Throughout the year we've reported on some of the larger stories, and some of the lesser-covered happenings, in our regular monthly digests. We look back through the archives and pick out one or two stories from each of our digests. This month, AI startup DeepSeek released DeepSeek R1, a reasoning model designed for good performance on logic, maths, and pattern-finding tasks. The company has also launched six smaller versions of R1 that are tiny enough to run locally on laptops. In Wired, Zeyi Yang reported on who is behind the startup, whilst Tongliang Liu (in The Conversation) looked at how DeepSeek achieved its results with a fraction of the cash and computing power of its competitors.

Over the course of 2025, we had the pleasure of finding out more about a whole range of AI topics from researchers around the world. Here, we highlight some of our favourite interviews from the past 12 months. We caught up with Erica Kimei to find out about her research studying gas emissions from agriculture, specifically ruminant livestock. Erica combines machine learning and remote sensing technology to monitor and forecast such emissions. We spoke to Yuki Mitsufuji, Lead Research Scientist at Sony AI, to find out more about two pieces of research that his team presented at the Conference on Neural Information Processing Systems (NeurIPS 2024).

The author, Kevin Zhu, now runs Algoverse, an AI research and mentoring company for high schoolers. The author, Kevin Zhu, now runs Algoverse, an AI research and mentoring company for high schoolers. Artificial intelligence research has a slop problem, academics say: 'It's a mess' AI research in question as author claims to have written over 100 papers on AI that one expert calls a'disaster' A single person claims to have authored 113 academic papers on artificial intelligence this year, 89 of which will be presented this week at one of the world's leading conference on AI and machine learning, which has raised questions among computer scientists about the state of AI research. Zhu himself graduated from high school in 2018. Papers he has put out in the past two years cover subjects like using AI to locate nomadic pastoralists in sub-Saharan Africa, to evaluate skin lesions, and to translate Indonesian dialects.

Background: Autonomous agents powered by Large Language Models (LLMs) are driving a paradigm shift toward an "Internet of Agents" (IoA). While offering immense potential, this vision also introduces novel and systemic risks to safety and security. Objectives: Unlike common threat-centric taxonomies, our survey provides a principled, architectural framework for engineering safe and reliable agentic systems. We aim to identify the architectural sources of vulnerabilities to establish a foundation for secure design. Methods: We perform a bottom-up deconstruction of agentic systems, treating each component as a dual-use interface. The analysis spans three levels of complexity: the foundational Single Agent, the collaborative Multi-Agent System (MAS), and the visionary Interoperable Multi-Agent System (IMAS). At each level, we identify core architectural components and their inherent security risks. Results & Conclusions: Our central finding is that agentic safety is an architectural principle, not an add-on. By identifying specific vulnerabilities and deriving mitigation principles at each level of the agentic stack, this survey serves as a foundational guide for building the capable, safe, and trustworthy AI needed to realize a secure Internet of Agents.

A central challenge in scaling up explicit state-space search for large tasks is compactly representing the set of generated states. Tree databases, a data structure from model checking, require constant space per generated state in the best case, but they need a large preallocation of memory. We propose a novel dynamic variant of tree databases for compressing state sets over propositional and numeric variables and prove that it maintains the desirable properties of the static counterpart. Our empirical evaluation of state compression techniques for grounded and lifted planning on classical and numeric planning tasks reveals compression ratios of several orders of magnitude, often with negligible runtime overhead.

Time series classification is a task of paramount importance, as this kind of data often arises in safety-critical applications. However, it is typically tackled with black-box deep learning methods, making it hard for humans to understand the rationale behind their output. To take on this challenge, we propose a novel approach, STELLE (Signal Temporal logic Embedding for Logically-grounded Learning and Explanation), a neuro-symbolic framework that unifies classification and explanation through direct embedding of trajectories into a space of temporal logic concepts. By introducing a novel STL-inspired kernel that maps raw time series to their alignment with predefined STL formulae, our model jointly optimises accuracy and interpretability, as each prediction is accompanied by the most relevant logical concepts that characterise it. This yields (i) local explanations as human-readable STL conditions justifying individual predictions, and (ii) global explanations as class-characterising formulae. Experiments demonstrate that STELLE achieves competitive accuracy while providing logically faithful explanations, validated on diverse real-world benchmarks.

