跳到正文
研踪PaperTrail

论文库

检索计算机领域论文，按研究方向、作者或关键词筛选。

部分采集异常
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+2已有论文修订
+100新增 AI 导读

arXiv 最近完成扫描：2026-11-15 01:33 · 全站最近入库：2026-11-14 15:09各信源进度 →

统计口径与数据新鲜度

统计 2026-11-14 02:33 — 2026-11-15 02:33（北京时间） · 点击数字查看对应简报；AI 相关包含在新增论文内。

最近完成检查：2026-11-15 01:33最近发现新增版本：2026-11-14 15:09库内论文最新更新时间：2026-11-13 02:59

每分钟刷新已保存的数据。空检查不代表采集异常；新收录可能包含历史论文。AI 导读按新增版本计数。

23,348 已收录论文19,580 AI 相关6,729 有修订112 HF 社区发现

浏览论文库

研究日报 / 周报 →按信源浏览 →

时间范围跟随排序：版本更新、本站收录或首次发表。最新收录可能包含历史论文。部分信源异常，查看状态 →

找到 4 篇论文 · 按版本更新时间排序上轮扫描起点 2026-11-15 01:32（北京时间）
新论文 · v1arXiv:2611.15732

Right Advice, Wrong Reasons: How Decision and Reasoning Alignment Shape Reliance on AI Advisors

Eunkyu Park, Vasudha Varadarajan, Wesley Hanwen Deng, Anisha Reddy, Gunhee Kim, Motahhare Eslami, Jana Schaich Borg, Maarten Sap

原文摘要

When people seek advice from AI on social situations, the system may recommend a decision they fully agree with but justify it through reasoning they disagree with. Conversely, it may offer reasoning they agree with, and yet reach a decision they reject. We distinguish decision alignment, whether an advisor recommends the option favored by a user's measured values, from reason alignment, whether its stated reasons align with the user's values. In this work, we ask how these two forms of alignment shape people's preferences for AI advisors and their willingness to use them in different roles. We conducted a within-subjects study (N=121) in which participants evaluated three AI advisors varying in decision and reason alignment across eight everyday moral dilemmas. Participants then chose which advisors they would consult, allow to draft messages for review, or allow to act on their behalf in new tasks. When seeking advice, participants most often preferred an advisor that reasoned through their prioritized values while recommending the opposing decision (40%). However, when participants had to pick advisors for new tasks, their preferences shifted toward advisors that also recommended decisions aligned with their values, rather than those that only shared their reasons. Also, as participants considered tasks that gave AI advisors greater autonomy, they became more likely to opt out of using any advisor, from 12% when consulting to 38% when allowing an advisor to act on their behalf. Our findings suggest that decision and reason alignment shape advisor preferences differently across contexts of AI use, and that alignment with a user's values does not necessarily imply willingness to delegate.

cs.HC

查看论文
arXiv ↗PDF ↗
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

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.23689

AutoDex: An Automated Real-World System for Dexterous Grasping Data Collection

Mingi Choi, Gunhee Kim, Jisoo Kim, Taeksoo Kim, Taeyun Ha, Jongbin Lim, Hanbyul Joo

原文摘要

Scaling real-world dexterous-grasp data collection is limited by the sustained human effort required to operate the robot, verify grasp outcomes, and reset objects between trials. Simulation can generate grasp candidates at scale, but their success on hardware remains uncertain. We present AutoDex, a system that collects real-world dexterous-grasp data without continuous human operation. Given generated grasp candidates, AutoDex plans and executes grasps, labels their outcomes, and resets the object to continue collection across stable poses. Multi-view object tracking supports labeling under hand-object occlusion, while residual-torque collision monitoring and pose-directed reset enable the loop to operate without continuous human supervision. Using AutoDex, we collect over 3,500 grasp trials across 100 objects with Allegro and Inspire hands, recording both successes and failures together with synchronized visual observations and robot states. AutoDex reduces human effort by more than an order of magnitude, requiring an estimated 0.2 human-hours to collect 500 trajectories compared with 9.3 for teleoperation. As AutoDex collects trials, it builds a library of physically validated grasps for subsequent execution. In new object placements and obstacle configurations, retrieval from this library achieves 76% success, compared with 34% for candidates screened only in simulation. Code and data are available at https://snuvclab.github.io/AutoDex/.

cs.RO · cs.LG

查看论文
arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.04867

Greedy Local Learning for Language Model Pretraining: Gaps and Objective Design

Jihwan Moon, Sheir A. Zaheer, Jinmyoung Lee, Gunhee Kim, Chan Y. Park

原文摘要

Greedy block-wise local learning splits a network into gradient-isolated blocks trained by local auxiliary losses, deleting the backward pass between blocks: inter-stage communication becomes forward-only and every block can step its optimizer independently, properties directly relevant to decentralized model-parallel training. Local learning is competitive with end-to-end backpropagation on image classification, and on small Transformers it is known to trade a worse best loss for parallel speedup. How this loss gap behaves in autoregressive language model (LM) pretraining at larger scale, and which auxiliary designs reduce it, has not been measured. We present a token-budget-matched empirical study at 125M and 400M parameters with $K \in \{1,2,4\}$ blocks at Chinchilla-optimal budgets, factorizing the auxiliary design into network architecture and training objective. We observe: (i) the gap to end-to-end training more than doubles from $K=2$ to $K=4$, but at $K=4$ shrinks from 125M to 400M; (ii) replacing an MLP auxiliary with a Transformer-based one is a strong network-side intervention, recovering 22-41% of the gap; (iii) a multi-token-prediction (MTP) auxiliary objective helps at the first block boundary, whereas adding it at deeper boundaries hurts, and restricting it to the first block yields the best $K=4$ configuration ($+0.062$ vs. $+0.075$ nats at 400M); and (iv) deployment-style per-block execution reduces activation memory by up to $2.2\times$. We frame these results as an empirically grounded method direction rather than a finalized method: local objectives should apply future-predictive pressure selectively across boundaries while resisting shortcuts that bypass predictive content.

cs.LG · cs.AI

查看论文
arXiv ↗PDF ↗