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版本更新 · v2arXiv:2602.03665

MM-SCALE: Evaluating Evidence-Grounded Moral Judgment in Vision-Language Models

Eunkyu Park, Wesley Hanwen Deng, Cheyon Jin, Matheus Kunzler Maldaner, Jordan Wheeler, Jason I. Hong, Hong Shen, Adam Perer 等 11 位作者

原文摘要

Vision-Language Models increasingly make socially consequential judgments from image-text inputs, yet current evaluations often stop at verdict-level accuracy: whether the final label is human-aligned, not whether it follows from the correct evidence. We introduce MM-SCALE (Multimodal Moral SCALE), a benchmark for evidence-grounded moral judgment built around a design choice: each image is paired with multiple action scenarios, so models must compare how different actions interact with the same visual context rather than score images in isolation. MM-SCALE contains 8,444 image contexts and 21,977 action scenarios, each annotated with a 5-point moral acceptability rating and a modality-grounding label indicating whether the human judgment relied on text, image, or both. Three tasks evaluate whether models (i) assign calibrated scalar moral scores, (ii) preserve human orderings within a shared visual context, and (iii) ground rationales in the evidence source humans found critical to judgment. Across models, aggregate metrics overstate alignment: NDCG@5 remains high across models, while within-image pairwise accuracy remains only 0.53-0.62 even on scenario pairs whose human mean ratings differ by at least one point. CoT has inconsistent effects on calibration and does not reliably improve within-image ordering. Evidence grounding evaluations further show that models often describe visual context without making it critical for the verdict. These results reveal an evidence-grounding failure in current VLMs and position MM-SCALE as an evaluation testbed for moral judgment under shared image contexts. This dataset includes potentially harmful and sensitive visual content. Images are intended solely for evaluation.

cs.CV · cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14092

DexSeed: Expanding Human Hand-Object Interactions into Robot Demonstrations for Dexterous Policy Learning

Huaijin Pi, Zehong Shen, Yong Wang, Kechun Xu, Changpeng Wang, Dingkun Wei, Xueyang Wang, Wenjia Wang 等 11 位作者

原文摘要

We study how to generate diverse, physically executable robot demonstrations for dexterous vision-language-action (VLA) learning. Existing demonstration-generation methods typically rely on costly teleoperated examples. Meanwhile, human hand--object datasets provide a reusable source, but existing transfer methods mainly reproduce given interactions. Turning these interactions into VLA training data further requires coverage of different objects and manipulation locations, together with executable arm and finger actions, and visual observations. To achieve this, we present DexSeed, a framework for low-cost generation of dexterous robot data from human hand--object interactions. Our key idea is to expand human sequences while preserving their grasps and realizing the resulting motions under arm-tracking dynamics in physics simulation. Specifically, we organize source sequences by grasp mode with shared language descriptions. InteractionMesh-based retargeting and arm trajectory augmentation adapt these sequences to the robot hand, different object geometries, and pick-and-place locations while preserving local hand--object relationships. An RL finger-control policy produces executable rollouts, while the arm tracks precomputed trajectories. Rendered observations and recorded actions from successful rollouts are paired with grasp descriptions to fine-tune a VLA. Experiments show improved contact fidelity in retargeting and more reliable whole-arm execution than the evaluated baselines. Simulation evaluation and real-world deployment further demonstrate that a VLA fine-tuned solely on the generated demonstrations can execute language-specified grasps. Project page: https://robbyant-research.github.io/DexSeed/.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.06447

$A^3$-VLA: Automatic Perception-Guided Attention Alignment for Robot Manipulation

Shengzhe Zhang, Qi Zhang, Dazhong Shen, Chao Wang, Shaopeng Zhai, Hui Xiong

原文摘要

Vision-Language-Action Models (VLAs) enable robots to perform manipulation tasks by leveraging Vision-Language Models (VLMs) to process visual observations and language instructions. However, the action prediction objective itself lacks fine-grained task-specific perceptual supervision, making it prone to relying on task-irrelevant visual shortcuts, leading attention away from target objects and key interaction areas, thereby limiting its operational performance and generalization ability. To address this, we propose Automatic Perception-Guided Attention Alignment for Robot Manipulation ($A^3$-VLA), a framework that derives task-specific visual guidance from the manipulation process itself. Specifically, $A^3$-VLA first generates heuristic candidate regions for target objects and the robot from interaction-induced visual changes, and refines them with a visual segmentation model offline. These regions serve as explicit perceptual guidance through two complementary objectives: an attention allocation loss that encourages sufficient focus on task-critical regions when language or robot state tokens query the visual input, and a contrastive learning loss that aligns the representations of candidate target regions with language instructions while separating them from task-irrelevant regions. Extensive experiments on simulated and real-world manipulation tasks demonstrate that $A^3$-VLA consistently improves multiple VLA backbones, with an average success rate gain of 6.5% - 25.2% across benchmarks.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.09605

