Search: “benchmark”

PACER: Acyclic Causal Discovery from Large-Scale Interventional Data

arXiv:2605.15353v1 Announce Type: cross Abstract: Inferring the structure of directed acyclic graphs (DAGs) from data is a central challenge in causal discovery, particularly in modern high-dimensiona

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PAGER: Bridging the Semantic-Execution Gap in Point-Precise Geometric GUI Control

DGX agent

arXiv:2605.15963v1 Announce Type: new Abstract: Large vision-language models have significantly advanced GUI agents, enabling executable interaction across web, mobile, and desktop interfaces. Yet the

model-releasesarxiv-cs-ai

18 May 2026

Petri Net Induced Heuristic Search for Resource Constrained Scheduling

arXiv:2605.15983v1 Announce Type: new Abstract: We formulate the Resource-Constrained Project Scheduling Problem (RCPSP) as optimal search over the reachability graph of a Timed Transition Petri Net w

PhyDetEx: Detecting and Explaining the Physical Plausibility of T2V Models

arXiv:2512.01843v2 Announce Type: replace Abstract: Driven by the growing capacity and training scale, Text-to-Video (T2V) generation models have recently achieved substantial progress in video qualit

Polynomial Neural Sheaf Diffusion: A Spectral Filtering Approach on Cellular Sheaves

arXiv:2512.00242v3 Announce Type: replace-cross Abstract: Sheaf Neural Networks equip graph structures with a cellular sheaf: a geometric structure which assigns local vector spaces (stalks) and a lin

safetyarxiv-cs-ai

Probabilistic Dating of Historical Manuscripts via Evidential Deep Regression on Visual Script Features

arXiv:2605.06475v1 Announce Type: cross Abstract: We introduce a probabilistic approach for dating historical manuscript pages from visual features alone. Instead of aggregating centuries into classes

Process Rewards with Learned Reliability

arXiv:2605.15529v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level feedback for reasoning, but current PRMs usually output only a single reward score for each step. Down

Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levels

arXiv:2605.15208v1 Announce Type: cross Abstract: Large Language Models are routinely compressed via post-training quantization to reduce inference costs and memory footprint for cloud and edge deploy

RAR: Retrieving And Ranking Augmented MLLMs for Visual Recognition

arXiv:2403.13805v2 Announce Type: replace-cross Abstract: CLIP (Contrastive Language-Image Pre-training) uses contrastive learning from noise image-text pairs to excel at recognizing a wide array of c

Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design

arXiv:2605.15243v1 Announce Type: cross Abstract: When reliable target structures are unavailable at scale or phenotypes arise from dysregulated pathways, transcriptomic perturbations provide a system

Representation Without Reward: A JEPA Audit for LLM Fine-Tuning

arXiv:2605.15394v1 Announce Type: cross Abstract: Joint-embedding predictive architectures (JEPAs) propose that a model should learn more useful abstractions when trained to predict latent representat

Reversing the Flow: Generation-to-Understanding Synergy in Large Multimodal Models

arXiv:2605.15792v1 Announce Type: new Abstract: The long-standing goal of multimodal AI is to build unified models in which visual understanding and visual generation mutually enhance one another. Des

safetyarxiv-cs-cv

SARVLM: A Vision Language Foundation Model for Semantic Understanding in SAR Imagery

arXiv:2510.22665v3 Announce Type: replace-cross Abstract: Synthetic Aperture Radar (SAR) is a critical imaging modality due to its all-weather operational capability. Although recent advances in self-

SDOF: Taming the Alignment Tax in Multi-Agent Orchestration with State-Constrained Dispatch

arXiv:2605.15204v1 Announce Type: new Abstract: Multi-agent orchestration frameworks such as LangChain, LangGraph, and CrewAI route tasks through graph-based pipelines but do not enforce the stage con

Searching on a Budget: HW-NAS with 10 Latency Probes

model-releasesarxiv-cs-lg

arXiv:2504.00663v2 Announce Type: replace Abstract: Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approxima

