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新论文 · v1arXiv:2611.10917

Remembering to Reason: Structured Memory for Scene and Workflow Understanding in Robot-Assisted Surgery

Zhiran Zhang, Hu Wang, Yichen Wu, Yutong Xie

原文摘要

Surgical video understanding is an important foundation for intelligent assistance in robot-assisted surgery. Similar surgical views can reflect different actions or procedural stages. Interpreting these views requires access to earlier observations, yet frame-centric prompting leaves this history unavailable. We introduce MemSurg, a framework that remembers surgical observations before reasoning over them. Its structured memory graph stores timestamped entity categories, locations, motion cues, and interaction summaries. Chronological links preserve recent context, while entity-associated links connect recurring observations across the procedure. Task-adaptive retrieval then supplies evidence-grounded prompts for scene and workflow reasoning, supporting both frozen inference and memory-conditioned supervised fine-tuning. Evaluation on robot-assisted prostatectomy and two complementary surgical benchmarks shows a 10.43-percentage-point gain over SurgRAW with GPT-4o. Memory conditioning adds 6.52 points under matched Qwen3-VL-8B fine-tuning, and an inference-time plug-in adds 7.04 points to EndoChat. These results support structured memory as a reusable source of procedural evidence for surgical video understanding. Our code will be publicly released.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2509.18937

Lang2Morph: Language-Driven Morphological Design of Robotic Hands

Yanyuan Qiao, Kieran Gilday, Yutong Xie, Josie Hughes

原文摘要

Designing task-specific robotic hands requires translating diverse task requirements into morphologies that are both functional and manufacturable. Existing parametric and optimization-based approaches provide systematic design tools, but often rely on expert-defined objectives, manual tuning, or simulation-based search. Large language models (LLMs), with broad semantic knowledge and reasoning capabilities, offer a promising way to interpret task requirements directly from natural language. We present Lang2Morph, an LLM-driven framework that maps natural-language task descriptions to fabricable robotic hand morphologies within the Open Parametric Hand (OPH) design space. Lang2Morph combines task reasoning, structured morphology generation, geometric constraints, and feedback-guided selection to produce task-conditioned hand designs. Morphology-level analyses demonstrate high design feasibility and task-dependent structural variation, while real-world experiments with three fabricated hands reveal distinct functional characteristics across manipulation tasks. These results highlight the potential of LLMs as practical interfaces for task-driven robotic hand design.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.19134

ScienceIDE: Turning World's Scientific Codebase into Agent Learnable Environments

Hejia Geng, Zesen Huang, Haoyang Li, Wenbin Li, Koutian Wu, Zihan Zhou, Yuanbo Pang, Weihao Liu 等 63 位作者

原文摘要

Scientific code repositories encode decades of human knowledge in executable models, methods, and tools. Yet fragmented toolchains, implicit domain conventions, and specialized correctness criteria make this knowledge difficult to convert into reliable learning experience-a challenge we call the scientific experience bottleneck. We introduce ScienceIDE, infrastructure for turning the world's scientific code into programmable environments for scientific agents. Guided by expert-defined scientific cases and acceptance criteria, agents transform repositories into executable environments that support task generation, execution, and scientific verification. These environments provide a shared foundation for supervised fine-tuning, reinforcement learning, and evaluation. Using verified interaction trajectories, we train PhAI-IDE-72B/9B/4B. The model family shows gains in held-out scientific-code repair and across selected general-purpose benchmarks in code, reasoning, and knowledge, providing evidence of positive transfer from scientific experience to broader capabilities. ScienceIDE lays the foundation for an integrated workspace for agent learning and scientific practice, making humanity's scientific software a shared substrate for developing scientific intelligence. Code: https://github.com/aitofound/ScienceIDE

cs.CL · cs.CY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2611.03953

P$^2$-MOPD: Accelerating Multi-Teacher On-Policy Distillation via Parameter Priors

Sinuo Wang, Jiacheng Lu, Zeyu Zhang, Maojia Song, Rui Sun, Beidi Luan, Yutong Xie, Jing Li 等 11 位作者

