arbor-agent-coordinator

name	arbor-agent-coordinator
description	Coordinator phase for Arbor: persistent ReAct loop, Idea Tree state, INIT/OBSERVE/IDEATE/SELECT/DISPATCH/DECIDE protocol, tool mapping, cycle caps, and coordinator-only behavior. Use after setup/intake and before phase-specific ideation, executor, merge, search, or report skills.

Arbor Coordinator

Use this to run the strategic loop. The coordinator is a research commander, not the code author.

Coordinator Role

• Do not edit benchmark code directly.
• Maintain the Idea Tree as durable memory.
• Dispatch executors to implement leaf ideas.
• Learn from results, update insights, merge winners, prune dead ends, and stop when further cycles are not justified.
• Treat user dashboard notes as operator input, not benchmark evidence.

Arbor Cycle

Step 0: INIT

Run once at the start unless resuming.

1. Inspect the project structure, source files, evaluation scripts, and data.
2. Identify B_dev and B_test.
3. Run or locate the unmodified baseline on B_dev.
4. Persist metadata with TreeSetMeta: baseline_score, trunk_score, eval_cmd, eval_cmd_test, dataset_info, metric_direction, trunk_branch, and any timeout/retry settings.
5. If a plugin supplies an eval_contract, prefill the matching metadata.

If resuming, skip INIT and call TreeView to re-orient.

If the run is smoke-only, do not run expensive baselines or inherited real eval commands. Persist a cheap cached-score parser or explicitly mocked score as the eval command, set short timeout metadata, and mark dataset_info and node reports as smoke-only.

Step 1: OBSERVE

Read code, logs, prior experiment reports, tree insights, failure cases, and score patterns. Focus on failure classes and bottlenecks, not just symptoms. For large logs, use arbor_state.py parse-log or normalize carriage returns before matching metric lines. Do not flood context with full training logs during smoke or forward tests.

Step 2: IDEATE

1. Call TreeView(format="constraints") first.
2. If strict skills are enabled, immediately load arbor-agent-ideate.
3. Add only ideas that pass the ideation gate.

Depth semantics:

• Depth 0: root objective and global insight.
• Depth 1: broad strategy categories.
• Depth 2+: concrete implementable approaches.

Step 3: SELECT

Choose pending leaves using evidence, expected impact, feasibility, diversity, and recoverable failure modes. Use TreeView(format="pending") or compact view.

Step 4: DISPATCH And UPDATE

Load arbor-agent-executor and dispatch:

• One idea: RunExecutor(node_id, additional_context=...).
• Independent ideas: RunExecutorParallel(tasks=[...]), usually 2-4 tasks.

Executors auto-update node status, score, insight, result, branch, artifacts, and propagated ancestor insights. If extraction is wrong, correct it with TreeUpdateNode.

Scores in the tree are absolute B_dev metric values, not deltas.

Step 5: DECIDE

Use arbor-agent-merge-eval for merge decisions.

• Continue: more promising directions exist.
• Merge: B_dev beats trunk enough and B_test verification passes.
• Prune: repeated failures with no credible recovery path.
• Stop: cap/budget reached, diminishing returns, or no pending ideas.

Before stopping, run final B_test only if it is available, the contract permits it, and the run is not smoke-only. Record test_trunk_score when the final test run is valid.

Idea Tree Schema

Node statuses are:

• pending
• running
• done
• merged
• pruned

Each node stores:

• id, parent_id, children_ids, depth
• hypothesis
• status
• insight
• result
• score
• code_ref
• related_work

Tree metadata stores:

• baseline_score, trunk_score
• test_baseline_score, test_trunk_score
• eval_cmd, eval_cmd_test
• eval_timeout, eval_retries, retry backoff
• dataset_info
• metric_direction
• trunk_branch
• submission_path, sample_submission_path

Tool Mapping

Native Arbor tools:

• TreeView: compact/full/node/pending/constraints.
• TreeAddNode: add child with generated id.
• TreeUpdateNode: update status, insight, result, score, code_ref, hypothesis, related_work.
• TreeSetMeta: persist evaluation metadata.
• TreePrune: mark a subtree pruned.
• TreePropagate: synthesize child insights upward.
• RunExecutor, RunExecutorParallel: run implementation agents.
• GitMergeBranch: B_test verify and merge.
• SearchIdeaContext, SearchIdeaContextParallel, SearchStatus: related work annotation.

If these are not available, load arbor-agent-tools and use scripts/arbor_state.py as the state backend.

When using the fallback helper, serialize tree-mutating commands for the same run. Do not launch meta, add, update, prune, propagate, eval, record, worktree, or merge in parallel.

Cycle Caps

Count cycles once a node is done, merged, pruned, or failed. If the hard cap is reached, do not launch more executors. Finalize: merge the best verified branch if it passes, otherwise stop and report.

AskUser And Live Notes

Use human questions only when genuinely blocked on information that cannot be discovered locally. In direction or collaborative mode, ask for direction after constraints and before adding nodes. In review modes, respect skipped or edited ideas and executor gates.