Background: Trustworthy AI serves as a foundational pillar for two major AI ethics conferences: AIES and FAccT. However, current research often adopts techno-centric approaches, focusing primarily on technical attributes such as reliability, robustness, and fairness, while overlooking the sociotechnical dimensions critical to understanding AI trustworthiness in real-world contexts. Objectives: This scoping review aims to examine how the AIES and FAccT communities conceptualize, measure, and validate AI trustworthiness, identifying major gaps and opportunities for advancing a holistic understanding of trustworthy AI systems. Methods: We conduct a scoping review of AIES and FAccT conference proceedings to date, systematically analyzing how trustworthiness is defined, operationalized, and applied across different research domains. Our analysis focuses on conceptualization approaches, measurement methods, verification and validation techniques, application areas, and underlying values. Results: While significant progress has been made in defining technical attributes such as transparency, accountability, and robustness, our findings reveal critical gaps. Current research often predominantly emphasizes technical precision at the expense of social and ethical considerations. The sociotechnical nature of AI systems remains less explored and trustworthiness emerges as a contested concept shaped by those with the power to define it. Conclusions: An interdisciplinary approach combining technical rigor with social, cultural, and institutional considerations is essential for advancing trustworthy AI. We propose actionable measures for the AI ethics community to adopt holistic frameworks that genuinely address the complex interplay between AI systems and society, ultimately promoting responsible technological development that benefits all stakeholders.

In automated planning, control parameters extend standard action representations through the introduction of continuous numeric decision variables. Existing state-of-the-art approaches have primarily handled control parameters as embedded constraints alongside other temporal and numeric restrictions, and thus have implicitly treated them as additional constraints rather than as decision points in the search space. In this paper, we propose an efficient alternative that explicitly handles control parameters as true decision points within a systematic search scheme. We develop a best-first, heuristic search algorithm that operates over infinite decision spaces defined by control parameters and prove a notion of completeness in the limit under certain conditions. Our algorithm leverages the concept of delayed partial expansion, where a state is not fully expanded but instead incrementally expands a subset of its successors. Our results demonstrate that this novel search algorithm is a competitive alternative to existing approaches for solving planning problems involving control parameters.

Welcome to our monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, recap recent events, and more. This month, we take a trip around some of the RoboCup leagues, check in at ICML, learn about the NASA onboard AI research platform, and explore feedback in human-robot interactions. This month saw the running of RoboCup 2025, with the event taking place in Salvador, Brazil, from 15-21 July. Ahead of kick-off, we spoke to the general chair Marco Simões and caught up with Ana Patrícia Magalhães, lead organizer for RoboCupJunior, to find out more about their plans for the week. You can find out what the participants got up to in our two round-ups from social media: #RoboCup2025: social media round-up 1 #RoboCup2025: social media round-up part 2. If you missed the action, you can find the recordings of the livestreams here.

The Thirty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI 2025), which took place alongside AAAI 2025, serves as a showcase for successful applications and novel uses of AI. One such application is the Onboard Artificial Intelligence Research (OnAIR) platform, introduced by Evana Gizzi and colleagues in their paper OnAIR: Applications of The NASA On-Board Artificial Intelligence Research Platform. This open-source software pipeline and cognitive architecture tool has been designed to aid space research and missions. We spoke to Evana, Artificial Intelligence Research Lead at NASA Goddard Space Flight Center, about the OnAIR platform, some of the particular challenges of deploying AI-based solutions in space, and how the tool has been used so far. OnAIR is an open-source software pipeline and cognitive architecture tool.