Search: “benchmark”

BioGraphletQA: Knowledge-Anchored Generation of Complex QA Datasets

arXiv:2604.26048v1 Announce Type: new Abstract: This paper presents a principled and scalable framework for systematically generating complex Question Answering (QA) data. In the core of this framewor

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Bridge: Basis-Driven Causal Inference Marries VFMs for Domain Generalization

DGX agent

arXiv:2604.26820v1 Announce Type: new Abstract: Detectors often suffer from degraded performance, primarily due to the distributional gap between the source and target domains. This issue is especiall

model-releasesarxiv-cs-cv

30 Apr 2026

CacheRAG: A Semantic Caching System for Retrieval-Augmented Generation in Knowledge Graph Question Answering

arXiv:2604.26176v1 Announce Type: cross Abstract: The integration of Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) has significantly advanced Knowledge Graph Question Answerin

Calibrated Surprise: An Information-Theoretic Account of Creative Quality

arXiv:2604.26269v1 Announce Type: cross Abstract: The essence of good creative writing is calibrated surprise: when constraints from all relevant dimensions act together, the feasible solution space c

Ceci n'est pas une explication: Evaluating Explanation Failures as Explainability Pitfalls in Language Learning Systems

arXiv:2604.26145v1 Announce Type: cross Abstract: AI-powered language learning tools increasingly provide instant, personalised feedback to millions of learners worldwide. However, this feedback can f

Cheeger--Hodge Contrastive Learning for Structurally Robust Graph Representation Learning

arXiv:2604.26301v1 Announce Type: new Abstract: Graph Contrastive Learning (GCL) has emerged as a prominent framework for unsupervised graph representation learning. However, relying on augmentation d

researcharxiv-cs-lg

ClawGym: A Scalable Framework for Building Effective Claw Agents

arXiv:2604.26904v1 Announce Type: cross Abstract: Claw-style environments support multi-step workflows over local files, tools, and persistent workspace states. However, scalable development around th

Compton Form Factor Extraction using Quantum Deep Neural Networks

arXiv:2504.15458v4 Announce Type: replace Abstract: We extract Compton form factors (CFFs) from deeply virtual Compton scattering measurements at the Thomas Jefferson National Accelerator Facility (JL

Consist-Retinex: One-Step Noise-Emphasized Consistency Training Accelerates High-Quality Retinex Enhancement

arXiv:2512.08982v2 Announce Type: replace-cross Abstract: Retinex-based low-light image enhancement benefits from separating reflectance and illumination, yet recent generative approaches often rely o

safetyarxiv-cs-ai

Correcting Performance Estimation Bias in Imbalanced Classification with Minority Subconcepts

arXiv:2604.26024v1 Announce Type: cross Abstract: Class-level evaluation can conceal substantial performance disparities across subconcepts within the same class, causing models that perform well on a

safetyarxiv-cs-ai

Cross-Domain Transfer of Hyperspectral Foundation Models

arXiv:2604.26478v1 Announce Type: new Abstract: Hyperspectral imaging (HSI) semantic segmentation typically relies on in-domain training, but limited data availability often restricts model performanc

DB-KSVD: Scalable Alternating Optimization for Disentangling High-Dimensional Embedding Spaces

arXiv:2505.18441v2 Announce Type: replace Abstract: Dictionary learning has recently emerged as a promising approach for mechanistic interpretability of large transformer models. Disentangling high-di

DenseStep2M: A Scalable, Training-Free Pipeline for Dense Instructional Video Annotation

arXiv:2604.26565v1 Announce Type: new Abstract: Long-term video understanding requires interpreting complex temporal events and reasoning over procedural activities. While instructional video corpora,

DepthPilot: From Controllability to Interpretability in Colonoscopy Video Generation

arXiv:2604.26232v1 Announce Type: cross Abstract: Controllable medical video generation has achieved remarkable progress, but it still lacks interpretability, which requires the alignment of generated

Deterministic Legal Agents: A Canonical Primitive API for Auditable Reasoning over Temporal Knowledge Graphs

arXiv:2510.06002v3 Announce Type: replace Abstract: In high-stakes legal domains, retrieval must preserve not only semantic relevance, but also the hierarchy, temporality, and causal provenance of leg

Agents

DreamProver: Evolving Transferable Lemma Libraries via a Wake-Sleep Theorem-Proving Agent

arXiv:2604.26311v1 Announce Type: new Abstract: We introduce DreamProver, an agentic framework that leverages a 'wake-sleep' program induction paradigm to discover reusable lemmas for formal theorem p

agentsarxiv-cs-ai

Efficient and Interpretable Transformer for Counterfactual Fairness

arXiv:2604.26188v1 Announce Type: new Abstract: The growing reliance of machine learning models in high-stakes, highly regulated domains such as finance and insurance has created a growing tension bet

safetyarxiv-cs-lg

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

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

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

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

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

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