Locy Errors and Limits¶

Common Error Classes¶

• Parse errors: invalid Locy syntax.
• Compile errors: invalid dependencies, type/schema mismatches, stratification violations.
• Runtime errors: timeout, iteration limit, memory constraints, mutation constraints.

Operational Limits (via LocyConfig)¶

• max_iterations: recursion cap per recursive stratum.
• timeout: overall evaluation budget.
• max_derived_bytes: derived fact memory bound.
• max_explain_depth: derivation tree depth bound.
• max_slg_depth: goal-directed recursion bound.
• max_abduce_candidates / max_abduce_results: abduction search bounds.
• deterministic_best_by: enforce deterministic tie-breaking in BEST BY clauses.
• strict_probability_domain: reject probability inputs outside [0, 1] instead of clamping.
• probability_epsilon: MPROD threshold for switching to log-space accumulation.
• exact_probability: enable BDD-based exact evaluation for shared-proof aggregate groups.
• max_bdd_variables: cap per-group BDD complexity before fallback.
• top_k_proofs: limit proof enumeration per aggregate group (controls memory/CPU for large proof spaces).

Warning Codes¶

Locy has two warning channels. Runtime warnings (RuntimeWarningCode) surface in result.warnings at evaluation time. Compile-time warnings (WarningCode) surface in compile_warnings when the program is compiled. Both are informational; the program still runs.

Runtime warnings (result.warnings)¶

• SharedProbabilisticDependency: multiple proof paths inside one MNOR/MPROD group reuse shared evidence.
• BddLimitExceeded: exact mode was enabled, but the group exceeded max_bdd_variables and fell back to independence mode.
• CrossGroupCorrelationNotExact: shared evidence spans multiple aggregate groups; each group is exact internally, but correlation across groups is still approximate.
• FuzzyNotProbabilistic: LocyConfig.semiring = MaxMinProb is active and a rule emits PROB. Fuzzy-truth math is being applied to a column declared as a probability; either pick the right semiring or drop the PROB annotation.
• TopKPruningCrossedDependency: under TopKProofs(k), pruning dropped a proof that shared a base fact with a kept proof. The kept set is an approximation; increase k for exactness.

Compile-time warnings (compile_warnings)¶

• SharedNeuralInputArgument: two or more model invocations in the same rule receive the same INPUT variable argument, so their outputs are correlated. Downstream MNOR under-estimates joint risk. Mark the models @independent to suppress.
• SharedNeuralFeatureValue: two or more model invocations in the same rule share an equivalent feature value expression — the same correlation concern even when binding variables differ. @independent suppresses.
• SharedRetrievalContext: multiple similar_to/semantic_match features in the same rule share the same query embedding. The features are not independent of each other; the rule's joint composition may be biased.
• UncalibratedNeuralPredicate: a rule invokes a PROB model declaring no CALIBRATION (or CALIBRATION none); the uncalibrated probability compounds miscalibration downstream.
• UncalibratedLLMLogprobs: an uncalibrated CREATE MODEL whose xervo_alias looks like an LLM provider — raw logprobs are not calibrated probabilities.
• ProbabilityDomainViolation: a probability input fell outside [0, 1] (clamped, or rejected under strict_probability_domain).
• FoldInRecursivePath: a clause has a recursive IS-ref and a FOLD aggregate but no ALONG — almost always a semantic mistake.
• EceBinningBias: VALIDATE METRICS ece was requested; equal-width-binned ECE is biased in the small-sample regime. Prefer debiased_ece.

Recommended Profiles¶

Development¶

• Lower iteration and timeout values.
• Keep deterministic tie-break enabled.

Production¶

• Set explicit timeout and memory budgets.
• Monitor command result sizes.
• Restrict unconstrained QUERY and ABDUCE patterns.

Escalation Playbook¶

1. Reduce goal scope.
2. Add stronger filters.
3. Split large programs by module.
4. Profile expensive rule strata.