The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k papers, providing the first naturally annotated dataset for this task. Experimental results demonstrate that our method significantly outperforms prior approaches, achieving state-of-the-art performance in taxonomy coherence, granularity, and interpretability.

All of the reviewers' minor comments and corrections have been added to Below, we address the reviewers' main questions. The paper focuses on HMC sampling. Unfortunately, HMC can't be applied in the discrete setting due to discontinuous How do you recommend setting π and g to best estimate β? Therefore, it's quite straightforward to implement pseudo-extended HMC within Stan by As a minor comment in line 58, it would be good to state that delta is an arbitrary differentiable function. This is a good point and we've corrected this in the paper. The experiments in 4.1 and 4.2 use the RMSE error of the target variables which is quite unusual.

PAPER ID: TMRB-06-24-OA-0958 1 Model-based optimisation for the personalisation of robot-assisted gait training Andreas Christou, Daniel F. N. Gordon, Theodoros Stouraitis, Juan C. Moreno and Sethu Vijayakumar Abstract--Personalised rehabilitation can be key to promoting gait independence and quality of life. Robots can enhance therapy by systematically delivering support in gait training, but often use one-size-fits-all control methods, which can be suboptimal. Here, we describe a model-based optimisation method for designing and fine-tuning personalised robotic controllers. As a case study, we formulate the objective of providing assistance as needed as an optimisation problem, and we demonstrate how musculoskeletal modelling can be used to develop personalised interventions. Eighteen healthy participants (age = 26 4) were recruited and the personalised control parameters for each were obtained to provide assistance as needed during a unilateral tracking task. A comparison was carried out between the personalised controller and the non-personalised controller. In simulation, a significant improvement was predicted when the personalised parameters were used. Experimentally, responses varied: six subjects showed significant improvements with the personalised parameters, eight subjects showed no obvious change, while four subjects performed worse. High interpersonal and intra-personal variability was observed with both controllers. This study highlights the importance of personalised control in robot-assisted gait training, and the need for a better estimation of human-robot interaction and human behaviour to realise the benefits of model-based optimisation. I. Introduction Motor function deficits are often the result of neurological disorders and can significantly impact the quality of This research was supported in part by the Engineering and Physical Sciences Research Council (EPSRC, grant reference EP/L016834/1) as part of the Centre for Doctoral Training in Robotics and Autonomous Systems at Heriot-Watt University and The University of Edinburgh, in part by the Alan Turing Institute, U.K., in part by Project I+D+i RED2022-134319-T (Spain), and in part by the Japan Science and Technology Agency (JST) Moonshot R&D Program (Grant No. JPMJMS2239). This includes one multimedia MP4 format movie clip, which provides scenes of the experimental setup. This material is 24.1 MB in size. T. Stouraitis is with DeepSea Technologies, 105 64 Athens, Greece (email: stoutheo@gmail.com).

Scientific writing involves retrieving, summarizing, and citing relevant papers, which can be time-consuming processes in large and rapidly evolving fields. By making these processes inter-operable, natural language processing (NLP) provides opportunities for creating end-to-end assistive writing tools. We propose SciLit, a pipeline that automatically recommends relevant papers, extracts highlights, and suggests a reference sentence as a citation of a paper, taking into consideration the user-provided context and keywords. SciLit efficiently recommends papers from large databases of hundreds of millions of papers using a two-stage pre-fetching and re-ranking literature search system that flexibly deals with addition and removal of a paper database. We provide a convenient user interface that displays the recommended papers as extractive summaries and that offers abstractively-generated citing sentences which are aligned with the provided context and which mention the chosen keyword(s). Our assistive tool for literature discovery and scientific writing is available at https://scilit.vercel.app