Search: “benchmark”

Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation

arXiv:2606.20196v2 Announce Type: replace Abstract: Continual Test-Time Adaptation (CTTA) aims to maintain model performance under evolving target domains by adapting online without labeled data. Howe

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Distilling Temporal Coherence into 2D Networks for Transrectal Ultrasound Prostate Video Segmentation

DGX agent

arXiv:2606.31198v1 Announce Type: cross Abstract: Real-time video segmentation of the prostate in Transrectal Ultrasound (TRUS) is essential for image-guided interventions. While conventional 2D metho

model-releasesarxiv-cs-ai

1 Jul 2026

Distilling the Essence: Efficient Reasoning Distillation via Sequence Truncation

arXiv:2512.21002v3 Announce Type: replace-cross Abstract: Distilling the capabilities from a large reasoning model (LRM) to a smaller student model often involves training on substantial amounts of re

DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments

arXiv:2606.31654v1 Announce Type: cross Abstract: Recent advances in multimodal large models have significantly improved UAV vision-language navigation (UAV-VLN) by enhancing high-level perception and

Early-Terminable Energy-Safe Iterative Coupling for Parallel Simulation of Partitioned Port-Hamiltonian Systems

arXiv:2603.16424v2 Announce Type: replace Abstract: Parallel simulation of robotic systems requires partitioning the dynamics into coupled subsystems. Finite-iteration coupling across the partition bo

ECHO: Prune to act, trace to learn with selective turn memory in agentic RL

arXiv:2606.31650v1 Announce Type: cross Abstract: Long-horizon language agents must repeatedly interact with tools, accumulate evidence, and make decisions under bounded context windows. Existing cont

Editing Everything Everywhere All at Once

arXiv:2606.31278v1 Announce Type: new Abstract: Editing multiple elements of an image in a single forward pass is a practical alternative to multi-turn image manipulation, offering improved efficiency

EgoVITA: Learning to Plan and Verify for Egocentric Video Reasoning

arXiv:2511.18242v3 Announce Type: replace Abstract: Egocentric video understanding requires procedural reasoning under partial observability and continuously shifting viewpoints. Current multimodal la

safetyarxiv-cs-cv

ELASTIC: Efficiently Learning to Adaptively Scale Test-Time Compute for Generative Control Policies

arXiv:2606.31132v1 Announce Type: new Abstract: Generative control policies (GCPs), such as diffusion policies and flow-based vision-language-action models, enable test-time scaling in robot control.

safetyarxiv-cs-ro

ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification Across the Periodic Table

model-releasesarxiv-cs-lg

arXiv:2606.30961v1 Announce Type: cross Abstract: Advances in deep learning architectures and representations have enabled ML-driven chemical property prediction, but state-of-the-art (SOTA) models ha

Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modeling

arXiv:2606.31252v1 Announce Type: new Abstract: Large language models can write plausible CAD scripts, but reliable industrial CAD modeling requires more than syntactically valid code: every feature,

Energy-Optimal Spatial Iterative Learning within a Virtual Tube

arXiv:2606.31487v1 Announce Type: new Abstract: Due to the limited endurance of embedded energy sources such as lithium-polymer (LiPo) batteries, the flight duration and operational range of unmanned

Enhancing Graph Representations with Neighborhood-Contextualized Message-Passing

arXiv:2511.11046v3 Announce Type: replace-cross Abstract: Graph neural networks (GNNs) have become an indispensable tool for analyzing relational data. Classical GNNs are broadly classified into three

Evo-PI: Aligning Medical Reasoning via Evolving Principle-Guided Supervision

arXiv:2606.31800v1 Announce Type: new Abstract: Despite recent progress, the reasoning capabilities of large multimodal language models (MLLMs) remain fundamentally constrained by static supervision,

Expected Gain-based Escalation in Vertical Federated Learning

arXiv:2606.31331v1 Announce Type: new Abstract: Collaborative inference can improve predictive performance by integrating complementary information across agents, but applying collaborative fusion to

researcharxiv-cs-lg

FaceMoE: Mixture of Experts for Low-Resolution Face Recognition

arXiv:2606.32040v1 Announce Type: new Abstract: Low-resolution face recognition (LR-FR) remains a challenging task due to poor feature extraction and aggregation, as probe images often contain limited

