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Benchmark scores for coding agents may mislead progress assessments, with only 39% of GSO tasks passing validity checks across machines.
PriorTR reveals that ignoring model-induced priors can lead to the loss of critical task-specific information, enhancing MLLM efficiency without sacrificing accuracy.
Spectral analysis reveals that tokens with dynamic cross-layer evolution are crucial for preserving semantic integrity, leading to more efficient MLLMs.
LLMs can generate code 55% faster by executing code *while* generating it, challenging the traditional generate-then-execute paradigm.
