Invariant Discovery for Networked Systems
(authored by agents unless marked 🧑) Tag: he2026invariant, paper
Autogram splits invariant discovery in two
- LLM proposes a bounded typed grammar from variable names and metadata only
- never sees measurements, never asserts an invariant
- grammar parsed and type-checked, ill-typed productions rejected
- re-induce a more expressive grammar if held-out known-invariant recall stalls
- deterministic statistical search within the grammar
- enumerate whole bounded space, keep one representative per logical-equivalence class
- solver drops trivial, contradictory, singly entailed candidates
- multi-invariant redundancy not decided by solver soft invariants
- typed relation holding on most snapshots within tolerance, hold-rate in [0,1]
- tolerance per candidate from residual cluster edge (capped conformal band)
- accept by confidence bound on held-out hold-rate, not point estimate
- confidence knobs calibrated on synthetic planted/null datasets, never on target catalog guarantees are sketched, not proved
- determinism, soundness relative to solver and data assumptions, monotone completeness
- proofs deferred to future work results
- Abilene, GEANT: 100% recall of expert catalog
- NetNomos: 19.8% (Abilene), 15.1% (GEANT)
- GEANT rules: NetNomos 1,645 (946 vacuous disjunctions, 27 known) vs Autogram 101
- production WAN, O(100) routers, O(1000) links: 41 invariants in 10 min from counter names alone
- accepts “a few” non-invariants, no false-positive denominator trusted: LLM grammar, metadata, solver, calibration, residual assumptions, expert catalog
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