Exploring Syntropic Frameworks in AI Alignment: A Philosophical Investigationcs.AI updates on arXiv.org arXiv:2512.03048v1 Announce Type: new
Abstract: I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms rather than encoding fixed human value content. The paper makes three philosophical contributions. First, I articulate the “specification trap” argument demonstrating why content-based value specification appears structurally unstable due to the conjunction of the is-ought gap, value pluralism, and the extended frame problem. Second, I propose syntropy — the recursive reduction of mutual uncertainty between agents through state alignment — as an information-theoretic framework for understanding multi-agent alignment dynamics. Third, I establish a functional distinction between genuine and simulated moral capacity grounded in compatibilist theories of guidance control, coupled with an embodied experimental paradigm and verification regime providing operational criteria independent of phenomenological claims. This paper represents the philosophical component of a broader research program whose empirical validation is being developed in a separate project currently in preparation. While the framework generates specific, falsifiable predictions about value emergence and moral agency in artificial systems, empirical validation remains pending.
arXiv:2512.03048v1 Announce Type: new
Abstract: I argue that AI alignment should be reconceived as architecting syntropic, reasons-responsive agents through process-based, multi-agent, developmental mechanisms rather than encoding fixed human value content. The paper makes three philosophical contributions. First, I articulate the “specification trap” argument demonstrating why content-based value specification appears structurally unstable due to the conjunction of the is-ought gap, value pluralism, and the extended frame problem. Second, I propose syntropy — the recursive reduction of mutual uncertainty between agents through state alignment — as an information-theoretic framework for understanding multi-agent alignment dynamics. Third, I establish a functional distinction between genuine and simulated moral capacity grounded in compatibilist theories of guidance control, coupled with an embodied experimental paradigm and verification regime providing operational criteria independent of phenomenological claims. This paper represents the philosophical component of a broader research program whose empirical validation is being developed in a separate project currently in preparation. While the framework generates specific, falsifiable predictions about value emergence and moral agency in artificial systems, empirical validation remains pending. Read More
A Learning-based Control Methodology for Transitioning VTOL UAVscs.AI updates on arXiv.org arXiv:2512.03548v1 Announce Type: cross
Abstract: Transition control poses a critical challenge in Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL UAV) development due to the tilting rotor mechanism, which shifts the center of gravity and thrust direction during transitions. Current control methods’ decoupled control of altitude and position leads to significant vibration, and limits interaction consideration and adaptability. In this study, we propose a novel coupled transition control methodology based on reinforcement learning (RL) driven controller. Besides, contrasting to the conventional phase-transition approach, the ST3M method demonstrates a new perspective by treating cruise mode as a special case of hover. We validate the feasibility of applying our method in simulation and real-world environments, demonstrating efficient controller development and migration while accurately controlling UAV position and attitude, exhibiting outstanding trajectory tracking and reduced vibrations during the transition process.
arXiv:2512.03548v1 Announce Type: cross
Abstract: Transition control poses a critical challenge in Vertical Take-Off and Landing Unmanned Aerial Vehicle (VTOL UAV) development due to the tilting rotor mechanism, which shifts the center of gravity and thrust direction during transitions. Current control methods’ decoupled control of altitude and position leads to significant vibration, and limits interaction consideration and adaptability. In this study, we propose a novel coupled transition control methodology based on reinforcement learning (RL) driven controller. Besides, contrasting to the conventional phase-transition approach, the ST3M method demonstrates a new perspective by treating cruise mode as a special case of hover. We validate the feasibility of applying our method in simulation and real-world environments, demonstrating efficient controller development and migration while accurately controlling UAV position and attitude, exhibiting outstanding trajectory tracking and reduced vibrations during the transition process. Read More
The promising potential of vision language models for the generation of textual weather forecastscs.AI updates on arXiv.org arXiv:2512.03623v1 Announce Type: cross
Abstract: Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond.
arXiv:2512.03623v1 Announce Type: cross
Abstract: Despite the promising capability of multimodal foundation models, their application to the generation of meteorological products and services remains nascent. To accelerate aspiration and adoption, we explore the novel use of a vision language model for writing the iconic Shipping Forecast text directly from video-encoded gridded weather data. These early results demonstrate promising scalable technological opportunities for enhancing production efficiency and service innovation within the weather enterprise and beyond. Read More
Introducing OpenAI for AustraliaOpenAI News OpenAI is launching OpenAI for Australia to build sovereign AI infrastructure, upskill more than 1.5 million workers, and accelerate innovation across the country’s growing AI ecosystem.
OpenAI is launching OpenAI for Australia to build sovereign AI infrastructure, upskill more than 1.5 million workers, and accelerate innovation across the country’s growing AI ecosystem. Read More
Bootstrap a Data Lakehouse in an AfternoonTowards Data Science Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB
The post Bootstrap a Data Lakehouse in an Afternoon appeared first on Towards Data Science.
Using Apache Iceberg on AWS with Athena, Glue/Spark and DuckDB
The post Bootstrap a Data Lakehouse in an Afternoon appeared first on Towards Data Science. Read More
5 Critical Feature Engineering Mistakes That Kill Machine Learning ProjectsKDnuggets Check out this comprehensive guide to building production-ready features that actually work.
Check out this comprehensive guide to building production-ready features that actually work. Read More
AWS re:Invent 2025: Frontier AI agents replace chatbotsAI News According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but
The post AWS re:Invent 2025: Frontier AI agents replace chatbots appeared first on AI News.
According to AWS at this week’s re:Invent 2025, the chatbot hype cycle is effectively dead, with frontier AI agents taking their place. That is the blunt message radiating from Las Vegas this week. The industry’s obsession with chat interfaces has been replaced by a far more demanding mandate: “frontier agents” that don’t just talk, but
The post AWS re:Invent 2025: Frontier AI agents replace chatbots appeared first on AI News. Read More
The Best Data Scientists are Always LearningTowards Data Science Why continuous learning matters & how to come up with topics to study
The post The Best Data Scientists are Always Learning appeared first on Towards Data Science.
Why continuous learning matters & how to come up with topics to study
The post The Best Data Scientists are Always Learning appeared first on Towards Data Science. Read More
Engineering more resilient crops for a warming climateGoogle DeepMind News Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops.
Scientists are using AlphaFold to strengthen a photosynthesis enzyme for resilient, heat-tolerant crops. Read More
Emergent Introspective Awareness in Large Language ModelsKDnuggets An overview, summary, and position of cutting-edge research conducted on the emergent topic of LLM introspection on self internal states
An overview, summary, and position of cutting-edge research conducted on the emergent topic of LLM introspection on self internal states Read More