Tootfinder

Meter, which sells and maintains packages of custom data center networking equipment, raised $170M led by General Catalyst at a $1B valuation (Michael J. de la Merced/New York Times)
https://www.nytimes.com/2025/06/12/business/dealbook/meter-n…

Meter, Networking Start-Up, Raises $170 Million
The company has collected $170 million in new capital as it seeks to help connect data centers and other businesses in the age of artificial intelligence.

A Framework for Inherently Safer AGI through Language-Mediated Active Inference
Bo Wen
https://arxiv.org/abs/2508.05766 https://arxiv.org/pdf/2508.05766

A Framework for Inherently Safer AGI through Language-Mediated Active Inference
This paper proposes a novel framework for developing safe Artificial General Intelligence (AGI) by combining Active Inference principles with Large Language Models (LLMs). We argue that traditional approaches to AI safety, focused on post-hoc interpretability and reward engineering, have fundamental limitations. We present an architecture where safety guarantees are integrated into the system's core design through transparent belief representations and hierarchical value alignment. Our framewor…

Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
Oliver Guest, Kevin Wei
https://arxiv.org/abs/2506.03497

Bridging the Artificial Intelligence Governance Gap: The United States' and China's Divergent Approaches to Governing General-Purpose Artificial Intelligence
The United States and China are among the world's top players in the development of advanced artificial intelligence (AI) systems, and both are keen to lead in global AI governance and development. A look at U.S. and Chinese policy landscapes reveals differences in how the two countries approach the governance of general-purpose artificial intelligence (GPAI) systems. Three areas of divergence are notable for policymakers: the focus of domestic AI regulation, key principles of domestic AI regul…

This https://arxiv.org/abs/2504.15585 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
The remarkable success of Large Language Models (LLMs) has illuminated a promising pathway toward achieving Artificial General Intelligence for both academic and industrial communities, owing to their unprecedented performance across various applications. As LLMs continue to gain prominence in both research and commercial domains, their security and safety implications have become a growing concern, not only for researchers and corporations but also for every nation. Currently, existing surveys…

GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
Yunan Zhang, Shuoran Jiang, Mengchen Zhao, Yuefeng Li, Yang Fan, Xiangping Wu, Qingcai Chen
https://arxiv.org/abs/2508.04676

GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
The continual learning capability of large language models (LLMs) is crucial for advancing artificial general intelligence. However, continual fine-tuning LLMs across various domains often suffers from catastrophic forgetting, characterized by: 1) significant forgetting of their general capabilities, and 2) sharp performance declines in previously learned tasks. To simultaneously address both issues in a simple yet stable manner, we propose General Sample Replay (GeRe), a framework that use usu…

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
James Alcala, Yat Tin Chow, Mahesh Sunkula
https://arxiv.org/abs/2506.07290

Stochastic Moving Anchor Algorithms and a Popov's Scheme with Moving Anchor
Since their introduction, anchoring methods in extragradient-type saddlepoint problems have inspired a flurry of research due to their ability to provide order-optimal rates of accelerated convergence in very general problem settings. Such guarantees are especially important as researchers consider problems in artificial intelligence (AI) and machine learning (ML), where large problem sizes demand immense computational power. Much of the more recent works explore theoretical aspects of this new…

A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
Xiaoxiao Long, Qingrui Zhao, Kaiwen Zhang, Zihao Zhang, Dingrui Wang, Yumeng Liu, Zhengjie Shu, Yi Lu, Shouzheng Wang, Xinzhe Wei, Wei Li, Wei Yin, Yao Yao, Jia Pan, Qiu Shen, Ruigang Yang, Xun Cao, Qionghai Dai
https://arxiv.org/abs/2507.00917

A Survey: Learning Embodied Intelligence from Physical Simulators and World Models
The pursuit of artificial general intelligence (AGI) has placed embodied intelligence at the forefront of robotics research. Embodied intelligence focuses on agents capable of perceiving, reasoning, and acting within the physical world. Achieving robust embodied intelligence requires not only advanced perception and control, but also the ability to ground abstract cognition in real-world interactions. Two foundational technologies, physical simulators and world models, have emerged as critical …

