Tootfinder

Hier kommt alle paar Minuten eine Taube aufs Fenstersims und schaut rein, ob ich noch da bin. Dasselbe passiert der Kollegin auch. Sie schaut uns beiden ganz genau in die Augen. Die Biester planen doch was!

Hier kommt alle paar Minuten eine Taube aufs Fenstersims und schaut rein, ob ich noch da bin. Dasselbe passiert der Kollegin auch. Sie schaut uns beiden ganz genau in die Augen. Die Biester planen doch was!

„Inakzeptabel“ – so bewertet SPD-Politikerin Nina Scheer die Kürzungspläne bei der Solarförderung. Wer private Dächer für die Energiewende gewinnen will, kürzt nicht ausgerechnet dort. Während gespart werden soll, pocht die SPD auf Planungssicherheit für Bürger und Branche. Energiewende funktioniert nicht per Haushaltsstreichliste.
@…

Wie funktioniert die #Knotenpunktwegweisung in #Brandenburg? 🗺️🚴‍♀️ Mit nummerierten #Knotenpunkten an Kreuzungen kann man in diesem Bundesland eine Route leicht planen und auch …

Didn't get a lot of sleep last night and everything kinda hurts so I've finished working earlier than usual. I am planning on a long relaxing bath before getting ready to go out for dinner with husband. And perhaps a pre and postprandial stroll will help iron out all the remaining kinks. It's still nicely Autumn in Canberra currently.
#Ageing

Statistical Query Lower Bounds for Smoothed Agnostic Learning
Ilias Diakonikolas, Daniel M. Kane
https://arxiv.org/abs/2602.21191 https://arxiv.org/pdf/2602.21191 https://arxiv.org/html/2602.21191
arXiv:2602.21191v1 Announce Type: new
Abstract: We study the complexity of smoothed agnostic learning, recently introduced by~\cite{CKKMS24}, in which the learner competes with the best classifier in a target class under slight Gaussian perturbations of the inputs. Specifically, we focus on the prototypical task of agnostically learning halfspaces under subgaussian distributions in the smoothed model. The best known upper bound for this problem relies on $L_1$-polynomial regression and has complexity $d^{\tilde{O}(1/\sigma^2) \log(1/\epsilon)}$, where $\sigma$ is the smoothing parameter and $\epsilon$ is the excess error. Our main result is a Statistical Query (SQ) lower bound providing formal evidence that this upper bound is close to best possible. In more detail, we show that (even for Gaussian marginals) any SQ algorithm for smoothed agnostic learning of halfspaces requires complexity $d^{\Omega(1/\sigma^{2} \log(1/\epsilon))}$. This is the first non-trivial lower bound on the complexity of this task and nearly matches the known upper bound. Roughly speaking, we show that applying $L_1$-polynomial regression to a smoothed version of the function is essentially best possible. Our techniques involve finding a moment-matching hard distribution by way of linear programming duality. This dual program corresponds exactly to finding a low-degree approximating polynomial to the smoothed version of the target function (which turns out to be the same condition required for the $L_1$-polynomial regression to work). Our explicit SQ lower bound then comes from proving lower bounds on this approximation degree for the class of halfspaces.
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Pollenallergiemenschen: Der DWD macht eine Umfrage zur Verbesserung der Pollenflug-Vorhersagen
https://www.dwd.de/DE/Home/_functions/aktuelles/2026/20260402_umfrage_copernicus.html