原文摘要

Multi-teacher on-policy distillation consolidates expert capabilities by supervising student-generated trajectories, whereas task arithmetic offers a training-free alternative through linear parameter combinations. We view the linear combination of student and expert parameters as a single differentiable model and propose P$^2$-MOPD, which jointly optimizes the mixing coefficients and the student weights under the same on-policy distillation objective. Unlike task arithmetic, which stays within the span of the frozen student and expert parameters for any choice of merging coefficients, P$^2$-MOPD keeps the student trainable, so the experts act as parameter priors without confining it to this span. Across four domains at 1.7B and 4B scales, P$^2$-MOPD achieves faster and more balanced recovery than MOPD from the same initialization, surpassing its final average recovery in half the training steps. At the end of training, our 1.7B model reaches 94.7% average recovery, versus 80.9% for MOPD.

cs.LG · cs.AI

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.35107

DoAtlas-2: A Foundation for Self-Evolving Causal Biomedical Discovery

Yulong Li, Rong Xia, Yuxuan Zhang, Jianxu Chen, Xiwei Liu, Haochen Xue, Maosheng Li, Yuhang Liu 等 15 位作者

原文摘要

We introduce DoAtlas-2, a foundation for self-evolving causal biomedical discovery that organizes knowledge around causal mechanisms and advances through external evidence from human populations. DoAtlas-2 integrates 771 research resources covering more than 720,000 participants in 48 countries, from longitudinal clinical phenotypes, medical imaging, and continuous physiological signals to eight molecular layers, together with an evidence network of approximately 4.7 million literature-derived records over 93,566 concepts and 149,383 candidate causal relations. DoAtlas-2 autonomously formulates research questions from evidence gaps and unresolved mechanisms, prespecifies their causal designs, and generates validated analyses. Supporting, challenging, and unresolved results continuously revise mechanistic interpretations, the causal evidence state, and the discovery frontier, so that DoAtlas-2 self-evolves within a closed loop of hypothesis generation, empirical testing, and renewed discovery. DoAtlas-2 has systematically evaluated 2,031 research questions. In the Human Phenotype Project (HPP), it formulated 4,014 candidate pathway questions across vascular, early-glycemic, and hepatic-metabolic systems, and screening of the first 1,079 yielded statistical support for 756. Representative studies identify blood pressure as a convergence node linking adiposity, hepatic, and lipid phenotypes to vascular outcomes, and show that an adiposity-inflammation-blood-pressure pathway is largely attenuated by joint adjustment for body mass index (BMI) and smoking. The discovered vascular network constitutes a completely interpretable predictive foundation, admitting exact attribution of every prediction and closed-form mediation effects. DoAtlas-2 thereby unifies causal mechanism discovery, population-evidence testing, and interpretable prediction within one continuously evolving foundation.

cs.AI · q-bio.QM

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arXiv ↗PDF ↗
首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.113ACL Anthology · ACL 2026

MMCLIP: Cross-Modal Attention Masked Modelling for Medical Language-Image Pre-Training

Biao Wu, Yutong Xie Mohamed bin Zayed University of Artificial Intelligence, Zeyu Zhang Australian National University, Vu Minh Hieu Phan, Qi Chen Adelaide University, Ling Chen University of Technology Sydney, Qi Wu The University of Adelaide

原文摘要

Vision-and-language pretraining (VLP) in medicine leverages contrastive learning on image–text pairs, often enhanced with masked modeling. However, existing methods face two challenges: difficulty reconstructing key pathological features due to limited data, and reliance on either paired or image-only datasets without combining both. To address this, we propose MMCLIP (Masked Medical Contrastive Language–Image Pre-training), which introduces two modules: AttMIM: Masks image features highly correlated with text to improve reconstruction of fine medical details. EntMLM: Masks key medical entities in text and reconstructs them using visual cues. Furthermore, MMCLIP incorporates unpaired data through disease-kind prompts, achieving state-of-the-art performance in zero-shot and fine-tuning across five benchmarks.

cs.CL

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