Beyond Text: Relational Shards for Music, Audio, Image, and Video: A Secretary Suite Project

Beyond Text: Relational Shards for Music, Audio, Image, and Video: A Secretary Suite Project 

DOI: [To be assigned]

John Swygert

August 25, 2026

Abstract

This paper generalizes the relational-shard architecture beyond language while preserving an important distinction: all digital media are ultimately encoded as bits and bytes, but the most useful shard boundaries for analysis, reuse, compression, provenance, and reconstruction usually occur at higher representational levels. In audio, a useful unit may be a sample block, spectral event, note, beat, motif, phrase, stem, or section. In images it may be a pixel block, feature region, object, layer, or scene component. In video it may be a frame region, motion segment, shot, transition, scene, object track, or narrative unit. The paper proposes a modality-neutral relational model, multiscale fingerprints, cross-modal provenance, adaptive routing, and future compression experiments. It explicitly distinguishes this work from established perceptual codecs and multimedia fingerprinting: the proposed contribution is an interoperable shard/provenance/reconstruction layer that can sit above or beside conventional codecs and exploit shared higher-order structure when doing so reduces total cost or improves traceability.

Governing principle: minimize total storage or transmission cost subject to explicit reconstruction, integrity, provenance, and reliability requirements.

1. Language as the First Laboratory

Text is a convenient starting point because symbolic boundaries can be inspected directly. The broader architecture should not depend on words or sentences.

A shard is better defined as a bounded relational information unit at a selected scale.

2. Modality-Neutral Ladder

Signal -> primitive units -> local patterns -> structures -> relational units -> higher-order compositions.

The exact meaning of each level depends on modality.

3. Audio Scales

Physical/sample scale: PCM samples or compressed frames. Signal scale: spectral components, transients, pitch contours, timbral features. Musical scale: notes, beats, chords, motifs, phrases, stems, sections, performances.

Different tasks should choose different shard boundaries.

4. Music as Relational Structure

Music depends heavily on relation: interval, rhythm, repetition, variation, harmony, orchestration, expectation, and temporal hierarchy.

Two passages may be structurally related despite transposition, tempo change, instrumentation change, or local ornamentation. Exact waveform matching alone cannot express those relations.

5. Image Scales

Pixels and blocks support physical compression. Higher levels may include edges, textures, segments, objects, layers, repeated backgrounds, layouts, and semantic regions.

A provenance-aware system could distinguish reuse of an exact asset from independent generation of a visually similar structure.

6. Video Scales

Frames are not the only natural units. Motion tracks, shots, edits, scenes, repeated intros, lower-thirds, backgrounds, animations, and narrative segments can become shards.

Temporal relation is fundamental: order and duration can change meaning without changing the frame inventory.

7. Existing Codecs Remain Essential

JPEG, AVIF, AAC, Opus, H.264/AVC, HEVC, AV1, and successors already provide sophisticated signal compression. The relational layer should not attempt to replace them where they are efficient.

Instead it can reuse known assets, shared segments, templates, and higher-order structures across files or projects.

8. Cross-Asset Deduplication

A production library may contain the same logo, intro, music bed, animation, background, or clip in thousands of outputs. Content-addressed physical blocks can remove exact duplicates; relational shards can also track the logical identity and permitted transformations of the asset.

9. Transform-Aware Reuse

A musical motif transposed to another key, an image resized or color-adjusted, or a video segment cropped and captioned may derive from the same source while differing physically.

Transform-aware provenance can represent source + operation + parameters rather than storing every conceptual relation only as unrelated files. Whether this saves bytes depends on the transform and codec.

10. Multimodal Fingerprints

Fingerprint vectors can contain physical hashes, perceptual hashes, spectral/audio features, motion signatures, object identities, temporal structures, relational patterns, and provenance paths.

Similarity and derivation must remain separate: perceptual resemblance does not prove copying.

11. Cross-Modal Shards

A scene may bind transcript, audio, video, captions, music, and metadata. Treating them as a relational bundle preserves synchronization and provenance while allowing each modality to use its own physical codec.

12. Storage Objective

Store exact recurring assets once where possible; store transformations or references when cheaper; preserve independent versions when reconstruction risk or compute would exceed savings.

Logical relationships should guide reuse without dictating physical encoding.

13. Transmission Objective

If sender and receiver share media assets, a manifest can transmit references, edits, timing, and novel payload instead of entire compositions. This resembles the text relational-codec strategy but must be tested against mature media codecs.

14. Computational Cost

Media reconstruction can be expensive. A tiny transform manifest that requires minutes of rendering may be inferior to sending a compressed asset. Total cost therefore remains the governing metric.

15. Rights and Provenance

Media systems require robust ownership, licensing, attribution, and transformation history. Provenance metadata can be as important as compression because an efficient reconstruction that violates usage rights is not an acceptable result.

16. Experimental Roadmap

Begin with exact repeated assets and simple deterministic transforms. Then test music motif matching, repeated video segments, image components, and cross-modal bundles. Compare against conventional perceptual fingerprinting and codec baselines.

