"We think that Mars could be green in our lifetime

This is not an Earth clone, but rather a thin, life-supporting envelope that still exhibits large day-to-night temperature swings but blocks most radiation. Such a state would allow people to live outside on the planet’s surface"

Very cool!

I was initially confused how they managed to do a randomized control trial on this. Seems they in each workflow randomly turned on the tool for a subset of the customers

the focus on practical capacities is very sensible! though on basis on that, I thought you would focus on what LLMs do to humans' practical capacity to feel empathy with other beings, rather than whether LLMs satisfy humans' need to be emphasized with

Interesting. Could the measure also be applied to the human, assessing changes to their empowerment over time?

Interesting, does the method rely on being able to set different goals for the LLM?

Interesting. I recall Rich Sutton made a similar suggestion in the 3rd edition of his RL book, arguing we should optimize average reward rather than discount

digital-strategy.ec.europa.eu/en/policies/... The Code also has two other, separate Chapters (Copyright, Transparency). The Chapter I co-chaired (Safety & Security) is a compliance tool for the small number of frontier AI companies to whom the “Systemic Risk” obligations of the AI Act apply.
2/3

This is an interesting explanation. But surely boys falling behind is nevertheless an important and underrated problem?

Interesting. But is case 2 *real* introspection? It infers its internal temperature based on its external output, which feels more like inference based on exospection rather than proper introspection. (I know human "intro"spection often works like this too, but still)

… and many more! Check out our paper arxiv.org/pdf/2506.01622, or come chat to @jonrichens.bsky.social, @dabelcs.bsky.social or Alexis Bellot at #ICML2025

Causality. In previous work we showed a causal world model is needed for robustness. It turns out you don’t need as much causal knowledge of the environment for task generalization. There is a causal hierarchy, but for agency and agent capabilities, rather than inference!

Emergent capabilities. To minimize training loss across many goals, agents must learn a world model, which can solve tasks the agent was not explicitly trained on. Simple goal-directedness gives rise to many capabilities (social cognition, reasoning about uncertainty, intent…).

Safety. Several approaches to AI safety require accurate world models, but agent capabilities could outpace our ability to build them. Our work gives a theoretical guarantee: we can extract world models from agents, and the model fidelity increases with the agent's capabilities.

Extracting world knowledge from agents. We derive algorithms that recover a world model given the agent’s policy and goal (policy + goal -> world model). These algorithms complete the triptych of planning (world model + goal -> policy) and IRL (world model + policy -> goal).

Fundamental limitations on agency. In environments where the dynamics are provably hard to learn, or where long-horizon prediction is infeasible, the capabilities of agents are fundamentally bounded.

No model-free path. If you want to train an agent capable of a wide range of goal-directed tasks, you can’t avoid the challenge of learning a world model. And to improve performance or generality, agents need to learn increasingly accurate and detailed world models.

These results have several interesting consequences, from emergent capabilities to AI safety… 👇