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University of Maryland, College Park
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Visual repair coupling can derail UI-to-code generation, but RubSE offers a structured solution that stabilizes self-evolution and boosts performance across models.
ORBITER significantly boosts decision-making reliability in last-mile delivery, outperforming existing models by up to 9.2% through enhanced reasoning about spatiotemporal cues.
None of the 30 LLM agents evaluated in CausalGame demonstrated reliable causal thinking, revealing a critical gap in AI's ability to perform scientific reasoning.
HALOMI bridges the human-to-humanoid gap, achieving 85% success in complex loco-manipulation tasks by leveraging active perception from human demonstrations.
LLMs are far more alike than you think: shared biases and failure modes mean that ensembling them is less effective than you'd hope.
LLMs can be guided to discover better solutions in open-ended scientific tasks by identifying and reasoning about causal factors that influence the evolutionary process.
Forget brute-force KV cache: CHESS achieves comparable quality to full KV cache using only 1% of the memory, unlocking 4.56x higher throughput for long-context LLMs.
