Search: “benchmark”

GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning

arXiv:2505.17022v2 Announce Type: replace-cross Abstract: Visual generation models have made remarkable progress in creating realistic images from text prompts, yet struggle with complex prompts that

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Governed Reasoning for Institutional AI

DGX agent

arXiv:2604.10658v1 Announce Type: new Abstract: Institutional decisions -- regulatory compliance, clinical triage, prior authorization appeal -- require a different AI architecture than general-purpos

model-releasesarxiv-cs-ai

14 Apr 2026

Graph-Coarsening Approach for the Capacitated Vehicle Routing Problem with Time Windows

arXiv:2510.22329v2 Announce Type: replace Abstract: The Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) is a fundamental NP-hard optimization problem in logistics. Solving large-scale i

Graph-RHO: Critical-path-aware Heterogeneous Graph Network for Long-Horizon Flexible Job-Shop Scheduling

arXiv:2604.10073v1 Announce Type: cross Abstract: Long-horizon Flexible Job-Shop Scheduling~(FJSP) presents a formidable combinatorial challenge due to complex, interdependent decisions spanning exten

Grounded World Model for Semantically Generalizable Planning

arXiv:2604.11751v1 Announce Type: cross Abstract: In Model Predictive Control (MPC), world models predict the future outcomes of various action proposals, which are then scored to guide the selection

H-SPAM: Hierarchical Superpixel Anything Model

arXiv:2604.11218v1 Announce Type: new Abstract: Superpixels offer a compact image representation by grouping pixels into coherent regions. Recent methods have reached a plateau in terms of segmentatio

researcharxiv-cs-cv

HeceTokenizer: A Syllable-Based Tokenization Approach for Turkish Retrieval

arXiv:2604.10665v1 Announce Type: new Abstract: HeceTokenizer is a syllable-based tokenizer for Turkish that exploits the deterministic six-pattern phonological structure of the language to construct

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria

arXiv:2604.10560v1 Announce Type: new Abstract: Profiled Sparse Networks (PSN) replace uniform connectivity with deterministic, heterogeneous fan-in profiles defined by continuous, nonlinear functions

Heterogeneous Consensus-Progressive Reasoning for Efficient Multi-Agent Debate

arXiv:2604.09679v1 Announce Type: cross Abstract: Multi-Agent Debate (MAD) is a collaborative framework in which multiple agents iteratively refine solutions through the generation of reasoning and al

agentsarxiv-cs-ai

HO-Flow: Generalizable Hand-Object Interaction Generation with Latent Flow Matching

arXiv:2604.10836v1 Announce Type: new Abstract: Generating realistic 3D hand-object interactions (HOI) is a fundamental challenge in computer vision and robotics, requiring both temporal coherence and

tutorialsarxiv-cs-cv

How Alignment Routes: Localizing, Scaling, and Controlling Policy Circuits in Language Models

arXiv:2604.04385v3 Announce Type: replace-cross Abstract: This paper localizes the policy routing mechanism in alignment-trained language models. An intermediate-layer attention gate reads detected co

How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts

arXiv:2604.11133v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly being explored for clinical question answering and decision support, yet safe deployment critically requir

HTAA: Enhancing LLM Planning via Hybrid Toolset Agentization & Adaptation

arXiv:2604.10917v1 Announce Type: new Abstract: Enabling large language models to scale and reliably use hundreds of tools is critical for real-world applications, yet challenging due to the inefficie

agentsarxiv-cs-cl

Human-like Working Memory Interference in Large Language Models

arXiv:2604.09670v1 Announce Type: cross Abstract: Intelligent systems must maintain and manipulate task-relevant information online to adapt to dynamic environments and changing goals. This capacity,

Hypergraph Neural Diffusion: A PDE-Inspired Framework for Hypergraph Message Passing

arXiv:2604.10955v1 Announce Type: new Abstract: Hypergraph neural networks (HGNNs) have shown remarkable potential in modeling high-order relationships that naturally arise in many real-world data dom

HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAG

arXiv:2601.17755v2 Announce Type: replace Abstract: Graph Retrieval-Augmented Generation (GraphRAG) has emerged as a promising paradigm that organizes external knowledge into structured graphs of enti

safetyarxiv-cs-cl

I Can't Believe TTA Is Not Better: When Test-Time Augmentation Hurts Medical Image Classification

arXiv:2604.09697v1 Announce Type: cross Abstract: Test-time augmentation (TTA)--aggregating predictions over multiple augmented copies of a test input--is widely assumed to improve classification accu

