Tootfinder

Falls ihr mal unbefriedigte Blockgelüste habt.
Und ja, ich bin hier, weil ich mein Profil NICHT durch "strategic following and content management tools" (vulgo Algorithmus) growen will.

ProSEA: Problem Solving via Exploration Agents
William Nguyen, Vinh Luong, Christopher Nguyen
https://arxiv.org/abs/2510.07423 https://arxiv.org/pdf/2510.0…

ProSEA: Problem Solving via Exploration Agents
Large language models (LLMs) have empowered AI agents to tackle increasingly complex tasks. However, most existing agents remain limited to static planning and brittle interactions, falling short of true collaboration or adaptive reasoning. We introduce ProSEA, a modular, general-purpose multi-agent framework designed for iterative problem solving through exploration and plan evolution. ProSEA features a hierarchical architecture in which a Manager Agent orchestrates domain-specialized Expert A…

I had over three hundred accounts following me that I didn't follow back, mostly because I lost track of all the new followers.
So I did the unthinkable thing:
I asked Perplexity to write me a python script to follow everyone back. I will just unfollow some of them later, if I don't like them enough. But for now, everyone gets the benefit of the doubt.😉
The script needed some adjustments, but it worked.
Here's the script if you need it:

followback
A short python script that looks for all your mastodon followers that you don't follow back, and follows them.

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Christopher A. Orrico, Hari Prasad Varadarajan, Matthijs van Berkel, Lennard Ceelen, Thomas O. S. J. Bosman, W. P. M. H. Heemels, Dinesh Krishnamoorthy
https://arxiv.org/abs/2510.04784

MPC strategies for density profile control with pellet fueling in nuclear fusion tokamaks under uncertainty
Control of the density profile based on pellet fueling for the ITER nuclear fusion tokamak involves a multi-rate nonlinear system with safety-critical constraints, input delays, and discrete actuators with parametric uncertainty. To address this challenging problem, we propose a multi-stage MPC (msMPC) approach to handle uncertainty in the presence of mixed-integer inputs. While the scenario tree of msMPC accounts for uncertainty, it also adds complexity to an already computationally intensive …

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

Robot Conga: A Leader-Follower Walking Approach to Sequential Path Following in Multi-Agent Systems
Pranav Tiwari, Soumyodipta Nath
https://arxiv.org/abs/2509.16482 https://

Robot Conga: A Leader-Follower Walking Approach to Sequential Path Following in Multi-Agent Systems
Coordinated path following in multi-agent systems is a key challenge in robotics, with applications in automated logistics, surveillance, and collaborative exploration. Traditional formation control techniques often rely on time-parameterized trajectories and path integrals, which can result in synchronization issues and rigid behavior. In this work, we address the problem of sequential path following, where agents maintain fixed spatial separation along a common trajectory, guided by a leader …

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…

Closing paths to cycles in symmetric graphs
Martin Milani\v{c}, {\DJ}or{\dj}e Mitrovi\'c
https://arxiv.org/abs/2510.05404 https://arxiv.org/pdf/2510.05…

Closing paths to cycles in symmetric graphs
It was shown by Beisegel, Chudnovsky, Gurvich, Milanič, and Servatius in 2022 that every induced $2$-edge path in a vertex-transitive graph closes to an induced cycle. Similar results were obtained for 3-edge paths closing to cycles in edge-transitive graphs, where the cycle can be assumed to be induced if the path is induced. Motivated by these results, we consider the following problem: For a given class of graphs, determine all integers $\ell\geq 0$ such that for every graph in the class, e…

Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
https://arxiv.org/abs/2512.05271 https://arxiv.org/pdf/2512.05271 https://arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
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