Search: “benchmark”

Odoriko: A Shape-Aware Multimodal Diffusion Framework for Human Motion

arXiv:2606.21135v1 Announce Type: new Abstract: Human motion generation has been widely studied across diverse input modalities, text, music, and video, and recent efforts have unified these into sing

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OFMU: Optimization-Driven Framework for Machine Unlearning

DGX agent

arXiv:2509.22483v2 Announce Type: replace Abstract: Large language models deployed in sensitive applications increasingly require the ability to unlearn specific knowledge, such as user requests, copy

safetyarxiv-cs-lg

23 Jun 2026

OGD4All: A Framework for Accessible Interaction with Geospatial Open Government Data Based on Large Language Models

arXiv:2602.00012v3 Announce Type: replace Abstract: We present OGD4All, a transparent, auditable, and reproducible framework based on Large Language Models (LLMs) to enhance citizens' interaction with

OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs

arXiv:2606.22617v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have achieved remarkable performance on 2D visual tasks, yet enhancing their spatial intelligence for real-worl

agentsarxiv-cs-cv

On-Manifold Variational Learning with Heat-Kernel Priors

arXiv:2606.18658v2 Announce Type: replace Abstract: Learning unsupervised representations of medical imaging cohorts can reveal clinically meaningful prototypes without expert labels, which are often

On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

arXiv:2606.22352v1 Announce Type: new Abstract: Dictionary learning has long been studied from both optimization and probabilistic perspectives. While formulations with element-wise sparsity regulariz

One Image is All You Need: Agentic One-Shot Image Generation via Text-Based World Models for Long-Tail Spatial Perception

arXiv:2606.20764v1 Announce Type: new Abstract: Reliable spatial decision automation, such as autonomous driving and maritime surveillance, critically depends on robust visual perception. However, rea

One Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation

arXiv:2606.21415v1 Announce Type: new Abstract: Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain

safetyarxiv-cs-lg

Open models, global networks: How AT&T and GSMA are accelerating telecom innovation with Gemma

model-releasesgoogle-cloud-ai

Telecommunications is an incredibly complex, highly specialized domain. Modern mobile networks are inherently multi-vendor, featuring diverse and often proprietary data structures. While AI has made m

Open Problem: Is AdamW Effective Under Heavy-Tailed Noise?

arXiv:2606.23676v1 Announce Type: new Abstract: AdamW is the de facto optimizer for training large language models (LLMs), yet the theory behind it still lives mostly in finite-variance regimes. This

OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

arXiv:2606.22101v1 Announce Type: cross Abstract: Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrie

Oracle-RLAIF: An Improved Fine-Tuning Framework for Multi-modal Video Models using Reinforcement Learning from Ranking Feedback

arXiv:2510.02561v2 Announce Type: replace Abstract: Recent advances in large video-language models (VLMs) rely on extensive fine-tuning techniques that strengthen alignment between textual and visual

ORBIT: Training-Free Multi-Attribute Behavioral Steering via Orthogonal Subspace Rotation

arXiv:2606.22357v1 Announce Type: cross Abstract: Language models are widely used in assistant settings, where controlling behavioral attributes is often essential. Activation steering modifies hidden

PACT: Preserving Anchored Cores in Task-vectors for Model Merging

arXiv:2606.18627v2 Announce Type: replace Abstract: Model merging has emerged as a training-free alternative to multi-task learning, aiming to combine multiple task-specific fine-tuned models into a s

PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation

arXiv:2606.18375v2 Announce Type: replace Abstract: World foundation models (WFMs) are powerful simulators, yet they predominantly operate in a single-view setting and lack the multi-view 3D consisten

safetyarxiv-cs-ro

PEAR: Permutation-Equivariant Adaptive Routing Multi-Agent Debate

arXiv:2606.20621v1 Announce Type: cross Abstract: Multi-agent debate improves the reliability of large language models (LLMs) through iterative peer critiques. However, fixed topologies often introduc

agentsarxiv-cs-lg

PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography

arXiv:2606.23226v1 Announce Type: new Abstract: Remote Photoplethysmography (rPPG) enables contactless pulse estimation from facial videos, serving as a vital tool for health monitoring. However, curr