Local Ai

Identity-Aware U-Net: Fine-grained Cell Segmentation via Identity-Aware Representation Learning

arXiv:2604.09702v1 Announce Type: cross Abstract: Precise segmentation of objects with highly similar shapes remains a challenging problem in dense prediction, especially in scenarios with ambiguous b

local-aiarxiv-cs-ai

Improving the Reasoning of Multi-Image Grounding in MLLMs via Reinforcement Learning

arXiv:2507.00748v3 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) perform well in single-image visual grounding but struggle with real-world tasks that demand cross-image re

tutorialsarxiv-cs-cv

Incentivizing Honesty among Competitors in Collaborative Learning and Optimization

arXiv:2305.16272v5 Announce Type: replace Abstract: Collaborative learning techniques have the potential to enable training machine learning models that are superior to models trained on a single enti

INCRT: An Incremental Transformer That Determines Its Own Architecture

arXiv:2604.10703v1 Announce Type: new Abstract: Transformer architectures are designed by trial and error: the number of attention heads, the depth, and the head size are fixed before training begins,

tutorialsarxiv-cs-lg

Infusing Theory of Mind into Socially Intelligent LLM Agents

arXiv:2509.22887v2 Announce Type: replace Abstract: Theory of Mind (ToM)-an understanding of the mental states of others-is a key aspect of human social intelligence, yet, chatbots and LLM-based socia

INSPATIO-WORLD: A Real-Time 4D World Simulator via Spatiotemporal Autoregressive Modeling

model-releasesarxiv-cs-cv

arXiv:2604.07209v2 Announce Type: replace Abstract: Building world models with spatial consistency and real-time interactivity remains a fundamental challenge in computer vision. Current video generat

Instruction Data Selection via Answer Divergence

arXiv:2604.10448v1 Announce Type: new Abstract: Instruction tuning relies on large instruction-response corpora whose quality and composition strongly affect downstream performance. We propose Answer

researcharxiv-cs-cl

Integrating Semi-Supervised and Active Learning for Semantic Segmentation

arXiv:2501.19227v2 Announce Type: replace-cross Abstract: In this paper, we propose a novel active learning approach integrated with an improved semi-supervised learning framework to reduce the cost o

Interactive Learning for LLM Reasoning

arXiv:2509.26306v4 Announce Type: replace Abstract: Existing multi-agent learning approaches have developed interactive training environments to explicitly promote collaboration among multiple Large L

safetyarxiv-cs-ai

Introspective Diffusion Language Models

arXiv:2604.11035v1 Announce Type: new Abstract: Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of intr

Investigating Bias and Fairness in Appearance-based Gaze Estimation

model-releasesarxiv-cs-cv

arXiv:2604.10707v1 Announce Type: new Abstract: While appearance-based gaze estimation has achieved significant improvements in accuracy and domain adaptation, the fairness of these systems across dif

Investigating Vaccine Buyer's Remorse: Post-Vaccination Decision Regret in COVID-19 Social Media Using Politically Diverse Human Annotation

arXiv:2604.09626v1 Announce Type: cross Abstract: A significant gap exists in datasets regarding post-COVID-19 vaccination experiences, particularly ``vaccine buyer's remorse''. Understanding the prev

Jailbreaking the Matrix: Nullspace Steering for Controlled Model Subversion

arXiv:2604.10326v1 Announce Type: cross Abstract: Large language models remain vulnerable to jailbreak attacks -- inputs designed to bypass safety mechanisms and elicit harmful responses -- despite ad

safetyarxiv-cs-ai

Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System

arXiv:2602.09667v2 Announce Type: replace Abstract: Integrating domain knowledge into neural networks is a central challenge in scientific machine learning. Three paradigms have emerged -- data-driven

LaMI: Augmenting Large Language Models via Late Multi-Image Fusion

arXiv:2406.13621v2 Announce Type: replace Abstract: Commonsense reasoning often requires both textual and visual knowledge, yet Large Language Models (LLMs) trained solely on text lack visual groundin

