallucination Detection and Recovery in Multi-Agent LLMs

arXiv:2604.23366v1 Announce Type: new Abstract: Autonomous multi-agent LLM systems are increasingly deployed to investigate operational incidents and produce structured diagnostic reports. Their trust

GSC-QEMit: A Telemetry-Driven Hierarchical Forecast-and-Bandit Framework for Adaptive Quantum Error Mitigation

model-releasesarxiv-cs-lg

arXiv:2604.24551v1 Announce Type: cross Abstract: Quantum error mitigation (QEM) is essential for extracting reliable results from near-term quantum devices, yet practical deployments must balance mit

HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization

arXiv:2511.08425v3 Announce Type: replace Abstract: Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distri

Hardware-Efficient Softmax and Layer Normalization with Guaranteed Normalization for Edge Devices

arXiv:2604.23647v1 Announce Type: cross Abstract: In Transformer models, non-GEMM (non-General Matrix Multiplication) operations -- especially Softmax and Layer Normalization (LayerNorm) -- often domi

Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis

arXiv:2604.23375v1 Announce Type: new Abstract: Convolutional neural networks (CNNs) have become increasingly difficult to deploy in resource-constrained environments due to their large memory and com

Improving Vision-language Models with Perception-centric Process Reward Models

arXiv:2604.24583v1 Announce Type: new Abstract: Recent advancements in reinforcement learning with verifiable rewards (RLVR) have significantly improved the complex reasoning ability of vision-languag

InCoM: Intent-Driven Perception and Structured Coordination for Mobile Manipulation

arXiv:2602.23024v2 Announce Type: replace Abstract: Mobile manipulation is a fundamental capability for general-purpose robotic agents, requiring both coordinated control of the mobile base and manipu

safetyarxiv-cs-ro

Information bottleneck for learning the phase space of dynamics from high-dimensional experimental data

arXiv:2604.24662v1 Announce Type: cross Abstract: Identifying the dynamical state variables of a system from high-dimensional observations is a central problem across physical sciences. The challenge

IoT-Enhanced CNN-Based Labelled Crack Detection for Additive Manufacturing Image Annotation in Industry 4.0

arXiv:2604.22857v1 Announce Type: new Abstract: This paper presents an IoT-enhanced deep learning framework for automated crack detection in Additive Manufacturing (AM) surfaces using convolutional ne

applicationsarxiv-cs-cv

Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis

arXiv:2604.23435v1 Announce Type: cross Abstract: Radiographic grading of knee osteoarthritis (KOA) with the Kellgren-Lawrence (KL) system is limited by inter-reader variability and the opacity of cur

Layerwise Convergence Fingerprints for Runtime Misbehavior Detection in Large Language Models

arXiv:2604.24542v1 Announce Type: cross Abstract: Large language models deployed at runtime can misbehave in ways that clean-data validation cannot anticipate: training-time backdoors lie dormant unti

Learn&Drop: Fast Learning of CNNs based on Layer Dropping

arXiv:2604.23403v1 Announce Type: cross Abstract: This paper proposes a new method to improve the training efficiency of deep convolutional neural networks. During training, the method evaluates score

Learning to Route Queries to Heads for Attention-based Re-ranking with Large Language Models

arXiv:2604.24608v1 Announce Type: cross Abstract: Large Language Models (LLMs) have recently been explored as fine-grained zero-shot re-rankers by leveraging attention signals to estimate document rel

LearnPruner: Rethinking Attention-based Token Pruning in Vision Language Models

arXiv:2604.23950v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in visual understanding and reasoning, but they also impose significant

safetyarxiv-cs-cv

LILogic Net: Compact Logic Gate Networks with Learnable Connectivity for Efficient Hardware Deployment

arXiv:2511.12340v2 Announce Type: replace Abstract: Efficient machine learning deployment requires models that account for hardware constraints. Because binary logic gates are the fundamental primitiv

Linear-Nonlinear Fusion Neural Operator for Partial Differential Equations

model-releasesarxiv-cs-lg

arXiv:2603.24143v2 Announce Type: replace Abstract: Neural operator learning directly constructs the mapping relationship from the equation parameter space to the solution space, enabling efficient di

