arXiv:2601.21028v1 Announce Type: new Abstract: AI-generated media is radically changing the way content is both consumed and produced on the internet, and in no place is this potentially more visible than in sexual content. AI-generated sexual content (AIG-SC) is increasingly enabled by an ecosystem of individual AI developers, specialized third-party applications, and foundation model providers. AIG-SC raises a number of concerns from old debates about the line between pornography and obscenity, to newer debates about fair use and […]

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arXiv:2601.02386v1 Announce Type: new Abstract: Diversified recommendation has attracted increasing attention from both researchers and practitioners, which can effectively address the homogeneity of recommended items. Existing approaches predominantly aim to infer the diversity of user preferences from observed user feedback. Nonetheless, due to inherent data biases, the observed data may not fully reflect user interests, where underexplored preferences can be overwhelmed or remain unmanifested. Failing to capture these preferences can lead to suboptimal diversity in recommendations. To fill […]

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arXiv:2601.21081v1 Announce Type: new Abstract: Multimodal models for text-to-image generation have achieved strong visual fidelity, yet they remain brittle under compositional structural constraints-notably generative numeracy, attribute binding, and part-level relations. To address these challenges, we propose Shape-of-Thought (SoT), a visual CoT framework that enables progressive shape assembly via coherent 2D projections without external engines at inference time. SoT trains a unified multimodal autoregressive model to generate interleaved textual plans and rendered intermediate states, helping the model capture shape-assembly […]

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arXiv:2601.09828v1 Announce Type: new Abstract: Effective retrieval across both seen and unseen categories is crucial for modern image retrieval systems. Retrieval on seen categories ensures precise recognition of known classes, while retrieval on unseen categories promotes generalization to novel classes with limited supervision. However, most existing deep hashing methods are confined to a single training paradigm, either pointwise or pairwise, where the former excels on seen categories and the latter generalizes better to unseen ones. To overcome this […]

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Hard-won lessons on how to scale agentic systems without scaling the chaos, including a taxonomy of core agent types. The post Why Your Multi-Agent System is Failing: Escaping the 17x Error Trap of the “Bag of Agents” appeared first on Towards Data Science.

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By Michael Nielsen, September 26 2019 Note: Hastily written, a few observations based on writing I happened to find of interest The opening of Chapter II of Carl Sagan’s “Cosmos”: All my life I have wondered about the possibility of life elsewhere. What would it be like? Of what would it be made? … There was once a time before life, when the Earth was barren and utterly desolate. Our world is now overflowing with life. How did […]

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Regarding neural network training, I think we are all guilty of doing this: we choose our batch sizes as powers of 2, that is, 64, 128, 256, 512, 1024, and so forth. There are some valid theoretical justifications for this, but how does it pan out in practice? We had some discussions about that in the last couple of days, and here I want to write down some of the take-aways so I can reference them in the […]

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arXiv:2601.04263v1 Announce Type: new Abstract: Knowledge distillation has proven effective for model compression by transferring knowledge from a larger network called the teacher to a smaller network called the student. Current knowledge distillation in time series is predominantly based on logit and feature aligning techniques originally developed for computer vision tasks. These methods do not explicitly account for temporal data and fall short in two key aspects. First, the mechanisms by which the transferred knowledge helps the student […]

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arXiv:2601.03418v1 Announce Type: new Abstract: Verifying system-generated summaries remains challenging, as effective verification requires precise attribution to the source context, which is especially crucial in high-stakes medical domains. To address this challenge, we introduce PCoA, an expert-annotated benchmark for medical aspect-based summarization with phrase-level context attribution. PCoA aligns each aspect-based summary with its supporting contextual sentences and contributory phrases within them. We further propose a fine-grained, decoupled evaluation framework that independently assesses the quality of generated summaries, citations, […]

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In this interview, we speak with two representatives from Quantori, a data science and digital transformation company specializing in research and development in the biopharma industry. Yuriy Gankin is a co-founder and chief scientific officer at Quantori. He’s a life science researcher, serial entrepreneur, and inventor and holds a Ph.D. in analytical chemistry from Tufts University and an MBA from MIT Sloan. Maksim Kazanskii is Quantori’s engineering manager in machine learning. With a background in theoretical physics and […]

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