A research community is only as strong as the way it divides its cognitive labour. The philosopher Philip Kitcher argued that researchers should not all pursue the most promising approach. Credit rules, not a central plan, keep some of them on other lines. We bring this question to autonomous research, where Large Language Model agents improve programs against a deterministic grader. Agents that share only a score follow the leader and converge onto one approach. Coral Discovery gives the community a direction board. A verifier labels every attempt with a direction, the mechanism earning the score, and a method, one change within it. Per direction, the board tracks how much each eval improves the score, adds an exploration bonus for little-tried directions, prices the option of opening a new direction, and suggests how many agents each direction deserves. Credit is earned against a direction's own best, so a lagging direction keeps its agents. Agents finish their lines, then treat the board as advice. We test on nine tasks with the same model and budget, without grader access except for Kernel. Coral Discovery beats the strongest open autonomous multi-agent system on 6 tasks, ties on 1, and loses on 2. Against published numbers, it is best or tied on 4. It improves the reported Kernel result from 1103 to 1095 cycles and passes 1103 at eval 275, under half of the 596 used before. It sets a new Polyominoes state of the art, 91.78 against 87.0. Direction labels show that the leading scores rest on directions a score-only community never entered. Code will be released shortly.
Figures & tables
Figure 1: The division of cognitive labour in a community of research agents. Left: a community that shares only a score answers “who scores highest”. Every agent modifies the leader, and approaches that nobody has tried are never evaluated. Right: a community that shares progress per direction sees which approaches exist and how each is paying off. It spreads its agents over them, keeps opening new ones, and earns credit against each direction’s own best, so a lagging direction keeps its agents. Sections 5 and 6 quantify both.
Figure 2: Overview of Coral Discovery. Left: one round of an agent (1 is the line finished, 2 read the board, 3 claim, 4 check out and edit, 5 eval, 6 insight), an unfinished line loops back and skips the board. Middle: the shared direction board, the community’s account of its division of labour, and the verifier, which files every eval by its diff. Right: the tree of node cards, where code lineage and direction labels are independent graphs, and a lagging direction still earns credit for its own records. Board numbers are from the Signal Proc. run after eval 13.
Figure 3: Directions and methods. Left: the two definitions, with a direction being an approach in Kitcher’s sense. Middle: the verifier’s judgement at every eval, with its verdict recorded on the node card. Right: code lineage (solid edges) and direction membership (node colours) are independent; borrowing is declared with --inspired-by (dashed edges), and direction labels attach to nodes rather than subtrees.
Figure 4: Best-so-far curves of the single agent, the 4-agent baseline and Coral Discovery on the nine tasks (arrows give the direction of improvement). Dots mark the eval at which each final best first appears, labels give the final value and best@k , and the dashed line is the SOTA cited in the MIT CORAL paper. Cloudcast shows the first 20 evals, within which all three methods reach 618.
Figure 5: Directions opened on the six tasks in phenomena b and c (Kernel Eng., Polyominoes, Signal Proc., Circle-Pack., Erdős Over., Txn Sched.), by re-labelled direction. (a) Over the whole run, and (b) in the first, middle and last third of the run. Blue-grey = 4-agent baseline, rose = Coral Discovery.
Figure 6: Lagging directions survive on the board and exploration continues alongside exploitation. Blue-grey = 4-agent baseline, rose = Coral Discovery.
Autonomous research agents use language models and tools to search literature, design and run experiments, analyze results, and support scientific discovery.