Tootfinder

A Federalism Bottleneck? -- Across America, officials in small government offices are reviewing ... (Ashley Splawinski/Can We Still Govern?)
https://donmoynihan.substack.com/p/a-federalism-bottleneck
http://www.memeorandum.com/260227/p36#a260227p36

🪰 New technology solves production bottleneck for black soldier fly larvae
#insects

New technology solves production bottleneck for black soldier fly larvae
Texas A&M AgriLife Research scientists have developed a patented breakthrough system that marks a major step forward in insect biomanufacturing, waste reduction and sustainable protein production.

Gimlet Labs, which says it is the first "multi-silicon inference cloud" for running AI workloads across diverse types of hardware, raised an $80M Series A (Julie Bort/TechCrunch)
https://techcrunch.com/2026/03/23/star

Startup Gimlet Labs is solving the AI inference bottleneck in a surprisingly elegant way | TechCrunch
Gimlet Labs just raised an $80 million Series A for tech that lets AI run across NVIDIA, AMD, Intel, ARM, Cerebras and d-Matrix chips, simultaneously.

Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
Bin Zhu, Qianghuai Jia, Tian Lan, Junyang Ren, Feng Gu, Feihu Jiang, Longyue Wang, Zhao Xu, Weihua Luo
https://arxiv.org/abs/2603.28376 https://arxiv.org/pdf/2603.28376 https://arxiv.org/html/2603.28376
arXiv:2603.28376v1 Announce Type: new
Abstract: Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems from the lack of explicit verification mechanisms in QA data synthesis, trajectory construction, and test-time scaling. Errors introduced at each stage propagate downstream and degrade the overall agent performance. To address this, we present Marco DeepResearch, a deep research agent optimized with a verification-centric framework design at three levels: \textbf{(1)~QA Data Synthesis:} We introduce verification mechanisms to graph-based and agent-based QA synthesis to control question difficulty while ensuring answers are unique and correct; \textbf{(2)~Trajectory Construction:} We design a verification-driven trajectory synthesis method that injects explicit verification patterns into training trajectories; and \textbf{(3)~Test-time scaling:} We use Marco DeepResearch itself as a verifier at inference time and effectively improve performance on challenging questions. Extensive experimental results demonstrate that our proposed Marco DeepResearch agent significantly outperforms 8B-scale deep research agents on most challenging benchmarks, such as BrowseComp and BrowseComp-ZH. Crucially, under a maximum budget of 600 tool calls, Marco DeepResearch even surpasses or approaches several 30B-scale agents, like Tongyi DeepResearch-30B.
toXiv_bot_toot

Global Food Supply Faces a Dangerous Bottleneck as Iran War Persists (Ana Swanson/New York Times)
https://www.nytimes.com/2026/03/27/business/economy/fertilizer-food-supply-iran-war.html?unlocked_article_code=1.WVA.zxen.9UWesjK_HkG7&smid=url-share
http://www.memeorandum.com/260327/p135#a260327p135

Bengaluru-based C2i Semiconductors, building a "grid-to-GPU" power system for datacenters, raised a $15M Series A led by Peak XV, bringing total funding to $19M (Jagmeet Singh/TechCrunch)
https://techcrunch.com/2026/02/15/as-a

As AI data centers hit power limits, Peak XV backs Indian startup C2i to fix the bottleneck | TechCrunch
C2i has raised $15 million as it tests a grid-to-GPU approach to reducing power losses in AI data centers.

🇺🇦 #NowPlaying on #BBC6Music's #6Musics90sForever
Mercury Rev:
🎵 Delta Sun Bottleneck Stomp
#MercuryRev
https://mercuryrev.bandcamp.com/track/delta-sun-bottleneck-stomp-4
https://open.spotify.com/track/2j4APtyKqOnp3BfuiBmuFV

