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State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China
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Table reasoning gets a reliability boost: TableMind++ uses uncertainty estimates to prune flawed plans and refine actions, outperforming prior models by synthesizing robust reasoning paths.
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Forget complex model architectures for cross-domain recommendation: Taesar shows that cleverly transforming your data can unlock better performance with standard models.
CoT reasoning can hurt recommender performance by drowning out important ID signals – unless you compress reasoning chains and use bias-subtracted contrastive decoding to realign the inference subspace.
Recommender systems can bootstrap their performance without external data via a recursive self-improvement loop that generates, filters, and learns from its own plausible user interaction sequences.
Platform-centric digital services may be prioritizing engagement over user well-being, but LLMs and on-device intelligence now make truly user-centric agents a feasible alternative.
