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新论文 · v1arXiv:2611.16339

Hand in Silhouette: Synthetic-to-Real Multi-View Hand Reconstruction via Binary Masks

Leiyao Cui, Lecheng Ruan, Zihang Zhao

原文摘要

Calibrated multi-view hand reconstruction has advanced rapidly, yet the effectiveness of existing methods depends strongly on real captured data, which is costly to acquire, limited in hand-geometry diversity, and often affected by imperfect annotations. Synthetic data offers a scalable alternative with accurate supervision, but a substantial synthetic-to-real appearance gap hinders its use. To address this, we introduce Hand in Silhouette (SilHand), a calibrated multi-view hand reconstruction framework that operates on binary hand masks, obtainable at low cost either automatically or manually, to reduce the synthetic-to-real observation gap. Rather than relying on RGB features for 3D lifting, SilHand projects metric 3D hypotheses into each view to query and fuse mask-ray evidence for hand reconstruction. This mask-centric formulation enables training on a large-scale synthetic dataset comprising 46.8k grasping poses and 60k free-hand samples, providing broad coverage of hand geometry while directly transferring to real captured hand geometry. Across five real-world benchmarks, SilHand improves relative reconstruction accuracy from 49.2% to 63.4% using masks rendered from reference hands. Moreover, reconstruction accuracy improves consistently as the synthetic training set grows, indicating that broader geometric coverage translates into better real-world performance. While achieving such diversity is difficult with real captured data, it becomes practical under our mask-centric formulation. Finally, SilHand remains robust under mask corruption, with reconstruction error increasing by at most 1.63 mm. Together, these results support binary masks as an effective interface for learning hand reconstruction from synthetic geometry.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.15490

ActionCodec2: A Streaming Action Tokenizer

Zibin Dong, Yicheng Liu, Shiduo Zhang, Tianyuan Yuan, Baijun Ye, Jinqiu Wang, Hang Zhao

原文摘要

Most action tokenizers for autoregressive vision language action (VLA) models compress motion into chunks that must be decoded in full before their actions become executable. We introduce ActionCodec2, a non-neural streaming tokenizer that releases actions incrementally while keeping trajectory reconstruction error within a prescribed tolerance. Its encoder quantizes each step relative to the decoder's reconstructed state, so the selected motion primitive corrects residual error rather than allowing errors from independently quantized increments to accumulate. Finding the smallest such primitive set is a geometric covering problem; we derive the exact minimum for translation and constant-factor-tight bounds for rotation. Set-BPE then losslessly merges co-occurring primitive symbols across coordinates and neighboring steps, without a fixed coordinate ordering. A universal version fitted on 20k hours of data from 24 embodiments averages 8.8 tokens per second for 15 Hz end-effector motion on this corpus. With the same VLA backbone, ActionCodec2 outperforms the compared tokenizers on three simulation benchmarks and in zero-shot real-robot evaluation. Its streaming representation allows policies to incorporate state feedback within an action chunk, substantially improving performance over open-loop execution under randomized dynamics, external disturbances, and moving objects. The same tokens also can serve as a direct control interface for VLM agents, which compose successful action sequences into reusable macros. These agent-built macros reduce control decisions and thinking-token usage across attempts and transfer to a held-out task. More results are available at https://zibindong.github.io/actioncodec2-page/

cs.RO

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.03602

SV-WAM: An Efficient Surround-View World-Action Model for End-to-End Autonomous Driving

Jinyang Wang, Shiwei Li, Junjian Wang, Zhiqiang Deng, Jianbin Gao, Yihang Zhao, Liu Liu, Yongjia Zhao 等 15 位作者

