You Don't Need to Read Papers Anymore: A Provenance-Disciplined Advisory Tier for Autonomous Research

Autonomous research agents that work alone fail in a characteristic way: one context holds every result and the story built on it, so each new result is read through that story and the agent checks its own hypotheses against its own framing of the evidence. In practice the agent clings to its first direction until outside feedback, usually a human's, arrives: it takes run after run to leave an approach that is failing, and keeps working around problems whose solution the literature already holds. We propose separating execution from epistemic control by layering knowledge by generality: a task tier that runs the experiments, a domain-supervisor tier that owns the domain literature, and an advisory tier that answers only from general ML knowledge. YoDA (You Don't need to read papers Anymore) implements the advisory tier as an agent outside the experiment loop. It shares neither context nor success criterion with its asker, receives only questions the supervisor has abstracted to general deep learning, answers each in a disposable per-thread agent, and marks every claim as corpus-grounded or not. Its substrate is a literature knowledge base consulted rather than browsed: 769 papers digested at registration into fixed-schema notes whose quantitative claims carry fixed provenance, under a contamination rule that never admits an asker's claim as evidence and a reading-priority score that corpus growth cannot move. On MLE-bench (22 competitions, private grading, one base model in every tier), the three-tier pipeline took 17 of 22 medals within one hour, the most of five pipelines, and 19 by 24 hours with each cell stopped at its first privately graded medal; the cell-level records show the supervisor tier producing the largest single gains and catching defects the executor could not see, and the executor overturning the supervisor's wrong priors by measurement.