Tootfinder

Fast $k$-means Seeding Under The Manifold Hypothesis
Poojan Shah, Shashwat Agrawal, Ragesh Jaiswal
https://arxiv.org/abs/2602.01104 https://arxiv.org/pdf/2602.01104 https://arxiv.org/html/2602.01104
arXiv:2602.01104v1 Announce Type: new
Abstract: We study beyond worst case analysis for the $k$-means problem where the goal is to model typical instances of $k$-means arising in practice. Existing theoretical approaches provide guarantees under certain assumptions on the optimal solutions to $k$-means, making them difficult to validate in practice. We propose the manifold hypothesis, where data obtained in ambient dimension $D$ concentrates around a low dimensional manifold of intrinsic dimension $d$, as a reasonable assumption to model real world clustering instances. We identify key geometric properties of datasets which have theoretically predictable scaling laws depending on the quantization exponent $\varepsilon = 2/d$ using techniques from optimum quantization theory. We show how to exploit these regularities to design a fast seeding method called $\operatorname{Qkmeans}$ which provides $O(\rho^{-2} \log k)$ approximate solutions to the $k$-means problem in time $O(nD) \widetilde{O}(\varepsilon^{1 \rho}\rho^{-1}k^{1 \gamma})$; where the exponent $\gamma = \varepsilon \rho$ for an input parameter $\rho < 1$. This allows us to obtain new runtime - quality tradeoffs. We perform a large scale empirical study across various domains to validate our theoretical predictions and algorithm performance to bridge theory and practice for beyond worst case data clustering.
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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/5]:
- Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization
Ru Wang, Wei Huang, Selena Song, Haoyu Zhang, Qian Niu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
https://arxiv.org/abs/2502.18273 https://mastoxiv.page/@arXiv_csCL_bot/114069031700102129
- Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech
Anja Ryser, Yingqiang Gao, Sarah Ebling
https://arxiv.org/abs/2504.00780 https://mastoxiv.page/@arXiv_csCL_bot/114267149909002069
- Cultural Biases of Large Language Models and Humans in Historical Interpretation
Fabio Celli, Georgios Spathulas
https://arxiv.org/abs/2504.02572 https://mastoxiv.page/@arXiv_csCL_bot/114278467094094490
- BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, et al.
https://arxiv.org/abs/2504.19467 https://mastoxiv.page/@arXiv_csCL_bot/114420036189999973
- Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potentia...
Yiming Huang, Biquan Bie, Zuqiu Na, Weilin Ruan, Songxin Lei, Yutao Yue, Xinlei He
https://arxiv.org/abs/2505.15392 https://mastoxiv.page/@arXiv_csCL_bot/114550277171100272
- Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
Raza, Qureshi, Farooq, Lotif, Chadha, Pandya, Emmanouilidis
https://arxiv.org/abs/2505.17870 https://mastoxiv.page/@arXiv_csCL_bot/114572956853819813
- LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Fu, Jiang, Hong, Li, Guo, Yang, Chen, Zhang
https://arxiv.org/abs/2506.14493 https://mastoxiv.page/@arXiv_csCL_bot/114703502552989170
- GHTM: A Graph-based Hybrid Topic Modeling Approach with a Benchmark Dataset for the Low-Resource ...
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
https://arxiv.org/abs/2508.00605 https://mastoxiv.page/@arXiv_csCL_bot/114969875643478303
- Link Prediction for Event Logs in the Process Industry
Anastasia Zhukova, Thomas Walton, Christian E. Lobm\"uller, Bela Gipp
https://arxiv.org/abs/2508.09096 https://mastoxiv.page/@arXiv_csCL_bot/115020938764936882
- AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Huang, Cao, Zhang, Kang, Wang, Wang, Luo, Zheng, Qian, Chen, Yu
https://arxiv.org/abs/2509.16952 https://mastoxiv.page/@arXiv_csCL_bot/115253526588472475
- Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortio...
Jun Seo Kim, Hyemi Kim, Woo Joo Oh, Hongjin Cho, Hochul Lee, Hye Hyeon Kim
https://arxiv.org/abs/2509.17292 https://mastoxiv.page/@arXiv_csCL_bot/115253586227941157
- Dual-Space Smoothness for Robust and Balanced LLM Unlearning
Han Yan, Zheyuan Liu, Meng Jiang
https://arxiv.org/abs/2509.23362 https://mastoxiv.page/@arXiv_csCL_bot/115293308293558024
- The Rise of AfricaNLP: Contributions, Contributors, Community Impact, and Bibliometric Analysis
Tadesse Destaw Belay, et al.
https://arxiv.org/abs/2509.25477 https://mastoxiv.page/@arXiv_csCL_bot/115298213432594791
- Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Reco...
Srivastav, Zheng, Bezzam, Le Bihan, Koluguri, \.Zelasko, Majumdar, Moumen, Gandhi
https://arxiv.org/abs/2510.06961 https://mastoxiv.page/@arXiv_csCL_bot/115343748052193267
- Neuron-Level Analysis of Cultural Understanding in Large Language Models
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka
https://arxiv.org/abs/2510.08284 https://mastoxiv.page/@arXiv_csCL_bot/115349533441895984
- CLMN: Concept based Language Models via Neural Symbolic Reasoning
Yibo Yang
https://arxiv.org/abs/2510.10063 https://mastoxiv.page/@arXiv_csCL_bot/115372392366793754
- Schema for In-Context Learning
Chen, Chen, Wang, Leong, Fung, Bernales, Aspuru-Guzik
https://arxiv.org/abs/2510.13905 https://mastoxiv.page/@arXiv_csCL_bot/115389057899856601
- Evaluating Latent Knowledge of Public Tabular Datasets in Large Language Models
Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei
https://arxiv.org/abs/2510.20351 https://mastoxiv.page/@arXiv_csCL_bot/115428615784704418
- LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data
Julian Valline, Cedric Lothritz, Siwen Guo, Jordi Cabot
https://arxiv.org/abs/2510.24434 https://mastoxiv.page/@arXiv_csCL_bot/115457025096322944
- Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs
Muhammed Saeed, Muhammad Abdul-mageed, Shady Shehata
https://arxiv.org/abs/2511.01187 https://mastoxiv.page/@arXiv_csCL_bot/115491321130591723
toXiv_bot_toot

