lineprior-cli-0.11.0 is not a library.

lineprior

lineprior is a Rust library and CLI for explainable action ranking: given historical (state, action, outcome) sequences, it builds a reproducible action prior for action selection — a ranked, confidence-scored list of candidate actions per state, learned offline from historical evidence, not online exploration. Given a state, it answers:

What actions have historically worked well from here?

It's built for search, planning, agents, games, and optimization — move/candidate ordering, opening-book-like historical guidance, or ranking which expensive experiment to try first. It's confidence-aware and abstention-friendly: sparse or unseen states return no candidates rather than a guess (see "Confidence modes" below for the selective-prediction machinery behind that). It is not a shogi opening book library, a chess-specific book format, a planner, a solver, a game engine, or a contextual-bandit/reinforcement-learning/online-learning library — no policy learning, no exploration, no online updates. See "lineprior vs. contextual bandit" below if that's the gap you're checking.

When to use lineprior

Use lineprior when you have historical (state, action, outcome) sequences and want an explainable, reproducible prior for ranking candidate actions before search, planning, simulation, or verification:

Search move ordering — rank candidate moves/actions to explore first.
Planner candidate ordering — prioritize which candidate step to expand first.
Agent action priors — give an agent a starting ranking over its candidate actions.
Optimization branch ordering — use past successful paths to order which branch to try first.
Opening-book-like historical guidance — reuse patterns from past games/runs; lineprior itself has no game- or domain-specific knowledge built in.
Expensive experiment/gate candidate prioritization — rank which candidate is worth an expensive run or verification first.

What it is not

lineprior does not decide the best action by itself. It is a prior, not an oracle:

It suggests candidate actions with a count, rate, and confidence attached.
The caller is expected to combine this with search, evaluation, rules, or verification before acting.
When data is sparse or a state is unseen, it returns no candidates rather than inventing one.

If historical data is biased, the prior will be biased too. lineprior can improve candidate ordering when historical sequences are relevant and representative — it does not guarantee better decisions.

lineprior vs. contextual bandit

If you landed here searching for a contextual bandit, here's the actual difference:

A contextual bandit (e.g. LinUCB, Thompson Sampling) learns a policy online: it explores, updates from feedback, and balances exploration vs. exploitation as it runs.
lineprior builds a prior offline, once, from a static historical log. It implements no bandit or reinforcement-learning algorithm, does no online exploration, and does no online policy updates.
lineprior is not a policy or a solver on its own — it's a ranking/confidence component meant to sit before a search, planner, bandit, or solver, the same way a chess opening book informs (without replacing) a search engine.

They're complementary, not competing: candidates ranked by lineprior could seed a bandit's initial arm set or a planner's move ordering, but lineprior itself never explores or learns online.

Building a prior book

lineprior build observations.jsonl \
  --out prior.jsonl \
  --min-count 1 \
  --smoothing-alpha 5.0

Useful flags: --max-step (drop observations past a given step), --max-actions-per-state (keep only the top N candidates), --tags (keep only observations carrying at least one of the given tags, comma-separated), --confidence-k (tune how fast confidence grows with sample size), --confidence-mode (heuristic (default), wilson-lower-bound, or hybrid — see "Confidence modes" below), --confidence-z (z-score for the Wilson lower bound, default 1.96, ignored under heuristic), --min-weighted-count / --min-confidence (filter on the weighted count or confidence directly, instead of just the raw --min-count), --draw-value (success credit for a draw outcome — default 0.5, since a draw is a genuine partial outcome in adversarial games, not a loss), --time-decay-half-life-days / --time-decay-reference-unix-seconds / --missing-timestamp-policy (age-based weight decay — see "Time decay and source reliability" below), --source-weights / --default-source-weight (per-source reliability multipliers, same section), --count-weight / --success-weight / --score-weight (relative weight of each term in the raw prior score — count_weight * ln(1 + weighted_count) + success_weight * success_rate + score_weight * mean_score — all default 1.0; also available as tune --param keys, see "Tuning" below), --config <path.json> (load the whole BuildConfig from a file instead of individual flags, e.g. one saved by lineprior tune --save-best-config — see "Tuning" below; errors if combined with any flag above), --strict (fail on the first invalid record instead of skipping it with a warning).

--min-confidence's meaning depends on --confidence-mode: under heuristic it's a pure sample-size floor, blind to outcome. Under wilson-lower-bound/hybrid it's success-rate-aware, so a high-count but mostly-failing action that used to pass the filter can now be dropped by it — switching --confidence-mode on an existing --min-confidence threshold is a real behavior change, not just an additive one.

Confidence modes

heuristic (default): weighted_count / (weighted_count + confidence_k) — a sample-size heuristic, blind to outcome. Not a statistical guarantee, but works even for score-only datasets with no outcome labels at all.
wilson-lower-bound: the Wilson score interval lower bound on the action's success rate — an actual statistical lower bound, useful once outcome labels are meaningful. Falls back to heuristic for an action with no decisive-outcome observations (nothing to bound).
hybrid: heuristic * wilson-lower-bound, so both low sample size and a weak success rate pull confidence down. Same fallback as wilson-lower-bound when there's no outcome data.

Weighted/fractional observations (--weight, draw outcomes under --draw-value) feed the Wilson bound through an effective sample size (sum(weight)^2 / sum(weight^2), Kish's formula) rather than the raw weighted count — an engineering approximation, exact for uniform weight 1.0 observations.

Time decay and source reliability

Not every observation deserves equal trust. build/eval can compute an effective_weight per observation — weight * time_decay_multiplier * source_reliability_multiplier — feeding everything downstream (prior, confidence, eval calibration) automatically. Both factors default to a no-op, so this is entirely opt-in.

