Tootfinder

Italy may be the first EU country to impose requirements on private charging points in its transposition of the EU REDIII into national law: newly installed private charging points must be able to communicate with smart meters from June 30 onwards.
https://www.normattiva.it/uri-res/N2Ls?u…

Can't we all just give up on Discord already and spin up a few self hosted forums instead?
I mean, IRC is fine, but I would argue that the onboarding isn't easy for everyone.

Here was the vice president defending the administration’s vile immigration policies
in a way that fundamentally degrades the experiences and traditions of his own family,
of people he is bound by vows —vows that should be sacred to a Christian —to love and protect.
It sums up Vance’s journey into public life and politics:
There is nobody he won’t betray,
and no principle he won’t cast aside, in his quest to accrue more fame and power.
This has been clear…

Who Is JD Vance? (No, Seriously. Who Is He?)
The vice president is a cynical shapeshifter, willing to say anything—and be anybody—to become the most powerful person in America.

What’s with all the grey cars and trucks lately? Why would anyone deliberately spend that much money to buy a vehicle that is nearly invisible in low-light conditions? Is this evidence of a death wish, or just another manifestation of the millennial grey fashion meme in clothes and interior decorating? Disclosure: my car is orange—highly visible. Official Subaru marketing name: Sunshine Orange. My unofficial name for it: Orange Sunshine, to commemorate some fun times in my youth.

Why Pass@k Optimization Can Degrade Pass@1: Prompt Interference in LLM Post-training
Anas Barakat, Souradip Chakraborty, Khushbu Pahwa, Amrit Singh Bedi
https://arxiv.org/abs/2602.21189 https://arxiv.org/pdf/2602.21189 https://arxiv.org/html/2602.21189
arXiv:2602.21189v1 Announce Type: new
Abstract: Pass@k is a widely used performance metric for verifiable large language model tasks, including mathematical reasoning, code generation, and short-answer reasoning. It defines success if any of $k$ independently sampled solutions passes a verifier. This multi-sample inference metric has motivated inference-aware fine-tuning methods that directly optimize pass@$k$. However, prior work reports a recurring trade-off: pass@k improves while pass@1 degrades under such methods. This trade-off is practically important because pass@1 often remains a hard operational constraint due to latency and cost budgets, imperfect verifier coverage, and the need for a reliable single-shot fallback. We study the origin of this trade-off and provide a theoretical characterization of when pass@k policy optimization can reduce pass@1 through gradient conflict induced by prompt interference. We show that pass@$k$ policy gradients can conflict with pass@1 gradients because pass@$k$ optimization implicitly reweights prompts toward low-success prompts; when these prompts are what we term negatively interfering, their upweighting can rotate the pass@k update direction away from the pass@1 direction. We illustrate our theoretical findings with large language model experiments on verifiable mathematical reasoning tasks.
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So in another dream I just woke up from, I was talking to someone about "the idea problem" (that it's becoming harder to monitize ideas, from a vox article written by an AI cooked reporter).
https://www.iheart.com/podcast/105-it-could-happen-here-30717896/episode/executive-disorder-white-house-weekly-46-313675864/
Basically, I was arguing that the majority of inventions target men because patriarchy puts economic control in men's hands. As men have started to help more with childcare, there have been more inventions related to childcare. (I don't have any idea if this is true. Seems legit, but I'm just relating my dream. I think I was also oversimplifying a bit to "men" and "women" because of my audience, but anyway it was a dream.) There's actually more low-hanging fruit, I pointed out, related to making care work easier.
So I argued that the real problem was a failure to invest in research into solving that problem. Today there are all these boondoggles built around killing people. What if, instead of all this government research into killing people, we dumped a ton of money into making it easier to support a household? That would be great for the economy. (Being asleep, I seem to have forgotten that working people need money.)
In the blur of being just awake I started thinking about how you could kickstart the US economy by taking the money from the AI boondoggle and other autonomous murder bots and create something like a program to build robots for housekeepers. You'd still be funding tech with government money, so the same horrible people get paid, but you're now actually solving real problems. It wouldn't even matter if it was a boondoggle, honestly. Just dumping money into something other than murdering people is good enough.
I imagined first if there was a program to fund a robot housecleaner, like robot dog with AI some laundry pickup, that would be provided, free of charge, to help people with children. It would work the same as the military boondoggle where a private company makes the government buy a piece of hardware from them and then also pay them to service it for some number of years. But instead of that hardware sitting around waiting to kill someone, it would be getting brought to people's houses to help them.
Then I thought, hey, you could even boost the economy more if you just had government funding for doulas and housecleaners and paid them a living wage. Hey, you could really kickstart the economy by nationalizing healthcare and including doula support as part of all births. Oh, and you could also just include the optional household help for families with children until the kids turn 18.
None of this is perfect (I don't actually think most of this is possible from any state), but the point is that it's actually wildly easy to figure out all kinds of ways to invest in the economy and monitize ideas as long as you aren't entirely focused on the same old "make money from spying on people and killing them." Funny that. Like they said in the podcast, maybe "finding ideas" isn't the problem.
Hope you enjoyed the weird semi-awake brain dump/rant.