原文摘要

World models (WMs) have demonstrated strong potential for end-to-end autonomous driving by learning predictive representations of future scene dynamics. However, generating future videos during inference introduces substantial computational overhead, leading many recent driving WMs to adopt a single front camera as input for efficient deployment. This design restricts spatial coverage in safety-critical maneuvers such as lane changes, merges, and turns. To address this limitation, we propose SV-WAM, a surround-view world-action model (WAM) that preserves full six-camera observations while maintaining efficient inference. SV-WAM leverages future-video prediction as dense training supervision for action learning within a shared generative model, rather than as an inference-time output. At the core of this design is an action-centered causal mask that prevents action tokens from attending to future-video tokens during joint action-video denoising. Consequently, the video branch can be discarded at deployment, enabling efficient action-only planning. Furthermore, we introduce a differentiable drivable-area compliance regularizer that penalizes vehicle-footprint corners approaching or crossing drivable boundaries, improving planning safety and boundary awareness. Extensive experiments on the closed-loop NAVSIMv2 benchmark and the open-loop nuScenes benchmark demonstrate that SV-WAM achieves state-of-the-art planning performance with low inference latency and competitive zero-shot transfer capability.

cs.CV · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.03186

The Covering Radius of the Reed--Muller Code \texorpdfstring{$\RM(3,8)$}{RM(3,8)} is 56

Zhihang Zhao, Yansheng Wu

原文摘要

We prove that the covering radius of the third-order Reed--Muller code in eight variables is fifty-six. The proof separates even- and odd-weight inputs by moment-controlled slicing. On the even side, a transport invariant on quadratic-label stars and consistency of singleton restrictions give a compatible cubic correction. On the odd side, symmetry of the third moment yields a covariance-rank obstruction. Its rank-two exception is a quartic plus three points of a two-plane; the zero-second-moment case is a prescribed-point perturbation of a quintic. We prove that every such point perturbation has third-order nonlinearity at most fifty-five by a marked derivative identity and complete restriction-code covers. Consequently the maximum among exact-degree-eight functions is fifty-five. The structural reductions are proved analytically, and complete finite certificates verify the explicitly stated local covering and compatibility assertions. The matching lower bound follows from the published quartic relative radius.

cs.IT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.39090

PALATE: Pretrained Autoregressive LATent Embeddings

Sizhang Zhao, Praveen Gondhi, Bin Li, Utsaw Kumar

原文摘要

Local-commerce and food-delivery marketplaces run large ads businesses: across hundreds of thousands of stores, merchants pay to be surfaced on browse and search surfaces, and a ranker decides which ads each consumer sees. Ranking well demands strong per-consumer personalization, yet a consumer's raw interaction history is sparse and high-cardinality. Our production ranker had personalized only through hand-engineered signals: aggregate engagement counts, categorical embeddings, and order-agnostic pooled history, with no learned per-consumer sequence representation. We learn one by training a GPT-style generative model of the consumer's engagement sequence to predict their next few engagements, and read its last-token hidden state as the consumer embedding. That single frozen vector becomes one shared personalization input for every ad surface: a daily job refreshes it, and each ranker consumes it as an ordinary dense feature. Offline it is the largest single-feature gain in our ranking-AUC ladder, a relative Eval-AUC gain of +0.59% on a browse-driven feed and +0.57% on a query-driven search surface. Online, it shipped as one component of bundled ranker retrains on both surfaces: Discovery (revenue +0.79%, conversion rate +1.17%) and Search (revenue +0.94%, conversion rate +1.50%). Three design choices adapt the generative objective to a repurchase-heavy marketplace, and are the core of our contribution: an exploration-weighted loss that up-weights businesses new to the consumer over ones already engaged; a two-stage curriculum: a click pretrain that keeps the representation broad and exploratory, then a conversion fine-tune that sharpens purchase intent; and a designed negative set that pairs a fixed popular-business pool and businesses shown but not ordered with random and in-batch negatives.

cs.IR · cs.LG

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2608.12759

NavSight in the Wild: Understanding Real-World Use of a Mobile Augmented Reality Application for People with Low Vision in Outdoor Navigation

