让AI研究像模糊测试一样，靠中间反馈探索而非盲目试错。

Agentic auto-research is emerging, but most systems treat scientific discovery as goal-oriented optimization against a final benchmark. This paradigm rewards a sparse final verdict and ignores the exploration that precedes it. When agents optimize only the final score, they overfit to the test conditions and sample blindly rather than search. Within a declared research problem, a research agent and a greybox fuzzer for software analysis face the same sparse feedback. A fuzzer rarely finds a bug directly, but coverage makes partial progress observable on every execution. Fuzzers use that dense signal to mutate inputs and allocate effort, rather than merely rank completed runs. Auto-research needs the same two capabilities. First, each experiment must expose a cheap, dense signal of epistemic progress before final scientific validation is available. Second, that signal must determine the next intervention so the agent searches rather than repeatedly samples. Because the progress signal provides guidance rather than a final verdict, final validation must still evaluate claims using evidence protected from adaptive reuse. We propose controlled tests to determine whether candidate signals predict validated progress, whether feedback-directed search yields more validated discoveries per unit cost than repeated sampling, and whether protected validation reduces false discoveries. In a simulated physics environment, an AI research agent that tracks its intermediate epistemic progress discovers a hidden physical law. Optimization-driven baselines fail because they repeatedly sample and overfit to their existing data instead of probing unfamiliar regimes. Feedback architecture, not generation capacity, is the central bottleneck in auto-research.