Stale data, decayed by age:

lineprior build observations.jsonl \
  --out prior.jsonl \
  --time-decay-half-life-days 30 \
  --time-decay-reference-unix-seconds 1783540000

--time-decay-reference-unix-seconds is required whenever --time-decay-half-life-days is set — there's no implicit "now," since that would make identical build/eval invocations produce different priors (and a different build_config_fingerprint) depending on when you happened to run them. An observation's weight decays as 0.5 ^ (age_days / half_life_days); a future-dated observation (observed_at_unix_seconds after the reference) clamps to age 0, silently. --missing-timestamp-policy (keep-base-weight, the default, or drop) decides what happens to an observation with no observed_at_unix_seconds — inert when decay is disabled.

Multiple sources of differing reliability:

lineprior build observations.jsonl \
  --out prior.jsonl \
  --source-weights engine_v012=1.0,engine_v010=0.6,human=0.8 \
  --default-source-weight 1.0

An observation's source field looks itself up in --source-weights; an absent or unrecognized source falls back to --default-source-weight (default 1.0, i.e. trust it same as any other). This is independent of time decay — you can use either, both, or neither.

Caveat: Kish's effective sample size (the same formula the Wilson bound above uses) is invariant to uniformly scaling every one of an action's own weights by the same factor. So when every observation behind an action shares the same age/source, pure wilson-lower-bound confidence does not reflect decay at all — only weighted_count (and therefore prior, and heuristic/hybrid confidence) does. Use hybrid, not bare wilson-lower-bound, if you want the confidence number itself to drop for stale or unreliable data.

You could always precompute weight yourself before feeding it to lineprior — this feature exists so the common case (decay by age, discount by source) is reproducible and folded into the config fingerprint, not as a replacement for custom weighting logic.

build also prints a one-line summary of what its filters actually did, e.g. stats: 950/1000 observations kept, 42/50 candidates kept (5 by min_count, ...) — useful for sanity-checking your own pre-filtering (e.g. a domain-specific ply/depth cutoff) against --min-count/etc. without re-deriving the numbers by hand. As a library, this is BuildOutput.stats (a BuildStats) returned alongside the book by build_prior_book_from_reader.

Querying a prior book

lineprior query prior.jsonl --state state_a --top-k 5

An unseen state prints nothing and still exits 0 — that's the expected fallback behavior, not an error.

Add --recent-actions action_x,action_y for a context-aware query (see "Variable-order context" below) — output becomes {"matched_order": N, "candidates": [...]} instead of one line per candidate.

As a library, PriorBook::candidates() gives you every (state, action) candidate across the whole book as a flat Vec<(String, PriorAction)>, for callers filtering or sampling candidates directly (e.g. building a domain-specific "opening suite") instead of working through the nested per-state structure entries_sorted() returns.

Other commands

lineprior summary prior.jsonl      # coverage, average confidence, per-state entropy
lineprior validate observations.jsonl   # parse and report issues without building

Input schema

One JSON object per line:

{"sequence_id":"case-001","step":0,"state":"state_a","action":"action_x","outcome":"success","score":0.8,"weight":1.0,"tags":["trusted"],"observed_at_unix_seconds":1783540000,"source":"engine_v012"}

Required: sequence_id, step, state, action. Optional, with defaults: outcome (unknown), score (null), weight (1.0), tags ([]), observed_at_unix_seconds (null, only consulted when time decay is enabled — see "Time decay and source reliability" above), source (null, only consulted via --source-weights).

Output schema

One JSON object per state, actions ranked by descending prior:

{"state":"state_a","actions":[{"action":"action_x","count":3,"weighted_count":3.0,"success_rate":0.667,"mean_score":0.633,"prior":0.557,"confidence":0.130}]}

success_rate and mean_score are the raw, unsmoothed observed rates (for transparency); prior is the smoothed, normalized ranking score; confidence is a heuristic sample-size indicator by default, or a real Wilson-bound statistical lower bound under --confidence-mode wilson-lower-bound/hybrid (see "Confidence modes" above). success_rate credits a success outcome as 1.0, a draw as --draw-value (default 0.5), and a failure as 0.0.

lineprior build's CLI output (and the library's save_prior_book_with_config) prepends a header line carrying a fingerprint of the BuildConfig used to build it, e.g. {"build_config_fingerprint":7592859384087124328}. load_prior_book/lineprior query/lineprior summary all skip this line transparently — it doesn't change how you read a prior book day to day.

With --context-order > 0, some lines additionally carry a context field — see "Variable-order context" below.

Detecting a stale cached prior book

If you cache a prior book on disk and rebuild it later under different BuildConfig values (a different --smoothing-alpha, --confidence-k, etc.), the raw confidence/prior numbers in the old file were computed under the old config's semantics — reusing it silently can be misleading. As a library:

// When saving, embed the config that produced it:
save_prior_book_with_config(&book, &config, writer)?;

// Later, check a cached file against your current config before trusting it:
match load_prior_book_with_config(reader, &config) {
    Ok(book) => { /* config matches (or the file predates this check) */ }
    Err(Error::BuildConfigMismatch { .. }) => { /* stale -- rebuild */ }
    Err(e) => { /* other error */ }
}

A file saved via plain save_prior_book (or by a version of lineprior that predates this) has no fingerprint to compare against, so load_prior_book_with_config accepts it unconditionally — there's nothing to detect drift against. The fingerprint is stable within a given lineprior version, not guaranteed forever-stable across upgrades (it hashes a JSON encoding of BuildConfig, and floats' exact byte layout isn't itself a cross-version guarantee) — it's meant to catch a stale cache within one project's lifetime, not serve as a long-term archival checksum.