Live Knowledge Tracing: Real-Time Adaptation using Tabular Foundation Models

arXiv:2602.06542v3 Announce Type: replace Abstract: Deep knowledge tracing models have achieved significant breakthroughs in modeling student learning trajectories. However, these architectures requir

LoGeR: Long-Context Geometric Reconstruction with Hybrid Memory

arXiv:2603.03269v2 Announce Type: replace Abstract: Feedforward geometric foundation models achieve strong short-window reconstruction, yet scaling them to minutes-long videos is bottlenecked by quadr

safetyarxiv-cs-cv

Long-Context Aware Upcycling: A New Frontier for Hybrid LLM Scaling

model-releasesarxiv-cs-cl

arXiv:2604.24715v1 Announce Type: new Abstract: Hybrid sequence models that combine efficient Transformer components with linear sequence modeling blocks are a promising alternative to pure Transforme

Machine-Learning-Based Classification of Radio Frequency Building Loss

arXiv:2604.24143v1 Announce Type: new Abstract: Accurate modeling of outdoor-to-indoor (O2I) and indoor-to-indoor (I2I) signal loss is important for improving indoor wireless network performance in de

applicationsarxiv-cs-lg

MEMCoder: Multi-dimensional Evolving Memory for Private-Library-Oriented Code Generation

arXiv:2604.24222v1 Announce Type: cross Abstract: Large Language Models (LLMs) excel at general code generation, but their performance drops sharply in enterprise settings that rely on internal privat

MIMIC: A Generative Multimodal Foundation Model for Biomolecules

arXiv:2604.24506v1 Announce Type: new Abstract: Biological function emerges from coupled constraints across sequence, structure, regulation, evolution, and cellular context, yet most foundation models

MIPIC: Matryoshka Representation Learning via Self-Distilled Intra-Relational and Progressive Information Chaining

arXiv:2604.24374v1 Announce Type: new Abstract: Representation learning is fundamental to NLP, but building embeddings that work well at different computational budgets is challenging. Matryoshka Repr

safetyarxiv-cs-cl

ML-Guided Primal Heuristics for Mixed Binary Quadratic Programs

arXiv:2604.23053v1 Announce Type: new Abstract: Mixed Binary Quadratic Programs (MBQPs) are an important and complex set of problems in combinatorial optimization. As solving large-scale combinatorial

applicationsarxiv-cs-lg

Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion

arXiv:2604.23753v1 Announce Type: new Abstract: Multimodal affective computing analyzes user-generated social media content to predict emotional states. However, a critical gap remains in understandin

MTRouter: Cost-Aware Multi-Turn LLM Routing with History-Model Joint Embeddings

arXiv:2604.23530v1 Announce Type: cross Abstract: Multi-turn, long-horizon tasks are increasingly common for large language models (LLMs), but solving them typically requires many sequential model inv

MVIGER: Multi-View Variational Integration of Complementary Knowledge for Generative Recommender

arXiv:2408.08686v4 Announce Type: replace-cross Abstract: Language Models (LMs) have been widely used in recommender systems to incorporate textual information of items into item IDs, leveraging their

applicationsarxiv-cs-ai

Agents

NeoAMT: Neologism-Aware Agentic Machine Translation with Reinforcement Learning

arXiv:2601.03790v3 Announce Type: replace-cross Abstract: Neologism-aware machine translation aims to translate source sentences containing neologisms into target languages. This field remains underex

agentsarxiv-cs-ai

NeSyCat: A Monad-Based Categorical Semantics of the Neurosymbolic ULLER Framework

arXiv:2604.24612v1 Announce Type: new Abstract: ULLER (Unified Language for LEarning and Reasoning) offers a unified first-order logic (FOL) syntax, enabling its knowledge bases to be used directly ac

Hardware

NeuroAPS-Net: Neuro-Anatomically Aware Point Cloud Representation for Efficient Alzheimer's Disease Classification

arXiv:2604.22883v1 Announce Type: cross Abstract: Alzheimer's disease (AD) is a progressive neurodegenerative disorder and a major cause of dementia. Structural MRI is widely used to analyze AD-relate

hardwarearxiv-cs-ai

Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation

arXiv:2603.14228v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limi