I made nog again

Suicide jokes are only funny when I make them.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/6]:
- Towards Attributions of Input Variables in a Coalition
Xinhao Zheng, Huiqi Deng, Quanshi Zhang
https://arxiv.org/abs/2309.13411
- Knee or ROC
Veronica Wendt, Jacob Steiner, Byunggu Yu, Caleb Kelly, Justin Kim
https://arxiv.org/abs/2401.07390
- Rethinking Disentanglement under Dependent Factors of Variation
Antonio Almud\'evar, Alfonso Ortega
https://arxiv.org/abs/2408.07016 https://mastoxiv.page/@arXiv_csLG_bot/112959235461894530
- Minibatch Optimal Transport and Perplexity Bound Estimation in Discrete Flow Matching
Etrit Haxholli, Yeti Z. Gurbuz, Ogul Can, Eli Waxman
https://arxiv.org/abs/2411.00759 https://mastoxiv.page/@arXiv_csLG_bot/113423933393275133
- Predicting Subway Passenger Flows under Incident Situation with Causality
Xiannan Huang, Shuhan Qiu, Quan Yuan, Chao Yang
https://arxiv.org/abs/2412.06871 https://mastoxiv.page/@arXiv_csLG_bot/113632934357523592
- Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling
Paul Joe Maliakel, Shashikant Ilager, Ivona Brandic
https://arxiv.org/abs/2501.08219 https://mastoxiv.page/@arXiv_csLG_bot/113831081884570770
- Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
https://arxiv.org/abs/2501.10538 https://mastoxiv.page/@arXiv_csLG_bot/113872351652969955
- Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian, Alexander Winkler, Patrick Schaber, Jakob Andert
https://arxiv.org/abs/2501.16613 https://mastoxiv.page/@arXiv_csLG_bot/113910356206562660
- A Statistical Learning Perspective on Semi-dual Adversarial Neural Optimal Transport Solvers
Roman Tarasov, Petr Mokrov, Milena Gazdieva, Evgeny Burnaev, Alexander Korotin
https://arxiv.org/abs/2502.01310
- Improving the Convergence of Private Shuffled Gradient Methods with Public Data
Shuli Jiang, Pranay Sharma, Zhiwei Steven Wu, Gauri Joshi
https://arxiv.org/abs/2502.03652 https://mastoxiv.page/@arXiv_csLG_bot/113961314098841096
- Using the Path of Least Resistance to Explain Deep Networks
Sina Salek, Joseph Enguehard
https://arxiv.org/abs/2502.12108 https://mastoxiv.page/@arXiv_csLG_bot/114023706252106865
- Distributional Vision-Language Alignment by Cauchy-Schwarz Divergence
Wenzhe Yin, Zehao Xiao, Pan Zhou, Shujian Yu, Jiayi Shen, Jan-Jakob Sonke, Efstratios Gavves
https://arxiv.org/abs/2502.17028 https://mastoxiv.page/@arXiv_csLG_bot/114063477202397951
- Armijo Line-search Can Make (Stochastic) Gradient Descent Provably Faster
Sharan Vaswani, Reza Babanezhad
https://arxiv.org/abs/2503.00229 https://mastoxiv.page/@arXiv_csLG_bot/114103018985567633
- Semantic Parallelism: Redefining Efficient MoE Inference via Model-Data Co-Scheduling
Yan Li, Zhenyu Zhang, Zhengang Wang, Pengfei Chen, Pengfei Zheng
https://arxiv.org/abs/2503.04398 https://mastoxiv.page/@arXiv_csLG_bot/114120014622063602
- A Survey on Federated Fine-tuning of Large Language Models
Wu, Tian, Li, Sun, Tam, Zhou, Liao, Xiong, Guo, Li, Xu
https://arxiv.org/abs/2503.12016 https://mastoxiv.page/@arXiv_csLG_bot/114182234054681647
- Towards Trustworthy GUI Agents: A Survey
Yucheng Shi, Wenhao Yu, Jingyuan Huang, Wenlin Yao, Wenhu Chen, Ninghao Liu
https://arxiv.org/abs/2503.23434 https://mastoxiv.page/@arXiv_csLG_bot/114263024618476521
- CONTINA: Confidence Interval for Traffic Demand Prediction with Coverage Guarantee
Chao Yang, Xiannan Huang, Shuhan Qiu, Yan Cheng
https://arxiv.org/abs/2504.13961 https://mastoxiv.page/@arXiv_csLG_bot/114380404041503229
- Regularity and Stability Properties of Selective SSMs with Discontinuous Gating
Nikola Zubi\'c, Davide Scaramuzza
https://arxiv.org/abs/2505.11602 https://mastoxiv.page/@arXiv_csLG_bot/114538965060456498
- RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization
Alonso Urbano, David W. Romero, Max Zimmer, Sebastian Pokutta
