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University of Sydney
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Deep transformers can encode complex grammatical structures in low-dimensional spaces, supporting the linear representation hypothesis.
Aggregating insights from diverse causal discovery experts with LLM-guided reweighting leads to significantly improved causal graph accuracy, even in ambiguous scenarios.
FlowBP redefines reward backpropagation by transforming the backward trajectory into a design object, leading to significant improvements in model alignment with human preferences.
Even minor real-world environment corruptions can cripple MLLM-powered computer-use agents, revealing a surprising fragility in their ability to execute desktop tasks.