Raiders 7-Round Mock: Aggressive trades show GM John Spytek means business https://raiderramble.com/2026/03/21/raiders-7-round-mock-aggressive-trades-show-gm-john-spytek-means-business/

Regret-Guided Search Control for Efficient Learning in AlphaZero
Yun-Jui Tsai, Wei-Yu Chen, Yan-Ru Ju, Yu-Hung Chang, Ti-Rong Wu
https://arxiv.org/abs/2602.20809 https://arxiv.org/pdf/2602.20809 https://arxiv.org/html/2602.20809
arXiv:2602.20809v1 Announce Type: new
Abstract: Reinforcement learning (RL) agents achieve remarkable performance but remain far less learning-efficient than humans. While RL agents require extensive self-play games to extract useful signals, humans often need only a few games, improving rapidly by repeatedly revisiting states where mistakes occurred. This idea, known as search control, aims to restart from valuable states rather than always from the initial state. In AlphaZero, prior work Go-Exploit applies this idea by sampling past states from self-play or search trees, but it treats all states equally, regardless of their learning potential. We propose Regret-Guided Search Control (RGSC), which extends AlphaZero with a regret network that learns to identify high-regret states, where the agent's evaluation diverges most from the actual outcome. These states are collected from both self-play trajectories and MCTS nodes, stored in a prioritized regret buffer, and reused as new starting positions. Across 9x9 Go, 10x10 Othello, and 11x11 Hex, RGSC outperforms AlphaZero and Go-Exploit by an average of 77 and 89 Elo, respectively. When training on a well-trained 9x9 Go model, RGSC further improves the win rate against KataGo from 69.3% to 78.2%, while both baselines show no improvement. These results demonstrate that RGSC provides an effective mechanism for search control, improving both efficiency and robustness of AlphaZero training. Our code is available at https://rlg.iis.sinica.edu.tw/papers/rgsc.
toXiv_bot_toot