Seeing is Understanding: Unlocking Causal Attention into Modality-Mutual Attention for Multimodal LLMs

arXiv:2503.02597v3 Announce Type: replace-cross Abstract: Recent Multimodal Large Language Models (MLLMs) have demonstrated significant progress in perceiving and reasoning over multimodal inquiries,

Seeing What Matters: Visual Preference Policy Optimization for Visual Generation

arXiv:2511.18719v4 Announce Type: replace Abstract: Reinforcement learning (RL) has become a powerful tool for post-training visual generative models, with Group Relative Policy Optimization (GRPO) in

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Local Ai

Segmentation, Detection and Explanation: A Unified Framework for CT Appearance Reasoning

arXiv:2605.15997v1 Announce Type: new Abstract: Recent progress in deep learning has significantly advanced CT image analysis, particularly for segmentation tasks. However, these advances are largely

local-aiarxiv-cs-cv

SemanticOpt: Towards LLM-Based Semantic Black-Box Optimization

arXiv:2510.25404v3 Announce Type: replace-cross Abstract: Optimizing an experimental system can be extremely challenging when each experiment is expensive, time-consuming, or difficult to perform. Exi

SFHand: Learning Embodied Manipulation by Streaming Egocentric 3D Hand Forecasting

arXiv:2511.18127v2 Announce Type: replace Abstract: Real-time 3D hand forecasting is a critical component for fluid human-computer interaction in applications like AR and assistive robotics. However,

researcharxiv-cs-cv

SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation

model-releasesarxiv-cs-cl

arXiv:2605.16117v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities across diverse NLP applications, such as translation, text generation, and question a

SkillSmith: Compiling Agent Skills into Boundary-Guided Runtime Interfaces

arXiv:2605.15215v1 Announce Type: new Abstract: Recently, skills have been widely adopted in large language model (LLM)-based agent systems across various domains. In existing frameworks, skills are t

SkyLink: A Large Vision-Language Model Driven Re-ranking Framework for Cross-View UAV geolocalization

arXiv:2603.08063v3 Announce Type: replace Abstract: Cross-view UAV geolocalization is fundamentally a challenging large-scale image retrieval task, aiming to determine the geographic coordinates of Un

Small Generalizable Prompt Predictive Models Can Steer Efficient RL Post-Training of Large Reasoning Models

arXiv:2602.01970v2 Announce Type: replace Abstract: Reinforcement learning enhances the reasoning capabilities of large language models but often involves high computational costs due to rollout-inten

Tutorials

SMCEvolve: Principled Scientific Discovery via Sequential Monte Carlo Evolution

arXiv:2605.15308v1 Announce Type: new Abstract: LLM-driven program evolution has emerged as a powerful tool for automated scientific discovery, yet existing frameworks offer no principled guide for de

tutorialsarxiv-cs-ai

Social-Mamba: Socially-Aware Trajectory Forecasting with State-Space Models

arXiv:2605.15424v1 Announce Type: new Abstract: Human trajectory forecasting is crucial for safe navigation in crowded environments, requiring models that balance accuracy with computational efficienc

Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning

arXiv:2601.12894v2 Announce Type: replace-cross Abstract: Diffusion Policy has dominated action generation due to its strong capabilities for modeling multi-modal action distributions, but its multi-s

Sparse Autoencoders enable Robust and Interpretable Fine-tuning of CLIP models

arXiv:2605.15961v1 Announce Type: new Abstract: Large-scale pre-trained vision-language models like CLIP demonstrate remarkable zero-shot performance across diverse tasks. However, fine-tuning these m

researcharxiv-cs-cv

Stabilizing Knowledge, Promoting Reasoning: Dual-Token Constraints for RLVR

arXiv:2507.15778v2 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has become an effective post-training method for improving the reasoning abilities of Large La

researcharxiv-cs-cl

STAR: A Stage-attributed Triage and Repair framework for RCA Agents in Microservices

arXiv:2605.15581v1 Announce Type: new Abstract: LLM-based root cause analysis (RCA) agents have recently emerged as a promising paradigm for incident diagnosis in microservice AIOps. However, their re

StippleDiffusion: Capacity-Constrained Stippling using Controlled Diffusion

arXiv:2605.15816v1 Announce Type: cross Abstract: Stipple patterns, point sets whose local density tracks a target image, are traditionally produced by per-density iterative optimizers, which are slow

