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.
Large language model (LLM)-based evolution is a promising approach for open-ended discovery, where progress requires sustained search and knowledge accumulation. Existing methods still rely heavily on fixed heuristics and hard-coded exploration rules, which limit the autonomy of LLM agents. We present CORAL, the first framework for autonomous multi-agent evolution on open-ended problems. CORAL replaces rigid control with long-running agents that explore, reflect, and collaborate through shared persistent memory, asynchronous multi-agent execution, and heartbeat-based interventions. It also provides practical safeguards, including isolated workspaces, evaluator separation, resource management, and agent session and health management. Evaluated on diverse mathematical, algorithmic, and systems optimization tasks, CORAL sets new state-of-the-art results on 10 tasks, achieving 3-10 times higher improvement rates with far fewer evaluations than fixed evolutionary search baselines across tasks. On Anthropic's kernel engineering task, four co-evolving agents improve the best known score from 1363 to 1103 cycles. Mechanistic analyses further show how these gains arise from knowledge reuse and multi-agent exploration and communication. Together, these results suggest that greater agent autonomy and multi-agent evolution can substantially improve open-ended discovery. Code is available at https://github.com/Human-Agent-Society/CORAL.
Ao Qu, Han Zheng, Zijian Zhou +14
MIT · Singapore-MIT Alliance for Research and Technology · NUS +7
Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model sees at each step, leaving the model only the role of proposer. The model knows nothing of the search beyond what it is shown. As models grow more capable, a question arises: does a search strategy chosen by a human before the run scale better than promoting the model from proposer to planner and letting it own the search? The Bitter Lesson suggests that choosing the strategy in advance is the kind of hand-designed structure that general methods eventually outscale. We introduce AgentDiscover, in which a coding agent plans the search using its context as working memory, runs experiments, and records every attempt in a database of ideas, candidates, and their relations. This database serves as the agent's long-term memory and is structured so that the selection rules of classical algorithms such as MAP-Elites and Monte Carlo tree search each reduce to a single query, which the agent is free to use, combine, or replace. A server maintains the database and steers the agent after every submission, keeping it on course over long runs. In our experiments, AgentDiscover is more cost-efficient than existing frameworks, reaching better scores at lower cost. On tasks in kernel engineering, biology, algorithm design, and mathematics, AgentDiscover outperforms prior discovery frameworks. Its programs would have placed first among human competitors in seven past AtCoder heuristic contests, and on eleven mathematical and systems optimization tasks it matches or exceeds every baseline that uses the same model. Our code is available at https://github.com/mhdfb/AgentDiscover.
Automated research with LLM agents has shown promising results in various domains. Through iterative refinement and scored verifiers, LLM agents are capable of discovering new ideas. There are different ways to scale automated research: let one agent iterate for longer, run several agents in parallel, or further let the parallel agents communicate and build on one another's candidates. However, we find that all three strategies eventually plateau as the agents accumulate history. Each agent becomes anchored to its own past attempts, making fewer and smaller edits. Beyond this point, additional budget brings little further improvement. In this paper, we show that the key to scaling discovery further is to forget. Periodically discarding the search state, including every agent's session and the candidate history, and restarting from the best candidate found so far, unblocks the search and pushes the same budget further. We implement forgetting in a team of agents with communication and evaluate it on four tasks spanning heuristic programming, kernel optimization and model training. The results show up to 15% improvement compared to parallel agents, and up to 39% compared to a single agent. Our results suggest that, for open-ended discovery, what an agent is allowed to remember matters as much as how much compute it is given.