Tootfinder

Mira Murati's Thinking Machines Lab makes Tinker, its API for fine-tuning language models, generally available, adds support for Kimi K2 Thinking, and more (Thinking Machines Lab)
https://thinkingmachines.ai/blog/tinker-general-availability/

Tinker: General Availability and Vision Input
Today we are announcing four updates to Tinker: No more waitlist New reasoning model: Kimi K2 Thinking New inference interface that is compatible with the OpenAI API Vision input support with Qwen3-VL General availability The waitlist is over! Everybody can use Tinker now; sign up here to get started. See the Tinker homepage for available models and pricing, and check out the Tinker cookbook for code examples. More reasoning with Kimi K2 Thinking Users can now fine-tune Kimi K2 Thinking on Ti…

🇺🇦 #NowPlaying on #BBC6Music's #6MusicArtistCollection
Florence The Machine:
🎵 Queen of Peace (Radio 2 Session 16/2/15)
#Florence TheMachine
https://djcapo.bandcamp.com/track/florence-the-machine-queen-of-peace-dj-capo-remix
https://open.spotify.com/track/6WRjufZUoxjaUNKOJ6QhWp

Tencent reports Q3 revenue up 15% YoY to ~$27B, above ~$26.6B est., net profit up 19% YoY to ~$8.9B, above ~$8B est., and domestic game revenue up 15% YoY (Sherry Qin/Wall Street Journal)
https://www.wsj.com/tech/tencent-delivers-

Hydrodynamic properties in soliton field theory
Qian Chen
https://arxiv.org/abs/2510.11244 https://arxiv.org/pdf/2510.11244

Hydrodynamic properties in soliton field theory
The crucial role of hydrodynamic instabilities in soliton field theory is revealed. We demonstrate that the essential of soliton formation mechanism is the sound mode instability induced by thermodynamic instability. This instability triggers phase separation, where new thermal phases are generated to produce solitons. These solitons can be regarded as a coexistence state composed of a matter phase and a vacuum phase, with an interface proving surface tension to maintain dynamical equilibrium. …

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
Zilin Kang, Chonghua Liao, Tingqiang Xu, Huazhe Xu
https://arxiv.org/abs/2510.08549

Entropy Regularizing Activation: Boosting Continuous Control, Large Language Models, and Image Classification with Activation as Entropy Constraints
We propose ERA, a new paradigm that constrains the sampling entropy above given thresholds by applying specially designed activations to the outputs of models. Our approach demonstrates broad effectiveness across different domains: 1) for large language models(LLMs), boosting the AIME 2025 score for Qwen2.5-Math-7B by 37.4%; 2) for continuous control reinforcement learning agents, improving performance by more than 30% over strong baselines such as SAC on the challenging HumanoidBench; 3) for i…

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
Buqing Xu, Jianfeng Zhu, Yichi Zhang, Qinyi Cai, Guanhua Li, Shaojun Wei, Leibo Liu
https://arxiv.org/abs/2510.10484

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
CPU simulators are vital for computer architecture research, primarily for estimating performance under different programs. This poses challenges for fast and accurate simulation of modern CPUs, especially in multi-core systems. Modern CPU peformance simulators such as GEM5 adopt the cycle-accurate and event-driven approach, which is timeconsuming to simulate the extensive microarchitectural behavior of a real benchmark running on out-of-order CPUs. Recently, machine leaning based approach has …

Rethinking Thinking Tokens: LLMs as Improvement Operators
Lovish Madaan, Aniket Didolkar, Suchin Gururangan, John Quan, Ruan Silva, Ruslan Salakhutdinov, Manzil Zaheer, Sanjeev Arora, Anirudh Goyal
https://arxiv.org/abs/2510.01123

Rethinking Thinking Tokens: LLMs as Improvement Operators
Reasoning training incentivizes LLMs to produce long chains of thought (long CoT), which among other things, allows them to explore solution strategies with self-checking. This results in higher accuracy, but inflates context length, token/compute cost, and answer latency. We ask: Can current models leverage their metacognition to provide other combinations on this Pareto frontier, e.g., better accuracy with lower context length and/or latency? Abstractly, we view the model as an improvement op…

Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications in AI-assisted Data Analysis
Qianou Ma, Kenneth Koedinger, Tongshuang Wu
https://arxiv.org/abs/2509.21890

Not Everyone Wins with LLMs: Behavioral Patterns and Pedagogical Implications in AI-assisted Data Analysis
LLMs promise to democratize technical work in complex domains like programmatic data analysis, but not everyone benefits equally. We study how students with varied expertise use LLMs to complete Python-based data analysis in computational notebooks in a non-major course. Drawing on homework logs, recordings, and surveys from 36 students, we ask: Which expertise matters most, and how does it shape AI use? Our mixed-methods analysis shows that technical expertise -- not AI familiarity or communic…

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
Buqing Xu, Jianfeng Zhu, Yichi Zhang, Qinyi Cai, Guanhua Li, Shaojun Wei, Leibo Liu
https://arxiv.org/abs/2510.10484

CAPSim: A Fast CPU Performance Simulator Using Attention-based Predictor
CPU simulators are vital for computer architecture research, primarily for estimating performance under different programs. This poses challenges for fast and accurate simulation of modern CPUs, especially in multi-core systems. Modern CPU peformance simulators such as GEM5 adopt the cycle-accurate and event-driven approach, which is timeconsuming to simulate the extensive microarchitectural behavior of a real benchmark running on out-of-order CPUs. Recently, machine leaning based approach has …

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Yanghao Li, Rui Qian, Bowen Pan, Haotian Zhang, Haoshuo Huang, Bowen Zhang, Jialing Tong, Haoxuan You, Xianzhi Du, Zhe Gan, Hyunjik Kim, Chao Jia, Zhenbang Wang, Yinfei Yang, Mingfei Gao, Zi-Yi Dou, Wenze Hu, Chang Gao, Dongxu Li, Philipp Dufter, Zirui Wang, Guoli Yin, Zhengdong Zhang, Chen Chen, Yang Zhao, Ruoming Pang, Zhifeng Chen

MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
Unified multimodal Large Language Models (LLMs) that can both understand and generate visual content hold immense potential. However, existing open-source models often suffer from a performance trade-off between these capabilities. We present Manzano, a simple and scalable unified framework that substantially reduces this tension by coupling a hybrid image tokenizer with a well-curated training recipe. A single shared vision encoder feeds two lightweight adapters that produce continuous embeddi…