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@hex@kolektiva.social
2026-05-29 22:15:58

I tried to write something up to talk about an idea, but it didn't quite work. I have a lot more I need to put into it. But I want to get an idea out, and, after talking with a person who pointed out some of the flaws in what I wrote, I think I can maybe write down the kernel of the idea here.
An acquaintance of mine did a deep dive on Operational Art and wrote his thesis (which prompted an earlier set of posts and an article I wrote for my professional-ish blog) on the intersection of the OODA loop and critical philosophy. I've been spending a lot of time thinking about Kilcullen's Three Pillars model (after watching Andrewism's wonderful video) and Beer's VSM. The TL;DR of it is that there's a much better insurgency model. Of course, the insurgency model also works for a bunch of other things, because cybernetics lets you do all kinds of cool abstraction like that.
So as I was reading the essay of a comrade the other day, that model popped back into my head and I'm going to try to share what I can of it.
When colonizers came to the Salish region, they saw what they believed to be an untouched wilderness. They failed to see the ways in which Salish people tended the land. Indigenous fire practices were common on the northwest coast, and the suppression of those practices remains a problem. There is an interrelationship between an environment and the systems within it. Systems, like people, animals, and cultures, adapt to the environment. In doing so, those systems will also change the environment.
Social technology was invisible, so colonizers defaulted to either some kind of Rousseauvian or Malthusian model of these people. They were not, for the colonizers, people who had developed advanced social technologies to live in harmony with their world. They were, rather, people in "a state of nature."
The European influenced left continues to draw this Rousseauvian model, which continues through a lot of Anarchist revolutionary thought. European anarchists were heavily influenced by observations and theories around the behavior of indigenous people. The remnants of this thought still exist in the idea that the system must only be destroyed for us to be free.
This is the same obliviousness to social technology, that social technology actually exists, often informs both early colonizers and modern radicals.
It is through this obliviousness that we fail to recognize how capitalism is a social technology that is managed into existence and maintained, and how changes in the environment can threaten institutions that have become over-adapted to a specific version of that environment.
We can extend Kilcullen's metaphor of a "conflict ecosystem" through cybernetics into a much more rich model, populated by viable systems. The ecosystem itself has a fitness function, which drives adaptation within the environment. But all actors in the environment also affect it. Some try to manage the environment. Revolutions are often over who manages a social ecosystem, over who controls the social technology and what it does.
Once we see this dynamic at play, calls of "riot" and "revolution" make a whole lot less sense. Rather, the question becomes, "how do we change the ecosystem in such a way that it cannot be 'managed' at all?"
Graeber/Wengrow talked about Turtle Island indigenous social technologies in Dawn of Everything, such as the system of moieties and clans described in the book. So I have a good reading list as I think through this model, but I hope the "ecosystem" model is helpful (if not completely fleshed out).
I'd be interested in any critiques or thoughts to help develop this idea more.

@Techmeme@techhub.social
2026-05-28 17:14:37

Anthropic launches Opus 4.8, saying it's "more likely to flag uncertainties about its work and less likely to make unsupported claims", at the same price as 4.7 (Russell Brandom/TechCrunch)
techcrunch.com/2026/05/28/anth

@aral@mastodon.ar.al
2026-03-26 15:27:03

You know how you’re in the middle of a process and you refresh a web page and it loses state?
So that sucks.
With Kitten¹ – when using the new state-maintaining/class-based and event model-based component model – it’s easy to have flowing interfaces that animate between states, etc., that don’t lose state if you refresh the page (or open another tab).
What you can’t do on the Web, however, is restore the state of any cross-origin iframes. (As you have no visibility into th…

Screenshot of the restored state of the Stripe component’s success state using a mock HTML/CSS snapshot of the state with some dynamic areas included. The screen is full of horizontal and vertical guides aligned to areas of the success message to ensure that the mock is pixel perfect.
@arXiv_csIT_bot@mastoxiv.page
2026-06-11 07:43:08

