All published intelligence, sovereign stack research, ecosystem news, and AI foundation model reviews.
Interactive world models are increasingly capable of generating environments and acting within them, yet deliberately editing an existing executable world remains underexplored. We formulate world editing as intervening on an existing world while preserving properties that…
Societal resilience research relies on access to useful and actionable data, which motivates our main research question: Can annual reports, processed at scale with LLMs, provide a useful signal about how companies disclose their response to AI? We test this by applying a…
Agent harnesses make many small, typed decisions per task: which model to call, which tool to use, whether retrieved text is relevant, whether an input carries an injection. System-1 decision models answer such questions in a single forward pass with class probabilities,…
ReAct-based agents typically rely on a single LLM policy to propose actions, interact with the environment, and decide when a task is complete. This coupling makes action authorization and completion control difficult to enforce independently, allowing errors to propagate and…
Text-to-image generation enables users to explore several images generated from the same prompt. For these generated images to be useful, each one must reflect the user's preferences, measured by a learned reward, and differ visually from the others to maintain diversity.…
In the UK, public healthcare staff report turning to machine translation (MT) - predominantly Google Translate (GT) - to communicate with patients across language barriers. Though intended to support their duty of care, potentially uninformed reliance on MT in such contexts…
Campus AI tutors based on retrieval-augmented generation (RAG) must ground answers in assigned course materials while keeping textbooks and student dialogue on institutional infrastructure. We present CourseChat, an on-premises, multi-course RAG tutor for undergraduate business…
Large language models (LLMs) rely on long-horizon tool invocation sequences for complex tasks, where each invocation can alter the task state and condition subsequent decisions. In long-horizon tool use, final-outcome rewards provide weak credit assignment over long interaction…
World models (WMs) simulate the transition dynamics of environments, enabling agents to plan over the consequences of their actions. In text-based environments, fine-tuning a Language Model (LM) to serve as a WM has emerged as a dominant paradigm. However, despite the widespread…
In Recirculating Aquaculture Systems (RAS), precision feeding is critical for minimizing costs and improving fish welfare. However, existing methods lack cognitive alignment between fish behaviors and management knowledge, impeding translation into executable, interpretable…
Large language models spend the same amount of computation on every token they generate, regardless of how difficult each token is to produce. Methods such as speculative decoding and model routing are built on the premise that much of this computation is unnecessary, yet the…
The operation of dynamically polarized targets in nuclear physics experiments relies on continuous tuning of the microwave frequency to compensate for radiation damage and evolving material properties, a task that is traditionally performed through manual trial-and-error by…
World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruction. However, existing WAMs struggle with long-horizon prediction, as generating dense video…
Flow matching models excel at generative modeling, and many downstream applications require their samples to satisfy prescribed constraints, such as observed measurements and physical laws. However, existing constrained samplers often face a trade-off: \textit{enforcing…
Recent years have seen the employment of a plethora of machine learning (ML) models in high-stakes domains, but they remain largely opaque to the practitioners who act on their predictions. While post-hoc explanation methods offer a lens into this model behavior, wielding them…
Developing cognitive models of algorithmic reasoning from behavioral data is a central problem in cognitive science that challenges current methods. Traditional approaches to cognitive modeling are interpretable and benefit from human expertise, but lack flexibility and…
Long-horizon research agents must decide both how to investigate and what to investigate next as evidence accumulates. This is hard to learn because such decisions are sparse in long execution traces, and their consequences may emerge several investigations later. We introduce…
LLM judges are increasingly used to assess whether AI outputs meet workplace requirements, but agreement on response rankings does not establish agreement on acceptance rates or occupational aggregates. We introduce O*NET-BENCH, an audit suite derived from an existing survey of…
Training-free safeguards for text-to-image generation often rely on a reusable safety signal, such as an unsafe direction or global toxic subspace, applied broadly across prompts. We provide a controlled geometric analysis of this global-unsafety assumption and reveal a…
As powerful AI systems reach and sometimes surpass the abilities of human experts across a range of cognitively demanding tasks, the problem of accurate oversight and supervision of these systems has become increasingly urgent. One promising approach is AI debate, which seeks to…
Natural Visibility Graph (NVG) based analysis characterizes network traffic through topological descriptors reflecting different structural properties. However, not all descriptors contribute equally to cyber-attack classification, and extracting a large metric set can increase…
Using a frontier model like Claude Opus as a meta-agent to generate terminal tasks and verifiers for RL training is increasingly common. Yet a runnable Docker image and executable test suite do not guarantee a faithful end-to-end pipeline for terminal agent training. We present…
⚠️ “OpenAI 文化已崩溃” — 公司资深员工因对 AI 安全的担忧而离职。大卫·罗宾逊在 OpenAI 工作了三年半，负责为重大发布准备安全报告。现在他辞职，因为他认为……
StarSkirmish让AI制作的《星际争霸》游戏机器人相互对战，也与人类制作的机器人对战。OpenAI的GPT-6 Astra和Claude Opus 5.5基本持平，成为表现最好的AI制作机器人，但它们无法超越Stardust——这款评分最高的人类制作机器人。周五,…
All right, so we have some sobering news today from the world of AI. A key figure at OpenAI quits, saying that the culture at OpenAI is broken. Specifically talking about the safety approach they're taking with building AI. Another employee that still works at OpenAI, who's a…
DevDay has passed: OpenAI, new Codex, updated Workspace, and of course, 24/7 bots and an agent with its own computer . Inside the OpenAI ecosystem, Anthropic releases the little brother, Claude 3.5. 30%faster, cheaper, and almost as smart. An unexpected frontier model release…
💸 OpenAI возвращает ChatGPT Pro за $200 в месяц — но лимиты будут в два раза меньше Подписка снова обойдётся в $200 (около ₽16 900) в месяц, однако пользоваться моделями можно будет заметно меньше: OpenAI решила сократить прежние лимиты вдвое. В компании объясняют это ростом…
✔️ Anthropic открыла моды для Claude Code Моды – небольшие функции на TypeScript, меняющие работу ассистента в CLI и десктопном приложении. Там, где хуки лишь получали управление по событию, мод перехватывает его и переписывает: подменяет промпт до отправки модели, блокирует или…
☢️ Нейронкам устроили проверку на человечность — и ответы сильно разошлись Исследователи протестировали 10 ИИ-моделей на моральной дилемме: допустимо ли применить насилие к одному человеку, если это необходимо, чтобы предотвратить ядерный апокалипсис. Единственной моделью,…