Search: “benchmark”

Efficient, VRAM-Constrained xLM Inference on Clients

arXiv:2604.26334v1 Announce Type: cross Abstract: To usher in the next round of client AI innovation, there is an urgent need to enable efficient, lossless inference of high-accuracy large language mo

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ELIQ: A Label-Free Framework for Quality Assessment of Evolving AI-Generated Images

DGX agent

arXiv:2602.03558v2 Announce Type: replace-cross Abstract: Generative text-to-image models are advancing at an unprecedented pace, continuously shifting the perceptual quality ceiling and rendering pre

model-releasesarxiv-cs-ai

30 Apr 2026

Evergreen: Efficient Claim Verification for Semantic Aggregates

arXiv:2604.26180v1 Announce Type: cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natur

EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation

arXiv:2604.26170v1 Announce Type: new Abstract: Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires ite

safetyarxiv-cs-cl

ext{PKS}^4:Parallel Kinematic Selective State Space Scanners for Efficient Video Understanding

arXiv:2604.26461v1 Announce Type: new Abstract: Temporal modeling remains a fundamental challenge in video understanding, particularly as sequence lengths scale. Traditional video models relying on de

Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference

model-releasesarxiv-cs-cl

arXiv:2604.26294v1 Announce Type: new Abstract: We present tensor and sequence parallelism (TSP), a parallel execution strategy that folds tensor parallelism and sequence parallelism onto a single dev

FutureWorld: A Live Environment for Training Predictive Agents with Real-World Outcome Rewards

arXiv:2604.26733v1 Announce Type: new Abstract: Live future prediction refers to the task of making predictions about real-world events before they unfold. This task is increasingly studied using larg

GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition

arXiv:2604.26255v1 Announce Type: new Abstract: Gait recognition is an attractive biometric modality for long-range and contact-free identification, but high-performing gait models often rely on deep

applicationsarxiv-cs-cv

GateMOT: Q-Gated Attention for Dense Object Tracking

arXiv:2604.26353v1 Announce Type: new Abstract: While large models demonstrate the strong representational power of vanilla attention, this core mechanism cannot be directly applied to Dense Object Tr

researcharxiv-cs-cv

GIFGuard: Proactive Forensics against Deepfakes in Facial GIFs via Spatiotemporal Watermarking

arXiv:2604.26519v1 Announce Type: new Abstract: The rapid evolution of deepfake technology poses an unprecedented threat to the authenticity of Graphics Interchange Format (GIF) imagery, which serves

Glance-or-Gaze: Incentivizing LMMs to Adaptively Focus Search via Reinforcement Learning

arXiv:2601.13942v2 Announce Type: replace-cross Abstract: Large Multimodal Models (LMMs) have achieved remarkable success in visual understanding, yet they struggle with knowledge-intensive queries in

safetyarxiv-cs-ai

HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing

arXiv:2601.21459v4 Announce Type: replace-cross Abstract: LLM role-playing, i.e., using LLMs to simulate specific personas, has emerged as a key capability in various applications, such as companionsh

High-Dimensional Noise to Low-Dimensional Manifolds: A Manifold-Space Diffusion Framework for Degraded Hyperspectral Image Classification

arXiv:2604.26279v1 Announce Type: new Abstract: Recently, Hyperspectral Image (HSI) classification has attracted increasing attention in remote sensing. However, HSI data are inherently high-dimension

applicationsarxiv-cs-cv

HIVE: Hidden-Evidence Verification for Hallucination Detection in Diffusion Large Language Models

arXiv:2604.26139v1 Announce Type: new Abstract: Diffusion large language models generate text through multi-step denoising, where hallucination signals may emerge throughout the trajectory rather than

researcharxiv-cs-cl

Identifying the Achilles' Heel: An Iterative Method for Dynamically Uncovering Factual Errors in Large Language Models

arXiv:2401.00761v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) like ChatGPT are foundational in various applications due to their extensive knowledge from pre-training and fine

applicationsarxiv-cs-ai

Information Extraction from Electricity Invoices with General-Purpose Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.25927v1 Announce Type: new Abstract: Information extraction from semi-structured business documents remains a critical challenge for enterprise management. This study evaluates the capabili

