Tootfinder

Laminar: A Scalable Asynchronous RL Post-Training Framework
Guangming Sheng, Yuxuan Tong, Borui Wan, Wang Zhang, Chaobo Jia, Xibin Wu, Yuqi Wu, Xiang Li, Chi Zhang, Yanghua Peng, Haibin Lin, Xin Liu, Chuan Wu
https://arxiv.org/abs/2510.12633

Laminar: A Scalable Asynchronous RL Post-Training Framework
Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performance. However, the scalability of existing RL frameworks is limited, as extreme long-tail skewness in RL trajectory generation causes severe GPU underutilization. Current asynchronous RL systems attempt to mitigate this, but they rely on global weight synchronization between the actor and all rollouts, which creates a ri…

Dual Shellability of Admissible Set and Cohen-Macaulayness of Local Models
Xuhua He, Qingchao Yu
https://arxiv.org/abs/2509.11581 https://arxiv.org/pdf/250…

Dual Shellability of Admissible Set and Cohen-Macaulayness of Local Models
We prove Görtz's combinatorial conjecture \cite{Go01} on dual shellability of admissible sets in Iwahori-Weyl groups, proving that the augmented admissible set $\widehat{\mathrm{Adm}}(μ)$ is dual shellable for any dominant coweight $μ$. This provides a uniform, elementary approach to establishing Cohen-Macaulayness of the special fibers of the local models with Iwahori level structure for all reductive groups-including residue characteristic $2$ and non-reduced root systems-circumventing geo…

HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction
Bingqing Wei, Lianmin Chen, Zhongyu Xia, Yongtao Wang
https://arxiv.org/abs/2509.11719

HeLoFusion: An Efficient and Scalable Encoder for Modeling Heterogeneous and Multi-Scale Interactions in Trajectory Prediction
Multi-agent trajectory prediction in autonomous driving requires a comprehensive understanding of complex social dynamics. Existing methods, however, often struggle to capture the full richness of these dynamics, particularly the co-existence of multi-scale interactions and the diverse behaviors of heterogeneous agents. To address these challenges, this paper introduces HeLoFusion, an efficient and scalable encoder for modeling heterogeneous and multi-scale agent interactions. Instead of relyin…

A Range-Based Sharding (RBS) Protocol for Scalable Enterprise Blockchain
M. Z. Haider, M. Dias de Assuncao, Kaiwen Zhang
https://arxiv.org/abs/2509.11006 https://

A Range-Based Sharding (RBS) Protocol for Scalable Enterprise Blockchain
Blockchain technology offers decentralization and security but struggles with scalability, particularly in enterprise settings where efficiency and controlled access are paramount. Sharding is a promising solution for private blockchains, yet existing approaches face challenges in coordinating shards, ensuring fault tolerance with limited nodes, and minimizing the high overhead of consensus mechanisms like PBFT. This paper proposes the Range-Based Sharding (RBS) Protocol, a novel sharding mecha…

Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
Duc-Thinh Ngo (STACK, LS2N), Kandaraj Piamrat, Ons Aouedi, Thomas Hassan, Philippe Raipin-Parv\'edy
https://arxiv.org/abs/2509.10499

Towards Scalable O-RAN Resource Management: Graph-Augmented Proximal Policy Optimization
Open Radio Access Network (O-RAN) architectures enable flexible, scalable, and cost-efficient mobile networks by disaggregating and virtualizing baseband functions. However, this flexibility introduces significant challenges for resource management, requiring joint optimization of functional split selection and virtualized unit placement under dynamic demands and complex topologies. Existing solutions often address these aspects separately or lack scalability in large and real-world scenarios. …

🔪 A scalpel that can diagnose? Scientists unveil a 'Lab-on-a-Scalpel' for real-time surgical insights
https://medicalxpress.com/news/2025-10-scalpel-scientists-unveil-lab-real.html

A scalpel that can diagnose? Scientists unveil a 'Lab-on-a-Scalpel' for real-time surgical insights
Imagine a surgeon in the middle of a complex operation, able to get instant biochemical feedback not from a lab down the hall, but from the very tool in their hand. This vision is now one step closer to reality thanks to researchers at the University of Chemistry and Technology, Prague (UCT Prague).

🇺🇦 #NowPlaying on KEXP's #VarietyMix
D’Angelo:
🎵 I Found My Smile Again
#DAngelo
https://traadsaru.bandcamp.com/track/dangelo-i-found-my-smile-again-traadsaru-soulful-house-remix
https://open.spotify.com/track/3S4EcM2vITQtPuVRzyZegT

Scalable Hardware Maturity Probe for Quantum Accelerators via Harmonic Analysis of QAOA
Chinonso Onah, Kristel Michielsen
https://arxiv.org/abs/2509.11450 https://

Scalable Hardware Maturity Probe for Quantum Accelerators via Harmonic Analysis of QAOA
As quantum processors begin operating as tightly coupled accelerators inside high-performance computing (HPC) facilities, dependable and reproducible behavior becomes a gating requirement for scientific and industrial workloads. We present a hardware-maturity probe that quantifies a device's reliability by testing whether it can repeatedly reproduce the provably global optima of single-layer Quantum Approximate Optimization Algorithm (QAOA) circuits. Using harmonic analysis, we derive closed-fo…

EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making
Zixing Lei, Sheng Yin, Yichen Xiong, Yuanzhuo Ding, Wenhao Huang, Yuxi Wei, Qingyao Xu, Yiming Li, Weixin Li, Yunhong Wang, Siheng Chen
https://arxiv.org/abs/2510.12072

EmboMatrix: A Scalable Training-Ground for Embodied Decision-Making
Embodied decision-making enables agents to translate high-level goals into executable actions through continuous interactions within the physical world, forming a cornerstone of general-purpose embodied intelligence. Large language models (LLMs), with their general decision-making capabilities, offer a promising path to realize this potential; however, LLMs trained solely on language lack exposure to physical environments, limiting their true embodied understanding. To bridge this gap, we propo…

CoRA: Covariate-Aware Adaptation of Time Series Foundation Models
Guo Qin, Zhi Chen, Yong Liu, Zhiyuan Shi, Haixuan Liu, Xiangdong Huang, Jianmin Wang, Mingsheng Long
https://arxiv.org/abs/2510.12681

CoRA: Covariate-Aware Adaptation of Time Series Foundation Models
Time Series Foundation Models (TSFMs) have shown significant impact through their model capacity, scalability, and zero-shot generalization. However, due to the heterogeneity of inter-variate dependencies and the backbone scalability on large-scale multivariate datasets, most TSFMs are typically pre-trained on univariate time series. This limitation renders them oblivious to crucial information from diverse covariates in real-world forecasting tasks. To further enhance the performance of TSFMs,…