FairJudge: An Adaptive, Debiased, and Consistent LLM-as-a-Judge

model-releasesarxiv-cs-cl

arXiv:2602.06625v2 Announce Type: replace Abstract: Existing LLM-as-a-Judge systems suffer from three fundamental limitations: limited adaptivity to task- and domain-specific evaluation criteria, syst

Tutorials

FedLAB: Traceable Semantic Codebooks for Federated Multimodal Graph Foundation Learning

arXiv:2606.32016v1 Announce Type: new Abstract: Multimodal graph foundation models aim to learn reusable knowledge from graphs enriched with text, images, attributes, and relational topology, thereby

tutorialsarxiv-cs-lg

Fork-Think with Confidence

arXiv:2606.31484v1 Announce Type: cross Abstract: Parallel thinking has enjoyed great success for boosting LLM performance on reasoning tasks without the need for any re-training. However, existing me

researcharxiv-cs-cl

From Grasps to Dexterity: Large-Scale Grasp Pretraining for Dexterous Manipulation

arXiv:2606.30749v1 Announce Type: new Abstract: Large-scale dexterous grasp datasets encode rich priors over hand-object interaction, but their use has largely been confined to grasp generation and pi

From Search to Synthesis: Training LLMs as Zero-Shot Workflow Generators

arXiv:2606.30704v1 Announce Type: cross Abstract: Large language models (LLMs) excel across a wide range of tasks, yet their instance-specific solutions often lack the structural consistency needed fo

FROST: Training-Free Few-Shot Segmentation with Frozen Features and Nonparametric Statistics

arXiv:2606.31136v1 Announce Type: new Abstract: Few-shot segmentation asks a model to delineate a target class in a query image from only a handful of annotated examples, a setting most acute in remot

G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation

arXiv:2601.03510v3 Announce Type: replace Abstract: Point cloud segmentation is critical for 3D scene understanding. However, sparse and irregular point distributions provide limited appearance eviden

safetyarxiv-cs-cv

Gaussian Belief Propagation Network for Depth Completion

arXiv:2601.21291v2 Announce Type: replace Abstract: Depth completion aims to predict a dense depth map from a color image with sparse depth measurements. Although deep learning methods have achieved s

Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior

arXiv:2606.31814v1 Announce Type: new Abstract: Lane topology reasoning aims to construct a lane graph from onboard sensor observations. Existing methods follow a detection and association paradigm th

Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR

arXiv:2606.31813v1 Announce Type: cross Abstract: Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) par

Harnessing Textual Refusal Directions for Multimodal Safety

arXiv:2606.31876v1 Announce Type: new Abstract: To improve safety in Large Language Models (LLMs) we can either perform post-training alignment or exploit refusal directions in the activation space. B

Agents

Hierarchical Message-Passing Policies for Multi-Agent Reinforcement Learning

arXiv:2507.23604v2 Announce Type: replace Abstract: Decentralized Multi-Agent Reinforcement Learning (MARL) methods allow for learning scalable multi-agent policies, but suffer from partial observabil

agentsarxiv-cs-lg

High-Speed Vision-Based Flight in Clutter with Safety-Shielded Reinforcement Learning

arXiv:2602.08653v2 Announce Type: replace Abstract: Quadrotor unmanned aerial vehicles (UAVs) are increasingly deployed in complex missions that demand reliable autonomous navigation and robust obstac

HistoriQA-ThirdRepublic: Multi-Hop Question Answering Corpus for Historical Research, Parliamentary Debates from the French Third Republic (1870-1940)

arXiv:2606.31325v1 Announce Type: new Abstract: We present HistoriQA-ThirdRepublic: a French-language dataset of multi-hop historical questions derived from parliamentary debates and newspapers of the

HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection

arXiv:2606.31172v1 Announce Type: new Abstract: Monocular 3D lane detection plays a critical role in autonomous driving, yet recovering reliable 3D geometry from a single image remains challenging due

Human-Agent Collaborative Paper-to-Page Crafting

arXiv:2510.19600v2 Announce Type: replace-cross Abstract: In the quest for scientific progress, communicating research is as vital as the discovery itself. Yet, researchers are often sidetracked by th

