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新论文 · v1arXiv:2611.08039

HITCH: Teaching a Vision-Language Model to Operate a Humanoid through a Whole-Body Harness

Qingzhou Lu, Chenxin Liu, Xinming Wang, Liu Cao, Yanjiang Guo, Mengdi Xu, Jianyu Chen

原文摘要

Enabling vision-language models (VLMs) to control humanoid robots requires both reliable whole-body interfaces and knowledge of how to use the body that carries over across tasks. We present HITCH (Harness Interfaces To Command Humanoids), a harness in which perception returns the 3D position of any point the VLM selects in its image, and execution turns commands for the pelvis, torso, hands, walking, and gaze into whole-body motion with a learned tracker. We find that both measuring the scene and executing commands accurately raise task success. We then fine-tune a VLM on trajectories in which teacher policies solve five base tasks through HITCH, so that it learns how to operate the humanoid. Without task-specific trajectories, the fine-tuned model completes 51% of SIMPLE episodes and 42.5% of HumanoidArena episodes, and adding five trajectories per SIMPLE task raises its SIMPLE success to 95%. On a real G1, it carries a box and walks to a goal without real training data.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.41675

Radial Kernel Attention for Global Feature Exchange in Polymer Machine Learning Force Fields

Wenbo Fu, Chenghui Mao, Chengwen Hu, Haifeng Lv, Zhou Lu

原文摘要

We introduce Radial Kernel Attention (RKA) for long-range modeling in polymer machine learning force fields. Existing approaches often rely on local neighbor graphs, which limit communication across polymer chains and their surrounding environments, whereas explicit all-pair attention incurs quadratic computational cost. RKA maps interatomic distances from the cluster centroid to learned radial features and combines their kernelized overlap with content-dependent queries and keys. Factorized aggregation and readout enable global feature exchange at linear complexity in the number of atoms for fixed feature dimensions. To ensure physically consistent predictions, we insert the invariant scalar update between EquiformerV3 local blocks and obtain conservative forces by differentiating the total energy, including its dependence on radial coordinate . Our models are trained on official OPoly26 training set for 12 epochs and evaluated directly after training on the complete official validation set. A matched 5M comparison shows that RKA reduces per-atom energy and force MAEs by 67.4% and 36.3%, respectively. The 20M RKA model attains a total-energy MAE of 15.81 meV (per-atom MAE of 0.1182 meV/atom) and a force MAE of 2.619 meV/Angstrom,improving all three metrics over UMA-S-1.2 and UMA-S-1.2.1 on the same validation structures. RKA provides a compact pathway for information exchange beyond local graph connectivity in polymer machine learning force fields.

cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.16867

GoodServe: Globally-coordinated Routing of Agentic LLM Inferences for High Goodput

Boxiao Du, Boning Huangfu, Yizhou Luo, Chen Chen, Zijun Li, Minchen Yu, Xiaoyi Fan, Minyi Guo

原文摘要

Agentic applications consume LLM inferences as complete outputs: a code patch or database query is useful only after its last token arrives. Their performance is thus measured by end-to-end deadline satisfaction and goodput, the number of inferences completed within their deadlines. In heterogeneous GPU pools, existing greedy routers favor the fastest or least-loaded instance, often wasting powerful GPUs on requests that could meet their deadlines on weaker ones. Maximizing goodput instead requires globally coordinated routing, informed by uncertain output lengths and dynamic instance performance. We present GoodServe, a goodput-oriented routing system that addresses these challenges through a predict-and-rectify design. A task-gated mixture-of-experts predictor estimates request output lengths, while an EMA-smoothed black-box estimator tracks queuing delays and per-token speeds, accounting for prefix-cache reuse. A just-enough policy routes each request to the weakest instance capable of meeting its deadline and rejects requests that cannot finish on time. Periodic risk checks trigger migration of endangered requests using token IDs, at 7-15 times lower cost than KV-cache transfer. On a four-GPU heterogeneous testbed with three agentic benchmarks, GoodServe achieves the highest goodput across all evaluated SLO levels, improving over the best existing router by up to 31.0%. Its routing overhead remains 5 ms per request at 10,000 requests per second across 512 simulated instances.

cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.38151

Estimating Implicit Expected Log Density Ratios

Avi Amalanshu, Chandler Squires, Yizhou Lu, Pradeep Ravikumar

原文摘要

When exact probabilities cannot be computed, \emph{density ratio estimation (DRE)} is a key subroutine in the wide range of applications where density ratios appear, from experimental design to generative modelling and reinforcement learning. In this paper, we introduce the concept of an \emph{expected log density ratio (ELDR)}: log density ratios that are integrated under some third, possibly distinct measure. We show many problems with implicit densities where DRE is naïvely used are actually ELDR estimation tasks, and prove fundamental limitations on finite-sample ELDR estimation. Based on these results, we argue that the typical DRE-based approaches are unsuitable for ELDR estimation, and provide some foundational motivation towards bespoke ELDR estimators. To demonstrate, we propose a simple modification to some DRE methods that allows samples convergent to the integral distribution to appear in training data. We show through benchmarks that this can improve ELDR estimation error.

cs.LG · cs.AI · stat.ML

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2610.10270

Video Prediction Policy 2: Predict Better, Act Better

Yanjiang Guo, Haodong Yan, Zhide Zhong, Zhongru Zhang, Qingyuan Yang, Qingzhou Lu, Xiaoyu Chen, Yen-Jen Wang 等 18 位作者

原文摘要

World action models (WAMs) have emerged as an important class of generalist robot policies, aiming to transfer video prediction priors to action learning. However, we find that existing WAMs frequently produce incorrect motion predictions in open-ended environment, leading to erroneous actions. We attribute this limitation to two factors: (1) base video models are not optimized for manipulation, and (2) naively incorporating action components into video models can substantially degrade their generalization capabilities. We introduce Video Prediction Policy 2 (VPP2), a WAM that enables strong zero-shot generalization in both video prediction and action generation. First, we curate a large-scale, diverse dataset of manipulation videos to continue pretraining the base video foundation model. We annotate video clips with detailed captions and perform \textit{event-level} video pretraining to promote generalization across open-ended manipulation tasks. Second, we post-train and distill the video model into a single-step visual planner with fixed prediction horizon. Finally, we introduce action module via a mixture-of-transformers (MoT) architecture to learn implicit inverse dynamics model. Experiments demonstrate three key results: (1) VPP2-14B outperforms Cosmos3-64B by 11.0\% points in video prediction instruction-following success rate on open-ended tasks; (2) VPP2 surpasses the strongest baseline by 18.5\% points in success rate on real-world zero-shot ALOHA manipulation tasks; and (3) following benchmark-specific post-training, VPP2 achieves the highest success rates among evaluated methods on the challenging LIBERO-Pro, LIBERO-OOD, and RoboDojo benchmarks.

cs.CV · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.17558

Firefly: Illuminating Large-Scale Verified Tool-Call Data Generation from Real APIs

Yuxuan Lu, Ziyi Wang, Yingzhou Lu, Yisi Sang, Jiri Gesi, Xianfeng Tang, Yimeng Zhang, Zhenwei Dai 等 15 位作者

原文摘要

Training tool-calling agents requires large-scale trajectory data with verifiable labels, yet existing approaches either synthesize environments that diverge from real API behavior or generate tasks without ground-truth outcomes for verification. We present FireFly, a pipeline for generating verified tool-call data from real-world MCP servers. Our key insight is to invert the standard synthesis pipeline: rather than generating tasks and hoping they are solvable, we first let a strong LLM explore real APIs along graph-guided DAG structures, then synthesize tasks backward from observed outcomes, guaranteeing label correctness by construction. To handle the scale of real-world tool spaces (${\sim}$1,000 tools), we build a pairwise tool graph and sample sub-DAGs to focus exploration on semantically coherent workflows. To address environment drift in live APIs, we construct a retrieval-augmented simulator that caches all exploration results and replays them during training and evaluation, enabling fully offline and reproducible RL. Applying this pipeline yields 5,144 verified tasks spanning 240 servers and 993 tools. A 4B-parameter model trained with GRPO on FireFly matches Claude Sonnet 4.6 on our held-out test set and shows improvements on multiple tool-calling benchmarks including Tau2-Bench, MCPMark, and MCP-Atlas.

cs.SE · cs.CL

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arXiv ↗PDF ↗