Dataset condensation aims to synthesize compact yet informative datasets that1 retain the training efficacy of full-scale data, offering substantial gains in efficiency.2 Recent studies reveal that the condensation process can be vulnerable to backdoor3 attacks, where malicious triggers are injected into the condensation dataset, manipu-4 lating model behavior during inference. While prior approaches have made progress5 in balancing attack success rate and clean test accuracy, they often fall short in6 preserving stealthiness, especially in concealing the visual artifacts of condensed7 data or the perturbations introduced during inference. To address this challenge,8 we introduce SNEAKDOOR, which enhances stealthiness without compromising9 attack effectiveness. SNEAKDOOR exploits the inherent vulnerability of class deci-10 sion boundaries and incorporates a generative module that constructs input-aware11 triggers aligned with local feature geometry, thereby minimizing detectability. This12 joint design enables the attack to remain imperceptible to both human inspection13 and statistical detection. Extensive experiments across multiple datasets demon-14 strate that SNEAKDOOR achieves a compelling balance among attack success rate,15 clean test accuracy, and stealthiness, substantially improving the invisibility of both16 the synthetic data and triggered samples while maintaining high attack efficacy.17

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.

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.

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.

Accelerating model convergence in resource-constrained environments is essential for fast and efficient neural network training. This work presents learn2mix, a new training strategy that adaptively adjusts class proportions within batches, focusing on classes with higher error rates.

Dynamic pricing algorithms typically assume continuous price variables, which may not reflect real-world scenarios where prices are often discrete. This paper demonstrates that leveraging discrete price information within a semi-parametric model can substantially improve performance, depending on the size of the support set of the price variable relative to the time horizon. Specifically, we propose a novel semi-parametric contextual dynamic pricing algorithm, namely BayesCoxCP, based on a Bayesian approach to the Cox proportional hazards model. Our theoretical analysis establishes high-probability regret bounds that adapt to the sparsity level γ, proving that our algorithm achieves a regret upper bound of eO(T(1+γ)/2 + dT) for γ < 1/3 and eO(T2/3 + dT) for γ 1/3, where γ represents the sparsity of the price grid relative to the time horizon T. Through numerical experiments, we demonstrate that our proposed algorithm significantly outperforms an existing method, particularly in scenarios with sparse discrete price points.

AF3 introduces: CMM (i) AF-Whisper, a unified audio encoder trainedPrevious SOTA (Closed Source) using a novel strategy for joint representation learning across all 3 modalities of speech, sound, and music; (ii) flexible, on-demand thinking, allowing the model to do chain-of-thought-type reasoning before answering; (iii) multi-turn, multiaudio chat; (iv) long audio understanding and reasoning (including speech) up MMSU to 10 minutes; and (v) voice-to-voice interaction. To enable these capabilities, (avg.)