Yuheng Wu, Kexin Zhang, Ben Kosa, Ru Wang, Sanbrita Mondal, Yuhang Zhao

原文摘要

The ability to navigate outdoors safely and independently is crucial yet challenging for people with low vision (PLV). While various augmented reality (AR) systems for low vision have been designed and evaluated in ideal lab environments, no research has investigated their real-world feasibility and challenges. We present NavSight, a mobile AR application that assists PLV in outdoor navigation by recognizing important outdoor objects (e.g., curb, vehicle) and rendering real-time visual augmentations. Through a seven-day diary study with 12 PLV in real-world settings, we characterize the impact of NavSight on scene perception, users' configuration strategies on what objects to augment and how to augment them across scenarios, how users made sense of and responded to recognition errors, and the social acceptability of using NavSight in public. We further identify environmental factors affecting recognition, such as weather conditions, lighting and shadows, and nonstandard road markings and textures, as well as usability issues in daily use. We discuss these real-world challenges and derive design implications for future AI-powered assistive AR systems for outdoor use.

cs.HC

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.35ACL Anthology · ACL 2026

ReEx-SQL: Reasoning with Execution-Aware Reinforcement Learning for Text-to-SQL

Yaxun Dai Soochow University, Wenxuan Xie, Xialie Zhuang, Tianyu Yang Meituan, Ziyi Liu Soochow University, Haiqin Yang, Yiying Yang, Yuhang Zhao 等 10 位作者

原文摘要

Current Text-to-SQL reasoning models often lack integrated execution feedback during generation, and most existing approaches utilize feedback only for post-hoc correction. This separation not only limits real-time error correction, but may also introduce mistakes by altering otherwise correct SQL queries. To address these challenges, we present ReEx-SQL (Reasoning with Execution-Aware Reinforcement Learning), a Text-to-SQL framework that interacts with the SQL execution engine during decoding and dynamically adjusts reasoning based on execution feedback, thereby enabling context-sensitive query refinement and improved accuracy. ReEx-SQL achieves this through structured prompts with markup tags and a stepwise rollout strategy that incorporates execution feedback at each generation stage. For policy supervision, we design a composite reward function—featuring an exploration reward—to explicitly encourage effective interaction with the database. Furthermore, ReEx-SQL adopts a tree-based decoding strategy to facilitate exploratory reasoning and primarily aims to enhance parallel decoding efficiency. Notably, ReEx-SQL achieves 89.1% accuracy on Spider and 65.3% on BIRD at the 7B scale, surpassing baseline models by 2.7% and 2.6%, respectively. In addition, its tree-based decoding accelerates inference by 51.9% compared to linear decoding during sampling.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.529ACL Anthology · ACL 2026

SHAPE: Unifying Safety, Helpfulness and Pedagogy for Educational LLMs

Sihang Zhao, Kangrui Yu, Youliang Yuan The Chinese University of Hong Kong-Shenzhen, Pinjia He The Chinese University of Hong Kong, Shenzhen, Hongyi Wen New York University, Shanghai

原文摘要

Large Language Models (LLMs) have been widely explored in educational scenarios. We identify a critical vulnerability in current educational LLMs, pedagogical jailbreaks, where students use answer-inducing prompts to elicit solutions rather than scaffolded instructions. To enable systematic study, we unify and formalize safe, helpful, and pedagogical behaviors with a knowledge-mastery graph and introduce SHAPE, a benchmark of 9,087 student-question pairs for evaluating tutoring behavior under adversarial pressure. We propose a graph-augmented tutoring pipeline that infers prerequisite concepts from queries, identifies mastery gaps, and routes generation between instructing and problem-solving via explicit gating. Experiments across multiple LLMs show that our method yields significantly improved safety under two pedagogical jailbreak settings, while maintaining near-ceiling helpfulness under the same evaluation protocol. Our code and data are available at https://github.com/MAPS-research/SHaPE

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表