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This paper empirically identifies two structural flaws rooted in geometric properties and dataset sparsity, and proposes a novel two-stage framework to close the long-short-view performance gap and introduces a modified softmax to counter prefix position bias.
This position paper draws on AAC as a setting where speech AI failures are most visible and their stakes highest, alongside other underserved speakers - people who stutter, multilingual speakers, and non-binary and transgender users.
The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how large language models and foundation models can be made more capable, reliable, interpretable, and efficient.
This study shows that multi-agent design is fundamentally an information-bottleneck optimization problem, and explains when bounded inter-agent communication helps or hurts, and observes that MAS consistently helps when relays are near-sufficient, especially for weaker models.
Large language models and foundation models are increasingly embedded in reasoning systems that plan, invoke tools, use memory, gather evidence, and iteratively refine their outputs. The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how these systems...
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