Upgrading to a lineprior version that adds new BuildConfig fields (like confidence_mode/confidence_z, time_decay_half_life_days/source_weights, or context_order) changes the fingerprint for every config, even when the new fields are at their inert defaults (heuristic mode, decay disabled, no source weights) — so a prior book cached before upgrading will trip BuildConfigMismatch once after upgrading. That's the fingerprint mechanism working as intended, not a regression.

Limitations

By default (--confidence-mode heuristic), confidence is a sample-size heuristic (weighted_count / (weighted_count + k)), not a statistical confidence interval. This remains the default for backward compatibility and for score-only datasets with no outcome labels. --confidence-mode wilson-lower-bound/hybrid give an actual statistical lower bound on the success rate when outcome data is meaningful (see "Confidence modes" above) — but they're still a lower bound on the observed rate, not a guarantee about future actions if the underlying data is biased or non-stationary.
A low-sample action does not get reported as certain just because it has a 100% success rate from one observation — smoothing pulls it toward the dataset's overall rate.
lineprior never invents actions: an unseen state or a state with no candidates above threshold returns an empty result.
The library does not parse any domain-specific format (SFEN, CSA, USI, FEN, PGN, etc.) — that mapping is the caller's job.

Examples for two domains

The same observations.jsonl shape works whether the "state" is a board position or a UI screen:

Automation:
  state  = "checkout_page"
  action = "click_pay_button"

Optimization:
  state  = "partial_solution_hash_42"
  action = "branch_left"

Domain-specific mappings (e.g. a chess/shogi position as state, a UCI/USI move as action) belong in adapters outside this crate, not in lineprior itself.

For a real domain example: examples/shogi_opening.jsonl uses state = an SFEN string and action = a USI move, the mapping described in AGENTS.md's Sekirei integration notes. Its generated prior (examples/shogi_prior.jsonl) ranks 7g7f above 2g2f despite 2g2f's raw observed rate being higher (100% vs. 83%) — 7g7f has one more supporting observation, and smoothing correctly refuses to let 2g2f's smaller sample outrank it on a single-observation-driven perfect record.

The non-game fixture examples/ui_automation.jsonl maps screen states to UI actions such as click:add-to-cart. The same CLI round-trip is available in examples/python/roundtrip.py and examples/node/roundtrip.mjs; both assert that repeated builds are byte-deterministic and that querying the built book returns the expected action. These are CLI integration examples, not language bindings: the Rust CLI remains the authoritative implementation until a maintained Python or WASM package is justified.

WASM / JavaScript boundary

The lineprior-wasm workspace crate exposes two thin wasm-bindgen functions: build_json takes JSONL observations plus a serialized BuildConfig and returns JSON containing sorted entries, warnings, and build stats; query_json takes a JSONL prior book and returns ranked candidates. They keep Rust scoring authoritative and return JavaScript errors for invalid input. The crate has no file I/O or domain-specific state representation. npm/wasm-pack packaging and browser smoke testing are not claimed yet.

Performance

Measured on an Apple M4 (macOS 26.5.1), release build, 1,000,000 observations across 50,000 unique (state, action) pairs (1,000 states × 50 actions):

wall-clock:        1.71s
peak RSS:          ~15.4 MB

Reproduce with:

awk 'BEGIN{
  for (s=0; s<1000; s++) for (a=0; a<50; a++) for (i=0; i<20; i++)
    printf "{\"sequence_id\":\"seq_%d_%d_%d\",\"step\":0,\"state\":\"state_%05d\",\"action\":\"action_%03d\",\"outcome\":\"%s\",\"score\":%.2f,\"weight\":1.0}\n", \
      s, a, i, s, a, (i % 3 == 0 ? "failure" : "success"), 0.5 + (i % 10) * 0.01
}' > large.jsonl
cargo build --release
time ./target/release/lineprior build large.jsonl --out /dev/null --min-count 1

Memory is now genuinely bounded by unique (state, action) pairs rather than total observation count, matching AGENTS.md's MVP performance goal: the CLI's build command streams straight from the input file into the prior book via build_prior_book_from_reader, folding each observation into a bounded accumulator as it's parsed instead of collecting a Vec<Observation> first. Peak RSS on the measurement above dropped from ~199MB (the old, fully-materializing path) to ~15.4MB — about 13x less, for the same 1,000,000-observation input and identical output.

Smaller, checked-in benchmarks live in crates/lineprior/benches/scoring.rs (run with cargo bench -p lineprior), covering both the eager build_prior_book and the streaming build_prior_book_from_reader at 1k/10k/50k-observation scales. A dedicated regression test (crates/lineprior/tests/streaming_memory.rs, Linux-only, runs in CI) fails if peak memory ever creeps back up toward the old per-observation scaling.

Evaluating a prior

A prior is only useful if it actually ranks the right action highly on data it wasn't built from. lineprior eval holds out part of the observation log, builds a prior from the rest, and reports ranking-quality metrics on the held-out slice:

lineprior eval observations.jsonl \
  --split-by sequence --train-ratio 0.8 --top-k 1,3,5 --out eval.json

The split is by sequence_id, not by individual observation, so every step of the same sequence lands on the same side — otherwise later steps could leak information about earlier ones across the train/test boundary. The split is a deterministic hash of the id, so re-running eval with the same --train-ratio reproduces the same split.

