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版本更新 · v2arXiv:2610.32813

HarnessMM: Recursive Self-Improvement for Harness Token Efficiency

Yuhua Jiang, Chang Xu, Feifei Gao, Li Zhao

原文摘要

HarnessMinusMinus (HarnessMM) is a research project on improving the efficiency of agent harnesses, the software coordinating model reasoning, tool execution, and feedback. This technical report consolidates its shared framework, implementations, and empirical studies across coding, tool-based, and visual-agent settings. Agent harnesses often invoke reasoning after each tool result, even when earlier calls have already determined subsequent actions. HarnessMM addresses this overhead through guarded decision reuse and recursive self-improvement (RSI). RSI refines author prompts, context settings, and adapter rules using training feedback, with validation selecting deployment configurations. At inference time, a controller reuses resolved decisions while keeping actor weights fixed. Executable Decision Graphs encode actions, arguments, dependencies, and guards. Guarded Decision Reuse activates eligible decisions from observed feedback, while Certified Callback Substitution executes them through native tool interfaces, replacing redundant reasoning callbacks. The original actor resumes when new reasoning is required or reuse conditions are unmet. On HumanEval, HarnessMM preserves Codex success at 90.82\% while reducing tokens per task from 66.72k to 47.02k (29.54\% saving). For DeepSeek Harness, success improves from 71.43\% to 85.71\% while tokens decrease from 96.28k to 11.62k (87.93\% saving). An MBPP ablation shows 26.91\% lower cost than single-use graphs and 19.52\% lower cost than retained callbacks. The report also summarizes the HarnessMM-V visual-agent study and documents optimization overhead and execution time where measured. Together, these studies investigate guarded decision reuse across diverse agent harness settings. Code is available at https://github.com/YuhuaJiang2002/HarnessMM.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2507.22017

Cyst-X：用于胰腺囊肿风险分层与跨机构AI的多中心MRI基准

Cyst-X: A Multicenter MRI Benchmark for Pancreatic Cyst Risk Stratification and Cross-Institutional AI

Hongyi Pan, Gorkem Durak, Halil Ertugrul Aktas, Ziliang Hong, Zhixiang Wang, Elif KelesDeniz Seyithanoglu, Zheyuan Zhang, Alpay Medetalibeyoglu 等 30 位作者

AI 速读 基于摘要

作者构建了覆盖七中心、764例患者的胰腺囊肿MRI公开基准，并提供分割与IPMN风险预测的参考AI模型及联邦学习模拟结果。

eess.IV · cs.CV

arXiv ↗PDF ↗
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Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic cancer precursors, and guideline-based risk stratification both over-treats indolent lesions and misses progression. We present Cyst-X, a public benchmark of 1,461 T1-weighted (T1W) and T2-weighted (T2W) MRI volumes from 764 patients at seven centers, with expert pancreas masks, risk labels, splits and reference AI risk-prediction models. PanSegNet segmented the pancreas with Dice of 86.34% (T1W) and 89.26% (T2W) on held-out centers. For the primary task, high-risk versus low-risk IPMN, a 3D DenseNet-121 achieved cross-validated AUCs of 74.59% and 73.45%; complementary broad binary and three-class tasks reached 78.67--81.28% and 73.47--75.76%. Because patient images often cannot be pooled across institutions, we also simulated federated learning across the seven centers. On T2W MRI, FedProx was similar to centralized training in center-averaged external AUC (71.00% versus 71.18%) but not in pooled AUC (62.35% versus 77.49%). Radiologist comparisons showed cohort-dependent sensitivity--specificity tradeoffs. Cyst-X supports reproducible method development and identifies within-IPMN risk discrimination and cross-center score comparability as open challenges.

版本更新 · v2arXiv:2610.34356

RynnVLA-Latent: Scaling Robotic Manipulation Foundation Models with 3D-Grounded Latent Actions

Haoyu Zhao, Xingyue Zhao, Zhian Su, Bohan Hou, Yinzhou Tang, Fengyi Wang, Kehan Li, Yuming Jiang 等 14 位作者

原文摘要

Scaling vision-language-action (VLA) models is constrained by scarce action-labeled demonstrations and incompatible control spaces across robot embodiments. We present RynnVLA-Latent, a framework that turns heterogeneous interaction videos into scalable supervision for robot policy pretraining. At its core, RynnLAM learns continuous, 3D-grounded latent actions from visual transitions without requiring native robot controls. By combining residual future-feature prediction, camera-compensated 3D-flow supervision, and camera-motion decoupling, RynnLAM encourages representations that capture control-relevant motion rather than appearance changes alone. We apply RynnLAM to 30.1K hours of human, robot, and simulation videos, producing 745.52M latent-action descriptors. RynnVLA-Latent then learns to predict latent-action chunks from visual observations and language through flow matching, before adapting its pretrained backbone and action expert to embodiment-specific robot actions. The resulting 4B policy achieves 98.8% success on LIBERO, 84.7% on LIBERO-Plus without benchmark-specific adaptation, 51.9% average success across five VLABench tracks, and 88.6% average success on the RoboTwin 2.0 bimanual benchmark. It also achieves the highest observed success rates among the compared methods on six real-world manipulation tasks across dexterous-hand and mobile-gripper platforms. Together with representation ablations and data-scaling results, these findings demonstrate the potential of latent-action supervision for scaling robot policy learning beyond native action-labeled data.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.37393

