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Introduction

Aegir is a hierarchical byte-level sequence model for semantic annotation of relational data. Given one or more tables, it predicts the semantic type of each column (Column Type Annotation, CTA), the relationships between columns (Column Property Annotation, CPA), and the cross-table groupings that constitute coherent real-world data elements — for example, a PaymentCard data element spanning card_number, expiry, and cardholder columns across billing, transaction, and customer tables. The model is paired with the Signals Data Governance (SDG) ontology — a BFO 2020 / CCO-grounded, HermiT-validated OWL artifact whose classes are the CTA/CPA annotation vocabulary; together they constitute a closed loop between the model and the structured-knowledge representation it learns from.

Two coupled research outputs

The project produces two outputs that are cited together:

  1. A hierarchical byte-level sequence model. All-RWKV-7 time-mixing with H-Net dynamic chunking, trained byte-level on a mixed corpus and fine-tuned for the column-annotation tasks above. The architecture is described in Architecture; the operational pretraining work is described in Pretraining and Training Regime.

  2. An ontology-grounded synthetic corpus and the SDG ontology that generates it. A BFO/CCO-grounded domain ontology, content-derived from FinePDFs and realized to a HermiT-validated OWL artifact, drives LLM generation of deterministically-verifiable, attribution-clean textbook chapters and a relational DDL spine. The corpus is byte-level pretraining data and an independent publishable deliverable (the corpora/ submodule). The ontology, its quality gates, and the disposal membranes that enforce them are documented in the SDG ontology chapter and the Ontology Authors Guide.

The two outputs share substrate — the SDG ontology, the family catalog of Manchester-syntax templates, the verbalization pipeline — and are coupled downstream: the ontology produces verbalized, verified chapters that feed back into the byte-level pretraining corpus. The ontology is treated as a primary research output in its own right, not as plumbing; its rigor program is described below.

The ontology rigor program

The SDG ontology is governed by propose / dispose: an agent proposes axioms and a stack of deterministic membranes disposes — admitting only what is well-formed (a Manchester/OWLAPI parse membrane), logically consistent under the reasoner (HermiT, with CCO imported as a reasoning authority so grounding is validated against CCO’s disjointness axioms), and ontologically clean (an OntoClean meta-property membrane). The two strongest membranes are un-fakeable: you cannot talk past a contradiction or an anti-rigidity violation. Quality is measured by a metric suite — IOF-derived rigor dimensions (definitional_completeness, bfo_grounded, realizable_machinery, def_annotation_coverage), field-standard OntoQA/OQuaRE structural metrics, and OntoClean taxonomic-correctness proxies — and a formal OQuaRE publish gate (six SQuaRE characteristics; floors of oquare_aggregate ≥ 3.5 and FunctionalAdequacy ≥ 3.0 plus HermiT consistency), wired hard into the publish path so a regression cannot ship. The Authors Guide is the canonical reference for every metric, band, and threshold.

There is also an in-flight RLVR research sub-track — a language-model policy trained with Group Relative Policy Optimization (GRPO) against a deterministic four-component verifier R(O, I) over OWL compositions — documented in the concept brief and the RLVR chapter. It shares the catalog and the verbalization pipeline with the rest of the ontology track but is distinct from the production realization-and-gate path above; its status is tracked in EVIDENCE.md.

Problem setting

Enterprise data warehouses contain thousands of tables with columns whose meaning is often opaque: generic names (col0, field_42), inconsistent conventions across teams, no machine-readable metadata. Understanding what each column represents — and which columns across different tables refer to the same real-world concept — is foundational to data governance, privacy compliance, and integration.

Two families of prior approaches exist. Pattern and heuristic methods identify column types through regex detectors, name matching, embedding similarity, and gradient-boosted classifiers on hand-engineered features. They work well for structurally distinct types but struggle with confusable pairs — columns whose value distributions are nearly identical but whose semantic types differ (advertising IDs versus GUIDs, bank account numbers versus payment card numbers). They also require manual enumeration of data-element patterns and do not generalize to novel relationship types. Learned sequence models — DODUO, RECA, REVEAL — treat the table as a token sequence and classify columns via fine-tuned transformers. REVEAL’s central insight is that context-column selection matters: choosing the right neighboring columns (via MMR diversity sampling) materially improves annotation accuracy. These models operate on single tables in isolation and use fixed subword tokenizers that fragment tabular data unpredictably.

Aegir bridges the two families. It is trained byte-level — no fixed tokenizer — and is designed to be deployed in situ alongside evidence-based classification pipelines: consuming the same serialized table representations as the surrounding stack, but learning cross-column and cross-table relationships end-to-end rather than relying on enumerated patterns. Standard column-annotation benchmarks remain reference points — SOTAB (Schema.org types over web tables), GitTables (large-scale column type detection across 1M+ CSV tables from GitHub), and WikiTables (Wikipedia HTML tables) — but the project’s own thesis is that the ontology is load-bearing for property-prediction (CPA) while it trades raw web-table distribution alignment, so the headline evaluation targets relational / data-element skill rather than assuming a single web-CTA benchmark is the goal (see Roadmap and EVIDENCE.md).

Methodological contributions

Algorithms. Byte-level dynamic chunking as differentiable tokenization: a routing module predicts boundary probabilities from consecutive hidden-state cosine similarity, and chunk representatives propagate to the next hierarchical stage. The H-Net primitive treats tokenization as a learned property of the architecture rather than a fixed preprocessing decision. Chunked-mode RWKV-7 time-mixing through flash-linear-attention Triton kernels provides constant-state recurrent computation with parallel training throughput. The ontology side contributes a propose / dispose discipline in which rigor is enforced by deterministic membranes (parse → HermiT/CCO → OntoClean) rather than asserted, and measured by an IOF-anchored OQuaRE gate — an intrinsic, reasoner-validated quality signal rather than a judge-mediated one.

Architecture. A recursive hierarchy in which each stage selects its own block-type mix from a block factory. Every current default arch_layout uses RWKV-7 time-mix at every stage; ROSA (a RWKV-8 suffix automaton for exact substring retrieval) and Mamba-2 SSD are additional block codes that the factory supports for hybrid configurations and ablations. The recursion alternates encoding, dynamic chunking, recursive inner processing, EMA dechunking, and decoding; the recurrent state at every RWKV-7 stage is constant in sequence length. This makes the recurrent state a fixed-size object that can be serialized, transmitted, and algebraically combined across agents — the substrate for the multi-agent state-fusion infrastructure described in Agent Swarm.

Objectives. The system pursues two complementary objectives. The first is competitive accuracy on column-annotation tasks, with an emphasis on relational / data-element skill — the application of the byte-level model. The second is a measurably rigorous SDG ontology and the verifiable, attribution-clean corpus it generates — evaluated by intrinsic, reasoner-validated gates rather than by a judge. The two objectives share the SDG ontology and the family catalog as substrate; the second feeds the first downstream as pretraining data.

Reading this document

  • A reader interested in the model architecture should read Architecture and its sub-pages on RWKV-7 time mixing, dynamic chunking, ROSA, and the block factory.
  • A reader interested in the ontology, its rigor program, and how to extend it should read the SDG ontology chapter and the Ontology Authors Guide; the in-flight RLVR sub-track is in the concept brief and the RLVR chapter.
  • A reader interested in byte-level pretraining and downstream fine-tune should read Pretraining and Training Regime.
  • A reader interested in who the system is built for and what workflows it commits to should read Personas.
  • A reader interested in the operational milestones and what has been delivered should read Roadmap.

The development guide and the worktree-aware development chapter cover operational concerns and the CUDA-extension build path that Aegir’s runtime depends on.