Headline fields in the JSON report:

top1_hit_rate / topk_hit_rate: how often the actual action taken was the prior's #1 pick (or within its top-k), among test observations where the prior had any candidate at all.
mean_reciprocal_rank: the same idea averaged over rank (1/rank, 0 if the action wasn't among the candidates), a softer signal than a hard hit/miss cutoff.
success_weighted_top1_hit_rate / success_weighted_mean_reciprocal_rank: the same two metrics, but each test observation is weighted by its outcome credit (a win counts fully, a draw counts for --draw-value, a loss or unrecorded outcome counts for nothing and drops out of the average entirely) instead of counted equally. top1_hit_rate can be inflated by matching actions that went on to fail — this restricts "did the prior agree with what was actually taken" to trials that actually worked. None when nothing in the test set earned positive credit.
failure_agreement_top1_hit_rate: the counterweight — top1_hit_rate restricted to test observations whose outcome was exactly failure. A high value here is a warning sign: the prior's top pick agrees with actions that are known to have failed. Caveat: all three of these credit/blame each observation by its own outcome field, not by a sequence's eventual result — if your data records a terminal outcome by copying it onto every step, an early good move in an eventually-lost sequence is scored as a failure too. This is a property of how outcome was recorded, not something these metrics can correct for. None when the test set has no failure observations.
coverage vs. fallback_rate: these intentionally do not sum to 1. coverage is state-weighted (the fraction of distinct test states for which the prior returned any candidate); fallback_rate is observation-weighted (the fraction of test observations whose state had none). One rarely-seen state with no candidates barely moves fallback_rate but still costs a full point of coverage — the report also includes the raw counts each rate is computed from, so either framing can be double-checked directly.

lineprior eval --help lists the full set of build-equivalent tuning flags (--min-count, --smoothing-alpha, --confidence-mode, --time-decay-half-life-days, --source-weights, etc.) — eval builds its train-side prior under the same knobs a real build run would use, so the two stay comparable.

Confidence calibration and threshold sweep

--calibration-bins/--thresholds turn eval into a selective-prediction tool: instead of just "how good is the prior overall," they answer "if I only trust the prior above confidence X, how much of my data can I still act on, and how accurate is it?"

lineprior eval observations.jsonl \
  --confidence-mode wilson-lower-bound \
  --calibration-bins 10 \
  --thresholds 0.3,0.5,0.7,0.9

confidence_calibration (from --calibration-bins N): N equal-width bins over [0, 1], always exactly N entries regardless of how many observations landed in each. Each bin reports top1_hit_rate/mean_reciprocal_rank among evaluated test observations whose #1 candidate's confidence fell in that bin — a well-calibrated confidence mode should show hit rate tracking bin confidence roughly 1:1.
threshold_sweep (from --thresholds): one entry per requested threshold, always in the requested order. covered_fraction is the fraction of all test observations where the state had a candidate and its #1 confidence was >= min_confidence; abstained_fraction = 1.0 - covered_fraction. These are a different weighting convention than the top-level coverage/fallback_rate above — both are observation-weighted here and sum to 1 by construction, whereas the top-level pair deliberately doesn't. top1_hit_rate/ mean_reciprocal_rank in each entry are computed among covered observations only (accuracy given a prediction was actually made), the same "conditioned on evaluated" convention the headline metrics already use.

Both are omitted (empty arrays) unless explicitly requested, so existing eval usage is unaffected.

Off-policy evaluation (explicitly opt-in)

The library also exposes evaluate_self_normalized_ips in a separate evaluation module. The caller must provide the logging policy propensity and the evaluated policy's probability for the action that was actually logged; lineprior never infers a counterfactual reward from a prior. The report includes ordinary IPS, self-normalized IPS, support fraction, overlap failures, and Kish effective sample size. Zero-support rows and importance weights above an optional cap are reported as overlap failures rather than silently treated as losses.

evaluate_doubly_robust is also available when the caller supplies both a reward-model estimate for the evaluated policy's expected reward and a reward-model estimate for the logged action. It adds the propensity-weighted residual correction to the model baseline; rows without overlap use the baseline only and remain visible in the support diagnostics.

bootstrap_self_normalized_ips adds deterministic percentile intervals for IPS and self-normalized IPS. Its seed, resample count, and confidence level are explicit; resamples with no supported rows are counted as skipped. This supports replayable uncertainty checks but does not replace a held-out, real-data evaluation.

The same diagnostics are available from the CLI with lineprior offpolicy log.jsonl --out report.json. Add --doubly-robust when every row contains the two reward-model fields, and --bootstrap-resamples N --bootstrap-seed S for deterministic intervals. The JSONL input is one OffPolicyObservation per line; malformed rows or invalid propensities exit with code 3.

The checked-in examples/offpolicy.jsonl is a small valid input boundary fixture. For example:

lineprior offpolicy examples/offpolicy.jsonl --out /tmp/lineprior-offpolicy.json \
  --doubly-robust --bootstrap-resamples 128 --bootstrap-seed 42

These are estimators and audit surfaces, not proof of causal improvement. Valid propensities, overlap, uncertainty intervals, and a held-out downstream comparison remain the caller's responsibility. A reward model is supplied by the caller and is never trained or inferred by lineprior.

Variable-order context

By default the prior is order-0: state -> action, with no memory of what happened earlier in a sequence. --context-order k additionally learns (recent-k-actions, state) -> action for order 1..=k, derived automatically from each sequence's own sequence_id/step history — no schema change, no new observation field. 0 (the default) disables this entirely; every existing book, config, and query behaves exactly as before.

lineprior build observations.jsonl --out prior.jsonl --context-order 2
lineprior query prior.jsonl --state state_a --recent-actions action_x,action_y
lineprior eval observations.jsonl --context-order 2

Backoff and transparency. A context-aware query tries the longest available context first, then "stupid backoff" — no interpolation smoothing — to shorter context, down to the plain order-0 lookup as the final rung. lineprior query --recent-actions prints {"matched_order": N, "candidates": [...]}; N is which depth actually answered the query (0 meaning plain state-only), the same "how much evidence backs this" transparency confidence already gives per action. Without --recent-actions, query is byte-for-byte unchanged.

