Tootfinder

HiPrFlame-An ab initio based real-fluid modeling approach for high-pressure combustion-I. Rationale, methodology, and application to laminar premixed flames
Ting Zhang, Tianzhou Jiang, Mingrui Wang, Hongjie Zhang, Ruoyue Tang, Xinrui Ren, Song Cheng
https://arxiv.org/abs/2509.06717

HiPrFlame-An ab initio based real-fluid modeling approach for high-pressure combustion-I. Rationale, methodology, and application to laminar premixed flames
High-pressure combustion is central to modern propulsion and power-generation systems, where operating pressures often exceed the critical point of working fluids, resulting in pronounced real-fluid effects that fundamentally alter thermodynamic and transport properties. High-pressure combustion is central to modern propulsion and power-generation systems, where operating pressures often exceed the critical point of working fluids, resulting in pronounced real-fluid effects that fundamentally a…

Revisiting Cases 2 and 11 of the Map Color Theorem
Timothy Sun
https://arxiv.org/abs/2509.06407 https://arxiv.org/pdf/2509.06407

Revisiting Cases 2 and 11 of the Map Color Theorem
In 1968, Ringel and Youngs solved the remaining cases of the orientable Map Color Theorem by finding genus embeddings of the complete graphs $K_n$, for sufficiently large $n \equiv 2, 8, 11 \pmod{12}$. Following the approach previously explored by the author for $n \equiv 8 \pmod{12}$, we aim to streamline their constructions for $n \equiv 2, 11 \pmod{12}$ by finding families of current graphs with simpler patterns for the arc labelings.

OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
Bing Li, Wuqi Wang, Yanan Zhang, Jingzheng Li, Haigen Min, Wei Feng, Xingyu Zhao, Jie Zhang, Qing Guo
https://arxiv.org/abs/2510.06952

OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects
LiDAR-based 3D object detectors are fundamental to autonomous driving, where failing to detect objects poses severe safety risks. Developing effective 3D adversarial attacks is essential for thoroughly testing these detection systems and exposing their vulnerabilities before real-world deployment. However, existing adversarial attacks that add optimized perturbations to 3D points have two critical limitations: they rarely cause complete object disappearance and prove difficult to implement in p…

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Tianshi Zheng, Kelvin Kiu-Wai Tam, Newt Hue-Nam K. Nguyen, Baixuan Xu, Zhaowei Wang, Jiayang Cheng, Hong Ting Tsang, Weiqi Wang, Jiaxin Bai, Tianqing Fang, Yangqiu Song, Ginny Y. Wong, Simon See
https://arxiv.org/abs/2510.07172

NewtonBench: Benchmarking Generalizable Scientific Law Discovery in LLM Agents
Large language models are emerging as powerful tools for scientific law discovery, a foundational challenge in AI-driven science. However, existing benchmarks for this task suffer from a fundamental methodological trilemma, forcing a trade-off between scientific relevance, scalability, and resistance to memorization. Furthermore, they oversimplify discovery as static function fitting, failing to capture the authentic scientific process of uncovering embedded laws through the interactive explora…

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
Miao Lu, Weiwei Sun, Weihua Du, Zhan Ling, Xuesong Yao, Kang Liu, Jiecao Chen
https://arxiv.org/abs/2510.06727

Scaling LLM Multi-turn RL with End-to-end Summarization-based Context Management
We study reinforcement learning (RL) fine-tuning of large language model (LLM) agents for long-horizon multi-turn tool use, where context length quickly becomes a fundamental bottleneck. Existing RL pipelines can suffer from degraded instruction following, excessive rollout costs, and most importantly, strict context limits. To address these challenges, we introduce summarization-based context management to training. In specific, it periodically compresses the tool using history by LLM-generate…

TrackVLA : Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
Jiahang Liu, Yunpeng Qi, Jiazhao Zhang, Minghan Li, Shaoan Wang, Kui Wu, Hanjing Ye, Hong Zhang, Zhibo Chen, Fangwei Zhong, Zhizheng Zhang, He Wang
https://arxiv.org/abs/2510.07134

TrackVLA++: Unleashing Reasoning and Memory Capabilities in VLA Models for Embodied Visual Tracking
Embodied Visual Tracking (EVT) is a fundamental ability that underpins practical applications, such as companion robots, guidance robots and service assistants, where continuously following moving targets is essential. Recent advances have enabled language-guided tracking in complex and unstructured scenes. However, existing approaches lack explicit spatial reasoning and effective temporal memory, causing failures under severe occlusions or in the presence of similar-looking distractors. To add…

One reason why we chose #Baiersbronn for our last vacation was the #Landesgartenschau (#Garden show?).
It would have been a gem for flower and garden

A combinatorial approach to Ramana's exact dual for semidefinite programming
Gabor Pataki
https://arxiv.org/abs/2510.07271 https://arxiv.org/pdf/2510.0…

A combinatorial approach to Ramana's exact dual for semidefinite programming
Thirty years ago, in a seminal paper Ramana derived an exact dual for Semidefinite Programming (SDP). Ramana's dual has the following remarkable features: i) it is an explicit, polynomial size semidefinite program ii) it does not assume that the primal is strictly feasible, nor does it make any other regularity assumptions iii) yet, it has strong duality with the primal. The complexity implications of Ramana's dual are fundamental, and to date still the best known. The most important of these i…

Domain coarsening in fractonic systems: a cascade of critical exponents
Jacopo Gliozzi, Federico Balducci, Giuseppe De Tomasi
https://arxiv.org/abs/2509.04556 https://

Domain coarsening in fractonic systems: a cascade of critical exponents
We study the dynamics of domain growth when multipole moments of the order parameter are conserved. Following a quench into the ordered phase of the Ising model, the typical size of domains grows with time as $R(t) \sim t^{1/2}$ in the absence of conserved quantities. When the order parameter is conserved, the domain growth slows to $R(t) \sim t^{1/3}$. Conservation of higher moments of the order parameter fundamentally modifies this behavior: coarsening proceeds via anomalously slow growth. We…