Tootfinder

Watching The Martian (2015). It features a plot point where only the Chinese space agency can rescue the American mission. So of course they all cooperate and get the thing done.

X refuses to cooperate with French probe into algorithm bias
https://www.lemonde.fr/en/pixels/article/2025/07/21/x-refuses-to-cooperate-with-french-probe-into-algorithm-bias_6743577_13.html

Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement
Ying Gao, Qingqing Wu, Weidong Mei, Guangji Chen, Wen Chen, Ziyuan Zheng
https://arxiv.org/abs/2506.21375

Integrating Movable Antennas and Intelligent Reflecting Surfaces for Coverage Enhancement
This paper investigates an intelligent reflecting surface (IRS)-aided movable antenna (MA) system, where multiple IRSs cooperate with a multi-MA base station to extend wireless coverage to multiple designated target areas. The objective is to maximize the worst-case signal-to-noise ratio (SNR) across all locations within these areas through joint optimization of MA positions, IRS reflection coefficients, and transmit beamforming. To achieve this while balancing the performance-cost trade-off, w…

Evolution of boundedly rational learning in games
Marta C. Couto, Fernando P. Santos, Christian Hilbe
https://arxiv.org/abs/2506.21498 https://

Evolution of boundedly rational learning in games
People make strategic decisions multiple times a day. We act strategically in negotiations, when we coordinate our actions with others, or when we choose with whom to cooperate. The resulting dynamics can be studied with evolutionary game theory. This framework explores how people adapt their decisions over time, in light of how effective their strategies have proven to be. A crucial quantity in respective models is the strength of selection. This quantity regulates how likely individuals switc…

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
Soheyb Ribouh, Osama Saleem, Mohamed Ababsa
https://arxiv.org/abs/2506.20597

Differential Transformer-driven 6G Physical Layer for Collaborative Perception Enhancement
The emergence of 6G wireless networks promises to revolutionize vehicular communications by enabling ultra-reliable, low-latency, and high-capacity data exchange. In this context, collaborative perception techniques, where multiple vehicles or infrastructure nodes cooperate to jointly receive and decode transmitted signals, aim to enhance reliability and spectral efficiency for Connected Autonomous Vehicle (CAV) applications. In this paper, we propose an end-to-end wireless neural receiver base…

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
Senyao Li, Haozhao Wang, Wenchao Xu, Rui Zhang, Song Guo, Jingling Yuan, Xian Zhong, Tianwei Zhang, Ruixuan Li
https://arxiv.org/abs/2507.16731

Collaborative Inference and Learning between Edge SLMs and Cloud LLMs: A Survey of Algorithms, Execution, and Open Challenges
As large language models (LLMs) evolve, deploying them solely in the cloud or compressing them for edge devices has become inadequate due to concerns about latency, privacy, cost, and personalization. This survey explores a collaborative paradigm in which cloud-based LLMs and edge-deployed small language models (SLMs) cooperate across both inference and training. We present a unified taxonomy of edge-cloud collaboration strategies. For inference, we categorize approaches into task assignment, t…

The biggest misnomer in cooperate speech is probably saying 'letting people go' like it's a bloody favour, when you just fire them
#cooperate #work

Tokyo-based Sakana AI details a new Monte Carlo tree search-based technique that lets multiple LLMs cooperate on a single task, outperforming individual models (Ben Dickson/VentureBeat)
https://venturebeat.com/ai/sakana-ais-

Sakana AI’s TreeQuest: Deploy multi-model teams that outperform individual LLMs by 30%
Sakana AI's new inference-time scaling technique uses Monte-Carlo Tree Search to orchestrate multiple LLMs to collaborate on complex tasks.

Interesting, letting AI models cooperate.
#ai #sakana

Sakana AI
Inference-Time Scaling and Collective Intelligence for Frontier AI

Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity
Tomoki Kurikawa
https://arxiv.org/abs/2506.07341 https://

Slow and Fast Neurons Cooperate in Contextual Working Memory through Timescale Diversity
Neural systems process information across a broad range of intrinsic timescales, both within and across cortical areas. While such diversity is a hallmark of biological networks, its computational role in nonlinear information processing remains elusive. In this study, we examine how heterogeneity in intrinsic neural timescales within the frontal cortex - a region central to cognitive control - enhances performance in a context-dependent working memory task. We develop a recurrent neural networ…