https://arxiv.org/abs/2505.13289 https://mastoxiv.page/@arXiv_csLG_bot/114539124884913788
- RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models
Yilang Zhang, Bingcong Li, Georgios B. Giannakis
https://arxiv.org/abs/2505.18877 https://mastoxiv.page/@arXiv_csLG_bot/114578778213033886
- SuperMAN: Interpretable and Expressive Networks over Temporally Sparse Heterogeneous Data
Bechler-Speicher, Zerio, Huri, Vestergaard, Gilad-Bachrach, Jess, Bhatt, Sazonovs
https://arxiv.org/abs/2505.19193 https://mastoxiv.page/@arXiv_csLG_bot/114578790124778172
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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
https://arxiv.org/abs/2505.19698 https://mastoxiv.page/@arXiv_csLG_bot/114578810521008766
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
https://arxiv.org/abs/2506.08660 https://mastoxiv.page/@arXiv_csLG_bot/114664238967892509
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
https://arxiv.org/abs/2506.10167 https://mastoxiv.page/@arXiv_csLG_bot/114675175949432731
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
https://arxiv.org/abs/2506.10914 https://mastoxiv.page/@arXiv_csLG_bot/114675529854402158
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
https://arxiv.org/abs/2506.18481 https://mastoxiv.page/@arXiv_csLG_bot/114738421450807709
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
https://arxiv.org/abs/2506.21220 https://mastoxiv.page/@arXiv_csLG_bot/114754764750730849
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
https://arxiv.org/abs/2507.04448 https://mastoxiv.page/@arXiv_csLG_bot/114818005803079705
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
https://arxiv.org/abs/2508.01115 https://mastoxiv.page/@arXiv_csLG_bot/114975999992144374
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
https://arxiv.org/abs/2508.13904 https://mastoxiv.page/@arXiv_csLG_bot/115060568241390847
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
https://arxiv.org/abs/2508.16815 https://mastoxiv.page/@arXiv_csLG_bot/115094785677272005
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
https://arxiv.org/abs/2508.21785 https://mastoxiv.page/@arXiv_csLG_bot/115128450608548173
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
https://arxiv.org/abs/2509.15796 https://mastoxiv.page/@arXiv_csLG_bot/115247429156900905
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
https://arxiv.org/abs/2509.19975 https://mastoxiv.page/@arXiv_csLG_bot/115264498084813952
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
https://arxiv.org/abs/2509.21895 https://mastoxiv.page/@arXiv_csLG_bot/115287261047939306
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
https://arxiv.org/abs/2509.22566 https://mastoxiv.page/@arXiv_csLG_bot/115287379672141023
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2509.23115 https://mastoxiv.page/@arXiv_csLG_bot/115293273559547106
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
https://arxiv.org/abs/2509.25424 https://mastoxiv.page/@arXiv_csLG_bot/115298579764580635
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
https://arxiv.org/abs/2509.26626 https://mastoxiv.page/@arXiv_csLG_bot/115298789487177431
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
https://arxiv.org/abs/2510.12402 https://mastoxiv.page/@arXiv_csLG_bot/115377759317818093
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
https://arxiv.org/abs/2510.15425 https://mastoxiv.page/@arXiv_csLG_bot/115405933861293858
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
https://arxiv.org/abs/2510.18114 https://mastoxiv.page/@arXiv_csLG_bot/115417332500265972
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
https://arxiv.org/abs/2510.22293 https://mastoxiv.page/@arXiv_csLG_bot/115451746201804373
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