When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.

Environmental sustainability, particularly in relation to climate change, is a key concern for consumers, producers, and policymakers. The carbon footprint, based on greenhouse gas emissions, is a standard metric for quantifying the contribution to climate change of activities and is often assessed using life cycle assessment (LCA). However, conducting LCA is complex due to opaque and global supply chains, as well as fragmented data. This paper presents a methodology that combines advances in LCA and publicly available databases with knowledge-augmented AI techniques, including retrieval-augmented generation, to estimate cradle-to-gate carbon footprints of food products. Our methodology is implemented as a chatbot interface that allows users to interactively explore the carbon impact of composite meals and relate the results to familiar activities.

We provide theoretical guarantees for CBPR's rapid convergence to the optimal policy once human partners alter their policies.

When interacting with people, AI agents do not just influence the state of the world - they also influence the actions people take in response to the agent, and even their underlying intentions and strategies.

We evaluate the performance of agents trained via self-play and population-based training.

In the current media landscape, it's a wonder it even got made. In a world where hallowed news organizations transform into conservative mouthpieces and milquetoast late-night jokes are grounds for suspension, satirical headlines from the Onion can feel closer to real life than parody. Now the site is taking on one of the most taboo subjects of all--disgraced sex offender and financier Jeffrey Epstein--in mockumentary form. It launches in theaters in New York City, Minneapolis, Los Angeles, and San Francisco for one day only on October 2; after that it will be available online. You can watch the trailer right here.

Onion CEO Ben Collins Hasn't Given Up on Print--or Buying Infowars A year after relaunching The Onion as a newspaper, Collins visits to talk about why "going into something and not ruining it is bravery." Ben Collins made a big bet. A year ago, just a few months after he'd been named CEO of The Onion, he relaunched its print edition. Once a favorite on university campuses, The Onion hadn't published a physical issue since 2013 . Common wisdom said that readership, and advertising dollars, just weren't there for newspapers. But Collins, a fan of the satirical paper since childhood, thought "that's dumb." Readers celebrated The Onion's relaunch and the ability to read all of its bitingly funny headlines on a single broadsheet. Collins wouldn't give exact numbers on how many people are currently subscribed to the print edition but did say they should be enough to keep its writers' room humming (a few weeks after we taped this episode, the Wall Street Journal reported that The Onion now boasts more than 53,000 paying subscribers). On this episode of, I spoke with Collins about his hopes for The Onion, the future of journalism, and his Balatro addiction. KATIE DRUMMOND: Do you have a recent favorite Onion headline? Can I look it up for you? "Ghislaine Maxwell Can't Help but Notice Interview Room Covered in Plastic Sheeting." The staff churns out like 15 a day that are great. I sit there, and I still don't know how they do it. When I say they throw away eight or nine of the best sentences I would ever write every day, I mean that sincerely.

Large language model (LLM) agents-especially smaller, open-source models-often produce causally invalid or incoherent actions in collaborative tasks due to their reliance on surface-level correlations rather than grounded causal reasoning. This limitation undermines their performance in terms of coordination and planning in dynamic environments. We address this challenge with CausalPlan, a two-phase framework that integrates explicit structural causal reasoning into the LLM planning process. At the core of CausalPlan is the Structural Causal Action (SCA) model, which learns a causal graph from agent trajectories to capture how prior actions and current environment states influence future decisions. This structure is then used to guide action selection by assigning causal scores to LLM-generated proposals, reweighting them accordingly, or falling back to causally grounded alternatives when needed. By embedding this causal knowledge directly into the decision loop, CausalPlan constrains planning to intervention-consistent behaviours without requiring fine-tuning of the LLM itself. We evaluate CausalPlan on the Overcooked-AI benchmark across five multi-agent coordination tasks and four LLMs of varying sizes: Gemma-7B, Llama-8B, Qwen-14B, and Llama-70B. Experimental results show that CausalPlan consistently reduces invalid actions and improves collaboration in both AI-AI and human-AI settings, outperforming strong reinforcement learning baselines. Our findings highlight the value of causality-driven planning for deploying efficient, interpretable, and generalisable multi-agent LLM systems.

Welcome to Slate's weekly news quiz. It's Friday, which means it's time to test your knowledge of the week's news events. Your host, Ray Hamel, has concocted questions on news topics ranging from politics to business, from culture to sports to science. At the end of the quiz, you'll be able to compare your score with that of the average contestant, as well as that of a Slatester who has agreed to take the quiz on the record. This week's contestant is senior editor Rebecca Onion.