Licht op groen voor Lelystad Airport, maar één cruciale factor blijft keer op keer onbereikbaar: ‘Overheid al jaren bottleneck’
https://www.destentor.nl/lelystad/licht-op-groen-voor-lelystad-airport-maar-een-cruciale-factor-blijft-keer-op-keer-onbereikbaar-overheid-al-jaren-bottleneck~a12eb9b1/
Bij elke opmerking over 'Lelystad' even terugkoppelen aan deze twee blokjes en je weet hoe ver het er mee staat.
Aangezien vliegen pas kan -na- verbetering vliegt er dus de komende jaren nog geen enkel toestel.
Daarnaast wordt bijzonder interessant wat de gevolgen van defensie op de neerslag is. Als die door defensie toeneemt .... 😇🥴

Extra Scrutiny of FEMA Aid to States Has Created a $17 Billion Bottleneck (Scott Dance/New York Times)
https://www.nytimes.com/2026/01/27/climate/fema-aid-kristi-noem.html
http://www.memeorandum.com/260127/p33#a260127p33

Drones caused a Qatari Helium-producing energy hub to shutter; crucial in chipmaking, Bloomberg says the closed hub makes up ~33% of global Helium production (Ines Ferré/Yahoo Finance)
https://finance.yahoo.com/news/iran-wa

Iran war could wreak havoc on farmers, create a potential 'bottleneck for the entire AI story'
It's not just the oil industry that has seen soaring prices. The cost of commodities like fertilizers and helium is also rising fast.

hm, rustc doesn't work with crates larger than 4 GiB of source code, and it looks to me like it only sometimes panics, so maybe it usually wanders into undefined behavior? ... it's probably fine
... well, Claude is $25 per 5M output tokens, so 4 GiB of slop-code is only ~$10000
... 4 GiB of source will probably fail at some other bottleneck anyway

Invasive species generally show signs of reduced genetic diversity from the bottleneck of introduction to a new habitat— but also usually recover a lot of diversity from hybridization among introduced populations
https://doi.org/10.1111/brv.70005