Sortedness precondition. Deriving a sequence's own recent-action window while streaming requires that sequence's rows be contiguous in the input, with strictly increasing step — only enforced when --context-order is nonzero. A violation is a hard error (SequenceNotSorted, exit code 3) independent of --strict: it's a structural precondition on the whole stream, not a per-record validity question --strict/non-strict already governs. If your data isn't already grouped this way, sort it first (jq -s 'sort_by(.sequence_id, .step)[]' or similar).

Output schema. A context entry adds a context field (the recent-action window, oldest first) to the usual {"state": ..., "actions": [...]} line: {"state":"state_a", "context":["action_x"],"actions":[...]}. Order-0 entries never carry this field, so a book built with --context-order 0 (the default) serializes identically to before this feature existed.

Memory. Peak memory grows from "bounded by unique (state, action) pairs" to "bounded by unique (state, action) pairs at order 0, plus unique (context, state, action) tuples across every order 1..=k" — an inherent cost of the feature (more precision needs more storage), not a regression. crates/lineprior/tests/streaming_memory.rs has a regression test for this shape too.

Evaluating whether context actually helps. lineprior eval --context-order k reports two new top-level fields alongside the usual order-0 ones, computed over the same test observations in the same run: context_top1_hit_rate / context_mean_reciprocal_rank (the context-aware counterparts of top1_hit_rate/mean_reciprocal_rank, which themselves stay order-0). The difference is the lift (or cost) context provides — a single-run, apples-to-apples comparison rather than two separate runs whose headline field would otherwise silently mean different things. hit_rate_by_matched_order breaks accuracy down by the depth backoff actually reached (not just how often each depth was reached), answering "is deeper context more accurate when available, or just rarer." All three are empty/None at --context-order 0. lineprior tune surfaces the same two fields per candidate in all_results, so --param context-order=0,1,2,3 sweeps show the lift directly — no new --objective needed, since the existing objectives already read the order-0 fields those sweeps vary.

Credit-assignment caveat, same shape as the outcome-weighted eval metrics above: context is derived purely from step order — it has no opinion on whether deeper context is causally meaningful for your domain, only on whether it's statistically predictive on your held-out data. Always check context_top1_hit_rate against the plain top1_hit_rate baseline before trusting a context-aware prior; a domain where state already encodes recent history (e.g. a full board position) may see little or no lift, and that's a legitimate, informative result — not a bug.

Opt-in similarity fallback

PriorBook::query_with_similarity accepts neighbors supplied by the caller, each with a state, non-negative distance, and opaque provenance label. It applies deterministic exponential distance weighting and returns only actions observed in those neighbor states; unknown actions are never invented. SimilarityConfig can cap neighbors or distance, and each result retains its evidence so callers can audit which states supported an action.

This is an integration boundary, not an embedding or vector-database implementation. Exact-match querying and abstention remain the default. The returned confidence is a weighted summary of the source confidences, not a new statistical guarantee, so callers should validate similarity fallback against an unseen-state split before enabling it in a decision loop.

The deterministic boundary fixture at crates/lineprior-similarity/tests/fixtures/unseen_states.jsonl and its integration test compare exact-match, no-prior abstention, and opt-in similarity recovery. It is a contract check only; it does not establish real-data quality or justify enabling similarity by default.

Sequence-level priors

PriorBook::score_sequence(path: &[(String, String)]) -> SequencePriorScore scores a caller- supplied candidate multi-step plan — how much historical precedent backs each step, and the plan as a whole — by walking context-aware backoff at each step:

let path = vec![
    ("state_a".to_string(), "action_x".to_string()),
    ("state_b".to_string(), "action_y".to_string()),
];
let score = book.score_sequence(&path);
// score.steps[i]: { state, action, matched_order, found, prior, confidence }
// score.min_confidence: the weakest-linked step's confidence, or None if none matched
// score.unseen_steps: how many steps had no historical precedent at all

Each step's context is the plan's own prior steps' actions (oldest first, mirroring how --context-order derives context while building) — not something the caller passes separately. lineprior has no model of environment dynamics: given (state, action) it doesn't know what state results, so the caller (who owns that mapping — their own planner or simulator) must supply both state and action at every step.

Aggregation is min, not an average. A chain is only as strong as its weakest link; averaging would let one very-weakly-supported step hide behind stronger ones, which cuts against "prior, not oracle" transparency. min_confidence is None (not 0.0) when every step is unseen — the same "absent data isn't a bad score" rule used elsewhere. Check steps directly, not just the aggregate, when unseen_steps > 0.

Backoff-shadowing caveat. Each step reuses query_with_context verbatim: whichever context depth resolves is the only depth searched for the caller's action. A sparse deep-context match on other actions can shadow abundant order-0 support for the action actually asked about, reading as found: false even though the action is well-supported at a shallower depth. This is the safe direction (under-reporting support, never over-reporting) and matches what query_with_context itself would have suggested to a caller asking "what should I do here" — not a bug, but worth knowing before treating found: false as "truly never seen."

Deliberately library-only. No CLI subcommand and no eval/tune integration in this round — a (state, action) path doesn't fit a comma-separated CLI flag, and scoring held-out sequences against their outcome would require inventing a "sequence's terminal outcome" concept the core model deliberately doesn't have an opinion on (see the credit-assignment caveat above). Both are natural upgrade paths if real demand shows up.