LASQ: A Low-resource Aspect-based Sentiment Quadruple Extraction Dataset

arXiv:2604.10417v1 Announce Type: new Abstract: In recent years, aspect-based sentiment analysis (ABSA) has made rapid progress and shown strong practical value. However, existing research and benchma

researcharxiv-cs-cl

LDEPrompt: Layer-importance guided Dual Expandable Prompt Pool for Pre-trained Model-based Class-Incremental Learning

arXiv:2604.11091v1 Announce Type: new Abstract: Prompt-based class-incremental learning methods typically construct a prompt pool consisting of multiple trainable key-prompts and perform instance-leve

researcharxiv-cs-cv

Applications

Learnable Motion-Focused Tokenization for Effective and Efficient Video Unsupervised Domain Adaptation

arXiv:2604.09955v1 Announce Type: new Abstract: Video Unsupervised Domain Adaptation (VUDA) poses a significant challenge in action recognition, requiring the adaptation of a model from a labeled sour

applicationsarxiv-cs-cv

Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories

arXiv:2604.11365v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) has been widely used for automated reasoning data exploration, but current supervision extraction methods remain ineffici

tutorialsarxiv-cs-ai

Learning from Emptiness: De-biasing Listwise Rerankers with Content-Agnostic Probability Calibration

arXiv:2604.10150v1 Announce Type: new Abstract: Generative listwise reranking leverages global context for superior retrieval but is plagued by intrinsic position bias, where models exhibit structural

safetyarxiv-cs-ai

Learning How Much to Think: Difficulty-Aware Dynamic MoEs for Graph Node Classification

arXiv:2604.11473v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures offer a scalable path for Graph Neural Networks (GNNs) in node classification tasks but typically rely on static

researcharxiv-cs-lg

Applications

Learning Preference-Based Objectives from Clinical Narratives for Sequential Treatment Decision-Making

arXiv:2604.10783v1 Announce Type: new Abstract: Designing reward functions remains a central challenge in reinforcement learning (RL) for healthcare, where outcomes are sparse, delayed, and difficult

applicationsarxiv-cs-ai

Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning

arXiv:2603.11346v2 Announce Type: replace Abstract: Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking w

agentsarxiv-cs-cv

Learning to Focus and Precise Cropping: A Reinforcement Learning Framework with Information Gaps and Grounding Loss for MLLMs

arXiv:2603.27494v2 Announce Type: replace-cross Abstract: To enhance the perception and reasoning capabilities of multimodal large language models in complex visual scenes, recent research has introdu

agentsarxiv-cs-ai

Learning to Play Piano in the Real World

arXiv:2503.15481v3 Announce Type: replace-cross Abstract: Towards the grand challenge of achieving human-level manipulation in robots, playing piano is a compelling testbed that requires strategic, pr

Lightweight Low-Light Image Enhancement via Distribution-Normalizing Preprocessing and Depthwise U-Net

arXiv:2604.11071v1 Announce Type: cross Abstract: We present a lightweight two-stage framework for low-light image enhancement (LLIE) that achieves competitive perceptual quality with significantly fe

LiveCLKTBench: Towards Reliable Evaluation of Cross-Lingual Knowledge Transfer in Multilingual LLMs

arXiv:2511.14774v3 Announce Type: replace-cross Abstract: Evaluating cross-lingual knowledge transfer in large language models is challenging, as correct answers in a target language may arise either

LMMs Meet Object-Centric Vision: Understanding, Segmentation, Editing and Generation

arXiv:2604.11789v1 Announce Type: new Abstract: Large Multimodal Models (LMMs) have achieved remarkable progress in general-purpose vision--language understanding, yet they remain limited in tasks req

researcharxiv-cs-cv

Local Ai

Long-Horizon Streaming Video Generation via Hybrid Attention with Decoupled Distillation

arXiv:2604.10103v1 Announce Type: new Abstract: Streaming video generation (SVG) distills a pretrained bidirectional video diffusion model into an autoregressive model equipped with sliding window att

local-aiarxiv-cs-cv

Local Ai

Look Twice before You Leap: A Rational Framework for Localized Adversarial Anonymization

arXiv:2512.06713v3 Announce Type: replace-cross Abstract: Current LLM-based frameworks for text anonymization usually rely on remote API services from powerful LLMs, which creates an inherent privacy

local-aiarxiv-cs-cl

LOOPE: Learnable Optimal Patch Order in Positional Embeddings for Vision Transformers

arXiv:2504.14386v2 Announce Type: replace-cross Abstract: Positional embeddings (PE) play a crucial role in Vision Transformers (ViTs) by providing spatial information otherwise lost due to the permut