Tootfinder

Robinhood Ventures Fund I discloses its first investments, buying $14.6M of Stripe shares and $20M of ElevenLabs' preferred stock in March (CoinDesk)
https://www.coindesk.com/markets/2026/03/17/robinhood-s-new-ventu…

Robinhood’s new venture fund just snapped up stakes in Stripe and ElevenLabs
The closed-end fund aims to give everyday investors exposure to private firms before they go public.

🔊 #NowPlaying on #BBCRadio3:
#Radio3InConcert
- Felix Mendelssohn's Symphony No. 3 ‘Scottish’
Conductor Nil Venditti joins Royal Northern Sinfonia and violinist Maria Włoszczowska at the Glasshouse in Gateshead to perform music by Anna Clyne, Prokofiev and Mendelssohn.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002sg9c

Sources: Kraken has paused its IPO plans, amid the downturn in crypto markets since October, and may revisit a listing when market conditions improve (CoinDesk)
https://www.coindesk.com/business/2026/03/

Payward, parent of crypto exchange Kraken, has put its IPO plans on hold
Kraken's parent company filed a draft S-1 registration statement with the SEC in November regarding the proposed initial public offering of its common stock.

Some City Some Nature V 🏙️🪾
一些城一些自然 V 🏙️🪾
📷 Nikon F4E
🎞️ ERA 100, expired 1993
#filmphotography #Photography #blackandwhite

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[4/5]:
- Retrieving Climate Change Disinformation by Narrative
Upravitelev, Solopova, Jakob, Sahitaj, M\"oller, Schmitt
https://arxiv.org/abs/2603.22015 https://mastoxiv.page/@arXiv_csCL_bot/116283633674519408
- PaperVoyager : Building Interactive Web with Visual Language Models
Dasen Dai, Biao Wu, Meng Fang, Wenhao Wang
https://arxiv.org/abs/2603.22999 https://mastoxiv.page/@arXiv_csCL_bot/116289015432093128
- Continual Robot Skill and Task Learning via Dialogue
Weiwei Gu, Suresh Kondepudi, Anmol Gupta, Lixiao Huang, Nakul Gopalan
https://arxiv.org/abs/2409.03166 https://mastoxiv.page/@arXiv_csRO_bot/113089412115632702
- Shifting Perspectives: Steering Vectors for Robust Bias Mitigation in LLMs
Zara Siddique, Irtaza Khalid, Liam D. Turner, Luis Espinosa-Anke
https://arxiv.org/abs/2503.05371 https://mastoxiv.page/@arXiv_csLG_bot/114136994263573386
- SkillFlow: Scalable and Efficient Agent Skill Retrieval System
Fangzhou Li, Pagkratios Tagkopoulos, Ilias Tagkopoulos
https://arxiv.org/abs/2504.06188 https://mastoxiv.page/@arXiv_csAI_bot/114306773220502860
- Large Language Models for Computer-Aided Design: A Survey
Licheng Zhang, Bach Le, Naveed Akhtar, Siew-Kei Lam, Tuan Ngo
https://arxiv.org/abs/2505.08137 https://mastoxiv.page/@arXiv_csLG_bot/114504972217393639
- Structured Agent Distillation for Large Language Model
Liu, Kong, Dong, Yang, Li, Tang, Yuan, Niu, Zhang, Zhao, Lin, Huang, Wang
https://arxiv.org/abs/2505.13820 https://mastoxiv.page/@arXiv_csLG_bot/114544636506163783
- VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction
Fan, Zhang, Li, Zhang, Chen, Hu, Wang, Qu, Zhou, Wang, Yan, Xu, Theiss, Chen, Li, Tu, Wang, Ranjan
https://arxiv.org/abs/2505.20279 https://mastoxiv.page/@arXiv_csCV_bot/114578817567171199
- Learning to Diagnose Privately: DP-Powered LLMs for Radiology Report Classification
Bhattacharjee, Tian, Rubin, Lo, Merchant, Hanson, Gounley, Tandon
https://arxiv.org/abs/2506.04450 https://mastoxiv.page/@arXiv_csCR_bot/114635189706505648
- L-MARS: Legal Multi-Agent Workflow with Orchestrated Reasoning and Agentic Search
Ziqi Wang, Boqin Yuan
https://arxiv.org/abs/2509.00761 https://mastoxiv.page/@arXiv_csAI_bot/115140304787881576
- Your Models Have Thought Enough: Training Large Reasoning Models to Stop Overthinking
Han, Huang, Liao, Jiang, Lu, Zhao, Wang, Zhou, Jiang, Liang, Zhou, Sun, Yu, Xiao
https://arxiv.org/abs/2509.23392 https://mastoxiv.page/@arXiv_csAI_bot/115293169353788311
- Person-Centric Annotations of LAION-400M: Auditing Bias and Its Transfer to Models
Leander Girrbach, Stephan Alaniz, Genevieve Smith, Trevor Darrell, Zeynep Akata
https://arxiv.org/abs/2510.03721 https://mastoxiv.page/@arXiv_csCV_bot/115332690912652473
- Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Zhang, Hu, Upasani, Ma, Hong, Kamanuru, Rainton, Wu, Ji, Li, Thakker, Zou, Olukotun
https://arxiv.org/abs/2510.04618 https://mastoxiv.page/@arXiv_csLG_bot/115332999596603375
- Mitigating Premature Exploitation in Particle-based Monte Carlo for Inference-Time Scaling
Giannone, Xu, Nayak, Awhad, Sudalairaj, Xu, Srivastava
https://arxiv.org/abs/2510.05825 https://mastoxiv.page/@arXiv_csLG_bot/115338159696513898
- Complete asymptotic type-token relationship for growing complex systems with inverse power-law co...
Pablo Rosillo-Rodes, Laurent H\'ebert-Dufresne, Peter Sheridan Dodds
https://arxiv.org/abs/2511.02069 https://mastoxiv.page/@arXiv_physicssocph_bot/115496283627867809
- ViPRA: Video Prediction for Robot Actions
Sandeep Routray, Hengkai Pan, Unnat Jain, Shikhar Bahl, Deepak Pathak
https://arxiv.org/abs/2511.07732 https://mastoxiv.page/@arXiv_csRO_bot/115535941444003568
- AISAC: An Integrated multi-agent System for Transparent, Retrieval-Grounded Scientific Assistance
Chandrachur Bhattacharya, Sibendu Som
https://arxiv.org/abs/2511.14043
- VideoARM: Agentic Reasoning over Hierarchical Memory for Long-Form Video Understanding
Yufei Yin, Qianke Meng, Minghao Chen, Jiajun Ding, Zhenwei Shao, Zhou Yu
https://arxiv.org/abs/2512.12360 https://mastoxiv.page/@arXiv_csCV_bot/115729238732682644
- RadImageNet-VQA: A Large-Scale CT and MRI Dataset for Radiologic Visual Question Answering
L\'eo Butsanets, Charles Corbi\`ere, Julien Khlaut, Pierre Manceron, Corentin Dancette
https://arxiv.org/abs/2512.17396 https://mastoxiv.page/@arXiv_csCV_bot/115762705911757243
- Measuring all the noises of LLM Evals
Sida Wang
https://arxiv.org/abs/2512.21326 https://mastoxiv.page/@arXiv_csLG_bot/115779597137785637
toXiv_bot_toot

