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 found out how foundation models are being used for conservation efforts, how AI can help with scarce resource allocation, and how color metaphors and LLMs can teach us about human cognition. We also went to ICRA and captured some footage of cutting-edge robots. In this latest interview in our AAAI Fellow series, we found out about Tanya Berger-Wolf's research developing a foundation model for biology, the insights this model can provide for conservation and protecting ecosystems, interesting collaborations over the years, and what the future has in store. In this interview, we chat to Sanmay Das, who was elected as a Fellow "for development of multiagent interaction mechanisms and learning techniques in the public interest, and for leadership service to the profession".

In financial applications, reinforcement learning (RL) agents are commonly trained on historical data, where their actions do not influence prices. However, during deployment, these agents trade in live markets where their own transactions can shift asset prices, a phenomenon known as market impact.

Recent monocular metric depth estimation (MMDE) methods have made notable progress towards zero-shot generalization.

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The increasing realism of synthetic images generated by advanced models such as VAEs, GANs, and LDMs poses significant challenges for synthetic image detection. To address this issue, we explore two artifact types introduced during the generation process: (1) latent distribution deviations and (2) decoding-induced smoothing effects, which manifest as inconsistencies in local textures, edges, and color transitions. Leveraging local pixel dependencies (LPD) properties rooted in Markov Random Fields, we reconstruct synthetic images using neighboring pixel information to expose disruptions in texture continuity and edge coherence. Building upon LPD, we propose FerretNet, a lightweight neural network with only 1.1M parameters that delivers efficient and robust synthetic image detection. Extensive experiments demonstrate that FerretNet--trained exclusively on the 4class ProGAN dataset--achieves an average accuracy of 97.1% on an open-world benchmark comprising 22 generative models.

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In recent years, large language models (LLMs) have shown remarkable performance in many problems. However, they fail to plan reliably. Specialized attempts to improve their planning capabilities still produce incorrect plans and fail to generalize to larger tasks. Furthermore, LLMs designed for explicit "reasoning" fail to compete with automated planners while increasing computational costs, which reduces one of the advantages of using LLMs. In this paper, we show how to use LLMs to always generate correct plans, even for out-of-distribution tasks of increasing size.

Musk's SpaceX buys AI coding start-up for $60bn days after IPO SpaceX has agreed to buy AI coding start-up Cursor for $60bn (£45bn) just days after its bumper initial public offering (IPO). Elon Musk's rocket company will take over Anysphere, which makes the artificial intelligence coding agent. The move comes after SpaceX joined New York's tech-focused Nasdaq stock exchange on Friday in the biggest ever listing, valuing it at more than $2tn and raising $85.7bn . A surge in SpaceX's share price on Monday and Tuesday saw the company overtake Amazon to become the world's fifth most valuable company. The companies have been partners since April, when SpaceX announced it had the right to either buy it for $60bn, or pay $10bn for the work they have done together.

AI-generated music is becoming increasingly common and increasingly difficult to recognise. Here are the tell-tale signs that reveal whether a song is AI-generated – and what this development means for the music industry.

What Do We Need From Our Homes Right Now? The global editorial directors of WIRED and Architectural Digest on teaming up to help you understand how we live today, and what comes next. There's no place like home--even if it keeps changing. After all, the places where we reside in 2026 look remarkably different than they did even a few decades ago: the style and decor, the technology and appliances, and even the way houses are insured and protected from natural disasters. The external forces shaping our day-to-day lives today, in turn, will inform what makes a home desirable--and safe--decades from now.