HyperVLP: Enhancing Hierarchical Surgical Video-Language Pre-training in Hyperbolic Space

arXiv:2606.31245v1 Announce Type: new Abstract: Surgical vision-language foundation models typically adopt educational materials, such as surgical lecture videos, to transfer surgical knowledge encode

HyPOLE: Hyperproperty-Guided Multi-Agent Reinforcement Learning under Partial Observation

arXiv:2606.30966v1 Announce Type: new Abstract: Formal specification is a powerful tool to guide the learning process and provides significant advantages over reward shaping: (1) mathematical rigor; (

Improving Certified Robustness via Adversarial Distillation

arXiv:2606.31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper

InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving

arXiv:2606.31109v1 Announce Type: new Abstract: Generating realistic, controllable, and temporally coherent urban environments is a critical yet unresolved challenge in the autonomous driving communit

safetyarxiv-cs-cv

InfoFlow KV: Information-Flow-Aware KV Recomputation for Long Context

arXiv:2603.05353v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) for long-context question answering is bottlenecked by inference-time prefilling over large retrieved contexts.

researcharxiv-cs-lg

Interface-Aware Neural Newton Preconditioning for Robust Cohesive Zone Model Simulations

arXiv:2606.31921v1 Announce Type: new Abstract: Cohesive Zone Models (CZMs) are widely used to simulate interface fracture, delamination, adhesive failure, and fiber--matrix debonding in aerospace com

researcharxiv-cs-lg

Agents

Investigating Multi-Agent Deliberation in Law

arXiv:2606.30906v1 Announce Type: new Abstract: Artificial Intelligence is increasingly applied to the field of law, and has the potential to increase access to justice. One particular movement that i

agentsarxiv-cs-ai

IterCAD: An Iterative Multimodal Agent for Visually-Grounded CAD Generation and Editing

arXiv:2606.13368v2 Announce Type: replace Abstract: Computer-Aided Design is pivotal in modern manufacturing, yet existing automated methods predominantly rely on open-loop, one-shot generation, creat

JacobianAvatar: Temporally Consistent Semi-rigid Avatar Reconstruction from a Monocular Video

arXiv:2606.31115v1 Announce Type: new Abstract: Generating realistic human avatars in complex motions--such as clothing dynamics--requires modeling of global and local deformations which remains chall

Knowledge Distillation from Large Reasoning Models to Compact Student Models: A Case Study on the John O Bryan Mathematics Competition

arXiv:2606.31048v1 Announce Type: cross Abstract: This paper investigates knowledge distillation from a large reasoning model (DeepSeek-R1) to a compact student model (Qwen2.5-7B). Using historical pr

Learning from Failure: Inference-Time Self-Improvement for Computer-Use Agents

arXiv:2606.31270v1 Announce Type: cross Abstract: Computer-use agents, which leverage multimodal large language models (MLLMs) to operate computers and complete tasks, have attracted significant atten

Learning Structurally Consistent Representations for Multi-View Radar Semantic Segmentation

arXiv:2606.31609v1 Announce Type: cross Abstract: Radar sensors provide reliable perception under adverse weather and lighting conditions, but their sparse, noisy, and weakly semantic measurements mak

Learning to Select, Not Relearn: Hard-Routed Mixtures of Reasoning LoRAs

arXiv:2606.31413v1 Announce Type: new Abstract: Composing independently trained LoRA adapters into a single large language model is useful for multi-domain adaptation, especially when the original tra

Hardware

Learning Video Dynamics with Predictive Differentiable Rendering

arXiv:2606.31050v1 Announce Type: cross Abstract: How to accurately predict a high-fidelity future world? While the visual world is inherently continuous, existing deterministic video prediction model

hardwarearxiv-cs-ai

Linguistic Distancing on Social Media: Indicators of Emotion Regulation Across Age Groups

arXiv:2606.30957v1 Announce Type: new Abstract: Managing our emotional responses to events is key to emotional well-being, a process referred to as emotion regulation in psychology. Previous work has

researcharxiv-cs-cl

LLM-as-a-judge validity in physics assessment depends more on the task than the model

model-releasesarxiv-cs-cl

arXiv:2603.14732v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly considered for automated assessment and feedback, understanding when LLM marking is valid is