Improving Fine-Grained Manipulation through Demonstration Expansion and Execution Design
Jingxu Gu, Dongze Lian
AI 速览：DEED 通过结构化示范扩展与执行设计，将预训练视觉-语言-动作策略适配到五个 MetaFine 细粒度操作任务，作者报告官方 Overall 得分为 85.55%。
Fine-grained manipulation requires a robot policy to follow instructions, remain robust to visual variations, and translate predicted actions into precise robot control. We present Demonstration Expansion and Execution Design (DEED), a framework for adapting a pretrained vision-language-action policy to the five MetaFine tasks through structured demonstration expansion and execution design. DEED generates demonstrations for different instructions from the same initial scene and renders each physical trajectory under different camera positions and lighting conditions while preserving its original states and action targets. A hierarchical sampling strategy balances instructions, trajectories, and visual conditions during supervised adaptation. At deployment, DEED converts predicted action chunks into robot commands using inverse kinematics, gripper mappings selected for each task, and closed-loop replanning. DEED achieves an official Overall score of 85.55%. Fixed-policy evaluations show that performance varies with execution horizon, denoising budget, and gripper mapping.
Chumeng Liang, Linxuan Wang, Xinyu Peng, Huabin Liu, XiaoXu Guo, Yuxin Chen, Ge Liu, Guang Lin 等 10 位作者
Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters have achieved breakthrough performance in frontier production models, but their effectiveness deteriorates as the entropy of target distributions increases due to insufficient draft diversity. To overcome this bottleneck without sacrificing parallelism, we introduce UBTree, a parallel drafter that couples a Unigram proposer with a Bigram selector to construct drafting Trees. The unigram proposer is trained with the standard cross-entropy objective to generate candidate tokens independently for each position, while a lightweight bigram selector predicts transition scores between adjacent candidate pairs. Unlike the proposer, the selector is trained with a renormalized KL objective on high-temperature data. This tree-native training broadens the supervision beyond the greedy path, encouraging plausible alternative branches that improve the chance of accepting additional tokens during tree verification. Across seven standardized benchmarks with Qwen3-4B and Qwen3-8B, UBTree achieves an average speedup of $5.84$--$6.94\times$ over autoregressive decoding and outperforms DARTree in all 28 comparisons. Production-scale evaluation further demonstrates UBTree's advantage over frontier baselines such as DSpark.
Changhao Li, Bradley A. Chase, Veena Vijayakumar, Mathys Rennela, Alessandro Cosentino, Naixu Guo, Sebastian Hassinger, Farrokh Labib 等 14 位作者
Quantum open-source software provides essential infrastructure for quantum science, and artificial intelligence (AI) is reshaping its capabilities and transforming its development. However, verifying quantum software at scale remains difficult, and AI-generated contributions can increase the workload of maintainers. In this Perspective, we examine verification as a central requirement for reliable AI-assisted quantum software development and research. We analyze public repositories, records covering 110 repositories from five annual quantum open-source hackathons that we organize, and responses from more than 1600 participants in a community survey. Our analysis connects limitations in automated verification with the review burden on quantum software maintainers. We propose verification practices and contribution standards that require contributors to support their changes with reproducible evidence for maintainer review. We also examine how quantum open-source software can provide infrastructure for AI-driven research whose results the community can independently verify and build upon. We maintain an open-source companion repository to support reproducibility and help projects adopt and refine these proposals. Together, these contributions help the quantum open-source software community translate advances in AI into reliable quantum research through reproducible verification.
Jiasen Gao, Xiaoliang Chen Xihua University, Duoqian Miao, Xu Gu, Xianyong Li Xihua University, Yajun Du
Spurious correlations cause deep learning models to rely on predictive shortcuts that hold in the training data but break under distribution shifts, leading to large performance drops for minority groups. Existing strategies often rely on costly group annotations or employ unstable adversarial training. In this paper, we propose Prototype-guided debiasing using Robust Invariant Feature Transformations (PRIFT), a novel framework that mitigates spurious correlations by manipulating latent space geometry. Specifically, we introduce a prototype-guided modeling approach that leverages natural language prompts to represent confounders, transforming abstract biases into interpretable geometric anchors without auxiliary classifiers. Based on these anchors, we introduce a centered projection operator that adaptively purifies representations by removing confounding deviations specific to instances while preserving essential semantic structure. Furthermore, PRIFT can handle confounding factor information at different levels, ranging from true labels to unsupervised latent inference. Experiments on four text classification benchmarks demonstrate the superiority of our method; notably, PRIFT outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset compared to standard empirical risk minimization.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表