Vision-Language-Action Models Meet World Models: Embodied Agentic AI for Low-Altitude Wireless Networks
Feibo Jiang, Li Dong, Lei Mao, Kezhi Wang, Cunhua Pan, Dong In Kim, Naofal Al-Dhahir
arxiv.org/abs/2606.11618 arxiv.org/pdf/2606.11618 arxiv.org/html/2606.11618
arXiv:2606.11618v1 Announce Type: new
Abstract: Low-Altitude Wireless Networks (LAWNs), composed of Unmanned Aerial Vehicles (UAVs) and other aerial platforms, provide integrated perception, communication, and computation services in low-altitude airspace. However, deploying large generative models in this domain faces three major challenges: 1) Limited embodied action mapping; 2) Inadequate physical environment modeling; 3) Insufficient closed-loop optimization. To address these challenges, this study proposes an Embodied Agentic UAV framework. Centered on a Vision-Language-Action (VLA) model as the execution core, the framework establishes an end-to-end embodied decision-making pipeline from multimodal environmental perception to continuous control generation. In addition, a World Model (WM) is introduced to capture the coupling between UAV actions and environmental state evolution, thereby supporting environment prediction, policy verification, and dynamic optimization. Furthermore, memory and reflection mechanisms are incorporated to form an adaptive closed-loop optimization paradigm of decision, execution, evaluation, and update, thereby enhancing the system's autonomous decision-making capability and continual evolution ability in complex dynamic environments. Experimental results validate its effectiveness in enabling robust, predictive, and sustainable autonomous control in LAWNs.
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@Techmeme@techhub.social
2026-06-04 00:36:01

Google releases macOS versions of AI Edge Gallery, which lets users run open models on their devices, and AI Edge Eloquent, an on-device voice dictation app (Google Developers Blog)
developers.googleblog.com/brin

@arXiv_csGT_bot@mastoxiv.page
2026-06-05 07:33:20

Should Demand Models Incorporate Competitor Prices? Oblivious Learning and Algorithmic Collusion
Yuhang Wu, Assaf Zeevi
arxiv.org/abs/2606.05363 arxiv.org/pdf/2606.05363 arxiv.org/html/2606.05363
arXiv:2606.05363v1 Announce Type: new
Abstract: On a platform with many sellers, should a pricing algorithm explicitly model competitors' prices when learning demand? Classical learning arguments suggest an affirmative answer: ignoring competitors induces model misspecification and inefficiency. In contrast, recent work on algorithmic collusion suggests that strategic obliviousness -- deliberately ignoring competitor prices -- may facilitate collusive outcomes and improve profits. We study this modeling choice in a stylized competitive market with unknown noisy demand, in which multiple sellers repeatedly set prices and estimate demand via iterated least squares, and either incorporate competitors' prices into their demand models (informed) or ignore them (oblivious). We first show that, relative to a monopolist, an oblivious seller in a competitive market must explore more aggressively to compensate for the loss of dynamic competitor information. Building on this insight, we characterize market dynamics when all sellers are oblivious and show that prices converge to the competitive outcome under sufficient exploration, while a continuum of pseudo-equilibria arises when exploration decays. Analyzing the resulting price trajectories, we uncover an excursion phenomenon that gives rise to transient collusive patterns that dissipate as learning progresses. In markets with both oblivious and informed sellers, the informed strictly out-earn the oblivious. Read as a strategy game, the modeling choice has a unique Nash equilibrium: the all-informed market, in which prices converge to the competitive outcome efficiently. Overall, our results indicate that collusive patterns are not robust and are not sustained by oblivious modeling; therefore, incorporating competitor information, together with sufficient price exploration, remains a reliable strategy for sellers in competitive markets.
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@arXiv_physicsaoph_bot@mastoxiv.page
2026-05-26 07:52:47

Volador 1.0: A Data-Driven Air-Sea Full-Coupling Regional Forecast Model with Submesoscale-Permitting Based on MOE-Swin-Transformer Framework
Yuhang Zhu, Jianxin Wang, Yu-kun Qian, Yineng Li, Yahui Liu, Yankun Gong, Shilin Tang, Shiqiu Peng, Tao Song
arxiv.org/abs/2605.24032 arxiv.org/pdf/2605.24032 arxiv.org/html/2605.24032
arXiv:2605.24032v1 Announce Type: new
Abstract: A data-driven air-sea full-coupling regional forecast model with submesoscale-permitting, named "Volador 1.0", is developed for the South China Sea (SCS). The model features a Swin-Transformer framework integrated with a Mixture-of-Experts (MoE) system, a latent space interaction architecture based on Cross-Grid Bidirectional Cross-Attention, and a fast-slow dual-branch architecture. Both the three-month hindcast test and the 15-day operational real-time forecasting demonstrate that Volador 1.0 has a very encouraging and promising performance in 0-72h forecasting of temperature and salinity in the 0-500m upper ocean as well as the sea surface height with root-mean-square-error (RMSE) or mean absolute error (MAE) smaller than or at least comparable to those from the reanalysis datasets REDOS V2.0 and GLORYS12 and the state-of-the-art regional numerical model Regional Ocean Modeling System (ROMS). In particular, Volador 1.0 demonstrates its capability of capturing/forecasting submesoscale processes including internal waves, with an energy spectrum well representing sub- to mesoscale energy cascade as expected by the classical turbulence theory. Further analysis based on ablation experiments shows that the air-sea full-coupling framework, which takes into account the dynamic exchanges of momentum and heat fluxes between the atmosphere and the ocean, indeed helps improve the model's performance compared to the non-full-coupling one. Volador 1.0, though still subject to refinement in the coming future with a large space for improvement, blazes a path for an accurate, fine and fast marine environment forecasting, and thus could help promote our capability of disaster prevention and mitigation in the SCS as well as in other coastal regions where these innovative techniques can be applied.
toXiv_bot_toot

