Tootfinder

Ukraine, EU partners finalize revised peace proposal, ready to present plan to US: https://benborges.xyz/2025/12/09/ukraine-eu-partners-finalize-revised.html

Robust equilibria in continuous games: From strategic to dynamic robustness
Kyriakos Lotidis, Panayotis Mertikopoulos, Nicholas Bambos, Jose Blanchet
https://arxiv.org/abs/2512.08138 https://arxiv.org/pdf/2512.08138 https://arxiv.org/html/2512.08138
arXiv:2512.08138v1 Announce Type: new
Abstract: In this paper, we examine the robustness of Nash equilibria in continuous games, under both strategic and dynamic uncertainty. Starting with the former, we introduce the notion of a robust equilibrium as those equilibria that remain invariant to small -- but otherwise arbitrary -- perturbations to the game's payoff structure, and we provide a crisp geometric characterization thereof. Subsequently, we turn to the question of dynamic robustness, and we examine which equilibria may arise as stable limit points of the dynamics of "follow the regularized leader" (FTRL) in the presence of randomness and uncertainty. Despite their very distinct origins, we establish a structural correspondence between these two notions of robustness: strategic robustness implies dynamic robustness, and, conversely, the requirement of strategic robustness cannot be relaxed if dynamic robustness is to be maintained. Finally, we examine the rate of convergence to robust equilibria as a function of the underlying regularizer, and we show that entropically regularized learning converges at a geometric rate in games with affinely constrained action spaces.
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Drei-Finger-Greifer: Roboter Atlas greift effizienter mit drehbarem Daumen
Boston Dynamics hat für seinen humanoiden Roboter Atlas einen robusten, effizienten Greifer entwickelt. Ein drehbarer Daumen lässt viele Griffvarianten zu.

Robust and Efficient Collaborative Learning
Abdellah El Mrini, Sadegh Farhadkhan, Rachid Guerraoui
https://arxiv.org/abs/2510.08311 https://arxiv.org/pdf/2…

Robust and Efficient Collaborative Learning
Collaborative machine learning is challenged by training-time adversarial behaviors. Existing approaches to tolerate such behaviors either rely on a central server or induce high communication costs. We propose Robust Pull-based Epidemic Learning (RPEL), a novel, scalable collaborative approach to ensure robust learning despite adversaries. RPEL does not rely on any central server and, unlike traditional methods, where communication costs grow in $\mathcal{O}(n^2)$ with the number of nodes $n$,…

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Wenbin Zhou, Shixiang Zhu
https://arxiv.org/abs/2510.07750 https://

When Robustness Meets Conservativeness: Conformalized Uncertainty Calibration for Balanced Decision Making
Robust optimization safeguards decisions against uncertainty by optimizing against worst-case scenarios, yet their effectiveness hinges on a prespecified robustness level that is often chosen ad hoc, leading to either insufficient protection or overly conservative and costly solutions. Recent approaches using conformal prediction construct data-driven uncertainty sets with finite-sample coverage guarantees, but they still fix coverage targets a priori and offer little guidance for selecting rob…

🇺🇦 #NowPlaying on KEXP's #MorningShow
Bob Dylan:
🎵 Highway 61 Revisited
#BobDylan
https://kyolsen.bandcamp.com/track/highway-61-revisited-bob-dylan

Doubly Robust Estimation with Stabilized Weights for Binary Proximal Outcomes in Micro-Randomized Trials
Jinho Cha, Eunchan Cha
https://arxiv.org/abs/2510.08359 https://

Doubly Robust Estimation with Stabilized Weights for Binary Proximal Outcomes in Micro-Randomized Trials
Micro-randomized trials (MRTs) are increasingly used to evaluate mobile health interventions with binary proximal outcomes. Standard inverse probability weighting (IPW) estimators are unbiased but unstable in small samples or under extreme randomization. Estimated mean excursion effect (EMEE) improves efficiency but lacks double robustness. We propose a doubly robust EMEE (DR-EMEE) with stabilized and truncated weights, combining per-decision IPW and outcome regression. We prove double robustne…

Journalists from The New York Times will not sign the Pentagon’s revised press pass policy,
which threatens to punish them for ordinary news gathering protected by the First Amendment.
Since the policy was first announced, we have expressed concerns that it constrains how journalists can report on the U.S. military,
which is funded by nearly $1 trillion in taxpayer dollars annually.
The public has a right to know how the government and military are operating.
T…

A Statement from The New York Times on the Pentagon’s Press Pass Policy | The New York Times Company
Journalists from The New York Times will not sign the Pentagon’s revised press pass policy, which threatens to punish them for ordinary news gathering protected by the First Amendment.

Robust Geometric Predicates for Bivariate Computational Topology
Petar Hristov, Ingrid Hotz, Talha Bin Masood
https://arxiv.org/abs/2510.07955 https://arxi…

Robust Geometric Predicates for Bivariate Computational Topology
We present theory and practice for robust implementations of bivariate Jacobi set and Reeb space algorithms. Robustness is a fundamental topic in computational geometry that deals with the issues of numerical errors and degenerate cases in algorithm implementations. Computational topology already uses some robustness techniques for the development of scalar field algorithms, such as those for computing critical points, merge trees, contour trees, Reeb graphs, Morse-Smale complexes, and persiste…

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Tajamul Ashraf, Umair Nawaz, Abdelrahman M. Shaker, Rao Anwer, Philip Torr, Fahad Shahbaz Khan, Salman Khan
https://arxiv.org/abs/2510.08567

MATRIX: Multimodal Agent Tuning for Robust Tool-Use Reasoning
Vision language models (VLMs) are increasingly deployed as controllers with access to external tools for complex reasoning and decision-making, yet their effectiveness remains limited by the scarcity of high-quality multimodal trajectories and the cost of manual annotation. We address this challenge with a vision-centric agent tuning framework that automatically synthesizes multimodal trajectories, generates step-wise preference pairs, and trains a VLM controller for robust tool-use reasoning. …