Search: “benchmark”

B-MoE: A Body-Part-Aware Mixture-of-Experts 'All Parts Matter' Approach to Micro-Action Recognition

arXiv:2603.24245v3 Announce Type: replace Abstract: Micro-actions, fleeting and low-amplitude motions, such as glances, nods, or minor posture shifts, carry rich social meaning but remain difficult fo

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BadSkill: Backdoor Attacks on Agent Skills via Model-in-Skill Poisoning

DGX agent

arXiv:2604.09378v1 Announce Type: cross Abstract: Agent ecosystems increasingly rely on installable skills to extend functionality, and some skills bundle learned model artifacts as part of their exec

model-releasesarxiv-cs-ai

13 Apr 2026

Bandwidth-constrained Variational Message Encoding for Cooperative Multi-agent Reinforcement Learning

arXiv:2512.11179v3 Announce Type: replace Abstract: Graph-based multi-agent reinforcement learning (MARL) enables coordinated behavior under partial observability by modeling agents as nodes and commu

agentsarxiv-cs-lg

Better Eyes, Better Thoughts: Why Vision Chain-of-Thought Fails in Medicine

arXiv:2603.06665v2 Announce Type: replace-cross Abstract: Large vision-language models (VLMs) often benefit from chain-of-thought (CoT) prompting in general domains, yet its efficacy in medical vision

safetyarxiv-cs-ai

Beyond Augmented-Action Surrogates for Multi-Expert Learning-to-Defer

arXiv:2604.09414v1 Announce Type: cross Abstract: Learning-to-Defer routes each input to the expert that minimizes expected cost, but it assumes that the information available to every expert is fixed

Beyond Flicker: Detecting Kinematic Inconsistencies for Generalizable Deepfake Video Detection

arXiv:2512.04175v2 Announce Type: replace Abstract: Generalizing deepfake detection to unseen manipulations remains a key challenge. A recent approach to tackle this issue is to train a network with p

Bias-Constrained Diffusion Schedules for PDE Emulations: Reconstruction Error Minimization and Efficient Unrolled Training

arXiv:2604.08357v2 Announce Type: replace Abstract: Conditional Diffusion Models are powerful surrogates for emulating complex spatiotemporal dynamics, yet they often fail to match the accuracy of det

safetyarxiv-cs-lg

Biologically-Grounded Multi-Encoder Architectures as Developability Oracles for Antibody Design

arXiv:2604.09369v1 Announce Type: cross Abstract: Generative models can now propose thousands of de novo antibody sequences, yet translating these designs into viable therapeutics remains constrained

Boosting Brain-inspired Path Integration Efficiency via Learning-based Replication of Continuous Attractor Neurodynamics

arXiv:2511.17687v2 Announce Type: replace Abstract: The brain's Path Integration (PI) mechanism offers substantial guidance and inspiration for Brain-Inspired Navigation (BIN). However, the PI capabil

Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models

arXiv:2604.08964v1 Announce Type: new Abstract: Diffusion Large Language Models (dLLMs) have recently become a promising alternative to autoregressive large language models (ARMs). Semi-autoregressive

Bridging SFT and RL: Dynamic Policy Optimization for Robust Reasoning

arXiv:2604.08926v1 Announce Type: new Abstract: Post-training paradigms for Large Language Models (LLMs), primarily Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL), face a fundamental dil

safetyarxiv-cs-lg

CatalogStitch: Dimension-Aware and Occlusion-Preserving Object Compositing for Catalog Image Generation

model-releasesarxiv-cs-cv

arXiv:2604.08836v1 Announce Type: new Abstract: Generative object compositing methods have shown remarkable ability to seamlessly insert objects into scenes. However, when applied to real-world catalo

CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention

arXiv:2603.18561v2 Announce Type: replace Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relatio

safetyarxiv-cs-cv

ChipSeek: Optimizing Verilog Generation via EDA-Integrated Reinforcement Learning

arXiv:2507.04736v2 Announce Type: replace Abstract: Large Language Models have emerged as powerful tools for automating Register-Transfer Level (RTL) code generation, yet they face critical limitation

safetyarxiv-cs-ai

Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity

arXiv:2501.19038v3 Announce Type: replace-cross Abstract: Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In