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/6]:
- Performance Asymmetry in Model-Based Reinforcement Learning
Jing Yu Lim, Rushi Shah, Zarif Ikram, Samson Yu, Haozhe Ma, Tze-Yun Leong, Dianbo Liu
https://arxiv.org/abs/2505.19698 https://mastoxiv.page/@arXiv_csLG_bot/114578810521008766
- Towards Robust Real-World Multivariate Time Series Forecasting: A Unified Framework for Dependenc...
Jinkwan Jang, Hyungjin Park, Jinmyeong Choi, Taesup Kim
https://arxiv.org/abs/2506.08660 https://mastoxiv.page/@arXiv_csLG_bot/114664238967892509
- Wasserstein Barycenter Soft Actor-Critic
Zahra Shahrooei, Ali Baheri
https://arxiv.org/abs/2506.10167 https://mastoxiv.page/@arXiv_csLG_bot/114675175949432731
- Foundation Models for Causal Inference via Prior-Data Fitted Networks
Yuchen Ma, Dennis Frauen, Emil Javurek, Stefan Feuerriegel
https://arxiv.org/abs/2506.10914 https://mastoxiv.page/@arXiv_csLG_bot/114675529854402158
- FREQuency ATTribution: benchmarking frequency-based occlusion for time series data
Dominique Mercier, Andreas Dengel, Sheraz Ahmed
https://arxiv.org/abs/2506.18481 https://mastoxiv.page/@arXiv_csLG_bot/114738421450807709
- Complexity-aware fine-tuning
Andrey Goncharov, Daniil Vyazhev, Petr Sychev, Edvard Khalafyan, Alexey Zaytsev
https://arxiv.org/abs/2506.21220 https://mastoxiv.page/@arXiv_csLG_bot/114754764750730849
- Transfer Learning in Infinite Width Feature Learning Networks
Clarissa Lauditi, Blake Bordelon, Cengiz Pehlevan
https://arxiv.org/abs/2507.04448 https://mastoxiv.page/@arXiv_csLG_bot/114818005803079705
- A hierarchy tree data structure for behavior-based user segment representation
Liu, Kang, Iyer, Malik, Li, Wang, Lu, Zhao, Wang, Liu, Liu, Liang, Yu
https://arxiv.org/abs/2508.01115 https://mastoxiv.page/@arXiv_csLG_bot/114975999992144374
- One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Lea...
Thanh Nguyen, Chang D. Yoo
https://arxiv.org/abs/2508.13904 https://mastoxiv.page/@arXiv_csLG_bot/115060568241390847
- Uncertainty Propagation Networks for Neural Ordinary Differential Equations
Hadi Jahanshahi, Zheng H. Zhu
https://arxiv.org/abs/2508.16815 https://mastoxiv.page/@arXiv_csLG_bot/115094785677272005
- Learning Unified Representations from Heterogeneous Data for Robust Heart Rate Modeling
Zhengdong Huang, Zicheng Xie, Wentao Tian, Jingyu Liu, Lunhong Dong, Peng Yang
https://arxiv.org/abs/2508.21785 https://mastoxiv.page/@arXiv_csLG_bot/115128450608548173
- Monte Carlo Tree Diffusion with Multiple Experts for Protein Design
Liu, Cao, Jiang, Luo, Duan, Wang, Sosnick, Xu, Stevens
https://arxiv.org/abs/2509.15796 https://mastoxiv.page/@arXiv_csLG_bot/115247429156900905
- From Samples to Scenarios: A New Paradigm for Probabilistic Forecasting
Xilin Dai, Zhijian Xu, Wanxu Cai, Qiang Xu
https://arxiv.org/abs/2509.19975 https://mastoxiv.page/@arXiv_csLG_bot/115264498084813952
- Why High-rank Neural Networks Generalize?: An Algebraic Framework with RKHSs
Yuka Hashimoto, Sho Sonoda, Isao Ishikawa, Masahiro Ikeda
https://arxiv.org/abs/2509.21895 https://mastoxiv.page/@arXiv_csLG_bot/115287261047939306
- From Parameters to Behaviors: Unsupervised Compression of the Policy Space
Davide Tenedini, Riccardo Zamboni, Mirco Mutti, Marcello Restelli
https://arxiv.org/abs/2509.22566 https://mastoxiv.page/@arXiv_csLG_bot/115287379672141023
- RHYTHM: Reasoning with Hierarchical Temporal Tokenization for Human Mobility
Haoyu He, Haozheng Luo, Yan Chen, Qi R. Wang
https://arxiv.org/abs/2509.23115 https://mastoxiv.page/@arXiv_csLG_bot/115293273559547106
- Polychromic Objectives for Reinforcement Learning
Jubayer Ibn Hamid, Ifdita Hasan Orney, Ellen Xu, Chelsea Finn, Dorsa Sadigh
https://arxiv.org/abs/2509.25424 https://mastoxiv.page/@arXiv_csLG_bot/115298579764580635
- Recursive Self-Aggregation Unlocks Deep Thinking in Large Language Models
Siddarth Venkatraman, et al.
https://arxiv.org/abs/2509.26626 https://mastoxiv.page/@arXiv_csLG_bot/115298789487177431
- Cautious Weight Decay
Chen, Li, Liang, Su, Xie, Pierse, Liang, Lao, Liu
https://arxiv.org/abs/2510.12402 https://mastoxiv.page/@arXiv_csLG_bot/115377759317818093
- TeamFormer: Shallow Parallel Transformers with Progressive Approximation
Wei Wang, Xiao-Yong Wei, Qing Li
https://arxiv.org/abs/2510.15425 https://mastoxiv.page/@arXiv_csLG_bot/115405933861293858
- Latent-Augmented Discrete Diffusion Models
Dario Shariatian, Alain Durmus, Umut Simsekli, Stefano Peluchetti
https://arxiv.org/abs/2510.18114 https://mastoxiv.page/@arXiv_csLG_bot/115417332500265972
- Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease using Machine Learning Method...
Mary E. An, Paul Griffin, Jonathan G. Stine, Ramakrishna Balakrishnan, Soundar Kumara
https://arxiv.org/abs/2510.22293 https://mastoxiv.page/@arXiv_csLG_bot/115451746201804373
toXiv_bot_toot

A major breakthrough in clean energy just dropped from Sweden.
Researchers have cracked the code on producing hydrogen from sunlight and water—without using platinum, the expensive and scarce metal that's been a bottleneck for years.
Instead, they're using conductive plastic particles. This could make green hydrogen way more affordable and scalable for renewable energy.

