Search: “benchmark”

ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models

arXiv:2604.24940v1 Announce Type: new Abstract: Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representa

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Agents

Agentic AI for Remote Sensing: Technical Challenges and Research Directions

DGX agent

arXiv:2604.24919v1 Announce Type: new Abstract: Earth Observation (EO) is moving beyond static prediction toward multi-step analytical workflows that require coordinated reasoning over data, tools, an

agentsarxiv-cs-cv

29 Apr 2026

Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses

arXiv:2604.25850v1 Announce Type: new Abstract: Harnesses have become a central determinant of coding-agent performance, shaping how models interact with repositories, tools, and execution environment

Tools

AI evals are becoming the new compute bottleneck

As AI models grow larger and more capable, the computational cost and time required to evaluate them has become a significant limiting factor in development, potentially surpassing training compute as

toolshugging-face

Analyzing LLM Reasoning to Uncover Mental Health Stigma

arXiv:2604.25053v1 Announce Type: new Abstract: While large language models (LLMs) are increasingly being explored for mental health applications, recent studies reveal that they can exhibit stigma to

Application of a Mixture of Experts-based Foundation Model to the GlueX DIRC Detector

arXiv:2604.24775v1 Announce Type: cross Abstract: We present a Mixture-of-Experts-based foundation model applied to the GlueX DIRC detector at Jefferson Lab, demonstrating its utility as a unified fra

ARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs

arXiv:2410.24214v3 Announce Type: replace-cross Abstract: Mixed precision quantization has become an important technique for optimizing the execution of deep neural networks (DNNs). Certified robustne

researcharxiv-cs-cv

asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics

model-releasesarxiv-cs-ro

arXiv:2604.24916v1 Announce Type: new Abstract: We introduce asRoBallet, to the best of our knowledge, the first successful deployment of reinforcement learning (RL) on a humanoid ballbot hardware. Hi

Backtranslation Augmented Direct Preference Optimization for Neural Machine Translation

arXiv:2604.25702v1 Announce Type: new Abstract: Contemporary neural machine translation (NMT) systems are almost exclusively built by training on supervised parallel data. Despite the tremendous progr

BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling

arXiv:2604.24999v1 Announce Type: new Abstract: Thoracic Computed Tomography (CT) scans offer detailed insights into the intricate branching network of the airway tree, which is essential for understa

BLASST: Dynamic BLocked Attention Sparsity via Softmax Thresholding

arXiv:2512.12087v3 Announce Type: replace Abstract: The growing demand for long-context inference capabilities in Large Language Models (LLMs) has intensified the computational and memory bottlenecks

Bye Bye Perspective API: Lessons for Measurement Infrastructure in NLP, CSS and LLM Evaluation

arXiv:2604.25580v1 Announce Type: new Abstract: The closure of Perspective API at the end of 2026 discards what has functioned as the de facto standard for automated toxicity measurement in NLP, CSS,

Cheaper, Better, Faster, Stronger: Robust Text-to-SQL without Chain-of-Thought or Fine-Tuning

arXiv:2505.14174v2 Announce Type: replace Abstract: LLMs are effective at code generation tasks like text-to-SQL, but is it worth the cost? Many state-of-the-art approaches use non-task-specific LLM t

Citation Failure: Definition, Analysis and Efficient Mitigation

arXiv:2510.20303v3 Announce Type: replace Abstract: Citations from LLM-based RAG systems are supposed to simplify response verification. However, this goal is undermined in cases of citation failure,

Combating Visual Neglect and Semantic Drift in Large Multimodal Models for Enhanced Cross-Modal Retrieval

arXiv:2604.25273v1 Announce Type: new Abstract: Despite significant progress in Unified Multimodal Retrieval (UMR) powered by Large Multimodal Models (LMMs), existing embedding methods primarily focus