@arXiv_econTH_bot@mastoxiv.page
2026-04-02 07:40:41

Solving Problems of Unknown Difficulty
Nicholas Wu
arxiv.org/abs/2604.00156 arxiv.org/pdf/2604.00156 arxiv.org/html/2604.00156
arXiv:2604.00156v1 Announce Type: new
Abstract: This paper studies how uncertainty about problem difficulty shapes problem-solving strategies. I develop a dynamic model where an agent solves a problem by brainstorming approaches of unknown quality and allocating a fixed effort budget among them. Success arrives from spending effort pursuing good approaches, at a rate determined by the unknown problem difficulty. The agent balances costly exploration (expanding the set of approaches) with exploitation (pursuing existing approaches). Failures could signal either a bad idea or a hard problem, and this uncertainty generates novel dynamics: optimal search alternates between trying new approaches and revisiting previously abandoned ones. I then examine a principal-agent environment, where moral hazard arises on the intensive margin: how the agent explores. Dynamic commitment leads contracts to frontload incentives, which can be counteracted by the presence of learning. The framework reflects scientific discovery, product development, and other creative work, providing insights into innovation and organizational design.
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@arXiv_qbioNC_bot@mastoxiv.page
2026-04-28 08:05:29

Triple Configuration of Brain Networks Based on Recurrent Neural Networks: The Synergistic Effects of Exogenous Stimuli, Task Demands, and Spontaneous Activity
Binghao Yang, Guangzong Chen
arxiv.org/abs/2604.23525 arxiv.org/pdf/2604.23525 arxiv.org/html/2604.23525
arXiv:2604.23525v1 Announce Type: new
Abstract: The foundation of cognitive flexibility and higher-order intelligence lies in the functional structure and activity of brain networks, which can be dynamically configured by both external environments and internal states. However, decoding these dynamics from high-dimensional neural data remains a challenge. In this study, we propose a computational framework using Recurrent Neural Networks (RNNs) with neural dynamic constraints to model source-localized resting-state EEG data from $114$ participants. We aim to clarify the "triple brain network configurations" driven by exogenous and endogenous factors, including external stimuli, information processing tasks, and spontaneous activities. Our model identifies the parietal network as a critical hub supporting these multiple configuration patterns. Furthermore, we reveal that the anterior and posterior parietal regions exhibit distinct functional specializations under different stimulus modalities. By formalizing a triple configuration framework, this work separates latent factors of brain dynamics and underscores the computational significance of parietal regions in orchestrating higher-order intelligence.
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@arXiv_csGT_bot@mastoxiv.page
2026-06-04 07:33:52

Extending the El Farol Bar Game with Partial Observability and Incentive Design
Iosif Polenakis, Kalliopi Kastampolidou, Theodore Andronikos
arxiv.org/abs/2606.04753 arxiv.org/pdf/2606.04753 arxiv.org/html/2606.04753
arXiv:2606.04753v1 Announce Type: new
Abstract: The El Farol Bar game is a classic model of coordination under uncertainty, traditionally treating the venue as a passive constraint. In this work, we re-conceptualize the problem by modeling the bar as a strategic player equipped with AI-driven learning capabilities. We extend the original framework to include partial observability, i.e., agents observe only subsets of past attendees, and transform the bar from a passive capacity threshold into an active mechanism designer that adjusts pricing policies to balance revenue, utilization, and sustainability constraints. Agents employ AI-based learning to form beliefs and adapt attendance strategies under incomplete information, while the bar uses policy learning to optimize dynamic pricing. The resulting two-sided learning system frames coordination as a co-evolutionary process between boundedly rational agents and an adaptive institution, offering insights into congestion management, resource allocation, and mechanism design in complex adaptive systems.
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