So to follow up on this, I've caught it in action. Models, when quantized a bit, just do a bit more poorly with short contexts. Even going from f32 (as trained) to bf16 (as usually run) to q8 tends to do okay for "normal" context windows. And q4 you start feeling like "this model is a little stupid and gets stuck sometimes” (it is! It's just that it's still mostly careening about in the space of "plausible" most of the time. Not good guesswork, but still in the zone). With long contexts, the probability of parameters collapsing to zero are higher, so the more context the more likelihood you are to see brokenness.
And then at Q2 (2 bits per parameter) or Q1, the model falls apart completely. Parameters collapse to zero easily. You start seeing "all work and no play makes jack a dull boy” sorts of behavior, with intense and unscrutinized repetition, followed by a hard stop when it just stops working.
And quantization is a parameter that a model vendor can turn relatively easily. (they have to regenerate the model from the base with more quantization, but it's a data transformation on the order of running a terabyte through a straightforward and fast process, not like training).
If you have 1000 customers and enough equipment to handle the requests of 700, going from bf16 to q8 is a no-brainer. Suddenly you can handle the load and have a little spare capacity. They get worse results, probably pay the same per token (or they're on a subscription that hides the cost anyway so you are even freer to make trade-offs. There's a reason that subscription products are kinda poorly described.)
It's also possible for them to vary this across a day: use models during quieter periods? Maybe you get an instance running a bf16 quantization. If you use it during a high use period? You get a Q4 model.
Or intelligent routing is possible. No idea if anyone is doing this, but if they monitor what you send a bit, and you generally shoot for an expensive model for simple requests? They could totally substitute a highly quantized version of the model to answer the question.
There are •so many tricks• that can be pulled here. Some of them very reasonable to make, some of them treading into outright misleading or fraudulent, and it's weirdly hard to draw the line between them.

At least someone's enjoying the thought of a by-election in Farrer
https://www.betootaadvocate.com/report-farrer-by-election-is-going-to-be-a-real-shitshow/

Report: Farrer By-Election Is Going To Be A Real Shitshow
ERROL PARKER | Editor-at-large | Contact For years, Farrer was the kind of seat political parties kept in the china cabinet. Safe. Reliable. Only taken out on special occasions. With the vacancy declared, what should have been a routine rural by-election has instead morphed into a full-blown political demolition derby. The Coalition

Kraken parent company Payward agrees to acquire Bitnomial, a digital asset derivatives platform, for up to $550M in cash and stock (Will Canny/CoinDesk)
https://www.coindesk.com/business/2026/04/

Kraken's parent company Payward to acquire derivatives exchange Bitnomial for $550 million in cash and stock.
The deal gives Payward control of a fully licensed U.S. crypto derivatives stack, accelerating its expansion in regulated markets.

🔊 #NowPlaying on #BBCRadio3:
#ThroughTheNight
- Prokofiev, Stravinsky, Bacewicz and Mozart from Stockholm
Nicolas Altstaedt joins the Swedish Radio Symphony Orchestra and conductor Maxim Emelyanychev in Bacewicz's Cello Concerto no 2, plus works by Prokofiev, Stravinsky and Mozart.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002tmgb

Filing: Gemini says its COO, CFO, and chief legal officer are leaving; Cameron Winklevoss will take on many of the COO's duties; GEMI falls 10% (Olivier Acuna/CoinDesk)
https://www.coindesk.com/business/2026/02/

Gemini ousts COO, CFO and Chief Legal Officer after international exit and 25% staff cuts
Newly public crypto exchange Gemini Space Station Inc. said Marshall Beard, Dan Chen and Tyler Meade are departing effective immediately, days after the company announced plans to shut down U.K., EU and Australia operations and refocus on the U.S. and prediction markets.