·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
The sameness problem behind those unappetizing AI-generated menus3h◆Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation3h◆Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection3h◆DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents3h◆Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents3h◆Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty3h◆CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning3h◆Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation3h◆SimSkill: A Lifelong Learning AI Agent for Autonomous Mastery of Traffic Simulation3h◆Rethinking World Models for Safety-Critical Embodied Systems3h◆IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations3h◆DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training3h◆Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM3h◆Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable3h◆The Natural Language Interaction Protocol and Standard for AI Agents3h◆Efficient Test-Time Adaptation through Human-AI Interaction3h◆Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments3h◆From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research3h◆A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms3h◆Rethinking On-Policy Distillation of Large Language Models II: One Training Example3h◆The sameness problem behind those unappetizing AI-generated menus3h◆Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation3h◆Dude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection3h◆DuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents3h◆Do GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents3h◆Beyond "Made with AI": Visualizing Provenance Density to Mitigate the Transparency Penalty3h◆CulturalMenuBench: Probing the Knowledge-Application Gap in Multimodal Culinary Reasoning3h◆Proactive Service Agents: A Unified Decision Framework, Methods, and Evaluation3h◆SimSkill: A Lifelong Learning AI Agent for Autonomous Mastery of Traffic Simulation3h◆Rethinking World Models for Safety-Critical Embodied Systems3h◆IRWOZ 2.0: A Large Language Model-driven Dialogue Dataset for Industrial Robot Conversations3h◆DRACO: Fine-Grained Credit Assignment with Dynamic Rubrics for Long-Horizon Agent Training3h◆Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM3h◆Epistemic Warrant for LLM Recommendations: Characterizing the Basis for Reliance When Ground Truth Is Unavailable3h◆The Natural Language Interaction Protocol and Standard for AI Agents3h◆Efficient Test-Time Adaptation through Human-AI Interaction3h◆Terminal-Universe: Turning Agent Trajectories into Scalable Terminal Environments3h◆From Deceptive Outputs to Deceptive Mechanisms: A Causal Framework for Language-Model Deception Research3h◆A Case Study on Emergent Cheating and Whistleblowing in Autonomous Research Swarms3h◆Rethinking On-Policy Distillation of Large Language Models II: One Training Example3h◆
News/CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning
arxiv
PublishedAugust 3, 2026 at 4:00 AM

CLIFT: Turning Gemini Robotics On-Device into Humanoid Specialists via Non-Invasive Closed-Loop Iterative Fine-Tuning

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

arXiv:2607.29172v1 Announce Type: cross Abstract: While robot foundation models are growing increasingly capable, the strongest models are typically trained on proprietary data and remain closed-source, limiting downstream users' ability to adapt them to new tasks, embodiments, and deployment settin

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

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

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivCaught in the Story: Narrative Captivity in Multi-turn LLMs Conversation3harxivDude: A Dual-Detection Multi-Agent System for Paper-Code Discrepancy Detection3harxivDuplexSpeechBench-IFEval: Evaluating Implicit Instruction Following in Full-Duplex Voice Agents3harxivDo GUI Agents Know When Not to Act? Enabling Conflict-Aware Termination for Multimodal GUI Agents3h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

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

Originally published on arxiv ↗
HomeModelsNews