Florent Delgrange won the Best Blue Sky Paper Award at AAMAS 2026 for his work Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments . We caught up with him to find out more about his vision for agent learning. What is the topic of your Blue Sky Ideas paper and why is it an interesting area for study? My Blue Sky Ideas paper asks a simple but difficult question: how can an autonomous agent keep learning as its world changes without quietly losing the guarantees that made its behavior trustworthy? Reinforcement learning and formal methods address complementary parts of this problem.

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What came into force with the EU's AI Act this week - and what didn't On August 2, the next phase of Europe's Artificial Intelligence Act came into force as the European Union frames this legislation as the world's first comprehensive law on AI. Like the General Data Protection Regulation (GDPR) before it, this new EU legislation is intended not to replace the economic bloc's existing digital rulebook but to complement it. GDPR has gone on to shape privacy practices well beyond Europe, becoming the benchmark against which many multinational organisations design their compliance programmes. The question now is whether the AI Act will prove just as influential for AI governance. What came into force this week?

The EPA has proposed a rule change that would let states decide how much input the public has on air pollution permits. It would be a huge boon for data centers increasingly reliant on fossil fuels. The Trump administration is quietly considering a rule change that could make it easier for polluters to build facilities--including certain gas plants and diesel generators that power data centers--with little to no notice to the public. On Wednesday, the Environmental Protection Agency held a public hearing on a proposed rule change that would hand the power to states to decide how the public participates in the permitting process for certain new sources of air pollution. The proposed rollback comes as data centers face greater pushback across the US, with many communities using the permitting process to try to slow down development.

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Netflix is implementing a new requirement for users to link separate email addresses to each profile on shared accounts, with no apparent opt-out option available. PCWorld reports that customer support screenshots and Reddit user experiences confirm this policy rollout, though an official announcement is still pending. While Netflix claims this enhances login security and personalization, critics view it as a data collection method that increases company control over users. Since 2023, it's been possible to share your Netflix account with someone who doesn't live in your household. Account sharing relies on separate profiles and comes with additional costs, but otherwise wasn't subject to strict rules. That now appears to be changing.

Optimization modeling is one of the most crucial but technical parts of operations research (OR). To automate the modeling process, existing works have leveraged large language models (LLMs), prompting them to break down tasks into steps for generating variables, constraints, and objectives. However, due to the highly complex mathematical structures inherent in OR problems, standard fixed-step decomposition often fails to achieve high performance. To address this challenge, we introduce OptiTree, a novel tree search approach designed to enhance modeling capabilities for complex problems through adaptive problem decomposition into simpler subproblems. Specifically, we develop a modeling tree that organizes a wide range of OR problems based on their hierarchical problem taxonomy and complexity, with each node representing a problem category and containing relevant high-level modeling thoughts. Given a problem to model, we recurrently search the tree to identify a series of simpler subproblems and synthesize the global modeling thoughts by adaptively integrating the hierarchical thoughts. Experiments show that OptiTree significantly improves the modeling accuracy compared to the state-of-theart, achieving over 10% improvements on the challenging benchmarks.

Language models must be adapted to understand and follow user instructions. Reinforcement learning is widely used to facilitate this - typically using fixed criteria such as "helpfulness" and "harmfulness". In our work, we instead propose using flexible, instruction-specific criteria as a means of broadening the impact that reinforcement learning can have in eliciting instruction following. We propose "Reinforcement Learning from Checklist Feedback" (RLCF). From instructions, we extract checklists and evaluate how well responses satisfy each item--using both AI judges and specialized verifier programs--then combine these scores to compute rewards for RL. We compare RLCF with other alignment methods on top of a strong instruction following model (Qwen2.5-7B-Instruct)

The "right to be forgotten" and the data privacy laws that encode it have motivated machine unlearning since its earliest days. Now, some argue that an inbound wave of artificial intelligence regulations -- like the European Union's Artificial Intelligence Act (AIA) -- may offer important new use cases for machine unlearning. However, this position paper argues, this opportunity will only be realized if researchers proactively bridge the (sometimes sizable) gaps between machine unlearning's state of the art and its potential applications to AI regulation. To demonstrate this point, we use the AIA as our primary case study. Specifically, we deliver a "state of the union" as regards machine unlearning's current potential (or, in many cases, lack thereof) for aiding compliance with various provisions of the AIA. This starts with a precise cataloging of the potential applications of machine unlearning to AIA compliance. For each, we flag the technical gaps that exist between the potential application and the state of the art of machine unlearning. Finally, we end with a call to action: for machine learning researchers to solve the open technical questions that could unlock machine unlearning's potential to assist compliance with the AIA -- and other AI regulations like it.

The rapid advancement of large language models (LLMs) has enabled the development of multi-agent systems where multiple LLM-based agents collaborate on complex tasks. However, existing systems often rely on centralized coordination, leading to scalability bottlenecks, reduced adaptability, and single points of failure. Privacy and proprietary knowledge concerns further hinder cross-organizational collaboration, resulting in siloed expertise. We propose AgentNet, a decentralized, Retrieval-Augmented Generation (RAG)-based framework that enables LLM-based agents to specialize, evolve, and collaborate autonomously in a dynamically structured Directed Acyclic Graph (DAG). Unlike prior approaches with static roles or centralized control, AgentNet allows agents to adjust connectivity and route tasks based on local expertise and context. AgentNet introduces three key innovations: (1) a fully decentralized coordination mechanism that eliminates the need for a central orchestrator, enhancing robustness and emergent intelligence; (2) dynamic agent graph topology that adapts in real time to task demands, ensuring scalability and resilience; and (3) a retrieval-based memory system for agents that supports continual skill refinement and specialization. By minimizing centralized control and data exchange, AgentNet enables fault-tolerant, privacy-preserving collaboration across organizations. Experiments show that AgentNet achieves higher task accuracy than both single-agent and centralized multi-agent baselines.