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[2/5]:
- POTSA: A Cross-Lingual Speech Alignment Framework for Speech-to-Text Translation
Li, Cui, Wang, Ge, Huang, Li, Peng, Lu, Tashi, Wang, Dang
https://arxiv.org/abs/2511.09232 https://mastoxiv.page/@arXiv_csCL_bot/115541846907664054
- Beyond Elicitation: Provision-based Prompt Optimization for Knowledge-Intensive Tasks
Yunzhe Xu, Zhuosheng Zhang, Zhe Liu
https://arxiv.org/abs/2511.10465 https://mastoxiv.page/@arXiv_csCL_bot/115547607561282911
- $\pi$-Attention: Periodic Sparse Transformers for Efficient Long-Context Modeling
Dong Liu, Yanxuan Yu
https://arxiv.org/abs/2511.10696 https://mastoxiv.page/@arXiv_csCL_bot/115564418836654965
- Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performanc...
Zijin Su, Huanzhu Lyu, Yuren Niu, Yiming Liu
https://arxiv.org/abs/2511.14073 https://mastoxiv.page/@arXiv_csCL_bot/115575715073023141
- HEAD-QA v2: Expanding a Healthcare Benchmark for Reasoning
Alexis Correa-Guill\'en, Carlos G\'omez-Rodr\'iguez, David Vilares
https://arxiv.org/abs/2511.15355 https://mastoxiv.page/@arXiv_csCL_bot/115581410328165116
- Towards Hyper-Efficient RAG Systems in VecDBs: Distributed Parallel Multi-Resolution Vector Search
Dong Liu, Yanxuan Yu
https://arxiv.org/abs/2511.16681 https://mastoxiv.page/@arXiv_csCL_bot/115603508442305146
- Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Transla...
Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, Marika Borovikova, Dage S\"arg, Kairit Sirts
https://arxiv.org/abs/2511.17290 https://mastoxiv.page/@arXiv_csCL_bot/115604083224487885
- A Systematic Study of In-the-Wild Model Merging for Large Language Models
O\u{g}uz Ka\u{g}an Hitit, Leander Girrbach, Zeynep Akata
https://arxiv.org/abs/2511.21437 https://mastoxiv.page/@arXiv_csCL_bot/115621178703846052
- CREST: Universal Safety Guardrails Through Cluster-Guided Cross-Lingual Transfer
Lavish Bansal, Naman Mishra
https://arxiv.org/abs/2512.02711 https://mastoxiv.page/@arXiv_csCL_bot/115655090475535157
- Multilingual Medical Reasoning for Question Answering with Large Language Models
Pietro Ferrazzi, Aitor Soroa, Rodrigo Agerri
https://arxiv.org/abs/2512.05658 https://mastoxiv.page/@arXiv_csCL_bot/115683267711014189
- OnCoCo 1.0: A Public Dataset for Fine-Grained Message Classification in Online Counseling Convers...
Albrecht, Lehmann, Poltermann, Rudolph, Steigerwald, Stieler
https://arxiv.org/abs/2512.09804 https://mastoxiv.page/@arXiv_csCL_bot/115700409397020978
- Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, an...
Hanyu Cai, Binqi Shen, Lier Jin, Lan Hu, Xiaojing Fan
https://arxiv.org/abs/2512.12812 https://mastoxiv.page/@arXiv_csCL_bot/115729149622659403
- Beg to Differ: Understanding Reasoning-Answer Misalignment Across Languages
Ovalle, Ross, Ruder, Williams, Ullrich, Ibrahim, Sagun
https://arxiv.org/abs/2512.22712 https://mastoxiv.page/@arXiv_csCL_bot/115808161882146194
- Activation Steering for Masked Diffusion Language Models
Adi Shnaidman, Erin Feiglin, Osher Yaari, Efrat Mentel, Amit Levi, Raz Lapid
https://arxiv.org/abs/2512.24143 https://mastoxiv.page/@arXiv_csCL_bot/115819533211103315
- JMedEthicBench: A Multi-Turn Conversational Benchmark for Evaluating Medical Safety in Japanese L...
Liu, Li, Niu, Zhang, Xun, Hou, Wang, Iwasawa, Matsuo, Hatakeyama-Sato
https://arxiv.org/abs/2601.01627 https://mastoxiv.page/@arXiv_csCL_bot/115847901607405421
- FACTUM: Mechanistic Detection of Citation Hallucination in Long-Form RAG
Dassen, Kotula, Murray, Yates, Lawrie, Kayi, Mayfield, Duh
https://arxiv.org/abs/2601.05866 https://mastoxiv.page/@arXiv_csCL_bot/115881545684182376
- {\dag}DAGGER: Distractor-Aware Graph Generation for Executable Reasoning in Math Problems
Zabir Al Nazi, Shubhashis Roy Dipta, Sudipta Kar
https://arxiv.org/abs/2601.06853 https://mastoxiv.page/@arXiv_csCL_bot/115887753245730019
- Symphonym: Universal Phonetic Embeddings for Cross-Script Name Matching
Stephen Gadd
https://arxiv.org/abs/2601.06932 https://mastoxiv.page/@arXiv_csCL_bot/115887767008671765
- LLMs versus the Halting Problem: Revisiting Program Termination Prediction
Sultan, Armengol-Estape, Kesseli, Vanegue, Shahaf, Adi, O'Hearn
https://arxiv.org/abs/2601.18987 https://mastoxiv.page/@arXiv_csCL_bot/115972010510378715
- MuVaC: A Variational Causal Framework for Multimodal Sarcasm Understanding in Dialogues
Diandian Guo, Fangfang Yuan, Cong Cao, Xixun Lin, Chuan Zhou, Hao Peng, Yanan Cao, Yanbing Liu
https://arxiv.org/abs/2601.20451 https://mastoxiv.page/@arXiv_csCL_bot/115977891530875024
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Towards simpler #btrfs raid setup with #disko:
https://discourse.nixos.org/t/btrfs-ra

Btrfs raid with Disko
I wrote a helper function mkBtrfsRAID to make defining btrfs raid collections more ergonomic in disko. It has the “other” disks formatted with an empty partition table, so disko knows about them and asks for wipe confirmation on formatting. Doesn’t solve the abovementioned problem when the first disk that’s actually tried to be mounted fails or is missing though. let # make a disko.devices.disk attrset that empty-format the given disks before anything else mkEmptyDisks = { before ?…

Kennt ihr #Center795?
Das war/ist eine russische Einheit bestehend aus #GRU und #FSB. Die sollten "Spezialaktionen" in der #Ukraine

#Ally43 : #OnRecrute en #Auvergne #HauteLoire
L’association