Continuous Orthogonal Mode Decomposition: Haptic Signal Prediction in Tactile Internet

arXiv:2604.09446v1 Announce Type: cross Abstract: The Tactile Internet demands sub-millisecond latency and ultra-high reliability, as high latency or packet loss could lead to haptic control instabili

researcharxiv-cs-lg

Cross-Lingual Attention Distillation with Personality-Informed Generative Augmentation for Multilingual Personality Recognition

arXiv:2604.08851v1 Announce Type: new Abstract: While significant work has been done on personality recognition, the lack of multilingual datasets remains an unresolved challenge. To address this, we

DeFakeQ: Enabling Real-Time Deepfake Detection on Edge Devices via Adaptive Bidirectional Quantization

model-releasesarxiv-cs-cv

arXiv:2604.08847v1 Announce Type: new Abstract: Deepfake detection has become a fundamental component of modern media forensics. Despite significant progress in detection accuracy, most existing metho

Dictionary-Aligned Concept Control for Safeguarding Multimodal LLMs

arXiv:2604.08846v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have been shown to be vulnerable to malicious queries that can elicit unsafe responses. Recent work uses prom

safetyarxiv-cs-ai

Distilling Genomic Models for Efficient mRNA Representation Learning via Embedding Matching

arXiv:2604.08574v1 Announce Type: cross Abstract: Large Genomic Foundation Models have recently achieved remarkable results and in-vivo translation capabilities. However these models quickly grow to o

researcharxiv-cs-ai

Distributionally Robust Token Optimization in RLHF

arXiv:2604.08577v1 Announce Type: cross Abstract: Large Language Models (LLMs) tend to respond correctly to prompts that align to the data they were trained and fine-tuned on. Yet, small shifts in wor

researcharxiv-cs-ai

Drift-Aware Online Dynamic Learning for Nonstationary Multivariate Time Series: Application to Sintering Quality Prediction

arXiv:2604.09358v1 Announce Type: new Abstract: Accurate prediction of nonstationary multivariate time series remains a critical challenge in complex industrial systems such as iron ore sintering. In

Dual Mamba for Node-Specific Representation Learning: Tackling Over-Smoothing with Selective State Space Modeling

arXiv:2511.06756v3 Announce Type: replace Abstract: Over-smoothing remains a fundamental challenge in deep Graph Neural Networks (GNNs), where repeated message passing causes node representations to b

researcharxiv-cs-lg

EchoTrail-GUI: Building Actionable Memory for GUI Agents via Critic-Guided Self-Exploration

arXiv:2512.19396v3 Announce Type: replace Abstract: Contemporary GUI agents, while increasingly capable due to advances in Large Vision-Language Models (VLMs), often operate with a critical limitation

agentsarxiv-cs-ai

Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning

arXiv:2604.08960v1 Announce Type: new Abstract: Offline goal-conditioned reinforcement learning (GCRL) is a practical reinforcement learning paradigm that aims to learn goal-conditioned policies from

Efficient Spatial-Temporal Focal Adapter with SSM for Temporal Action Detection

arXiv:2604.09164v1 Announce Type: new Abstract: Temporal human action detection aims to identify and localize action segments within untrimmed videos, serving as a pivotal task in video understanding.

applicationsarxiv-cs-cv

Envisioning the Future, One Step at a Time

model-releasesarxiv-cs-ai

arXiv:2604.09527v1 Announce Type: cross Abstract: Accurately anticipating how complex, diverse scenes will evolve requires models that represent uncertainty, simulate along extended interaction chains

Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation

arXiv:2604.08627v1 Announce Type: cross Abstract: Pretrained models have become standard in both vision and language, yet they typically do not provide reliable measures of confidence. Existing uncert

applicationsarxiv-cs-ai

EvoLen: Evolution-Guided Tokenization for DNA Language Model

arXiv:2604.08698v1 Announce Type: new Abstract: Tokens serve as the basic units of representation in DNA language models (DNALMs), yet their design remains underexplored. Unlike natural language, DNA

safetyarxiv-cs-lg

EXAONE 4.5 Technical Report

arXiv:2604.08644v1 Announce Type: new Abstract: This technical report introduces EXAONE 4.5, the first open-weight vision language model released by LG AI Research. EXAONE 4.5 is architected by integr

Fast-dVLM: Efficient Block-Diffusion VLM via Direct Conversion from Autoregressive VLM

arXiv:2604.06832v2 Announce Type: replace Abstract: Vision-language models (VLMs) predominantly rely on autoregressive decoding, which generates tokens one at a time and fundamentally limits inference

agentsarxiv-cs-cl

FDIF: Formula-Driven supervised Learning with Implicit Functions for 3D Medical Image Segmentation

arXiv:2603.23199v2 Announce Type: replace Abstract: Deep learning-based 3D medical image segmentation methods relies on large-scale labeled datasets, yet acquiring such data is difficult due to privac

Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving

model-releasesarxiv-cs-ai

arXiv:2603.13842v3 Announce Type: replace-cross Abstract: End-to-end autonomous driving is typically built upon imitation learning (IL), yet its performance is constrained by the quality of human demo

FIRE-CIR: Fine-grained Reasoning for Composed Fashion Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.09114v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve a target image that depicts a reference image modified by a textual description. While recent vision-lan

FIT-GNN: Faster Inference Time for GNNs that 'FIT' in Memory Using Coarsening

arXiv:2410.15001v5 Announce Type: replace Abstract: Scalability of Graph Neural Networks (GNNs) remains a significant challenge. To tackle this, methods like coarsening, condensation, and computation

FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes

model-releasesarxiv-cs-ai

arXiv:2604.08586v1 Announce Type: cross Abstract: Computational fluid dynamics (CFD) provides high-fidelity simulations of fluid flows but remains computationally expensive for many-query applications

Folks, this is not Jevon's Paradox. this is just normal supply and demand. It turns out the utility of AI is high enough that people have hi…

applicationsethan-mollick--x

Folks, this is not Jevon's Paradox. this is just normal supply and demand. It turns out the utility of AI is high enough that people have high demand, which is outstripping supply (so prices will go u

Local Ai

From Frames to Events: Rethinking Evaluation in Human-Centric Video Anomaly Detection

arXiv:2604.09327v1 Announce Type: new Abstract: Pose-based Video Anomaly Detection (VAD) has gained significant attention for its privacy-preserving nature and robustness to environmental variations.

local-aiarxiv-cs-cv

From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

arXiv:2604.09459v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models (LLMs) increasingly relies on sparse, outcome-level rewards -- yet determining which actions withi

Ge^ext{2}mS-T: Multi-Dimensional Grouping for Ultra-High Energy Efficiency in Spiking Transformer

arXiv:2604.08894v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) offer superior energy efficiency over Artificial Neural Networks (ANNs). However, they encounter significant deficienci

researcharxiv-cs-ai

GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimisation

arXiv:2604.09095v1 Announce Type: new Abstract: Automated algorithm selection in continuous black-box optimisation typically relies on fixed landscape descriptors computed under a limited probing budg

How to find the sweet spot between cost and performance

model-releasesgoogle-cloud-ai

At Google Cloud, we often see customers asking themselves: 'How can we manage our generative AI costs effectively without sacrificing the performance and availability our applications demand?' This is

I am catching glimpses in my feed that there is a backlash against Mythos as 'marketing hype,' and it is a little confusing. I don't think a…

I am catching glimpses in my feed that there is a backlash against Mythos as 'marketing hype,' and it is a little confusing. I don't think anyone who has used the latest agentic coding tools, would th

safetyethan-mollick--x

Identification and Anonymization of Named Entities in Unstructured Information Sources for Use in Social Engineering Detection

arXiv:2604.09016v1 Announce Type: cross Abstract: This study addresses the challenge of creating datasets for cybercrime analysis while complying with the requirements of regulations such as the Gener

applicationsarxiv-cs-ai

Improving Model Performance by Adapting the KGE Metric to Account for System Non-Stationarity

arXiv:2604.03906v2 Announce Type: replace Abstract: Geoscientific systems tend to be characterized by pronounced temporal non-stationarity, arising from seasonal and climatic variability in hydrometeo

Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning

arXiv:2403.19253v3 Announce Type: replace Abstract: Effective agent coordination is crucial in cooperative Multi-Agent Reinforcement Learning (MARL). While agent cooperation can be represented by grap

InsEdit: Towards Instruction-based Visual Editing via Data-Efficient Video Diffusion Models Adaptation

arXiv:2604.08646v1 Announce Type: new Abstract: Instruction-based video editing is a natural way to control video content with text, but adapting a video generation model into an editor usually appear

Interactive ASR: Towards Human-Like Interaction and Semantic Coherence Evaluation for Agentic Speech Recognition

arXiv:2604.09121v1 Announce Type: cross Abstract: Recent years have witnessed remarkable progress in automatic speech recognition (ASR), driven by advances in model architectures and large-scale train

agentsarxiv-cs-ai