Physics-Informed Neural Networks for Computing the Morse Index of the Critical Catenoid

arXiv:2606.21725v1 Announce Type: cross Abstract: The Morse index of a free boundary minimal surface is encoded in its Jacobi-Steklov spectrum, and we test how faithfully a physics-informed neural net

PolicyTrim: Boosting Intrinsic Policy Efficiency of Vision-Language-Action Models

arXiv:2606.22540v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models provide a unified paradigm for robotic manipulation, yet their real-world deployment is often bottlenecked by execut

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking

arXiv:2606.23604v1 Announce Type: new Abstract: The tracking-by-detection paradigm in multi-object tracking (MOT) typically relies on static appearance descriptors to complement motion estimation. How

Pose Anything Anywhere:Model-free Object Poses from Arbitrary References

arXiv:2606.23634v1 Announce Type: new Abstract: Estimating the 6D pose of unseen objects is a fundamental yet challenging problem for open-world robotics and embodied perception. Model-based methods a

Post-Training Speech Enhancement Language Models with Perceptual Rewards

arXiv:2606.21458v1 Announce Type: new Abstract: Speech enhancement language models achieve strong results when trained on discrete audio tokens, but their optimization relies on token-level cross-entr

Pre-Generation Hallucination Detection in Large Language Models via Soft-Target Attention Probing

arXiv:2606.21917v1 Announce Type: cross Abstract: Detecting hallucination risk before generation enables abstention, retrieval augmentation, and routing decisions without incurring the cost of decodin

Predicting Immune Biomarkers with MultiModal Mixture-of-Expert Pathology Foundation Models Empowers Precision Oncology

arXiv:2606.18123v2 Announce Type: replace Abstract: Predicting immune biomarkers associated with the tumor immune microenvironment (TIME) is critical for advancing precision oncology, yet existing app

Prefix-Guided On-Policy Distillation: Mining Golden Trajectories from Rollouts

arXiv:2606.21994v1 Announce Type: new Abstract: On-policy distillation (OPD) improves reasoning models by applying dense teacher supervision on student-sampled trajectories. However, scaling OPD to lo

safetyarxiv-cs-lg

Privacy-Preserving Federated Temporal Graph Learning with Digital Twin--Guided Adaptive Deception for Cyber-Resilient IoMT

arXiv:2606.21513v1 Announce Type: new Abstract: The rapid proliferation of IoT and IoMT devices introduces critical cybersecurity vulnerabilities in healthcare and industrial environments where resour

agentsarxiv-cs-lg

Prompt-Calibrated SAM 3 for Open-Vocabulary Remote Sensing Semantic Segmentation

arXiv:2606.21863v1 Announce Type: new Abstract: Open-vocabulary semantic segmentation (OVSS) in remote sensing images aims to segment categories beyond a fixed label space. Recent SAM 3-based methods

PromptDyG: Test-Time Prompt Adaptation on Dynamic Graphs

arXiv:2606.22914v1 Announce Type: new Abstract: Activities in numerous evolving systems can be represented as dynamic graphs in snapshot form at different time intervals, i.e., discrete-time dynamic g

PROTON: Prototype-Based Test-Time Online OOD Detection for Medical VLMs

arXiv:2606.20913v1 Announce Type: new Abstract: Medical vision-language models (VLMs) enable zero-shot clinical image classification, yet reliably detecting out-of-distribution (OOD) inputs at deploym

Provably Sub-Linear Two-Timescale NeuroEvolution with Online Plasticity

arXiv:2606.20817v1 Announce Type: cross Abstract: NeuroEvolution of Augmenting Topologies (NEAT) is a widely used neuroevolution algorithm for learning neural network architectures and weights for con

QeHDC: Hyperdimensional Computing based on Quantum-enhanced binding and SuperClass Construction

arXiv:2606.22421v1 Announce Type: new Abstract: Hyperdimensional Computing (HDC) is a robust computational framework inspired by human cognition characterized by simple and efficient operations within

QuantKAN: A Unified Quantization Framework for Kolmogorov Arnold Networks

arXiv:2511.18689v4 Announce Type: replace Abstract: Kolmogorov--Arnold Networks (KANs) replace linear weights with spline-based functions, offering strong expressivity but posing challenges for low-pr

