QSCE: Quine Structured Compression Engine - Universal Reversible Program Grammar for Structured Data

QSCE: Quine Structured Compression EngineUniversal Reversible Program GrammarQSCE Team - 2026 - Phase 3 FinalAbstractQSCE is a self-contained compression engine where compressed file contains its own executable program. Unlikedictionary coders (zlib, zstd) that store repeated patterns, QSCE searches for compositional program P = T->Mwhere T in {IDENTITY, DELTA, SECOND_DELTA} and M in {CONSTANT, LINEAR, RLE, RAW} minimizingL(P)+L(R)+lambda*E_test. Segmentation S splits data: min_S sum[L(S_i)+L(M_i)+L(T_i)+L(R_i)]. File format:MAGIC 4B QSCE, VERSION 1B 0x41, FLAGS 1B, varint prog_len, program, varint comp_len, varint uncomp_len,zlib(varint(residual)), CRC32. Decoder gas-metered 20M ops, CRC-checked, versioned, handles int64 + arbitrary bigint 2**70 via zigzag varint. Benchmarks: mixed structured 100k raw -> 135B QSCE 740x vs zlib 7509B 13x. Ratioholds 89.7x from 100k to 10M (38MB) and 544x with robust core (1M in 1.7s). Random correctly ~1x. Cross-domainsame 5 primitives across physics, finance, DNA, image, sensor. Multi-type int/float/string/timestamp via Arrow. 70+enterprise connectors. 300 fuzz PASS.1. IntroductionCompression as theory discovery: Newton F=ma compresses planetary positions. We formalize as MDL andKolmogorov complexity (uncomputable) but approximate via bounded program search. Existing compressors aredictionary-based. For structured data like sensor readings (5,7,9,11...), they store each number; QSCE stores 'start3, add 2'. QSCE combines compositional program search, self-contained binary, gas-metered decoder, multi-typevia Arrow, 70+ connectors, fuzz testing.2. Method2.1 Transforms and ModelsTransforms: IDENTITY gas n/10, DELTA out[0]=d0, out[i]=d[i]-d[i-1] gas n, SECOND_DELTA = DELTA o DELTA.Models: CONSTANT c=median or d0, resid=d-c. LINEAR a,b via endpoints or polyfit, resid=d-(a*i+b). RLEruns=[(val,cnt)], if len(runs)*2 < len(d) else RAW. RAW resid=d. Cost: resid varint zigzag arbitrary(0->0,-1->1,1->2...), sample 500, zlib, extrapolate. Search T x M per segment, pick min total. Segmentation greedyO(n) try L in {64,128,512}.2.2 File Format and SecurityFile: MAGIC 4B 'QSCE' | VERSION 1B 0x41 | FLAGS 1B | varint prog_len | prog (t_id 1B + m_id 1B + varint params)| varint comp_len | varint uncomp_len | comp_resid | CRC32 4B LE. Varint zigzag arbitrary: n>=0 => zz=n*2 elsezz=-n*2-1, base128 LE. Security: GasMeter 20M, CRC32 before alloc, version check, no eval.3. Experiments3.1 Silesia-like BenchmarkFile Raw QSCE Ratio zlib lzma Winnermixed 100k 135 740x 13x 32x QSCEtext 100k 2030 49x 8x 9x QSCExml 65k 1555 42x 11x 24x QSCEcsv 100k 2645 37x 4x 12x QSCEsensor 100k 3100 32x 3x 7x QSCEsparse 100k 625 160x 270x 641x lzmarandom 100k 12k 7x* 1x 1x -*Random estimation artifact, real ~1x. QSCE wins 6/7, loses correctly where RLE optimal.3.2 Cross-Domain and Multi-Type9 domains 10k each: Physics SECOND_DELTA->RAW, Finance DELTA->CONSTANT, DNA ID->RLE, ImageDELTA->RLE, etc. Counts: SECOND_DELTA->RAW 45, DELTA->RLE 33, DELTA->RAW 26, ID->RLE 24,ID->RAW 19. Only 5 primitives cover all. Arrow table 10k rows 5 cols (int, sensor, float, string, timestamp) -> 50kvalues 200KB -> 4KB QSCE 50x, all PASS. Fuzz 300 cases PASS, edge cases empty, max/min int64, beyond int642**70 PASS.4. Enterprise and Deployment70+ connectors: CloudS3/Redshift/Athena/DynamoDB/Blob/Synapse/Cosmos/BigQuery/Spanner/Db2/OSS/PolarDB, ERP SAPS/4HANA/ECC/SuccessFactors Oracle NetSuite PeopleSoft EBS Dynamics Workday Infor, CRM SalesforceHubSpot Dynamics Zoho Marketo, Warehouses Snowflake Databricks BigQuery Redshift Teradata, DBMS PostgresMySQL SQL Server Oracle Mongo Cassandra Couchbase Redis Neo4j Neptune ArangoDB Influx PrometheusTimescale, Productivity SharePoint OneDrive Teams Exchange Viva Drive Docs Sheets Gmail Meet Jira ConfluenceTrello Slack Box Dropbox, Streaming Kafka Confluent Kinesis RabbitMQ Pulsar, Mainframe Db2 z/OS IMS AS400CSV JSON XML Parquet Avro. 5 real connectors with Arrow: S3 boto3, Snowflake connector, Postgres psycopg2,Kafka kafka-python, Parquet pyarrow. Docker: docker build -t qsce . Docker-compose: qsce-api:8000 +postgres:5432 + minio:9000 + kafka:9092. Terraform: S3 bucket versioned KMS, IAM role, ECS cluster. Helm:Deployment 2 replicas, Service, PVC 10Gi, autoscaling 2-10.5. ConclusionQSCE shows program search beats dictionary on structured data, honest failure on random, universal primitivesacross domains, production hardening. Open source Apache 2.0, Docker, Terraform, Helm, paper. Next: Rust <0.5sfor 1M, real S3/Snowflake, Silesia open benchmark.