Tootfinder

Series C, Episode 05 - The Harvest of Kairos
VILA: We got it, we got it!
TARRANT: Yes, I know, only I was saving that trick for when they both attacked at once.
VILA: There's only two left now; no trouble.
https://blake.torpidity.net/m/305/300 B7B3

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Series C, Episode 05 - The Harvest of Kairos
VILA: We got it, we got it!
TARRANT: Yes, I know, only I was saving that trick for when they both attacked at once.
VILA: There's only two left now; no trouble.
https://blake.torpidity.net/m/305/300 B7B3

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
Zihan Wang, Rui Pan, Jiarui Yao, Robert Csordas, Linjie Li, Lu Yin, Jiajun Wu, Tong Zhang, Manling Li, Shiwei Liu
https://arxiv.org/abs/2506.18945

Chain-of-Experts: Unlocking the Communication Power of Mixture-of-Experts Models
We propose Chain-of-Experts (CoE), a new Mixture-of-Experts (MoE) architecture that introduces sequential expert communication within each layer. Unlike traditional MoE models, where experts operate independently in parallel, CoE processes tokens iteratively across a chain of experts inside a layer. To support dynamic expert selection across iterations, CoE employs a dedicated router at each iteration step within a layer. This design allows tokens to re-evaluate and select different experts dur…

Towards AI Search Paradigm
Yuchen Li, Hengyi Cai, Rui Kong, Xinran Chen, Jiamin Chen, Jun Yang, Haojie Zhang, Jiayi Li, Jiayi Wu, Yiqun Chen, Changle Qu, Keyi Kong, Wenwen Ye, Lixin Su, Xinyu Ma, Long Xia, Daiting Shi, Jiashu Zhao, Haoyi Xiong, Shuaiqiang Wang, Dawei Yin
https://arxiv.org/abs/2506.17188

Towards AI Search Paradigm
In this paper, we introduce the AI Search Paradigm, a comprehensive blueprint for next-generation search systems capable of emulating human information processing and decision-making. The paradigm employs a modular architecture of four LLM-powered agents (Master, Planner, Executor and Writer) that dynamically adapt to the full spectrum of information needs, from simple factual queries to complex multi-stage reasoning tasks. These agents collaborate dynamically through coordinated workflows to e…

LIVE NOW on Twitch! Exploring the mysteries of Aeternum in New World on my Bazzite Linux PC. Come join the adventure! #NewWorld

Exploring Speaker Diarization with Mixture of Experts
Gaobin Yang, Maokui He, Shutong Niu, Ruoyu Wang, Hang Chen, Jun Du
https://arxiv.org/abs/2506.14750 h…

Exploring Speaker Diarization with Mixture of Experts
In this paper, we propose a novel neural speaker diarization system using memory-aware multi-speaker embedding with sequence-to-sequence architecture (NSD-MS2S), which integrates a memory-aware multi-speaker embedding module with a sequence-to-sequence architecture. The system leverages a memory module to enhance speaker embeddings and employs a Seq2Seq framework to efficiently map acoustic features to speaker labels. Additionally, we explore the application of mixture of experts in speaker dia…

SRG/eROSITA No. 5: Discovery of quasi-periodic eruptions every ~3.7 days from a galaxy at z>0.1
R. Arcodia, P. Baldini, A. Merloni, A. Rau, K. Nandra, J. Chakraborty, A. J. Goodwin, M. J. Page, J. Buchner, M. Masterson, I. Monageng, Z. Arzoumanian, D. Buckley, E. Kara, G. Ponti, M. E. Ramos-Ceja, M. Salvato, K. Gendreau, I. Grotova, M. Krumpe
http…

SRG/eROSITA No. 5: Discovery of quasi-periodic eruptions every ~3.7 days from a galaxy at z>0.1
Quasi-periodic eruptions (QPEs) are repeating soft X-ray bursts from the nuclei of galaxies, tantalizingly proposed to be extreme mass ratio inspirals. Here, we report the discovery of a new galaxy showing X-ray QPEs, the fifth found through a dedicated blind search in the \emph{SRG}/eROSITA all-sky survey data, hereafter named eRO-QPE5. Its QPE duration ($t_{\rm dur}\sim0.6$\,d), recurrence time ($t_{\rm recur}\sim3.7\,$d), integrated energy per eruption ($\sim3.4 \times 10^{47}\,$erg), and bl…

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Houyi Li, Ka Man Lo, Ziqi Wang, Zili Wang, Wenzhen Zheng, Shuigeng Zhou, Xiangyu Zhang, Daxin Jiang
https://arxiv.org/abs/2506.12119

Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
Mixture-of-Experts (MoE) language models dramatically expand model capacity and achieve remarkable performance without increasing per-token compute. However, can MoEs surpass dense architectures under strictly equal resource constraints - that is, when the total parameter count, training compute, and data budget are identical? This question remains under-explored despite its significant practical value and potential. In this paper, we propose a novel perspective and methodological framework to …

Series C, Episode 05 - The Harvest of Kairos
[Space Command]
SERVALAN: How did that happen?
JARVIK: They fired through their own force shield.
SERVALAN: Don't be absurd.
JARVIK: You asked, I told you.
[Liberator flight deck]
https://blake.torpidity.net/m/305/294

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Series B, Episode 05 - Pressure Point
TRAVIS: Come on, come on. Where were you to rendezvous with Blake?
KASABI: You can rot, Travis. I'll tell you nothing, nothing. [Kasabi spits on Travis. Travis crosses to Servalan.]
https://blake.torpidity.net/m/205/170 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.