arXiv:2609.40159v1 Announce Type: cross Abstract: Sparse triangular solve (SpTRSV) is a fundamental kernel in numerous scientific and engineering applications. However, the data dependencies inherent...
arXiv:2604. 16509v2 Announce Type: replace-cross Abstract: Many robotic exploration algorithms rely on graph structures for frontier-based exploration and dynamic path planning.
arXiv:2601. 15158v4 Announce Type: replace-cross Abstract: Transformers trained via Reinforcement Learning (RL) with outcome-based supervision can spontaneously develop the ability to generate intermediate reasoning steps (Chain-of-Thought).
The paper presents a reinforcement learning method, HSAC, that builds covering structures without relying on rigid, pre‑planned sequences. It uses graph‑structured state representations and a mixed action space to select blocks and adjust their placement continuously, while an efficient exploration strategy incorporates unilateral edges into graph neural networks. HSAC outperforms the prior hybrid‑PPO approach, shows strong sample efficiency, robustness to hyperparameters, and successfully transfers policies from simulation to a real two‑robot 3D‑printed block construction task.
arXiv:2606. 15197v1 Announce Type: cross Abstract: Optimization modeling is inherently hierarchical, requiring a precise sequence of symbolic commitments.
arXiv:2608. 09366v1 Announce Type: new Abstract: Large-scale learning systems often face the challenge of balancing multiple, potentially competing objectives, such as fairness, accuracy, and latency.
The paper introduces a novel end‑to‑end, size‑agnostic graph reinforcement learning framework for the one‑dimensional bin packing problem (1D‑BPP). It models packing as a Markov decision process on an item‑compatibility graph, where a graph neural network actor‑critic policy learns to merge compatible partial bins. Empirical results on the BPPLIB benchmark show that the learned policy reduces the mean optimality gap of a constructive heuristic from 2.66 % to 2.31 %, performs competitively against other learned methods, and outperforms a state‑of‑the‑art learned solver on the hardest benchmark family.
The paper presents HSAC, a reinforcement learning method that builds covering structures without predefined plans, using graph-structured states and a mixed action space of discrete block selection and continuous placement. It extends soft actor-critic with unilateral edges in graph neural networks to efficiently explore while simulating stability. Experiments show HSAC outperforms hybrid-PPO, remains robust to hyperparameters, and successfully transfers to a real two-robot 3D‑printed arch construction.
arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...
arXiv:2603. 10395v2 Announce Type: replace Abstract: Graph generation is a fundamental task with broad applications, such as drug discovery.
arXiv:2507. 04136v2 Announce Type: replace Abstract: This survey offers a comprehensive foundation on the integration of RL with language models, highlighting prominent algorithms such as Proximal Policy Optimization (PPO), Q-Learning, and Actor-Critic methods.
arXiv:2606. 08610v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a powerful paradigm for robot learning, particularly in sim-to-real settings, but its broader adoption remains limited by the engineering pipeline surrounding the algorithms.