Tootfinder

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks
Jiun-Cheng Jiang, Morris Yu-Chao Huang, Tianlong Chen, Hsi-Sheng Goan
https://arxiv.org/abs/2509.14026

Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks
Variational quantum circuits (VQCs) are central to quantum machine learning, while recent progress in Kolmogorov-Arnold networks (KANs) highlights the power of learnable activation functions. We unify these directions by introducing quantum variational activation functions (QVAFs), realized through single-qubit data re-uploading circuits called DatA Re-Uploading ActivatioNs (DARUANs). We show that DARUAN with trainable weights in data pre-processing possesses an exponentially growing frequency …

Ist Jens #Spahn noch in dieser Welt oder schwebt er völlig abgehoben in seinen Strategiespielen und Karrierephantasien.
Es sollte dringend abgelöst werden. Er hat das von ihm verursachte Maskenfiasko immer noch nicht eingesehen. Er ist einsichtsresistent und damit wohl auch nicht lernfähig.

Spahn bleibt in Corona-Enquetekommission bei seiner Linie
Ex-Gesundheitsminister Spahn hat in einer Anhörung sein Krisenmanagement in der Corona-Pandemie verteidigt: Oberstes Ziel sei die Vermeidung einer Überlastung des Gesundheitswesens gewesen. Der Opposition reicht das nicht aus.

Characterizing the Multiclass Learnability of Forgiving 0-1 Loss Functions
Jacob Trauger, Tyson Trauger, Ambuj Tewari
https://arxiv.org/abs/2510.08382 https://

Characterizing the Multiclass Learnability of Forgiving 0-1 Loss Functions
In this paper we will give a characterization of the learnability of forgiving 0-1 loss functions in the finite label multiclass setting. To do this, we create a new combinatorial dimension that is based off of the Natarajan Dimension \citep{natarajan1989learning} and we show that a hypothesis class is learnable in our setting if and only if this Generalized Natarajan Dimension is finite. We also show a connection to learning with set-valued feedback. Through our results we show that the learna…

Es gelang mir heute, durch Schweigen und bedauernde Hinweise auf meine Abwesenheit in den nächsten zwei Wochen, Aufgaben zu vermeiden, von denen ich denke, ich hätte sie besser als andere erledigt, aber es wird auch nicht unbedingt nötig sein. #LernEffekte

Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Yuxiang Zhang, Jiangming Shu, Ye Ma, Xueyuan Lin, Shangxi Wu, Jitao Sang
https://arxiv.org/abs/2510.12635

Memory as Action: Autonomous Context Curation for Long-Horizon Agentic Tasks
Large Language Models face challenges in long-horizon agentic tasks as their constrained memory is easily overwhelmed by distracting or irrelevant context. Existing working memory methods typically rely on external, heuristic mechanisms that are decoupled from the agent's core policy. In this work, we reframe working memory management as a learnable, intrinsic capability. We propose a novel framework, Memory-as-Action, where an agent actively manages its working memory by executing explicit edi…

from my link log —
Search and replace tricks with ripgrep.
https://learnbyexample.github.io/substitution-with-ripgrep/
saved 2020-09-17 ht…

Adapting Noise to Data: Generative Flows from 1D Processes
Jannis Chemseddine, Gregor Kornhardt, Richard Duong, Gabriele Steidl
https://arxiv.org/abs/2510.12636 https://

Adapting Noise to Data: Generative Flows from 1D Processes
We introduce a general framework for constructing generative models using one-dimensional noising processes. Beyond diffusion processes, we outline examples that demonstrate the flexibility of our approach. Motivated by this, we propose a novel framework in which the 1D processes themselves are learnable, achieved by parameterizing the noise distribution through quantile functions that adapt to the data. Our construction integrates seamlessly with standard objectives, including Flow Matching an…

Attention to Order: Transformers Discover Phase Transitions via Learnability
\c{S}ener \"Oz\"onder
https://arxiv.org/abs/2510.07401 https://arxiv…

Attention to Order: Transformers Discover Phase Transitions via Learnability
Phase transitions mark qualitative reorganizations of collective behavior, yet identifying their boundaries remains challenging whenever analytic solutions are absent and conventional simulations fail. Here we introduce learnability as a universal criterion, defined as the ability of a transformer model containing attention mechanism to extract structure from microscopic states. Using self-supervised learning and Monte Carlo generated configurations of the two-dimensional Ising model, we show t…

ToolMem: Enhancing Multimodal Agents with Learnable Tool Capability Memory
Yunzhong Xiao, Yangmin Li, Hewei Wang, Yunlong Tang, Zora Zhiruo Wang
https://arxiv.org/abs/2510.06664

ToolMem: Enhancing Multimodal Agents with Learnable Tool Capability Memory
Agents utilizing tools powered by large language models (LLMs) or vision-language models (VLMs) have demonstrated remarkable progress in diverse tasks across text and visual modalities. Unlike traditional tools such as calculators, which give deterministic outputs, neural tools perform uncertainly across task scenarios. While different tools for a task may excel in varied scenarios, existing agents typically rely on fixed tools, thus limiting the flexibility in selecting the most suitable tool …

PAC Learnability in the Presence of Performativity
Ivan Kirev, Lyuben Baltadzhiev, Nikola Konstantinov
https://arxiv.org/abs/2510.08335 https://arxiv.org/p…

PAC Learnability in the Presence of Performativity
Following the wide-spread adoption of machine learning models in real-world applications, the phenomenon of performativity, i.e. model-dependent shifts in the test distribution, becomes increasingly prevalent. Unfortunately, since models are usually trained solely based on samples from the original (unshifted) distribution, this performative shift may lead to decreased test-time performance. In this paper, we study the question of whether and when performative binary classification problems are…