Cogent Security, which aims to use AI agents to decide which software bugs to remediate, raised a $42M Series A led by Bain, taking its total raised to $53M (Lily Mae Lazarus/Fortune)
https://fortune.com/2026/02/18/exclusi

Micron, which has undertaken a $200B US expansion, says it can currently meet about 50%-66% of demand for some key customers, as AI drives memory chip demand (Robbie Whelan/Wall Street Journal)
https://www.wsj.com/tech/micro…

AgentCgroup: Understanding and Controlling OS Resources of AI Agents
Yusheng Zheng, Jiakun Fan, Quanzhi Fu, Yiwei Yang, Wei Zhang, Andi Quinn
https://arxiv.org/abs/2602.09345 https://arxiv.org/pdf/2602.09345 https://arxiv.org/html/2602.09345
arXiv:2602.09345v1 Announce Type: new
Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations. We present a systematic characterization of OS-level resource dynamics in sandboxed AI coding agents, analyzing 144 software engineering tasks from the SWE-rebench benchmark across two LLM models. Our measurements reveal that (1) OS-level execution (tool calls, container and agent initialization) accounts for 56-74% of end-to-end task latency; (2) memory, not CPU, is the concurrency bottleneck; (3) memory spikes are tool-call-driven with a up to 15.4x peak-to-average ratio; and (4) resource demands are highly unpredictable across tasks, runs, and models. Comparing these characteristics against serverless, microservice, and batch workloads, we identify three mismatches in existing resource controls: a granularity mismatch (container-level policies vs. tool-call-level dynamics), a responsiveness mismatch (user-space reaction vs. sub-second unpredictable bursts), and an adaptability mismatch (history-based prediction vs. non-deterministic stateful execution). We propose AgentCgroup , an eBPF-based resource controller that addresses these mismatches through hierarchical cgroup structures aligned with tool-call boundaries, in-kernel enforcement via sched_ext and memcg_bpf_ops, and runtime-adaptive policies driven by in-kernel monitoring. Preliminary evaluation demonstrates improved multi-tenant isolation and reduced resource waste.
toXiv_bot_toot

EAG-PT: Emission-Aware Gaussians and Path Tracing for Indoor Scene Reconstruction and Editing
Xijie Yang, Mulin Yu, Changjian Jiang, Kerui Ren, Tao Lu, Jiangmiao Pang, Dahua Lin, Bo Dai, Linning Xu
https://arxiv.org/abs/2601.23065 https://arxiv.org/pdf/2601.23065 https://arxiv.org/html/2601.23065
arXiv:2601.23065v1 Announce Type: new
Abstract: Recent reconstruction methods based on radiance field such as NeRF and 3DGS reproduce indoor scenes with high visual fidelity, but break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, physically based inverse rendering relies on mesh representations and path tracing, which enforce correct light transport but place strong requirements on geometric fidelity, becoming a practical bottleneck for real indoor scenes. In this work, we propose Emission-Aware Gaussians and Path Tracing (EAG-PT), aiming for physically based light transport with a unified 2D Gaussian representation. Our design is based on three cores: (1) using 2D Gaussians as a unified scene representation and transport-friendly geometry proxy that avoids reconstructed mesh, (2) explicitly separating emissive and non-emissive components during reconstruction for further scene editing, and (3) decoupling reconstruction from final rendering by using efficient single-bounce optimization and high-quality multi-bounce path tracing after scene editing. Experiments on synthetic and real indoor scenes show that EAG-PT produces more natural and physically consistent renders after editing than radiant scene reconstructions, while preserving finer geometric detail and avoiding mesh-induced artifacts compared to mesh-based inverse path tracing. These results suggest promising directions for future use in interior design, XR content creation, and embodied AI.
toXiv_bot_toot

ByteDance's Seedance 2.0 AI model is held back by limited compute resources that create a bottleneck, forcing users to wait hours to generate a single video (Zeyi Yang/Wired)
https://www.wired.com/story/made-in-china-bytedances-…

ByteDance’s AI Ambitions Are Being Hampered by Compute Restraints and Copyright Concerns
ByteDance’s new Seedance 2.0 AI video model seemed unstoppable—until heavy demand strained the company’s compute capacity and copyright complaints began piling up.