Tuning: choosing a BuildConfig automatically

eval scores one config at a time; tune grid-searches many and picks the best one, using the same deterministic train/test split for every candidate so they're directly comparable:

lineprior tune observations.jsonl \
  --split-by sequence --train-ratio 0.8 \
  --param confidence-mode=heuristic,wilson-lower-bound,hybrid \
  --param min-confidence=0.0,0.3,0.5,0.7 \
  --param smoothing-alpha=1.0,5.0,10.0 \
  --param time-decay-half-life-days=none,30,90 \
  --time-decay-reference-unix-seconds 1783540000 \
  --objective covered-mrr --min-covered-fraction 0.4 \
  --out tune.json --save-best-config best_config.json

Each --param key=v1,v2,... sweeps one BuildConfig field (repeat --param for more than one); any field never named in a --param stays at its BuildConfig::default() for every candidate. Supported keys: confidence-mode, min-confidence, smoothing-alpha, confidence-k, confidence-z, min-count, min-weighted-count, draw-value, time-decay-half-life-days (accepts none), default-source-weight, count-weight, success-weight, score-weight. --time-decay-reference-unix-seconds is a single value applied to every candidate (never swept) — required whenever a swept time-decay-half-life-days value isn't none, same reproducibility rule build/eval already use.

--objective (default covered-mrr) is what candidates are ranked by:

objective	meaning
`mrr`	`mean_reciprocal_rank`, among covered test observations only
`top1`	`top1_hit_rate`, among covered test observations only
`covered-mrr` (default)	`covered_fraction * mean_reciprocal_rank` — MRR averaged across all test observations, an uncovered one contributing `0`
`top1-at-min-coverage`	same as `top1`, but requires `--min-covered-fraction` also be set
`success-weighted-mrr`	`success_weighted_mean_reciprocal_rank` — like `mrr`, but a failed or unrecorded-outcome test observation contributes nothing
`success-weighted-top1`	`success_weighted_top1_hit_rate`, the same idea applied to `top1`

covered-mrr is the default because optimizing mrr alone tends to pick configs that abstain (report no candidate) except when very confident, while optimizing coverage alone tolerates a sloppy prior — covered-mrr penalizes both.

--min-covered-fraction / --max-fallback-rate / --min-top1-hit-rate reject a candidate from being best, but it still shows up in the JSON report's all_results (with meets_constraints: false) so you can see what got excluded and why, rather than it silently vanishing.

The JSON report's pareto_front is the non-dominated set over (mrr, covered_fraction) — every config on it is the best some MRR/coverage tradeoff, independent of --objective, in case you'd rather eyeball the tradeoff yourself than trust the single best pick.

--save-best-config best_config.json writes the winning candidate's BuildConfig as JSON; build and eval both accept it back via --config best_config.json (errors if combined with any individual build-config flag like --min-count, since it's a whole-config replacement, not an overlay) — so a config chosen once by tune is reused exactly, not re-typed by hand:

lineprior build observations.jsonl --out prior.jsonl --config best_config.json

tune is exactly as domain-agnostic as the rest of lineprior (it only ever sees state/ action/sequence_id/outcome data) and doesn't change what lineprior fundamentally is — a prior, not an oracle. It automates what you'd otherwise do by hand-sweeping eval; it doesn't make the resulting prior any less something the caller should verify before acting on.

Gate outcome prediction (library only)

A different question from the rest of this crate: not "what action should I take," but "is this training candidate worth an expensive real evaluation (a 'gate' run of many games) at all?" GateModel::fit/GateModel::predict (in gate.rs) fit a small, regularized surrogate that predicts a candidate's real-gate Elo delta -- and how much to trust that prediction -- from cheap validation-time diagnostics, so gate runs can be reserved for candidates likely to be worth them.

let output = GateModel::fit(&observations, &GateModelConfig::default())?;
// output.report: selected_lambda, weighted_rmse, and a probability_positive calibration report --
// check this before trusting predictions from output.model.

let prediction = output.model.predict(&GateQuery { features });
// prediction.expected_elo, .interval_low/.interval_high, .probability_positive,
// .leverage, .support_distance, .nearest_group_distance, .missing_feature_fraction,
// .prediction_status, .recommend_for_gate

predict_verdict maps the Gaussian latent-Elo posterior into PASS, FAIL, and INCONCLUSIVE regions using explicit GateVerdictConfig thresholds. acquire computes standard expected improvement over an incumbent baseline_elo, divided by a caller-supplied expected gate cost; it does not multiply EI by probability_positive a second time. Both surfaces preserve the model's OOD recommendation flag.

lineprior gate gate_history.jsonl --feature valid_cp_mse_delta=0.12 \
  --feature output_std=0.03 --monotonic valid_cp_mse_delta=increasing \
  --expected-gate-cost 100 --out gate-report.json

The gate CLI fits the experimental model from strict JSONL GateObservation rows and optionally emits prediction, three-way verdict probabilities, acquisition score, and fit diagnostics. The GateModel remains in the main crate: the CLI currently has no independent schema or dependencies, so extracting lineprior-gate would add packaging surface without a demonstrated consumer boundary.