Polynomials in $c$-free random variables with applications to free denoising
Adrian Celestino, Franz Lehner, Kamil Szpojankowski
https://arxiv.org/abs/2603.21372 https://arxiv.org/pdf/2603.21372 https://arxiv.org/html/2603.21372
arXiv:2603.21372v1 Announce Type: new
Abstract: We study distributions of polynomials in conditionally free (c-free) random variables, a notion of independence for two-state noncommutative probability spaces introduced by Bozejko, Leinert and Speicher. To this end we establish recursive relations between the joint Boolean cumulants of c-free random variables, analogous to previously found recursions for Boolean cumulants of free random variables. The algebraic reformulation of these recursions on the free associative algebra provides an effective formal machinery for the computation of the moment generating functions and thus the distributions of arbitrary self-adjoint polynomials in c-free random variables. As an application of a recent observation, our approach can be used to determine conditional expectations of the form $E[a|P(a,b)]$, where $P(a,b)$ is a self-adjoint polynomial in free (in the sense of Voiculescu) random variables $a,b$. We illustrate this with an example where $P(a,b)=i[a,b]$. Finally we define orthogonal projections that formally play the role of conditional expectations in the framework of c-freeness and share some properties with the conditional expectations of free variables. In particular they can be used to re-derive by purely algebraic methods the formula of Popa and Wang for the $\Sigma$-transform for the c-free multiplicative convolution.
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Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/2]:
- The Geometry of Harmful Intent: Training-Free Anomaly Detection via Angular Deviation in LLM Resi...
Isaac Llorente-Saguer
https://arxiv.org/abs/2603.27412 https://mastoxiv.page/@arXiv_csLG_bot/116323180390164201
- LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, et al.
https://arxiv.org/abs/2603.27538 https://mastoxiv.page/@arXiv_csCV_bot/116323299668026852
- Emergent Social Intelligence Risks in Generative Multi-Agent Systems
Huang, Jiang, Wang, Zhuang, Luo, Ma, Xu, Chen, Moniz, Lin, Chen, Chawla, Dziri, Sun, Zhang
https://arxiv.org/abs/2603.27771 https://mastoxiv.page/@arXiv_csMA_bot/116322908437739020
- KVSculpt: KV Cache Compression as Distillation
Bo Jiang, Sian Jin
https://arxiv.org/abs/2603.27819 https://mastoxiv.page/@arXiv_csLG_bot/116323241993833314
- Q-Bridge: Code Translation for Quantum Machine Learning via LLMs
Runjia Zeng, Priyabrata Senapati, Ruixiang Tang, Dongfang Liu, Qiang Guan
https://arxiv.org/abs/2603.27836 https://mastoxiv.page/@arXiv_quantph_bot/116323164660887506
- EffiSkill: Agent Skill Based Automated Code Efficiency Optimization
Zimu Wang, Yuling Shi, Mengfan Li, Zijun Liu, Jie M. Zhang, Chengcheng Wan, Xiaodong Gu
https://arxiv.org/abs/2603.27850 https://mastoxiv.page/@arXiv_csSE_bot/116322989347928729
- Efficient Inference of Large Vision Language Models
Surendra Pathak
https://arxiv.org/abs/2603.27960 https://mastoxiv.page/@arXiv_csLG_bot/116323256085918152
- CDH-Bench: A Commonsense-Driven Hallucination Benchmark for Evaluating Visual Fidelity in Vision-...
Kesheng Chen, Yamin Hu, Qi Zhou, Zhenqian Zhu, Wenjian Luo
https://arxiv.org/abs/2603.27982 https://mastoxiv.page/@arXiv_csCV_bot/116323319000206060
- MOSS-VoiceGenerator: Create Realistic Voices with Natural Language Descriptions
Huang, Fan, Jiang, Jiang, Tu, Zhu, Zhang, Zhao, Yang, Fei, Li, Yang, Cheng, Qiu
https://arxiv.org/abs/2603.28086 https://mastoxiv.page/@arXiv_csSD_bot/116322971980743316
- Does Claude's Constitution Have a Culture?
Parham Pourdavood
https://arxiv.org/abs/2603.28123 https://mastoxiv.page/@arXiv_csCY_bot/116322911684465443
- MiroEval: Benchmarking Multimodal Deep Research Agents in Process and Outcome
Fangda Ye, et al.
https://arxiv.org/abs/2603.28407 https://mastoxiv.page/@arXiv_csAI_bot/116323220038984883
- IsoQuant: Hardware-Aligned SO(4) Isoclinic Rotations for LLM KV Cache Compression
Zhongping Ji
https://arxiv.org/abs/2603.28430 https://mastoxiv.page/@arXiv_csLG_bot/116323286231537351
- Entropic Claim Resolution: Uncertainty-Driven Evidence Selection for RAG
Davide Di Gioia
https://arxiv.org/abs/2603.28444 https://mastoxiv.page/@arXiv_csAI_bot/116323220366355511
- Moving Beyond Review: Applying Language Models to Planning and Translation in Reflection
Seyed Parsa Neshaei, Richard Lee Davis, Tanja K\"aser
https://arxiv.org/abs/2603.28596 https://mastoxiv.page/@arXiv_csHC_bot/116323161382060848
- ResAdapt: Adaptive Resolution for Efficient Multimodal Reasoning
Huanxuan Liao, Zhongtao Jiang, Yupu Hao, Yuqiao Tan, Shizhu He, Jun Zhao, Kun Xu, Kang Liu
https://arxiv.org/abs/2603.28610 https://mastoxiv.page/@arXiv_csCV_bot/116323344559859277
- The Ultimate Tutorial for AI-driven Scale Development in Generative Psychometrics: Releasing AIGE...
Lara Russell-Lasalandra, Hudson Golino, Luis Eduardo Garrido, Alexander P. Christensen
https://arxiv.org/abs/2603.28643 https://mastoxiv.page/@arXiv_csAI_bot/116323236095523987
- SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot Reinforcement Learning
Philip Schroeder, Thomas Weng, Karl Schmeckpeper, Eric Rosen, Stephen Hart, Ondrej Biza
https://arxiv.org/abs/2603.28730 https://mastoxiv.page/@arXiv_csRO_bot/116323253135037252
- ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining
Anuj Diwan, Eunsol Choi, David Harwath
https://arxiv.org/abs/2603.28737 https://mastoxiv.page/@arXiv_eessAS_bot/116322903493463665
toXiv_bot_toot

Crosslisted article(s) found for math.OA. https://arxiv.org/list/math.OA/new
[1/1]:
- Time-Scaled Intertwining Cocycles and Identifiability of Multi-Semigroup Mixtures on Hilbert Oper...
Anton Alexa
https://arxiv.org/abs/2603.20322 https://mastoxiv.page/@arXiv_mathFA_bot/116283090397448396
- Universal Coefficients and Mayer-Vietoris for Moore Homology of Ample Groupoids
Luciano Melodia
https://arxiv.org/abs/2603.20861 https://mastoxiv.page/@arXiv_mathAT_bot/116283018602632921
- Cocycles and positive functionals in higher cohomology
Antonio L\'opez Neumann, Piotr W. Nowak
https://arxiv.org/abs/2603.21431 https://mastoxiv.page/@arXiv_mathAT_bot/116283163474031677
- Maximality Levels of the classical permutation group in the quantum permutation group
J. P. McCarthy
https://arxiv.org/abs/2603.21759 https://mastoxiv.page/@arXiv_mathQA_bot/116283160850717727
- Triangular Decomposition of the Crystal Lattice of Quantized Function Algebras: Revisited
Ayan Dey
https://arxiv.org/abs/2603.21868 https://mastoxiv.page/@arXiv_mathQA_bot/116283166064403090
- Cyclicity of stable matrix free polynomials over non-commutative operator unit balls
Jeet Sampat, Maximilian Tornes
https://arxiv.org/abs/2603.22129 https://mastoxiv.page/@arXiv_mathFA_bot/116283334169858979
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