L2RU: a Structured State Space Model with prescribed L2-bound

arXiv:2503.23818v3 Announce Type: replace-cross Abstract: Structured state-space models (SSMs) have recently emerged as a powerful architecture at the intersection of machine learning and control, fea

Laplace Approximation for Bayesian Tensor Network Kernel Machines

arXiv:2604.26673v1 Announce Type: cross Abstract: Uncertainty estimation is essential for robust decision-making in the presence of ambiguous or out-of-distribution inputs. Gaussian Processes (GPs) ar

researcharxiv-cs-lg

Large Language Models for Multilingual Code Intelligence: A Survey

arXiv:2604.25960v1 Announce Type: cross Abstract: Large language models have transformed AI-assisted software engineering, but current research remains biased toward high-resource languages such as Py

applicationsarxiv-cs-lg

Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

arXiv:2604.26932v1 Announce Type: cross Abstract: The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depen

Learning to Ask: When LLM Agents Meet Unclear Instruction

arXiv:2409.00557v4 Announce Type: replace-cross Abstract: Equipped with the capability to call functions, modern large language models (LLMs) can leverage external tools for addressing a range of task

Agents

Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use

arXiv:2602.20426v2 Announce Type: replace Abstract: While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agen

agentsarxiv-cs-ai

LogitSpec: Accelerating Retrieval-based Speculative Decoding via Next Next Token Speculation

arXiv:2507.01449v3 Announce Type: replace Abstract: Speculative decoding (SD), where a small draft model is employed to propose draft tokens in advance and then the target model validates them in para

researcharxiv-cs-cl

Lyapunov-Guided Self-Alignment: Test-Time Adaptation for Offline Safe Reinforcement Learning

arXiv:2604.26516v1 Announce Type: cross Abstract: Offline reinforcement learning (RL) agents often fail when deployed, as the gap between training datasets and real environments leads to unsafe behavi

Agents

MappingEvolve: LLM-Driven Code Evolution for Technology Mapping

arXiv:2604.26591v1 Announce Type: cross Abstract: Technology mapping is a critical yet challenging stage in logic synthesis. While Large Language Models (LLMs) have been applied to generate optimizati

agentsarxiv-cs-ai

MemOVCD: Training-Free Open-Vocabulary Change Detection via Cross-Temporal Memory Reasoning and Global-Local Adaptive Rectification

arXiv:2604.26774v1 Announce Type: cross Abstract: Open-vocabulary change detection aims to identify semantic changes in bi-temporal remote sensing images without predefined categories. Recent methods

researcharxiv-cs-ai

MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification

arXiv:2604.26138v1 Announce Type: new Abstract: Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestri

Local Ai

Multiple Consistent 2D-3D Mappings for Robust Zero-Shot 3D Visual Grounding

arXiv:2604.26261v1 Announce Type: new Abstract: Zero-shot 3D Visual Grounding (3DVG) is a critical capability for open-world embodied AI. However, existing methods are fundamentally bottlenecked by th

local-aiarxiv-cs-cv

Naamah: A Large Scale Synthetic Sanskrit NER Corpus via DBpedia Seeding and LLM Generation

arXiv:2604.26456v1 Announce Type: cross Abstract: The digitisation of classical Sanskrit literature is impeded by a scarcity of annotated resources, particularly for Named Entity Recognition. While re

NeuralFLoC: Neural Flow-Based Joint Registration and Clustering of Functional Data

arXiv:2602.03169v2 Announce Type: replace-cross Abstract: Clustering functional data in the presence of phase variation is challenging, as temporal misalignment can obscure intrinsic shape differences

researcharxiv-cs-lg

NTIRE 2026 3D Restoration and Reconstruction in Real-world Adverse Conditions: RealX3D Challenge Results

arXiv:2604.04135v2 Announce Type: replace Abstract: This paper presents a comprehensive review of the NTIRE 2026 3D Restoration and Reconstruction (3DRR) Challenge, detailing the proposed methods and

Agents

OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory

arXiv:2604.26622v1 Announce Type: new Abstract: Autonomous LLM agents increasingly operate in long-horizon, interactive settings where success depends on reusing experience accumulated over extended h

agentsarxiv-cs-cl

OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms

arXiv:2604.26211v1 Announce Type: new Abstract: In order to automate AI research we introduce a full, end-to-end framework, OMEGA: Optimizing Machine learning by Evaluating Generated Algorithms, that

