ResBased research · Anamnesis Rising system · North Star release

Structured semantic reconstruction

Represent meaning as structured state—then prove the boundary view can be reconstructed.

ANLS studies queryable semantic packets, lineage, deltas, and governed reconstruction with honest baseline comparisons.

PacketsReconstructionBenchmark
Benchmark boundary

Two bounded experiments use different representations and denominators. ANLS used fewer bytes than literal JSON in one synthetic fixture, but zlib was substantially smaller. A separate compact hot-state prototype reduced duplication relative to an earlier expanded project representation. Neither result establishes universal compression, intelligence, network efficiency, or production performance.

Semantic structure

Warm Index

A bounded index for locating structured state.

ANLS packets

Versioned semantic payloads with explicit templates, coordinates, and slots.

Deltas and lineage

Changes remain connected to predecessor state and transformation evidence.

Reconstruction receipts

Proof that an expected boundary view can be rebuilt from governed inputs.

Privacy classification

Publication and access decisions travel separately from semantic representation.

Synthetic fixture byte counts · 80 records

RepresentationAggregate bytesSavings vs literalInterpretation
Literal canonical JSON27,975 B0.0000%Uncompressed literal baseline
Canonical JSONL27,925 B0.1787%One full canonical record per line
zlib level 92,961 B89.4155%Smallest in every measured cohort
Structural template store27,272 B2.5130%Larger than literal on the heterogeneous cohort
ANLS coordinate-plus-delta14,833 B46.9777%Queryable semantic structure with exact reconstruction

Fixture and measurement boundary

The public fixture contains 80 project-generated synthetic records: 64 repeat-heavy billing, claim, and Microsoft 365 migration-assessment records plus 16 heterogeneous governance notes. It contains no customer data and performs no live database read or host storage scan.

Every total includes the payload plus all template, dictionary, index, envelope, or compression-manifest bytes needed for exact reconstruction. The benchmark does not measure wall-clock latency or establish production throughput.

The purpose of ANLS is queryable semantic structure, lineage, and governed reconstruction—not universal compression superiority. zlib remained smaller in every measured cohort.

Bounded compact-state evidence · tradeoffs kept beside results

ExperimentObserved resultRequired interpretation
10K observationsV002 hot-state bytes were 96.45056% lower than the earlier V001 expanded hot store; exact reconstruction and tested query equality passedA project-internal representation comparison—not total machine memory, network bandwidth, or an optimized conventional-system comparison
50K observationsV002 hot-state bytes were 96.657398% lower than the earlier V001 expanded hot store; exact reconstruction and tested query equality passedThe compact hot-plus-cold total was only about 14.74% smaller than the SQLite reference, while replay was about 9.7× slower
80 synthetic records14,833 B versus a 27,975 B literal baseline: 53.02% of literal size, or 46.9777% smallerOrdinary zlib was substantially smaller; the result supports reconstructible structure, not compression superiority
Custom incremental adapterChanged-index updates were cheaper than rebuilding the complete in-memory index in the tested scenariosDurable serialization, fsync, recovery, rollback, and a conventional incremental implementation were excluded

What the measurements support

The evidence supports a narrow engineering idea: invariant structure can be shared, attributable differences can remain explicit, and an expanded boundary view can be reconstructed when its dependencies are present.

The measured adapters did not outperform ordinary preindexed controls for lookup or hydration, and the compact prototype did not outperform SQLite for replay. The next question is usefulness: when does compact, reconstructible structure improve perception and cooperation rather than merely reduce one kind of duplication?