·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
Data center backlash is uniting America1h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7h◆Scoped Semantic Invariance as a Foundation for LLM Reliability7h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems7h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?7h◆How To Build A Modern Diffusion Language Model7h◆Data Optimization for LLMs: A Survey7h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery7h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting7h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents7h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems7h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing7h◆Counterfactual Model Testing Under Distribution Shift7h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security7h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems7h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks7h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches7h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models7h◆Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network7h◆Data center backlash is uniting America1h◆AdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7h◆Scoped Semantic Invariance as a Foundation for LLM Reliability7h◆GEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7h◆Estimation of Propagated Risk in LLM-based Multi-Agent Systems7h◆AgentOrch: How Close Can Global Orchestration Come to Step-by-Step Interaction?7h◆How To Build A Modern Diffusion Language Model7h◆Data Optimization for LLMs: A Survey7h◆LegoSR: A Unified Framework for Self-improving Agent-Guided Scientific Discovery7h◆The Future Is Finer: Multivariate Time Series Super-Resolution Forecasting7h◆SkillShift-Bench: Benchmarking Skill Learning under Environment Evolution for Web Agents7h◆CoEvolve: Counterfactual-Guided Co-Evolution of Memory and Collaboration in Multi-Agent Systems7h◆Learning When to Collaborate: Selective Multi-Agent Medical Reasoning via Uncertainty-Aware Routing7h◆Counterfactual Model Testing Under Distribution Shift7h◆Data Governance for Generative and Agentic AI : Technology, Law, Economics, and Security7h◆When Generated Information Returns: Error Persistence in LLM-Mediated Information Ecosystems7h◆Graph-dependent pooling: adaptive granularity in hierarchical graph neural networks7h◆RAQ-MLA: Role-Aware Quantization for Multi-Head Latent Attention Caches7h◆Jointly Modeling Preferences and Imperfect Decision-Making in Large Language Models7h◆Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network7h◆
News/UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following
arxiv
PublishedJuly 16, 2026 at 4:00 AM
▲bullish

UESF-Bench: Benchmarking and Probing for Unified Embodied Seeking and Following

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.13621v1 Announce Type: new Abstract: Language-guided human following is an important capability for embodied agents, but existing benchmarks typically assume that the target person is visible at the start of an episode. This setting simplifies the problem and overlooks a more realistic re

Stay posted· Newsletter

Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Discussion
Mentioned models
01
  • 01
    SeekFollow-VLA
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#benchmark#embodied-agents#language-guided

No replies yet. Be first.

Mentioned models
01
  • 01
    SeekFollow-VLA
Source
↗
arxiv
Read original ↗All from arxiv →
Tags
03
#benchmark#embodied-agents#language-guided

Related coverage

More from ARXIV
arxivAdaPRA: Process Reward Agents with Adaptive Rubrics for Data Science7harxivScoped Semantic Invariance as a Foundation for LLM Reliability7harxivGEMS-RAG: Graph-grounded Engineering Multimodal Standards Retrieval-Augmented Generation7harxivEstimation of Propagated Risk in LLM-based Multi-Agent Systems7h
The Bubble Brief
MON · WED · FRI

Read benchmark insights every Monday, Wednesday and Friday — top movers, new models, story of the week.

// no spam · unsubscribe one-click · free forever

Your email is used only to send The Bubble Brief. Privacy policy

Originally published on arxiv ↗
PrivacyTermsRefundsLegal notice|AboutMethodologyContact

© 2026 Databubble

Built by Marouane Gazouzi · Readyo, AI interview coach

HomeModelsNews