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[5/6]:
- Towards General Continuous Memory for Vision-Language Models
Wenyi Wu, Zixuan Song, Kun Zhou, Yifei Shao, Zhiting Hu, Biwei Huang

Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Zenan Xu, Zexuan Qiu, Guanhua Huang, Kun Li, Siheng Li, Chenchen Zhang, Kejiao Li, Qi Yi, Yuhao Jiang, Bo Zhou, Fengzong Lian, Zhanhui Kang
https://arxiv.org/abs/2507.06829

Adaptive Termination for Multi-round Parallel Reasoning: An Universal Semantic Entropy-Guided Framework
Recent advances in large language models (LLMs) have accelerated progress toward artificial general intelligence, with inference-time scaling emerging as a key technique. Contemporary approaches leverage either sequential reasoning (iteratively extending chains of thought) or parallel reasoning (generating multiple solutions simultaneously) to scale inference. However, both paradigms face fundamental limitations: sequential scaling typically relies on arbitrary token budgets for termination, le…

I coined a new phrase!
Basilisk puppet (n). A person who relentlessly shills for artificial intelligence. Makes strong claims that general AI is happening any day now or that existing AIs can make everyone more efficient and happier. See also Roko's Basilisk.

This https://arxiv.org/abs/2505.15173 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

AvatarShield: Visual Reinforcement Learning for Human-Centric Video Forgery Detection
The rapid advancement of Artificial Intelligence Generated Content (AIGC) technologies, particularly in video generation, has led to unprecedented creative capabilities but also increased threats to information integrity, identity security, and public trust. Existing detection methods, while effective in general scenarios, lack robust solutions for human-centric videos, which pose greater risks due to their realism and potential for legal and ethical misuse. Moreover, current detection approach…

An Empirical Study on Embodied Artificial Intelligence Robot (EAIR) Software Bugs
Zeqin Liao, Zibin Zheng, Peifan Reng, Henglong Liang, Zixu Gao, Zhixiang Chen, Wei Li, Yuhong Nan
https://arxiv.org/abs/2507.18267

An Empirical Study on Embodied Artificial Intelligence Robot (EAIR) Software Bugs
Embodied Artificial Intelligence Robots (EAIR) is an emerging and rapidly evolving technological domain. Ensuring their program correctness is fundamental to their successful deployment. However, a general and in-depth understanding of EAIR system bugs remains lacking, which hinders the development of practices and techniques to tackle EAIR system bugs. To bridge this gap, we conducted the first systematic study of 885 EAIR system bugs collected from 80 EAIR system projects to investigate the…

#AI is a marketing term; before we discuss "AI is fake" vs "AI is real" we need to unfold what we *mean* by AI.
For example, "artificial general intelligence" is fake and can't hurt you. Layoffs excused by "AI efficiency" are real and can hurt you.
Linkedin discourse is fake - but it CAN hurt you.

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
Pranta Saha, Joyce Reimer, Brook Byrns, Connor Burbridge, Neeraj Dhar, Jeffrey Chen, Steven Rayan, Gordon Broderick
https://arxiv.org/abs/2507.04432

Reconstructing Biological Pathways by Applying Selective Incremental Learning to (Very) Small Language Models
The use of generative artificial intelligence (AI) models is becoming ubiquitous in many fields. Though progress continues to be made, general purpose large language AI models (LLM) show a tendency to deliver creative answers, often called "hallucinations", which have slowed their application in the medical and biomedical fields where accuracy is paramount. We propose that the design and use of much smaller, domain and even task-specific LM may be a more rational and appropriate use of this tec…

This just occured to me (too much sun and gin lemonade could be a factor): English is a funny language and when they say Artificial they mean Automated, and when they say Intelligence they don't mean smarts, they mean covertly gathering intel from prospective enemies!
Hence #ArtificialIntelligence, often promoted to General.
The purpose of any system is what it does, not what it consistently fails to do.

Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance
G\'abor Baranyi, Zsolt Csibi, Kristian Fenech, \'Aron F\'othi, Zs\'ofia Ga\'al, Joul Skaf, Andr\'as L\H{o}rincz
https://arxiv.org/abs/2507.08624

Adaptive Framework for Ambient Intelligence in Rehabilitation Assistance
This paper introduces the Ambient Intelligence Rehabilitation Support (AIRS) framework, an advanced artificial intelligence-based solution tailored for home rehabilitation environments. AIRS integrates cutting-edge technologies, including Real-Time 3D Reconstruction (RT-3DR), intelligent navigation, and large Vision-Language Models (VLMs), to create a comprehensive system for machine-guided physical rehabilitation. The general AIRS framework is demonstrated in rehabilitation scenarios following…

Gauging Growth: AGI Mathematical Metrics for Economic Progress
Davit Gondauri
https://arxiv.org/abs/2506.03156 https://arxiv.org/pdf/…

Gauging Growth: AGI Mathematical Metrics for Economic Progress
Today, the economy is greatly influenced by Artificial General Intelligence (AGI). The purpose of this paper is to determine the impact of the quantitative relations of AGI on the country's economic parameters. The authors use the analysis of historical data in the research, develop a new mathematical algorithm that refers to the level of AGI development, and conduct a regression analysis. The economic effect of AGI is deduced if it affects the growth of real GDP. As a result of the analysis, i…

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space
Amar Khelloufi, Huansheng Ning, Sahraoui Dhelim, Jianguo Ding
https://arxiv.org/abs/2506.22487

AGI Enabled Solutions For IoX Layers Bottlenecks In Cyber-Physical-Social-Thinking Space
The integration of the Internet of Everything (IoX) and Artificial General Intelligence (AGI) has given rise to a transformative paradigm aimed at addressing critical bottlenecks across sensing, network, and application layers in Cyber-Physical-Social Thinking (CPST) ecosystems. In this survey, we provide a systematic and comprehensive review of AGI-enhanced IoX research, focusing on three key components: sensing-layer data management, network-layer protocol optimization, and application-layer …

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
Sohan Shankar, Yi Pan, Hanqi Jiang, Zhengliang Liu, Mohammad R. Darbandi, Agustin Lorenzo, Junhao Chen, Md Mehedi Hasan, Arif Hassan Zidan, Eliana Gelman, Joshua A. Konfrst, Jillian Y. Russell, Katelyn Fernandes, Tianze Yang, Yiwei Li, Huaqin Zhao, Afrar Jahin, Triparna Ganguly, Shair Dinesha, Yifan Zhou, Zihao Wu, Xinliang Li, Lokesh Adusumilli, Aziza Hussein, Sagar Nook…

Bridging Brains and Machines: A Unified Frontier in Neuroscience, Artificial Intelligence, and Neuromorphic Systems
This position and survey paper identifies the emerging convergence of neuroscience, artificial general intelligence (AGI), and neuromorphic computing toward a unified research paradigm. Using a framework grounded in brain physiology, we highlight how synaptic plasticity, sparse spike-based communication, and multimodal association provide design principles for next-generation AGI systems that potentially combine both human and machine intelligences. The review traces this evolution from early c…

Q&A with Hugging Face Chief Ethics Scientist Margaret Mitchell on aligning AI development with human needs, the "illusion of consensus" around AGI, and more (Melissa Heikkilä/Financial Times)
https://www.ft.com/content/7089bff2-25fc-4a25-98bf-8828ab24…

Margaret Mitchell: artificial general intelligence is ‘just vibes and snake oil’
One of the pioneers of AI ethics explains why human needs should be the central driver in the development of the technology

Evaluating Gemini in an arena for learning
LearnLM Team, Abhinit Modi, Aditya Srikanth Veerubhotla, Aliya Rysbek, Andrea Huber, Ankit Anand, Avishkar Bhoopchand, Brett Wiltshire, Daniel Gillick, Daniel Kasenberg, Eleni Sgouritsa, Gal Elidan, Hengrui Liu, Holger Winnemoeller, Irina Jurenka, James Cohan, Jennifer She, Julia Wilkowski, Kaiz Alarakyia, Kevin R. McKee, Komal Singh, Lisa Wang, Markus Kunesch, Miruna P\^islar, Niv Efron, Parsa Mahmoudieh, Pierre-Alexandre Kamienny, Sara Wiltb…

