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A developer describes the challenge of building multi-agent AI assistants that fail to handle unexpected situations gracefully, relying on explicit rules that lead to a whack-a-mole problem instead of enabling autonomous reasoning about ambiguity.
Introduces PRIG, a gradient attribution method that localizes prompt ambiguity in large language models by training a linear probe to distinguish clear from ambiguous prompts and attributing the probe score to token representations in the residual stream, achieving strong performance on synthetic and human-written benchmarks.