arXiv subjects

Yikai Zheng

Publications and source records attributed to Yikai Zheng.

3 recordsLinked to original sources

arXiv2026

Zetta $ζ$: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence

Embodied agents are increasingly used to close the gap left by end-to-end policy models. Yet the agentic path has not realized closed-loop learning in physical execution: existing harnesses remain largely open-loop, following fixed skills during rollout and reflecting only after an episode completes. Such post-hoc reflection cannot govern execution as it unfolds, because physical interaction requires decisions to track rapidly changing robot-environment states at a frequency beyond today's large agentic models. We present Zetta, a closed-loop embodied harness that evolves code-based runtime critics and recovery skills online while keeping the base policy frozen. Through three timescale-separated loops, Zetta provides action-frequency governance, rollout-level critic-recovery proposal, and validation-gated skill updates. Together with Z-Infra, a rollout infrastructure decoupling agent logic from heterogeneous execution resources, Zetta achieves state-of-the-art success on LIBERO-Pro and RoboCasa under our current rollout budget, reaching 90.8% and 93.6%, with an 11.1x inference speedup; success continues to scale with self-exploration experience; learned skills transfer zero-shot, and clear robotic "Aha Moments" emerge. These results show that closed-loop harness self-evolution opens a scaling path for reliable physical intelligence.

cs.RO↗

arXiv2026

Em-Garde: A Propose-Match Framework for Proactive Streaming Video Understanding

Recent advances in Streaming Video Understanding has enabled a new interaction paradigm where models respond proactively to user queries. Current proactive VideoLLMs rely on per-frame triggering decision making, which suffers from an efficiency-accuracy dilemma. We propose Em-Garde, a novel framework that decouples semantic understanding from streaming perception. At query time, the Instruction-Guided Proposal Parser transforms user queries into structured, perceptually grounded visual proposals; during streaming, a Lightweight Proposal Matching Module performs efficient embedding-based matching to trigger responses. Experiments on StreamingBench and OVO-Bench demonstrate consistent improvements over prior models in proactive response accuracy and efficiency, validating an effective solution for proactive video understanding under strict computational constraints.

cs.CV↗

arXiv2026

Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning

Large language models (LLMs) struggle in real-world clinical consultations. Single-turn consultation systems require patients to describe all symptoms at once, which often leads to unclear complaints and vague diagnoses. Traditional dialogue models, constrained by static supervised learning, are limited to superficially imitating existing dialogue patterns and lack the ability to actively construct understanding in dynamic interactions, thus failing to achieve genuine clinical reasoning.To address these challenges, we propose DoctorAgent-RL, a reinforcement learning (RL)-based multi-agent collaborative framework, and train a doctor agent on Qwen2.5-7B-Instruct using this framework. Within this framework, a medical consultation is modeled as a dynamic decision-making process under uncertainty. The core intelligence of the doctor agent is shifted from knowing the answer to learning and mastering a questioning methodology aimed at achieving an optimal diagnosis. Through strategic questioning, it guides the progressive emergence of key patient information in multi-turn dialogues. To support this high-fidelity simulation of the real diagnostic process, we constructed MTMedDialog, a novel English multi-turn medical consultation dataset designed for dynamic, interactive training.To validate its real-world effectiveness, rigorous evaluations including blinded human assessments and trials with real patients were conducted. DoctorAgent-RL outperformed frontier models and achieved a 70% exact diagnostic match rate, confirming its potential as a collaborative tool. By handling initial screenings, it can free clinicians to focus on complex cases, thereby addressing critical issues like physician shortages and misdiagnosis risks while alleviating the strain on healthcare resources.

cs.CL↗