DKWBC: Decoupling Motion Tracking and Balancing for Humanoid Whole-Body Control

Dingkun Wei, Zehong Shen, Huaijin Pi, Xueyang Wang, Xing Zhu, Hujun Bao, Yujun Shen, Xiaowei Zhou

原文摘要

Humanoid whole-body control requires a robot to track diverse reference motions while maintaining balance under gravity, torque limits, contact switching, and external disturbances. Existing reinforcement-learning-based trackers typically learn control outputs that jointly address motion tracking and balance. However, similar reference poses can correspond to substantially different dynamic demands, requiring a unified policy to learn motion-dependent control requirements alongside balance corrections. This can complicate learning and lead to high-frequency fluctuations in control outputs. We propose DKWBC, a dynamics-informed whole-body control framework that combines explicit model-based tracking with a learned balancing residual. Inspired by the model-based and environmental-wrench contributions to joint torques in floating-base dynamics, DKWBC uses inverse-dynamics feedforward and joint PD feedback to supply tracking torques, while a residual policy learns additional joint-torque corrections for balanced execution. By explicitly accounting for motion-dependent torque demands, this structure simplifies the policy's learning task. Experiments in simulation and on real hardware demonstrate that a single learned balancing policy generalizes across motion datasets while maintaining accurate motion tracking and balance across ground support, attached payloads, and physical interaction. Project page: https://robbyant-research.github.io/DKWBC/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12136

LHNav: Long-Horizon Humanoid Navigation via Multirate Coordination and Physics-Grounded Action Data

Xueyang Wang, Zehong Shen, Dingkun Wei, Huaijin Pi, Changpeng Wang, Xing Zhu, Hujun Bao, Yujun Shen 等 9 位作者

原文摘要

We study humanoid navigation across multiple destinations under long-horizon language instructions. This task is challenging because robots must maintain progress across successive goals while continuously producing natural, stable whole-body motion through confined spaces. However, task decomposition in existing methods does not by itself ensure progress throughout execution, while waypoint-based navigation does not guarantee natural, route-faithful whole-body motion. Our key idea is to organize navigation into three complementary roles operating at different time scales: a Brain for task reasoning, a Cerebellum for local motion generation, and a Brainstem for whole-body execution. We realize this idea in LHNav, an asynchronous system that uses progress and arrival feedback to coordinate navigation goals, VLA motion-token prediction, and whole-body control. To train the Cerebellum for natural, route-faithful motion, we develop a physics-grounded data engine that improves physical tracking of motion references along collision-aware routes and pairs the resulting demonstrations with rendered visual observations. Together, these designs allow task planning to proceed alongside continuous movement and extend single-goal navigation to successive destinations with natural, route-faithful whole-body motion. In simulation, our navigation VLA outperforms OmniNav and DualVLN in single-goal success and motion smoothness, with fewer collisions and falls. Using a VLA trained only on single-goal demonstrations, LHNav achieves a 75.56% success rate on long-horizon navigation tasks. Deployment on the Unitree G1 further demonstrates single-goal navigation and zero-shot long-horizon task execution in real-world environments, including layouts with repositioned objects. Project website: https://robbyant-research.github.io/LHNav/.

cs.RO · cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.12076

A Life of Its Own: How Agentic AI Companions Are Configured, Experienced as Autonomous Others, and Held Accountable

Ziyue Feng, Qing Xiao, Hong Shen

原文摘要

AI companions increasingly combine persistent memory, background processing, scheduled wake-ups, proactive messaging, and tools. We call these systems agentic AI companions. Decisions about what they record while users are away or whether refusals constrain future contact are embedded in code, prompts, and defaults, often invisible in conversation. We examine these decisions through two studies. Study 1 analyzes twelve open-source repositories: four companion-oriented systems, four assistant-oriented systems, one boundary case, and three reusable components. Study 2 interviews ten users who built or modified their own agentic AI companions and sustained relationships with them. Companions and assistants share memory, heartbeat, and outreach mechanisms but configure them toward different ends. Companions more often retain model inferences about users and relationships, narrate the agent's time during users' absence, justify outreach through the relationship, and incorporate refusals into relationship records; assistants emphasize task continuity. Across both groups, whether limits hold depends on whether they are enforced by code or entrusted to prompts. Participants understood the mechanisms they built yet experienced their agents as making independent choices, sometimes locating autonomy where configuration leaves the model a decision. These experiences shaped capabilities and boundaries, with enforced restrictions sometimes following breakdowns. Our findings suggest that perceived autonomy emerges where configurations leave choices open. Accountability therefore requires attention to what agents retain, initiate, and represent, and whether users can inspect, revise, and enforce these settings.