CORAL: Adaptive Retrieval Loop for Culturally-Aligned Multilingual RAG

arXiv:2604.25676v1 Announce Type: new Abstract: Multilingual retrieval-augmented generation (mRAG) is often implemented within a fixed retrieval space, typically via query or document translation or m

safetyarxiv-cs-cl

CoreFlow: Low-Rank Matrix Generative Models

arXiv:2604.24959v1 Announce Type: new Abstract: Learning matrix-valued distributions from high-dimensional and possibly incomplete training data is challenging: ambient-space generative modeling is co

researcharxiv-cs-lg

Applications

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

arXiv:2602.01433v2 Announce Type: replace Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-te

applicationsarxiv-cs-lg

Tutorials

DDA-Thinker: Decoupled Dual-Atomic Reinforcement Learning for Reasoning-Driven Image Editing

arXiv:2604.25477v1 Announce Type: new Abstract: Recent image editing models have achieved strong visual fidelity but often struggle with tasks requiring complex reasoning. To investigate and enhance t

tutorialsarxiv-cs-cv

DEGround: An Effective Baseline for Ego-centric 3D Visual Grounding with a Homogeneous Framework

arXiv:2506.05199v3 Announce Type: replace Abstract: A core task in embodied intelligence is ego-centric 3D visual grounding. Existing methods typically adopt two-stage, heterogeneous pipelines that pa

safetyarxiv-cs-cv

Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge

arXiv:2512.08323v2 Announce Type: replace Abstract: Teeth landmark detection is a key task in modern orthodontics, supporting advanced diagnosis, personalized treatment planning, and effective monitor

DGLight: DQN-Guided GRPO Fine-Tuning of Large Language Models for Traffic Signal Control

arXiv:2604.25259v1 Announce Type: new Abstract: Traffic signal control (TSC) plays a central role in reducing congestion and maintaining urban mobility. This dissertation introduces DGLight, a critic-

safetyarxiv-cs-lg

DIAL: Decoupling Intent and Action via Latent World Modeling for End-to-End VLA

arXiv:2603.29844v2 Announce Type: replace-cross Abstract: The development of Vision-Language-Action (VLA) models has been significantly accelerated by pre-trained Vision-Language Models (VLMs). Howeve

DiffAdapt: Difficulty-Adaptive Reasoning for Token-Efficient LLM Inference

arXiv:2510.19669v4 Announce Type: replace Abstract: Recent reasoning Large Language Models (LLMs) demonstrate remarkable problem-solving abilities but often generate long thinking traces whose utility

DiRe-RAPIDS: Topology-faithful dimensionality reduction at scale

arXiv:2604.25209v1 Announce Type: new Abstract: Dimensionality reduction methods such as UMAP and t-SNE are central tools for visualising high-dimensional data, but their local-neighborhood objectives

DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors

arXiv:2604.25050v1 Announce Type: new Abstract: Unlike chatbots, physical AI must act while the world keeps evolving. Therefore, the inter-chunk pause of synchronous executors are fatal for dynamic ta

safetyarxiv-cs-ro

Diverse Image Priors for Black-box Data-free Knowledge Distillation

arXiv:2604.25794v1 Announce Type: cross Abstract: Knowledge distillation (KD) represents a vital mechanism to transfer expertise from complex teacher networks to efficient student models. However, in

researcharxiv-cs-cv

Doing More With Less: Revisiting the Effectiveness of LLM Pruning for Test-Time Scaling

arXiv:2604.25098v1 Announce Type: cross Abstract: While current Large Language Models (LLMs) exhibit remarkable reasoning capabilities through test-time compute scaling (TTS), their massive parameter

Dont Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination

arXiv:2604.24978v1 Announce Type: new Abstract: Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We

Local Ai

DouC: Dual-Branch CLIP for Training-Free Open-Vocabulary Segmentation

arXiv:2604.24997v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation requires assigning pixel-level semantic labels while supporting an open and unrestricted set of categories. Traini

local-aiarxiv-cs-cv

Local Ai

Dynamic Decision Learning: Test-Time Evolution for Abnormality Grounding in Rare Diseases

arXiv:2604.24972v1 Announce Type: new Abstract: Clinical abnormality grounding for rare diseases is often hindered by data scarcity, making supervised fine-tuning impractical and single-pass inference

local-aiarxiv-cs-cl

Egocentric Tactile and Proximity Sensors as Observation Priors for Humanoid Collision Avoidance

arXiv:2604.25554v1 Announce Type: cross Abstract: Collision-free motion is often aided by tactile and proximity sensors distributed on the body of the robot due to their resistance to occlusion as opp

Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis

arXiv:2603.16877v2 Announce Type: replace Abstract: Financial analysts face significant challenges extracting information from lengthy 10-K reports, which often exceed 100 pages. This paper presents a

ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation

arXiv:2604.24876v1 Announce Type: new Abstract: Text guided 3D medical image segmentation offers a flexible alternative to class based and spatial prompt based models by allowing users to specify regi

EvoTSC: Evolving Feature Learning Models for Time Series Classification via Genetic Programming

arXiv:2604.25499v1 Announce Type: new Abstract: Time series classification is an important analytical task across diverse domains. However, its practical application is often hindered by the scarcity

Explainable AI for Jet Tagging: A Comparative Study of GNNExplainer, GNNShap, and GradCAM for Jet Tagging in the Lund Jet Plane

arXiv:2604.25885v1 Announce Type: cross Abstract: Graph neural networks such as ParticleNet and transformer based networks on point clouds such as ParticleTransformer achieve state-of-the-art performa

researcharxiv-cs-lg

Exploring Reasoning Reward Model for Agents

arXiv:2601.22154v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (Agentic RL) has achieved notable success in enabling agents to perform complex reasoning and tool use. However

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

arXiv:2604.25135v1 Announce Type: new Abstract: Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environme

FARM: Enhancing Molecular Representations with Functional Group Awareness

arXiv:2410.02082v4 Announce Type: replace Abstract: We introduce Functional Group-Aware Representations for Small Molecules (FARM), a novel foundation model designed to bridge the gap between SMILES,

Agents

From Soliloquy to Agora: Memory-Enhanced LLM Agents with Decentralized Debate for Optimization Modeling

arXiv:2604.25847v1 Announce Type: cross Abstract: Optimization modeling underpins real-world decision-making in logistics, manufacturing, energy, and public services, but reliably solving such problem

agentsarxiv-cs-lg

From Syntax to Emotion: A Mechanistic Analysis of Emotion Inference in LLMs

arXiv:2604.25866v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used in emotionally sensitive human-AI applications, yet little is known about how emotion recognition is

G-Loss: Graph-Guided Fine-Tuning of Language Models

arXiv:2604.25853v1 Announce Type: new Abstract: Traditional loss functions, including cross-entropy, contrastive, triplet, and su pervised contrastive losses, used for fine-tuning pre-trained language

Generalizable Human Gaussian Splatting via Multi-view Semantic Consistency

arXiv:2604.25466v1 Announce Type: new Abstract: Recently, generalizable human Gaussian splatting from sparse-view inputs has been actively studied for the photorealistic human rendering. Most existing

GeoSearch: Augmenting Worldwide Geolocalization with Web-Scale Reverse Image Search and Image Matching

arXiv:2604.25390v1 Announce Type: cross Abstract: Worldwide image geolocalization, which aims to predict the GPS coordinates of any image on Earth, remains challenging due to global visual diversity.

researcharxiv-cs-cv

Golden RPG: Confidence-Adaptive Region-Aware Noise for Compositional Text-to-Image Generation

arXiv:2604.25314v1 Announce Type: new Abstract: Compositional text-to-image (T2I) generation requires a model to honour multiple sub-prompts that describe distinct image regions. Recent work shows tha

Applications

GramSR: Visual Feature Conditioning for Diffusion-Based Super-Resolution

arXiv:2604.25457v1 Announce Type: new Abstract: Despite recent advances, single-image super-resolution (SR) remains challenging, especially in real-world scenarios with complex degradations. Diffusion

applicationsarxiv-cs-cv

GraphPL: Leveraging GNN for Efficient and Robust Modalities Imputation in Patchwork Learning

arXiv:2604.25352v1 Announce Type: new Abstract: Current research on distributed multi-modal learning typically assumes that clients can access complete information across all modalities, which may not

Grothendieck Graph Neural Networks Framework: An Algebraic Platform for Crafting Topology-Aware GNNs

arXiv:2412.08835v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) are almost universally built on a single primitive: the neighborhood. Regardless of architectural variations, message p

researcharxiv-cs-lg