Evaluating Gemini in an arena for learning
Artificial intelligence (AI) is poised to transform education, but the research community lacks a robust, general benchmark to evaluate AI models for learning. To assess state-of-the-art support for educational use cases, we ran an "arena for learning" where educators and pedagogy experts conduct blind, head-to-head, multi-turn comparisons of leading AI models. In particular, $N = 189$ educators drew from their experience to role-play realistic learning use cases, interacting with two models se…

Cada vez que veo noticias sobre las IAs como esta pienso que aquí nos enteramos de estas miserias, se comparten, se comentan... pero ¿y fuera? Por ejemplo, la gente en general es consciente de lo que supone para su privacidad que una IA grabe y "transcriba" una conversación confidencial con su médico?

Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack
Khachatur Nazaryan, Filippo Gaggioli, Yi Teng, Liang Fu
https://arxiv.org/abs/2507.13322

Artificial Intelligence for Quantum Matter: Finding a Needle in a Haystack
Neural networks (NNs) have great potential in solving the ground state of various many-body problems. However, several key challenges remain to be overcome before NNs can tackle problems and system sizes inaccessible with more established tools. Here, we present a general and efficient method for learning the NN representation of an arbitrary many-body complex wave function. Having reached overlaps as large as $99.9\%$ for as many as $25$ particles, we employ our neural wave function for pre-tr…

Augmenting Von Neumann's Architecture for an Intelligent Future
Rajpreet Singh, Vidhi Kothari
https://arxiv.org/abs/2507.16628 https://

Augmenting Von Neumann's Architecture for an Intelligent Future
This work presents a novel computer architecture that extends the Von Neumann model with a dedicated Reasoning Unit (RU) to enable native artificial general intelligence capabilities. The RU functions as a specialized co-processor that executes symbolic inference, multi-agent coordination, and hybrid symbolic-neural computation as fundamental architectural primitives. This hardware-embedded approach allows autonomous agents to perform goal-directed planning, dynamic knowledge manipulation, and …

Neural Cellular Automata for ARC-AGI
Kevin Xu, Risto Miikkulainen
https://arxiv.org/abs/2506.15746 https://arxiv.org/pdf/2506.15746…

Neural Cellular Automata for ARC-AGI
Cellular automata and their differentiable counterparts, Neural Cellular Automata (NCA), are highly expressive and capable of surprisingly complex behaviors. This paper explores how NCAs perform when applied to tasks requiring precise transformations and few-shot generalization, using the Abstraction and Reasoning Corpus for Artificial General Intelligence (ARC-AGI) as a domain that challenges their capabilities in ways not previously explored. Specifically, this paper uses gradient-based train…

A New Perspective On AI Safety Through Control Theory Methodologies
Lars Ullrich, Walter Zimmer, Ross Greer, Knut Graichen, Alois C. Knoll, Mohan Trivedi
https://arxiv.org/abs/2506.23703

A New Perspective On AI Safety Through Control Theory Methodologies
While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged t…

Generative Representational Learning of Foundation Models for Recommendation
Zheli Zhou, Chenxu Zhu, Jianghao Lin, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
https://arxiv.org/abs/2506.11999

Generative Representational Learning of Foundation Models for Recommendation
Developing a single foundation model with the capability to excel across diverse tasks has been a long-standing objective in the field of artificial intelligence. As the wave of general-purpose foundation models sweeps across various domains, their influence has significantly extended to the field of recommendation systems. While recent efforts have explored recommendation foundation models for various generative tasks, they often overlook crucial embedding tasks and struggle with the complexit…

A look at the lack of consensus in the tech industry on what AGI is, whether LLMs are the best path to it, and what AGI might look like if or when it arrives (Melissa Heikkilä/Financial Times)

Why Big Tech cannot agree on artificial general intelligence
It has been tipped as the next big breakthrough out of Silicon Valley, but is it a scientific goal — or a marketing buzzword?

Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Sanyam Jain, Stefano Nichele
https://arxiv.org/abs/2507.18674 https://arxiv.o…

Frequency-Histogram Coarse Graining in Elementary Cellular Automata and 2D CA
Cellular automata and other discrete dynamical systems have long been studied as models of emergent complexity. Recently, neural cellular automata have been proposed as models to investigate the emerge of a more general artificial intelligence, thanks to their propensity to support properties such as self-organization, emergence, and open-endedness. However, understanding emergent complexity in large scale systems is an open challenge. How can the important computations leading to emergent comp…

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Huanjin Yao, Jiaxing Huang, Yawen Qiu, Michael K. Chen, Wenzheng Liu, Wei Zhang, Wenjie Zeng, Xikun Zhang, Jingyi Zhang, Yuxin Song, Wenhao Wu, Dacheng Tao
https://arxiv.org/abs/2506.23563

MMReason: An Open-Ended Multi-Modal Multi-Step Reasoning Benchmark for MLLMs Toward AGI
Reasoning plays a crucial role in advancing Multimodal Large Language Models (MLLMs) toward Artificial General Intelligence. However, existing MLLM benchmarks often fall short in precisely and comprehensively evaluating long-chain reasoning abilities from three key aspects: (1) lack of difficulty and diversity, (2) susceptibility to guessability and memorization, (3) inadequate assessment of intermediate reasoning steps. To fill this gap, we introduce MMReason, a new benchmark designed to preci…

AI, AGI, and learning efficiency
My 4-month-old kid is not DDoSing Wikipedia right now, nor will they ever do so before learning to speak, read, or write. Their entire "training corpus" will not top even 100 million "tokens" before they can speak & understand language, and do so with real intentionally.
Just to emphasize that point: 100 words-per-minute times 60 minutes-per-hour times 12 hours-per-day times 365 days-per-year times 4 years is a mere 105,120,000 words. That's a ludicrously *high* estimate of words-per-minute and hours-per-day, and 4 years old (the age of my other kid) is well after basic speech capabilities are developed in many children, etc. More likely the available "training data" is at least 1 or 2 orders of magnitude less than this.
The point here is that large language models, trained as they are on multiple *billions* of tokens, are not developing their behavioral capabilities in a way that's remotely similar to humans, even if you believe those capabilities are similar (they are by certain very biased ways of measurement; they very much aren't by others). This idea that humans must be naturally good at acquiring language is an old one (see e.g. #AI #LLM #AGI

Against racing to AGI: Cooperation, deterrence, and catastrophic risks
Leonard Dung, Max Hellrigel-Holderbaum
https://arxiv.org/abs/2507.21839 https://arxi…

Against racing to AGI: Cooperation, deterrence, and catastrophic risks
AGI Racing is the view that it is in the self-interest of major actors in AI development, especially powerful nations, to accelerate their frontier AI development to build highly capable AI, especially artificial general intelligence (AGI), before competitors have a chance. We argue against AGI Racing. First, the downsides of racing to AGI are much higher than portrayed by this view. Racing to AGI would substantially increase catastrophic risks from AI, including nuclear instability, and underm…

Replaced article(s) found for econ.GN. https://arxiv.org/list/econ.GN/new
[1/1]:
- Measuring artificial intelligence: a systematic assessment and implications for governance
Kerstin H\"otte, Taheya Tarannum, Vilhelm Verendel, Lauren Bennett

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/7]:
A philosophical and ontological perspective on Artificial General Intelligence and the Metaverse

The Relationship between Cognition and Computation: "Global-first" Cognition versus Local-first Computation
Lin Chen
https://arxiv.org/abs/2506.17970

The Relationship between Cognition and Computation: "Global-first" Cognition versus Local-first Computation
What fundamental research questions are essential for advancing toward brain-like AI or AGI (Artificial General Intelligence) capable of performing any intellectual task a human can? Should it be something like the Turing machine (1936), which answers the question "What is computation?" and lays the foundation for the entire field of computer science? Or should it be something like Shannon's mathematical theory of communication (1948), which answers the question "What is information?" and forms…

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Shova Kuikel, Aritran Piplai, Palvi Aggarwal
https://arxiv.org/abs/2506.13746

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability
Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the interpretable reasoning with classification and explainability that underpin phishing judgments remains challenging. Due to recent advancement in Natural Language Processing, Large Language Models (LLMs) sho…