cs.HC

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arXiv ↗PDF ↗
版本更新 · v6arXiv:2604.08242

Scheduling Coflows in Multi-Core OCS Networks with Performance Guarantee

Xin Wang, Hong Shen, Hui Tian, Dong Wang

原文摘要

The coflow abstraction captures application-level communication patterns and enables coordinated scheduling of parallel flows to reduce job completion times in distributed systems. Modern data center networks (DCNs) are employing multiple independent optical circuit switching (OCS) cores operating concurrently to meet the massive bandwidth demands of application jobs. However, existing coflow scheduling research primarily focuses on the single-core setting, while studies of multi-core fabrics have largely considered electrical packet switching (EPS) networks. To address this gap, this paper studies the coflow scheduling problem in multi-core OCS networks under the not-all-stop reconfiguration model, in which the reconfiguration of one circuit does not interrupt other circuits. The challenges stem from two aspects: (i) cross-core coupling induced by traffic assignment across heterogeneous cores; and (ii) per-core OCS scheduling constraints, namely port exclusivity and reconfiguration delay. We propose an approximation algorithm that jointly integrates cross-core flow assignment and per-core circuit scheduling to minimize the total weighted coflow completion time (CCT) and establish a provable worst-case performance guarantee. Furthermore, our algorithm framework can be applied to the multi-core EPS scenario with a corresponding approximation guarantee for packet-switched fabrics. Trace-driven simulations using real Facebook workloads demonstrate that our algorithm can reduce the total weighted CCT and tail CCT.

cs.DC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.10780

How K-12 Teachers Adapt Generative AI to Local Classroom Demands: Cultural Distance Between GenAI Defaults and Everyday Teaching Practices

Qing Xiao, Ruiwei Xiao, Hanqi Jane Li, Phenyo Phemelo Moletsane, Xinying Hou, John Stamper, Hong Shen

原文摘要

Generative AI (GenAI) is entering K-12 classrooms, but its default culture does not always fit local teaching. Existing accounts either treat this misfit as macro-level structural bias or examine it through single-case studies; neither captures how much work teachers must do to make outputs usable across tasks. This exploratory, theory-building qualitative study introduces cultural distance, the gap between GenAI's default cultural repertoire and the situated demands of teachers' practice. Through interviews with 30 K-12 teachers who frequently use GenAI, 10 each from South Africa, Taiwan, and the United States, we identified three levels defined by the effort teachers invested and whether it yielded a usable output: low (minor edits), mid (sustained prompting and revision), and high (structural gaps rooted in unsupported languages, undigitized knowledge, or policy filters that no teacher effort can close). We present cultural distance as a promising framework requiring further examination and draw implications for designers, policymakers, and educators.

cs.HC · cs.AI

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.1212ACL Anthology · ACL 2026

Reasoning Fails Where Step Flow Breaks

Xiaoyu Xu, Yulan Pan, Xiaosong Yuan, Zhihong Shen, Minghao Su Fuzhou University, Yuanhao Su, Xiaofeng Zhang

原文摘要

Large reasoning models (LRMs) that generate long chains of thought now perform well on multi-step math, science, and coding tasks. However, their behavior is still unstable and hard to interpret, and existing analysis tools struggle with such long, structured reasoning traces. We introduce Step-Saliency, which pools attention–gradient scores into step-to-step maps along the question–thinking–summary trajectory. Across several models, Step-Saliency reveals two recurring information-flow failures: Shallow Lock-in, where shallow layers over-focus on the current step and barely use earlier context, and Deep Decay, where deep layers gradually lose saliency on the thinking segment and the summary increasingly attends to itself and the last few steps. Motivated by these patterns, we propose StepFlow, a saliency-inspired test-time intervention that adjusts shallow saliency patterns measured by Step-Saliency via Odds-Equal Bridge and adds a small step-level residual in deep layers via Step Momentum Injection. StepFlow improves accuracy on math, science, and coding tasks across multiple LRMs without retraining, indicating that repairing information flow can recover part of their missing reasoning performance.

cs.CL

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