Tootfinder

Series B, Episode 08 - Hostage
BLAKE: You all right, Jenna?
JENNA: [Into bracelet.] Bring us up, Cally.
INGA: Come on, father.
[Surface of Exbar. Night. Travis is walking disconsolately and stumbles into a mutoid and Servalan.]
https://blake.torpidity.net/m/208/714 B7B4

The General Expiration Streaming Model: Diameter, $k$-Center, Counting, Sampling, and Friends
Lotte Blank, Sergio Cabello, MohammadTaghi Hajiaghayi, Robert Krauthgamer, Sepideh Mahabadi, Andr\'e Nusser, Jeff M. Phillips, Jonas Sauer
https://arxiv.org/abs/2509.07587

The General Expiration Streaming Model: Diameter, $k$-Center, Counting, Sampling, and Friends
An important thread in the study of data-stream algorithms focuses on settings where stream items are active only for a limited time. We introduce a new expiration model, where each item arrives with its own expiration time. The special case where items expire in the order that they arrive, which we call consistent expirations, contains the classical sliding-window model of Datar, Gionis, Indyk, and Motwani [SICOMP 2002] and its timestamp-based variant of Braverman and Ostrovsky [FOCS 2007]. …

Visual Analysis of Time-Dependent Observables in Cell Signaling Simulations
Lena Cibulski, Fiete Haack, Adelinde Uhrmacher, Stefan Bruckner
https://arxiv.org/abs/2509.08589 http…

Visual Analysis of Time-Dependent Observables in Cell Signaling Simulations
The ability of a cell to communicate with its environment is essential for key cellular functions like replication, metabolism, or cell fate decisions. The involved molecular mechanisms are highly dynamic and difficult to capture experimentally. Simulation studies offer a valuable means for exploring and predicting how cell signaling processes unfold. We present a design study on the visual analysis of such studies to support 1) modelers in calibrating model parameters such that the simulated s…

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Sankalp Tattwadarshi Swain, Anshika Krishnatray, Dhruv Kumar, Jagat Sesh Challa
https://arxiv.org/abs/2509.07389

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents
Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language acquisition. We propose a novel experimental framework in which an LLM agent is evaluated on its abi…

looks like there'll be a #NoKings event at Sunset Dunes! cool crossover
https://www.mobilize.us/nokings/event/847587/
main march in SF here:

NO KINGS Ocean Beach, San Francisco · No Kings
**In America, we don’t put up with would-be kings.** Join us for No Kings Day Ocean Beach! Saturday, October 18 • 12:00 PM • Sunset Dunes (Upper Great Highway & Judah), SF Be part of a fun, family-friendly Resistance Festival on the westside of San Francisco. Together we’ll celebrate community and resistance with: 🎨 Art and creative expression 🎶 Live music and performances 📚 Information tables on Bay Resistance, Prop 50, voter registration and more 🤝 Family-friendly ac…

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Pierre Cazals, Amalia Sorondo, Victor Onofre, Constantin Dalyac, Wesley da Silva Coelho, Vittorio Vitale
https://arxiv.org/abs/2508.06130

Quantum Optimization on Rydberg Atom Arrays with Arbitrary Connectivity: Gadgets Limitations and a Heuristic Approach
Programmable quantum systems based on Rydberg atom arrays have recently emerged as a promising testbed for combinatorial optimization. Indeed, the Maximum Weighted Independent Set problem on unit-disk graphs can be efficiently mapped to such systems due to their geometric constraints. However, extending this capability to arbitrary graph instances typically necessitates the use of reduction gadgets, which introduce additional experimental overhead and complexity. Here, we analyze the complexity…

Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
Jannek Ulm, Kevin Du, V\'esteinn Sn{\ae}bjarnarson
https://arxiv.org/abs/2510.08245 http…

Contrastive Decoding for Synthetic Data Generation in Low-Resource Language Modeling
Large language models (LLMs) are trained on huge amounts of textual data, and concerns have been raised that the limits of such data may soon be reached. A potential solution is to train on synthetic data sampled from LLMs. In this work, we build on this idea and investigate the benefits of contrastive decoding for generating synthetic corpora. In a controlled setting, we experiment with sampling corpora using the relative difference between a good and bad model trained on the same original cor…

Cyberbullying Detection via Aggression-Enhanced Prompting
Aisha Saeid, Anu Sabu, Girish A. Koushik, Ferrante Neri, Diptesh Kanojia
https://arxiv.org/abs/2508.06360 https://

Cyberbullying Detection via Aggression-Enhanced Prompting
Detecting cyberbullying on social media remains a critical challenge due to its subtle and varied expressions. This study investigates whether integrating aggression detection as an auxiliary task within a unified training framework can enhance the generalisation and performance of large language models (LLMs) in cyberbullying detection. Experiments are conducted on five aggression datasets and one cyberbullying dataset using instruction-tuned LLMs. We evaluated multiple strategies: zero-shot, …

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
5 Team, Aohan Zeng, Xin Lv, Qinkai Zheng, Zhenyu Hou, Bin Chen, Chengxing Xie, Cunxiang Wang, Da Yin, Hao Zeng, Jiajie Zhang, Kedong Wang, Lucen Zhong, Mingdao Liu, Rui Lu, Shulin Cao, Xiaohan Zhang, Xuancheng Huang, Yao Wei, Yean Cheng, Yifan An, Yilin Niu, Yuanhao Wen, Yushi Bai, Zhengxiao Du, Zihan Wang, Zilin Zhu, Bohan Zhang, Bosi Wen, Bowen Wu, Bowen Xu, Can Huang, Casey Zhao, Changpeng Cai, Chao Yu, Chen Li, Chendi …

GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
We present GLM-4.5, an open-source Mixture-of-Experts (MoE) large language model with 355B total parameters and 32B activated parameters, featuring a hybrid reasoning method that supports both thinking and direct response modes. Through multi-stage training on 23T tokens and comprehensive post-training with expert model iteration and reinforcement learning, GLM-4.5 achieves strong performance across agentic, reasoning, and coding (ARC) tasks, scoring 70.1% on TAU-Bench, 91.0% on AIME 24, and 64…

Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media
Yukai Song, Pengfei Zhou, C\'esar Escobar-Viera, Candice Biernesser, Wei Huang, Jingtong Hu
https://arxiv.org/abs/2510.08365

Two-Stage Voting for Robust and Efficient Suicide Risk Detection on Social Media
Suicide rates have risen worldwide in recent years, underscoring the urgent need for proactive prevention strategies. Social media provides valuable signals, as many at-risk individuals - who often avoid formal help due to stigma - choose instead to share their distress online. Yet detecting implicit suicidal ideation, conveyed indirectly through metaphor, sarcasm, or subtle emotional cues, remains highly challenging. Lightweight models like BERT handle explicit signals but fail on subtle impli…