Era

ERA — empirical-software search via Flat UCB Tree Search

Faithful port of Google Research's ERA (Nature 2026, repo): an AI system that helps write high-quality empirical software. An LLM proposes whole candidate programs, a sandbox scores each, and a flat PUCT bandit (FUTS) decides which existing solution to improve next — keeping a population and escaping local optima instead of greedily climbing one trajectory. The candidate is the source code; ERA evolves the algorithm/model/pipeline, not a config.

What ERA is for: optimizing a problem that already has (a) a runnable seed program, (b) a fast automated scalar score, (c) a fixed problem definition. It evolves the code within that frame — it does not invent the frame. Skip ERA for problems with no automated score, or a single known change (just make it).

The engine (scaffold/futs.py — FROZEN, vendored verbatim)

futs.py is vendored verbatim from google-research/era (implementation/futs.py, commit eb56676ce0a9b22b0c357c218de09ac61e94fc4b, Apache-2.0). It is the neutral judge — never edit it during a search (that invalidates all node scores and cross-run comparison).

Primitives:

Problem(description)         # the problem statement + interface + scoring contract
Solution(program)            # a complete, self-contained program (source text)
Node(index, parent_index, solution, score, num_visits, rank_score, puct)
search(problem, init_sol, init_score, generate_fn, execute_fn, num_iterations, c_puct=1.0)
generate_fn: (problem, parent_solution, parent_score) -> Solution
execute_fn:  (problem, solution) -> float

PUCT selection each iteration picks the node maximising rank_score + c_puct · (1/N) · √(total_visits) / (1 + num_visits), where rank_score = rank/(N−1) ∈ [0,1] is the node's score normalised by rank, the prior is uniform 1/N, and a new node's visit backpropagates up its ancestors (backpropagate_visit). "Flat" = one global pool ranked by score; the only tree structure is parent links + ancestor visit-backprop. The 1/N prior is load-bearing: a constant prior makes exploration grow unbounded and never converge.

Concurrency + logging (scaffold/futs_batched.py — labeled extension)

Upstream ships only the sequential search() with no node hook. This separate, non-upstream module adds, reusing the frozen primitives unchanged:

• search(..., on_node=...) — identical behaviour to futs.search plus a per-node callback (for live tree.json). Parity is asserted in selftest.py.
• batched_search(..., batch_size) — selects B parents per round via virtual visits so B candidates are executed concurrently (B independent SLURM jobs). Generation stays sequential (one LLM call each); only execution is parallel. batch_size=1 reduces to the sequential path.

The pieces you author (per problem)

Copy scaffold/ to a working dir. Only problem.py + config.yaml change.

File	Role
problem.py	get_problem() -> (Problem, initial_solution, initial_score). Start from problem_template.py. The Problem.description states the problem, the data/interface, the rules + fixed budget, and the exact scoring contract. initial_solution is a real runnable seed program (anchors the root); initial_score=None makes the driver score it once, or pass a float. No baseline? Ship a stub printing ERA_SCORE: -1e9 and cold-start.
config.yaml	backend, exec_mode, num_iterations, batch_size, c_puct, SLURM knobs, paths.
generate.py / execute.py	The generic generator/executor (below). Rarely need edits.

Scoring contract. Every candidate program must run under the fixed budget and print exactly one line ERA_SCORE: <float> (higher = better). Crash / OOM / timeout / missing footer → executor returns -1e9 and FUTS abandons the node. Define one clear objective in the problem and don't change it mid-search (it invalidates cross-node comparison). The candidate runs in its own temp workdir; it must be self-contained (no editing the scaffold or shared files).

Generator — subscription-authed CLI, with failover

generate_fn shells out to a terminal coding-CLI in headless mode, billing your subscription (included tokens), never a metered API key. Both run with a hard timeout and stdin-piped prompt (Cursor's -p can hang otherwise).

• Default: Claude / latest Opus — claude -p --model opus --output-format text.
• Failover: Cursor / latest Composer — cursor-agent -p -m composer --output-format text. On a Claude rolling-limit hit (detected from output), switch for a cooldown, then re-probe and switch back once it recovers.
• Check availability: python generate.py --check.

Execution — SLURM job per run (default) or interactive (off-cluster)

• sbatch_per_run (default). Each candidate is its own short SLURM job; a batch is submitted concurrently and harvested as jobs finish. All cluster specifics are config-driven: account, gres (omit for CPU clusters), cpus, mem, time_limit, and setup_cmds (env/module lines). Nothing cluster-specific is hardcoded.
• interactive. Runs each candidate as a local subprocess — on a held salloc allocation, or just locally for small problems / the regression example / selftest.

Running a search

# from this skill's own directory (where this SKILL.md and scaffold/ live):
cp -r scaffold <workdir> && cd <workdir>
cp examples/regression/problem.py ./problem.py   # or write your own from problem_template.py
python selftest.py            # verify the harness (no GPU/tokens/SLURM)
python generate.py --check    # verify claude / cursor-agent are logged in
python run.py --config config.yaml

Output: tree.json (every node, rewritten live → resumable/inspectable) and a printed top-k diverse portfolio — a set of strong, distinct programs, not just the single best. A portfolio winner is a hypothesis: scores come off one evaluation, so confirm a winner with a repeat run before trusting it.

Worked example

examples/regression/ — California Housing tabular regression (faithful to the repo's playground_s3e1): the candidate is a whole program (load → features → model → evaluate), scored by negative held-out RMSE. Runs locally with exec_mode: interactive (no GPU/SLURM). Expect ERA to evolve the linear baseline into feature engineering + gradient boosting.

Defaults & knobs

• c_puct (default 1.0): lower → greedier exploit (→0 ≈ greedy hill-climb); higher → more breadth (too high → random thrash). Collapsing to one lineage → raise it; thrashing → lower it.
• batch_size (default 4): candidates per round = concurrent jobs (serial in interactive).
• num_iterations: total candidates generated+executed.
• Whole-program candidates are higher-variance than config knobs — expect many -1e9 (broken imports, shape errors, NaN). Keep the seed clean and the scoring contract crisp; raise c_puct modestly so the search doesn't abandon the lineages that compile.

Anti-patterns

• Editing futs.py (the frozen judge) or putting non-upstream logic inside it — extensions go in futs_batched.py and reuse its primitives.
• Non-self-contained candidates (editing the scaffold or shared files).
• A composite score with no clear main term, or changing the metric mid-search.
• Treating it as deep MCTS — it is flat UCB (one ranked pool; only ancestor visit-backprop, no value back-prop).