Structure Abstraction and Generalization in a Hippocampal-Entorhinal Inspired World Model

arXiv:2605.15733v1 Announce Type: cross Abstract: Humans abstract experiences into structured representations to facilitate pattern inference and knowledge transfer. While the hippocampal-entorhinal (

Structure-BiEval: A Self-Supervised, Dual-Track Framework for Decoupling Structure and Content in LLM Evaluation for Web Information Systems

arXiv:2601.19923v2 Announce Type: replace-cross Abstract: As Large Language Models (LLMs) evolve into the core of Web-based autonomous agents and complex Web Information Systems, their ability to fait

STS: Efficient Sparse Attention with Speculative Token Sparsity

model-releasesarxiv-cs-cl

arXiv:2605.15508v1 Announce Type: cross Abstract: The quadratic complexity of attention imposes severe memory and computational bottlenecks on Large Language Model (LLM) inference. This challenge is p

SurvivalPFN: Amortizing Survival Prediction via In-Context Bayesian Inference

model-releasesarxiv-cs-lg

arXiv:2605.15488v1 Announce Type: new Abstract: Survival analysis provides a powerful statistical framework for modeling time-to-event outcomes in the presence of censoring. However, selecting an appr

TokenButler: Token Importance is Predictable

arXiv:2503.07518v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) rely on the Key-Value (KV) Cache to store token history, enabling efficient decoding of tokens. As the KV-Cache g

Towards Efficient Large Language Reasoning Models via Extreme-Ratio Chain-of-Thought Compression

model-releasesarxiv-cs-lg

arXiv:2602.08324v3 Announce Type: replace Abstract: Chain-of-Thought (CoT) reasoning successfully enhances the reasoning capabilities of Large Language Models (LLMs), yet it incurs substantial computa

Tutorials

Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation

arXiv:2605.15913v1 Announce Type: cross Abstract: Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reus

tutorialsarxiv-cs-ai

Tube Loss: A Novel Approach for Prediction Interval Estimation

arXiv:2412.06853v4 Announce Type: replace-cross Abstract: This paper proposes a novel loss function, called 'Tube Loss', for simultaneous estimation of bounds of a Prediction Interval (PI) in the regr

Unlocking Dense Metric Depth Estimation in VLMs

arXiv:2605.15876v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text

Unsupervised Domain Shift Detection with Interpretable Subspace Attribution

arXiv:2605.15920v1 Announce Type: cross Abstract: We developed a tool for detecting domain shifts, namely subtle differences in the probability distributions of datasets. We identify these shifts usin

researcharxiv-cs-lg

VideoSeeker: Incentivizing Instance-level Video Understanding via Native Agentic Tool Invocation

arXiv:2605.16079v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have shown significant progress in video understanding, yet they face substantial challenges in tasks requiring p

VSPO: Vector-Steered Policy Optimization for Behavioral Control

arXiv:2605.15604v1 Announce Type: cross Abstract: Modern language models often need to optimize a primary accuracy objective while also accommodating secondary behavioral preferences, such as verbosit

safetyarxiv-cs-cl

When Does Sparse MoE Help in Vision? The Role of Backbone Compute Leverage in Sparse Routing

arXiv:2605.15484v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) networks promise favorable accuracy-compute trade-offs, yet practical vision deployments are hindered by expert collapse and li

researcharxiv-cs-cv

When Importance Sampling Misallocates Credit: Asymmetric Ratios for Outcome-Supervised RL

arXiv:2510.06062v2 Announce Type: replace Abstract: Reinforcement learning (RL) has shown great promise in large language models (LLMs) post-training, which typically rely on token-level clipping to m

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Agents

WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation

arXiv:2605.15964v1 Announce Type: cross Abstract: Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D enviro

agentsarxiv-cs-cv

3D Skew-Normal Splatting

arXiv:2605.15010v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) has emerged as a leading representation for real-time novel view synthesis and been widely adopted in various downstream ap

15 May 2026

A cross-species neural foundation model for end-to-end speech decoding