Trump threatens to arrest Mamdani if he doesn't cooperate with ICE (Sareen Habeshian/Axios)
https://www.axios.com/2025/07/01/trump-mamdani-arrest-ice-immigration
http://www.memeorandum.com/250701/p86#a250701p86

"I think it is a huge mistake for people to assume that they can trust AI when they do not trust each other. The safest way to develop superintelligence is to first strengthen trust between humans, and then cooperate with each other to develop superintelligence in a safe manner. But what we are doing now is exactly the opposite. Instead, all efforts are being directed toward developing a superintelligence."
#AGI #AI
https://www.wired.com/story/questions-answered-by-yuval-noah-harari-for-wired-ai-artificial-intelligence-singularity/

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Gustavo C. Mangold, Heitor C. M. Fernandes, Mendeli H. Vainstein
https://arxiv.org/abs/2507.02211

Dilution, Diffusion and Symbiosis in Spatial Prisoner's Dilemma with Reinforcement Learning
Recent studies in the spatial prisoner's dilemma games with reinforcement learning have shown that static agents can learn to cooperate through a diverse sort of mechanisms, including noise injection, different types of learning algorithms and neighbours' payoff knowledge.In this work, using an independent multi-agent Q-learning algorithm, we study the effects of dilution and mobility in the spatial version of the prisoner's dilemma. Within this setting, different possible actions for the algor…

This https://arxiv.org/abs/2505.23847 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or viola…

This https://arxiv.org/abs/2410.08850 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Learning to Stop: Deep Learning for Mean Field Optimal Stopping
Optimal stopping is a fundamental problem in optimization with applications in risk management, finance, robotics, and machine learning. We extend the standard framework to a multi-agent setting, named multi-agent optimal stopping (MAOS), where agents cooperate to make optimal stopping decisions in a finite-space, discrete-time environment. Since solving MAOS becomes computationally prohibitive as the number of agents is very large, we study the mean-field optimal stopping (MFOS) problem, obtai…

Interactions across multiple games: cooperation, corruption, and organizational design
Jonathan Bendor, Lukas Bolte, Nicole Immorlica, Matthew O. Jackson
https://arxiv.org/abs/2507.03030

Interactions across multiple games: cooperation, corruption, and organizational design
It is socially beneficial for teams to cooperate in some situations (``good games'') and not in others (``bad games;'' e.g., those that allow for corruption). A team's cooperation in any given game depends on expectations of cooperation in future iterations of both good and bad games. We identify when sustaining cooperation on good games necessitates cooperation on bad games. We then characterize how a designer should optimally assign workers to teams and teams to tasks that involve varying arr…

This https://arxiv.org/abs/2505.05428 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Empowering Scientific Workflows with Federated Agents
Agentic systems, in which diverse agents cooperate to tackle challenging problems, are exploding in popularity in the AI community. However, the agentic frameworks used to build these systems have not previously enabled use with research cyberinfrastructure. Here we introduce Academy, a modular and extensible middleware designed to deploy autonomous agents across the federated research ecosystem, including HPC systems, experimental facilities, and data repositories. To meet the demands of scien…

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Ronny Ko, Jiseong Jeong, Shuyuan Zheng, Chuan Xiao, Taewan Kim, Makoto Onizuka, Wonyong Shin
https://arxiv.org/abs/2505.23847

Seven Security Challenges That Must be Solved in Cross-domain Multi-agent LLM Systems
Large language models (LLMs) are rapidly evolving into autonomous agents that cooperate across organizational boundaries, enabling joint disaster response, supply-chain optimization, and other tasks that demand decentralized expertise without surrendering data ownership. Yet, cross-domain collaboration shatters the unified trust assumptions behind current alignment and containment techniques. An agent benign in isolation may, when receiving messages from an untrusted peer, leak secrets or viola…

FastSet: Parallel Claim Settlement
Xiaohong Chen, Grigore Rosu
https://arxiv.org/abs/2506.23395 https://arxiv.org/pdf/2506.23395

FastSet: Parallel Claim Settlement
FastSet is an actor-based distributed protocol for decentralized finance and settlement, which is inspired from blockchains. Account holders cooperate by making claims, which can include payments, holding and transferring assets, accessing and updating shared data, medical records, digital identity, and mathematical theorems, among many others. The claims are signed by their owners and are broadcast to a decentralized network of validators, which validate and settle them. Validators replicate t…