Monotonic constraints are opt-in. GateModelConfig::monotonic_constraints projects named coefficients to the requested increasing/decreasing sign orthant. The constrained fit is a conservative shape constraint, and its closed-form ridge uncertainty is only an approximation; validate it against real gate history before using it for scheduling.
Named features, not a fixed schema. GateObservation.features/GateQuery.features are a caller-named BTreeMap<String, f64> (e.g. valid_cp_mse_delta, output_std, conflict_rate), so the diagnostic set can evolve without a schema break. Deliberately excludes anything like a training seed -- a categorical id, not a quantity a linear model can treat as "more" or "less."
Group-aware, not a random split. GateObservation.group_id is an opaque caller-composed key (e.g. an experiment family/recipe/lineage/dataset version joined together) used for k-fold cross-validation when selecting the ridge regularization strength -- never parsed by this crate. Falls back to leave-one-group-out when fewer than the requested fold count has distinct groups.
Uncertainty is latent-strength confidence, not next-gate-run noise. interval_low/ interval_high -- on both GatePrediction and GateOofPrediction below -- describe how much to trust the point estimate as a read on the candidate's true strength (a closed-form Bayesian-ridge posterior variance), not the added sampling noise of one hypothetical future gate match. This holds everywhere in the module; it is not a per-call opt-in. A missing feature at query time is imputed as its training-set mean and reported via missing_features, never invented silently.
Validating Round A itself, before building anything on top of it. GateModel::fit_with_validation returns everything fit does, plus a per-candidate out-of-fold audit table:
```
let validated = GateModel::fit_with_validation(&observations, &GateModelConfig::default())?;
// validated.interval_level: the two-sided confidence level interval_low/interval_high represent
// (e.g. ~0.95 at the default interval_z), stated once here rather than repeated per row.
for row in &validated.oof_predictions {
    // row.candidate_id, .group_id, .actual_elo, .predicted_elo, .residual, .prediction_stddev,
    // .interval_low/.interval_high, .probability_positive, .outer_fold, .inner_selected_lambda,
    // .leverage, .support_distance, .nearest_group_distance, .missing_feature_fraction,
    // .prediction_status, .recommend_for_gate
}
```
Every row comes from the same nested group cross-validation report.weighted_rmse/ report.calibration are built from -- not a second CV run solely to populate the table, so the aggregate metrics and the per-row audit can never describe a different population of predictions. Rows are sorted deterministically by (outer_fold, group_id, candidate_id), and a repeated candidate_id in the input is preserved as separate rows rather than collapsed. fit itself is a thin wrapper over fit_with_validation that discards the table (still computed either way -- this only spares a caller who only wants the model from receiving/reading it) -- both share one fitting path, so the two entry points can never disagree about the model or its aggregate metrics.
Elo observation uncertainty: not every label is equally trustworthy. A GateObservation may carry actual_elo_stddev (or elo_ci_low/elo_ci_high, from which a stddev is implied assuming a symmetric normal interval at GateModelConfig::observation_ci_z -- a separate knob from interval_z, since the caller's CI was computed at whatever confidence level they used, independent of how wide this model's own output intervals should be) alongside gate_elo_delta -- a 20-pair burn-in Elo and a 1700-pair formal-gate Elo are not equally trustworthy teacher labels. When present, this becomes the ridge fit's reliability weight (1 / stddev^2, inverse-variance) in place of the gate_games_played-based weight, per row -- mixed datasets (some rows with a stated stddev, some without) combine correctly. completed_pairs and gate_status (this candidate's actual historical PASS/FAIL/INCONCLUSIVE verdict) are also accepted, audit-only for now -- never fed into features or the fit, same reasoning as the existing training_seed exclusion. A provenance: BTreeMap<String, String> field carries opaque caller-composed provenance (experiment/dataset/ teacher-manifest ids, seeds, schema version, ...), same "never parsed by this crate" convention as group_id.
Exactly one uncertainty source per observation, never a silent priority. Specifying both actual_elo_stddev and a complete elo_ci_low/elo_ci_high pair on the same observation is rejected (Error::ConflictingGateUncertaintySources), as is providing only one bound of a CI (Error::IncompleteGateConfidenceInterval) or a gate_elo_delta outside its own stated [elo_ci_low, elo_ci_high] (Error::GateEloOutsideConfidenceInterval). An observation with neither falls back to the gate_games_played-based weight, unchanged.
Extreme per-row weights can't dominate the fit. After normalizing to mean 1.0, each row's reliability weight is clamped to [1 / max_weight_ratio, max_weight_ratio] (GateModelConfig::max_weight_ratio, default 100.0, must be >= 1.0) -- otherwise one observation with a tiny stated actual_elo_stddev (a data-entry slip, or a genuinely near-noiseless measurement) could produce an inverse-variance weight thousands of times any other row's and effectively dictate the fit alone. This clamp is deliberately not followed by a second renormalization, since re-normalizing clamped weights back to mean 1.0 could push a just-clamped weight back outside the bound it was promised; the trade-off is that the weight mean can drift (only when the clamp actually engages) instead. GateFitReport surfaces min_observation_weight/max_observation_weight/effective_sample_size/ clamped_observation_count so this isn't a silent safety net.
GateFitReport.dispersion_factor: a calibration check on the stated stddevs themselves. Some only when every observation in the fit supplied a usable stddev -- an out-of-fold reduced chi-square (sum((actual_elo - predicted_elo)^2 / stddev^2) / n, from the same nested-CV predictions weighted_rmse/calibration are built from). Roughly 1.0 means the stated stddevs are well-calibrated against how far predictions actually land from real outcomes; >> 1 means real noise exceeds what's being reported or the linear model is missing structure (the two aren't separable by this statistic alone); << 1 means stated stddevs are overstated.
Out-of-distribution abstention: reported, not enforced. In GateOofPrediction, every OOD quantity below is computed from a support model (standardizer, group centroids, mean leverage) fit on only that outer fold's training rows -- the same rows the fold's coefficients came from, never the held-out row itself or any other fold's rows. The final deployed GateModel (after all CV) fits its own support model on the complete training set, same as its coefficients. Every GatePrediction/GateOofPrediction also carries leverage (the ridge-analogue hat/leverage term, growing without bound the further a query sits from the training feature mean), support_distance (leverage.sqrt()), nearest_group_distance (standardized-space distance to the nearest training group's centroid), missing_feature_fraction, and prediction_status (Supported/Extrapolation/Unsupported). expected_elo/probability_positive are still computed and returned regardless of status -- same "report, don't refuse" convention as missing_features -- a caller decides for itself whether to act on Extrapolation/Unsupported. Classification checks missing_feature_fraction first and unconditionally (GateModelConfig::ood_missing_fraction_threshold, default 0.5): an all-missing query imputes to the training feature mean, the lowest possible leverage, which would otherwise look like maximum support while carrying zero real information. Otherwise Extrapolation fires when leverage exceeds ood_leverage_ratio_threshold (default 3.0) times the model's own mean leverage (df / n_eff, the ridge-correct analogue of the OLS hat matrix's uniform p/n -- not the classical 2p/n/3p/n rule of thumb, which assumes uniform OLS leverage and doesn't hold once lambda > 0), or when nearest_group_distance exceeds the largest nearest-neighbor distance among the training groups' own centroids (a self-calibrating reference scale, no arbitrary constant).
recommend_for_gate: the yes/no gating decision, without faking the estimate. Exactly prediction_status == Supported, nothing else -- a caller that only wants a boolean reads this instead of matching on prediction_status itself. expected_elo/interval_low/interval_high/ probability_positive are always the model's real prediction, even when recommend_for_gate is false: an out-of-distribution query is flagged, never silently zeroed or replaced.
This remains an experimental diagnostic surface. Verdict probabilities, acquisition, and monotonic constraints are implemented, but real gate-history calibration and downstream validation are still required before scheduling expensive runs. The CLI is intentionally thin and the model remains in the main crate until an independent schema/dependency boundary appears.