Quantum Convolutional Neural Networks for Groundwater Heat Plume Prediction: A Surrogate Modeling Approach

arXiv:2606.23411v1 Announce Type: cross Abstract: Quantum machine learning methods are increasingly explored for modeling complex environmental systems, including groundwater heat plume dynamics. In t

Rebuttals Move Peer-Review Scores, but Initial-Review Structure Bounds the Movement

arXiv:2606.22166v1 Announce Type: cross Abstract: Author rebuttals are the main post-submission window in peer review, but their effect on reviewer scores remains hard to measure because score updates

ReconMIL: Synergizing Latent Space Reconstruction with Bi-Stream Mamba for Whole Slide Image Analysis

arXiv:2603.19925v2 Announce Type: replace-cross Abstract: Whole slide image (WSI) analysis heavily relies on multiple instance learning (MIL). While recent methods benefit from large-scale foundation

Reinforcement Learning to Disentangle Multiqubit Quantum States from Partial Observations

arXiv:2406.07884v2 Announce Type: replace-cross Abstract: Using partial knowledge of a quantum state to control multiqubit entanglement is a largely unexplored paradigm in the emerging field of quantu

agentsarxiv-cs-lg

Reliability-Guided Adaptive Ensembling for Robust Test-Time Adaptation

arXiv:2606.22351v1 Announce Type: cross Abstract: Test-time adaptation (TTA) can mitigate domain shift without source data, but it is highly brittle under adversarially contaminated test streams, wher

Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation

arXiv:2606.21188v1 Announce Type: new Abstract: Long horizon, contact-rich manipulation is inherently partially observable. This is as a single visual observation rarely captures a robot's full action

safetyarxiv-cs-ro

ReNIO: Reweighting Negative Trajectory Importance for LLM On-Policy Distillation

arXiv:2606.23104v1 Announce Type: new Abstract: On-policy distillation (OPD) improves LLM reasoning by training a student model on its own generated outputs, but standard OPD treats all student-genera

Residue-Level Attributions in Protein Language Models Do Not Recover Allergen Epitopes

arXiv:2606.22181v1 Announce Type: new Abstract: Deep allergenicity classifiers are increasingly used in safety screening of novel foods, and recent protein language models have substantially improved

Rethinking Molecular Graph Backdoors under Chemistry-aware Admission

arXiv:2606.23361v1 Announce Type: new Abstract: Backdoor attacks on molecular graph neural networks (GNNs) are typically evaluated as abstract graph edits, but real molecular learning pipelines do not

Rethinking Object-Centric Representations for Video Dynamics Modeling

arXiv:2606.23436v1 Announce Type: new Abstract: Unsupervised video object tracking aims to decompose dynamic scenes into persistent, object-centric entities without manual annotations. Many recent app

Retrieval-Augmented Multimodal Learning for Enzyme-Substrate Interaction Prediction Under Low-Homology Shift

arXiv:2606.22823v1 Announce Type: new Abstract: Enzyme substrate interaction (ESI) prediction is a fundamental computational task for biocatalyst discovery and reaction screening in large biochemical

Revisiting OmniAnomaly for Anomaly Detection: performance metrics and comparison with PCA-based models

arXiv:2603.18985v2 Announce Type: replace-cross Abstract: Deep learning models have become the dominant approach for multivariate time series anomaly detection (MTSAD), often reporting substantial per

Right Knowledge, Wrong Answer: Test-Time Steering for Temporal Fact Conflicts in Open-Weight Language Models

arXiv:2606.20959v1 Announce Type: new Abstract: Large language models can store both outdated facts and newer superseding facts in their parameters, but standard prompting may still elicit the outdate

Local Ai

RIZZ: Routing Interactions to Near Zero-Interference Zones for Continual Adaptation of Black-Box Agents

arXiv:2606.20638v1 Announce Type: cross Abstract: Large language models are increasingly deployed as long-lived agents that must adapt across users, tasks, domains, modalities, and feedback regimes wi

local-aiarxiv-cs-lg

RLM-Cascade: Response-Level Speculative Decoding for Cost-Efficient LLM API Serving

arXiv:2606.22840v1 Announce Type: new Abstract: We present RLM-Cascade, a proxy-layer system that applies speculative decoding at the response level to reduce LLM API costs without requiring model arc