Operating-Layer Controls for Onchain Language-Model Agents Under Real Capital

arXiv:2604.26091v1 Announce Type: new Abstract: We study reliability in autonomous language-model agents that translate user mandates into validated tool actions under real capital. The setting is DX

safetyarxiv-cs-ai

Local Ai

OVGGT: O(1) Constant-Cost Streaming Visual Geometry Transformer

arXiv:2603.05959v3 Announce Type: replace Abstract: Reconstructing 3D geometry from streaming video requires continuous inference under bounded resources. Recent geometric foundation models achieve im

local-aiarxiv-cs-cv

PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners

arXiv:2604.26573v1 Announce Type: new Abstract: Improving large language model (LLM) reasoning requires supervision that is both aligned with the model's own test-time states and informative at the to

safetyarxiv-cs-lg

Tutorials

PromptEvolver: Prompt Inversion through Evolutionary Optimization in Natural-Language Space

arXiv:2604.06061v2 Announce Type: replace Abstract: Text-to-image generation has progressed rapidly, but faithfully generating complex scenes requires extensive trial-and-error to find the exact promp

tutorialsarxiv-cs-lg

Quantum Feature Selection with Higher-Order Binary Optimization on Trapped-Ion Hardware

arXiv:2604.26834v1 Announce Type: cross Abstract: We present a quantum feature-selection framework based on a higher-order unconstrained binary optimization (HUBO) formulation that explicitly incorpor

QYOLO: Lightweight Object Detection via Quantum Inspired Shared Channel Mixing

arXiv:2604.26435v1 Announce Type: cross Abstract: The rapid advancement of object detection architectures has positioned single stage detectors as the dominant solution for real-time visual perception

RADIO-ViPE: Online Tightly Coupled Multi-Modal Fusion for Open-Vocabulary Semantic SLAM in Dynamic Environments

arXiv:2604.26067v1 Announce Type: new Abstract: We present RADIO-ViPE (Reduce All Domains Into One -- Video Pose Engine), an online semantic SLAM system that enables geometry-aware open-vocabulary gro

Random Cloud: Finding Minimal Neural Architectures Without Training

arXiv:2604.26830v1 Announce Type: cross Abstract: I propose the Random Cloud method, a training-free approach to neural architecture search that discovers minimal feedforward network topologies throug

ReGATE: Learning Faster and Better with Fewer Tokens in MLLMs

arXiv:2507.21420v3 Announce Type: replace-cross Abstract: The computational cost of training multimodal large language models (MLLMs) grows rapidly with the number of processed tokens. Existing effici

researcharxiv-cs-cl

Rethinking KV Cache Eviction via a Unified Information-Theoretic Objective

arXiv:2604.25975v1 Announce Type: cross Abstract: Key-value (KV) caching is essential for large language model inference, yet its memory overhead poses a critical bottleneck for long-context generatio

researcharxiv-cs-ai

Retrieval-Augmented LLMs for Evidence Localization in Clinical Trial Recruitment from Longitudinal EHR Narratives

arXiv:2604.05190v2 Announce Type: replace-cross Abstract: Screening patients for enrollment is a well-known, labor-intensive bottleneck that leads to under-enrollment and, ultimately, trial failures.

Robust Federated Learning under Adversarial Attacks via Loss-Based Client Clustering

arXiv:2508.12672v4 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative model training across multiple clients without sharing private data. We consider FL scenarios wh

researcharxiv-cs-ai

Safety Is Not Universal: The Selective Safety Trap in LLM Alignment

arXiv:2601.04389v2 Announce Type: replace-cross Abstract: Current safety evaluations of large language models (LLMs) create a dangerous illusion of universal protection by aggregating harms under gene

Local Ai

SecMate: Multi-Agent Adaptive Cybersecurity Troubleshooting with Tri-Context Personalization

arXiv:2604.26394v1 Announce Type: cross Abstract: Recent advances in large language models and agentic frameworks have enabled virtual customer assistants (VCAs) for complex support. We present SecMat

local-aiarxiv-cs-ai

Seeking Consensus: Geometric-Semantic On-the-Fly Recalibration for Open-Vocabulary Remote Sensing Semantic Segmentation

arXiv:2604.26221v1 Announce Type: cross Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images is a promising task that employs textual descriptions for identifying undefined

safetyarxiv-cs-ai