Academic positioning

lineprior is an engineering-oriented Rust implementation inspired by existing ideas in case-based planning, plan reuse, sequence prediction, variable-order Markov models, and policy-guided search. It is not a new theoretical algorithm.

Development

cargo fmt --all -- --check
cargo clippy --all-targets --all-features --locked -- -D warnings
cargo test --all-features --locked
RUSTDOCFLAGS="-D warnings" cargo doc --workspace --all-features --no-deps --locked
cargo deny check licenses
sh scripts/check_candidate_contract.sh
sh scripts/run_examples_smoke.sh
sh scripts/run_wasm_build_smoke.sh

Dependency licenses are checked against the narrow SPDX allowlist in deny.toml; adding a new license requires an explicit policy review.

The candidate contract script checks the fixed workspace version, JSON fixtures, language-example syntax, formatting, and whitespace. It does not replace runtime tests, WASM packaging, or real-data measurement gates.

After building lineprior-cli, sh scripts/run_examples_smoke.sh runs the maintained Node.js and Python round-trip examples against the same binary. CI runs this smoke workflow; it checks the Rust-CLI integration boundary, not WASM packaging or real-data quality.

The same built binary can run sh scripts/run_offpolicy_smoke.sh to evaluate the checked-in OPE fixture twice and compare the complete JSON report, including IPS, DR, and the bootstrap seed. This is a replayability check, not evidence of causal improvement.

With the wasm32-unknown-unknown target installed, sh scripts/run_wasm_build_smoke.sh verifies that the lineprior-wasm crate compiles with the locked dependency graph. This is a compilation boundary only; npm/wasm-pack packaging and browser execution remain separate gates.

See CHANGELOG.md for release history, including which versions are published to crates.io and, from 0.9.0 on, notes on Rust source compatibility (distinct from JSON/serde input compatibility) for public API changes.

Pluggable scoring strategies

BuildConfig::scoring_strategy supports WeightedSum (the backward-compatible default), Bayesian, Ucb, and Softmax. The CLI exposes these through --scoring-strategy and strategy parameters. These are ranking strategies, not quality guarantees.

Compact binary books

JSONL remains the interchange format. Deterministic LPB v1 is available for local caching via save_prior_book_binary / load_prior_book_binary, or the CLI's lineprior pack and lineprior unpack. It preserves context entries, has a magic/version header and allocation caps, and rejects trailing bytes.

veridict on/off recipe

See examples/veridict_prior_comparison.md and its manifest. The checked-in recipe is protocol-only until a real veridict run supplies downstream evidence.

Macro-actions and multi-source merge

build_macro_actions extracts bounded contiguous action windows from ordered histories. It is intentionally eager because a sequence window must be held in memory; the normal streaming builder is unchanged. Independently-built books can be combined with merge_prior_books and explicit PriorBookSource weights, including context entries.

The official typed domain boundaries are in lineprior-adapters and cover Sekirei, UI automation, LLM-agent tool traces, and retrosynthesis. They keep domain values opaque and do not claim legality, execution success, or chemical validity.

Terminal credit and Trie representation

Set BuildConfig::terminal_credit_weight (or --terminal-credit-weight) to propagate the final known outcome of each sequence back to its kept steps. The default 0.0 preserves per-step labels; the opt-in mode buffers only the current sequence and requires grouped input.

PriorBook::to_trie() materializes context entries into a deterministic PriorTrie for repeated longest-suffix queries. The flat book remains the canonical serialization format, and trie performance is still a measurement item rather than a quality claim.

lineprior-cli 0.11.0