zephyr27/SMA-SSB

Structure Synthesis Benchmark (SSB)

Dataset summary

The Structure Synthesis Benchmark (SSB) is a zero-lexical-overlap structural retrieval benchmark designed to test whether a retrieval system matches on relational structure alone, with no surface vocabulary shared between the query and the target analog.

SSB is part of the SMA-1 evaluation suite (paper: "Structure-Mapping Agentic Memory", Ayaz Khan, 2026, under review at Nature Machine Intelligence).

Headline result (from reports/confirmatory/ssb_summary.csv, seeds 41 and 43, n = 100 per seed):

Method	Forced-choice r@1	Library r@1
SMA (structure-mapping)	1.000 (SD 0.000)	0.895 (SD 0.007)
BM25	0.000 (SD 0.000)	0.000 (SD 0.000)
TF-IDF Dense	0.000 (SD 0.000)	0.000 (SD 0.000)

Cliff's δ = 0.895, p_Holm = 0.0004 (paired bootstrap, 10 000 resamples).

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Dataset construction

Core principle: disjoint per-triple vocabularies

Each benchmark triple (base, target, distractor) uses independently sampled random functor names so that the query and its true analog share zero surface vocabulary. The only bridge is a declared predicate lattice: the query functor and the analog functor are mapped to the same abstract concept in the lattice, and retrieval requires ascending at most δ = 2 hops (penalised by ρ^distance = 0.95^hops).

This construction directly operationalises Gentner's systematicity principle: a system that matches on surface co-occurrence will score every candidate equally (zero lexical signal); only a system that ascends the predicate lattice to find structural correspondences will score the true analog higher.

Previous (broken) construction — what we fixed

Prior versions used a far_ / near_ prefix naming trick: the same functor vocabulary was reused across triples, and the "far" analog was simply the renamed version. This was circular — the benchmark unknowingly told the system which triple was the analog. The current generator eliminates this entirely:

• Each triple receives a fresh randomly-sampled vocabulary (UUID-derived functor names with no shared substrings).
• Distractors are star-rewired (provably non-isomorphic to chain-structured analogs for width ≥ 3) so a correct matcher cannot accidentally score a distractor 1.0.
• The MAC screening stage uses the ancestor-closure feature vector (blueprint §2.7) so lattice-bridged vocabularies intersect at screening; the Lemma-2 bound remains admissible with ancestor features.

Generation parameters (frozen at prereg-v1)

Parameter	Value
Lattice ascension depth δ	2 hops
Ascension penalty ρ	0.95 per hop
Structural width	≥ 3 (chain structure)
Vocabulary collision probability	< 10⁻⁶ (UUID-space)
Seeds used in confirmatory runs	41, 43 (n = 100 each)
Seeds used in development / calibration	29, 31

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Dataset splits

Split	Purpose	Seeds	n triples	Status
dev	Calibration (rho dial)	29, 31	200	Released
test	Confirmatory evaluation	41, 43	200	Released
forced_choice	One analog + one distractor per query	all	see above	Released
library	Query against a pool of 24 indexed cases	all	see above	Released

The forced-choice split presents each query with exactly one true analog and one star-rewired distractor; r@1 = 1 means the system always ranks the analog first.

The library split presents each query against a 24-case indexed library; r@1 measures whether the structural match tops the library ranking.

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Intended use

SSB is intended as a diagnostic test for retrieval systems that claim to match on relational structure rather than surface similarity:

1. Structural retrieval systems (structure-mapping engines, SME-style, predicate-lattice-based) — the benchmark was designed to reward these.
2. Ablation studies — a system that degrades from structural to surface retrieval should drop from r@1 ≈ 1.0 to r@1 ≈ 0.0 on SSB.
3. Unit testing — the forced-choice split is included in the SMA-1 test suite as gate G4 (test_macfac_lattice_bridges_disjoint_vocabularies).

SSB is not intended as a benchmark for:

• General-purpose information retrieval (no natural language; no text documents)
• Semantic similarity (there is no semantic content — functors are arbitrary symbols)
• Knowledge graph completion (no real-world entities or relations)

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Limitations and caveats

1. Synthetic by construction. SSB uses randomly generated functor names and artificially constructed predicate lattices. Performance on SSB does not directly predict performance on real-domain retrieval tasks (see the 5-domain agentic suite in the SMA-1 paper for real-domain evaluation).

2. The lattice is declared, not learned. The predicate lattice used by SMA is supplied at evaluation time. A system without a compatible lattice (e.g. pure BM25, dense RAG) will score 0.000 by construction — the benchmark is a test of whether the mechanism exists, not how well it generalises to unseen lattices.

3. Chain structure only. The current generator uses chain-shaped base cases (linear predicate chains). Richer topologies (trees, DAGs) are not yet in the benchmark.

4. Small n. Confirmatory evaluation uses n = 100 per seed × 2 seeds = 200 triples. Statistical power is sufficient for the observed effect sizes (δ = 0.895) but marginal tests may lack power.

5. Development contamination risk. The calibration seeds (29, 31) were used to set the rho = 0.95 dial; confirmatory seeds (41, 43) were reserved and never inspected during development. The protocol is documented in configs/preregistration.md.

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Source repository

https://github.com/ayazkhan27/sma-1

Generating script: scripts/confirmatory_battery.py --task ssb

Confirmatory outputs: reports/confirmatory/ssb_{rows,stats,summary}.csv

Gate test: tests/test_macfac.py::test_macfac_lattice_bridges_disjoint_vocabularies

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Citation

@software{khan2026sma,
  author  = {Ayaz Khan},
  title   = {SMA-1: Structure-Mapping Agentic Memory},
  year    = {2026},
  license = {Apache-2.0},
  url     = {https://github.com/ayazkhan27/sma-1}
}

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License

Apache-2.0. See LICENSE at the repository root.

Benchmark construction uses no third-party data; all functor names are randomly generated. The predicate lattice is fully synthetic.