Learning Quality-Controllable Robot Policies with Iterative Hindsight Relabeling

Sinjae Kang, Dong Kyu Shin, Jaehong Kim, Kamin Lee, Hyoeun Kim, Kaixin Wang, Li Zhao, Seonghyeon Ye 等 9 位作者

原文摘要

Robot policy evaluation often relies on a binary success signal for task completion. In real-world deployment, however, desired execution behaviors are also defined by quality attributes such as execution speed and motion smoothness, which need to be dynamically controlled according to task requirements. To address this, we introduce Quality-Controllable Policy (QCP), a robot policy that enables inference-time control over both individual quality attributes and their combinations. To extend the controllable quality range beyond initial demonstrations, we further propose an iterative hindsight relabeling loop: QCP collects online rollouts under target quality labels, adds successful trajectories to the dataset, relabels the accumulated trajectories in hindsight according to their measured quality, and fine-tunes the policy. Experiments on Robomimic and RoboEval demonstrate that QCP consistently improves both task execution quality and success rates across iterations, producing successful rollouts that exceed the quality of the initial demonstrations. Finally, we validate QCP on real-world manipulation tasks, where controlling speed increases throughput in conveyor operations and controlling transport stability improves success when carrying wine glasses, demonstrating that quality-controllable policies yield practical gains in physical deployment.

cs.RO · cs.AI · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.36791HF 社区推荐

RynnWorld-Latent: A Cross-Embodiment World Model Conditioned on Universal Latent Actions

Haoyu Zhao, Yinzhou Tang, Zixuan Wang, Minghao Zhu, Dongchi Huang, Xingyue Zhao, Qin Zhao, Kehan Li 等 14 位作者

原文摘要

Learning controllable world dynamics from heterogeneous interaction videos is challenging because robot embodiments use incompatible action spaces, while human videos lack native control labels. We present RynnWorld-Latent, a cross-embodiment video world model that uses the 3D-grounded latent actions introduced by RynnLAM as a shared conditioning interface. This representation enables joint pretraining on human interaction videos, robot demonstrations, and simulation rollouts without requiring a common native action space. Built on a pretrained video transformer, RynnWorld-Latent combines global action tokens with an identity-initialized local pathway that aligns actions with future video latents. We pretrain on 23 datasets comprising approximately 553.5M frames and adapt the model to target robots through post-training. The framework supports two data-generation routes: digital teleoperation uses target robot commands to generate action-video demonstrations, while latent-action synthesis transfers source motion to an edited initial frame and pairs the resulting rollout with retargeted pseudo robot actions. Experiments show improved video prediction metrics on public benchmarks and two real-robot platforms, alongside qualitative action following when different action sequences share the same initial observation. Synthetic mid-training improves policy success rates on LIBERO and LIBERO-Plus, and both generation routes improve real-world policy success rates over real-demonstration-only training in the evaluated settings. These results support universal latent actions as a scalable interface for learning controllable visual dynamics across heterogeneous embodiments.

cs.RO

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.32ACL Anthology · ACL 2026

UNIVID: Unified Vision-Language Model for Video Moderation

Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang 等 9 位作者

原文摘要

Global-scale video moderation faces a dual challenge: the need for fine-grained multimodal reasoning and the demand for interpretable outputs to support downstream enforcement. Traditional moderation systems often rely on fragmented black-box classifiers that are difficult to maintain and lack transparency.In this paper, we present UNIVID, a Unified Vision-Language model for Video Moderation. Unlike standard classification models, UNIVID generates policy-aware captions that serve as an interpretable intermediate representation, enabling human-verifiable decisions and multi-task reusability. While existing open-source and commercial VLMs often suffer from safety-guardrail refusals and lack fine-grained policy alignment, we develop a specialized training data recipe that combines expert human-refined labels with synthetic data to align the model with our safety guidelines.By integrating UNIVID as the core captioner, we design a novel end-to-end video moderation system that reduces violation leakage by 42.7% and overkill rate by 37.0% relatively. Meanwhile, by replacing over 1,000 policy-specific models with a single UNIVID backbone, we recycle extensive computational resources while significantly reducing engineering maintenance overhead. To our knowledge, this is one of the first reports of a high-efficiency captioning VLM successfully supporting industrial-scale moderation and cross-functional business.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表