In this supplementary material, we first explain the details of spectral clustering algorithm in Sec. B. We also analyze the sensitivity of FreeSel to the values of hyperparameters in3 Sec. C. Besides, FreeSel is compared with other intuitive baselines using the general-purpose model4 in Sec. D. Finally, implementation details of our experiments are explained in Sec. E. Our code will5 be made publicly available.6 In this section, we explain the spectral clustering algorithm [14, 18] in the semantic pattern extraction8 process for each image I (Sec.

The classes on the horizontal axis are sorted based on the performance of the task model without DTL. Dashed line shows the median performance of all classes. The implementation for MSTCN [2] and ASFormer [6] are from existing opensource code provided by corresponding authors. The result is shown in Fig.A1 and Fig.A2. Weanticipatemoreperformance improvement with more general constraints that go beyond knowledge in the annotations in future works.

We propose Differentiable Temporal Logic (DTL), a model-agnostic framework that introduces temporal constraints to deep networks. DTL treats the outputs of a network as a truth assignment of a temporal logic formula, and computes a temporal logic loss reflecting the consistency between the output and the constraints.

To tackle this, test-time adaptation (TTA) [44] isproposed toboost model performance atinference time. The proposed objective in Eq.(10) requires the joint training of task modelfθ(x) and diffusion model φ(xt,t,cy) over all conditions{cy : y Y} simutaneously.

Remarkable capabilities have been achieved by robotics and AI, mastering complex tasks and environments. Yet, humans often remain passive observers, fascinated but uncertain how to engage. Robots, in turn, cannot reach their full potential in human-populated environments without effectively modeling human states and intentions and adapting their behavior. To achieve a synergistic human-robot collaboration (HRC), a continuous information flow should be established: humans must intuitively communicate instructions, share expertise, and express needs. In parallel, robots must clearly convey their internal state and forthcoming actions to keep users informed, comfortable, and in control. This review identifies and connects key components enabling intuitive information exchange and skill transfer between humans and robots. We examine the full interaction pipeline: from the human-to-robot communication bridge translating multimodal inputs into robot-understandable representations, through adaptive planning and role allocation, to the control layer and feedback mechanisms to close the loop. Finally, we highlight trends and promising directions toward more adaptive, accessible HRC.

The Directed Acyclic Graph (DAG) task model for real-time scheduling finds its primary practical target in Robot Operating System 2 (ROS 2). However, ROS 2's publish/subscribe API leaves DAG precedence constraints unenforced: a callback may publish mid-execution, and multi-input callbacks let developers choose topic-matching policies. Thus preserving DAG semantics relies on conventions; once violated, the model collapses. We propose the Function-as-Subtask (FasS) API, which expresses each subtask as a function whose arguments/return values are the subtask's incoming/outgoing edges. By minimizing description freedom, DAG semantics is guaranteed at the API rather than by programmer discipline. We implement a DAG-native scheduler using FasS on a Rust-based experimental kernel and evaluate its semantic fidelity, and we outline design guidelines for applying FasS to Linux Linux sched_ext.

Data - driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability . To improve predictions, techniques such as transfer learning and multi - task learning have been used. T he performance of multi - task learning models depend s on the strength of the underlying correlations between tasks and the completeness of the dataset . S tandard multi - task models tend to underperform when trained on sparse datasets with weakly correlated properties. To address this gap, we fuse deep - learned embeddings generated by independent pre - trained single - task models, resulting in a multi - task model that inherit s rich, property - specific representations. By re - using (rather than re - training) these embeddings, the resulting fused model outperforms standard multi - task models and can be extended with fewer trainable parameters . We demonstrate this technique on a widely used benchmark dataset of quantum chemistry data for small molecules as well as a newly compiled sparse dataset of experimental data collected from literature and our own quant um chemistry and thermochemical calculations.

The ability to continually learn, retain and deploy skills to accomplish goals is a key feature of intelligent and efficient behavior. However, the neural mechanisms facilitating the continual learning and flexible (re-)composition of skills remain elusive. Here, we study continual learning and the compositional reuse of learned computations in recurrent neural network (RNN) models using a novel two-system approach: one system that infers what computation to perform, and one that implements how to perform it. We focus on a set of compositional cognitive tasks commonly studied in neuroscience. To construct the what system, we first show that a large family of tasks can be systematically described by a probabilistic generative model, where compositionality stems from a shared underlying vocabulary of discrete task epochs. The shared epoch structure makes these tasks inherently compositional. We first show that this compositionality can be systematically described by a probabilistic generative model. Furthermore, We develop an unsupervised online learning approach that can learn this model on a single-trial basis, building its vocabulary incrementally as it is exposed to new tasks, and inferring the latent epoch structure as a time-varying computational context within a trial. We implement the how system as an RNN whose low-rank components are composed according to the context inferred by the what system. Contextual inference facilitates the creation, learning, and reuse of low-rank RNN components as new tasks are introduced sequentially, enabling continual learning without catastrophic forgetting. Using an example task set, we demonstrate the efficacy and competitive performance of this two-system learning framework, its potential for forward and backward transfer, as well as fast compositional generalization to unseen tasks.