17. Binary Substrate and Meaningful Scale

Every representation ultimately becomes bits and bytes for storage and transmission. That does not imply that bit boundaries are always the best analytical boundaries.

The architecture should move freely between physical and semantic scales, selecting the level that provides the best verified efficiency for the task.

18. Conclusion

The Shard Library can become modality-neutral without pretending that every medium behaves like text. The common principle is relational: preserve identity, transformation, provenance, order, scope, and state at whatever scale makes reconstruction, reuse, compression, or analysis measurably better.

Methodological Guardrails

• Compare against strong existing baselines; never claim gains relative only to raw/uncompressed data.

• Count manifests, hashes, provenance, indices, repair traffic, and routing overhead as real cost.

• Keep logical shard boundaries separate from physical storage/chunk boundaries.

• Distinguish exact byte reconstruction from functional or semantic reconstruction.

• Treat similarity as evidence of resemblance, not proof of derivation or shared provenance.

• Publish crossover points and negative results where conventional methods win.

• Use cryptographic integrity checks for exact reconstruction experiments.

• Treat security, privacy, and access boundaries as constraints, not optional afterthoughts.

• Mappability to TSTOEAO is not validation; empirical advantage must be demonstrated.

References

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423, 623-656.

Deutsch, P. (1996). DEFLATE Compressed Data Format Specification version 1.3. RFC 1951.

Korn, D. G., MacDonald, J. P., Mogul, J. C., & Vo, K.-P. (2002). The VCDIFF Generic Differencing and Compression Data Format. RFC 3284.

Tridgetgell, A., & Mackerras, P. (1996). The rsync algorithm. Australian National University Technical Report TR-CS-96-05.

Alakuijala, J., & Szabadka, Z. (2016). Brotli Compressed Data Format. RFC 7932.

Collet, Y., & Kucherawy, M. (2018). Zstandard Compression and the application/zstd media type. RFC 8878.

Xia, W., Jiang, H., Feng, D., Douglis, F., Shilane, P., Hua, Y., Fu, M., Zhang, Y., & Zhou, Y. (2016). FastCDC: a Fast and Efficient Content-Defined Chunking Approach for Data Deduplication. USENIX Annual Technical Conference.

Swygert, J. (2026). 200 From Language to Computation: Linguistics, Punctuation, Mathematics, and Programming as a Unified Relational Architecture in Large Language Models. Ivory Tower Publishing.

Swygert, J. (2026). 203 The Shard as a Relational Unit: Deconstruction, Reconstruction, and Knowledge Architecture. Ivory Tower Publishing.

Swygert, J. (2026). 204 The Statistical Shard Library: Measuring Retrieval, Reuse, Frequency, and Relational Importance. Ivory Tower Publishing.

Swygert, J. (2026). 205 Symbolic Fingerprints: Provenance, Reuse Detection, and Plagiarism Prevention through Relational Analysis. Ivory Tower Publishing.

Swygert, J. (2026). 206 Relational Reconstruction: Building Novel Outputs from Provenance-Aware Knowledge Shards. Ivory Tower Publishing.

Swygert, J. (2026). 207 The Relational Intelligence Engine: From Language Structure to Adaptive Machine Reasoning. Ivory Tower Publishing.

Swygert, J. (2026). 208 The Empirical Relational Benchmark: A Hybrid Character, Word, Shard, and Provenance Test Architecture for Language Models and Knowledge Systems. Ivory Tower Publishing.

ITU-T. (2021). H.264: Advanced video coding for generic audiovisual services.

Alliance for Open Media. (2019). AV1 Bitstream & Decoding Process Specification.

Valin, J.-M., Vos, K., & Terriberry, T. B. (2012). Definition of the Opus Audio Codec. RFC 6716.

Multiscale Compression Benchmark: Bits, Bytes, Characters, Tokens, Shards, and Relational Templates: A Secretary Suite Project

Multiscale Compression Benchmark: Bits, Bytes, Characters, Tokens, Shards, and Relational Templates: A Secretary Suite Project 

DOI: [To be assigned]

John Swygert

August 25, 2026

Abstract

This paper specifies a reproducible benchmark for evaluating compression and reconstruction across multiple representational scales. It treats bits and bytes as the physical substrate, character and token sequences as intermediate symbolic layers, shards as logical content units, and relational templates as reusable higher-order structures. The benchmark does not presume that higher-order representations outperform established compressors. Instead, it measures when each scale contributes useful savings and when its metadata, computation, or synchronization overhead makes it inferior. Workloads include repeated documents, evolving documents, code, structured records, conversational memory, and eventually media. Metrics include compression ratio, transmitted bytes, unique stored bytes, manifest overhead, encoding and decoding latency, peak memory, reconstruction depth, integrity failures, provenance fidelity, and total cost. Ablations isolate each layer so that any improvement can be attributed rather than assumed.

Governing principle: minimize total storage or transmission cost subject to explicit reconstruction, integrity, provenance, and reliability requirements.

1. Why a Multiscale Benchmark

Compression effectiveness depends on the scale at which redundancy is visible. Byte compressors exploit local statistical redundancy. Deduplication exploits repeated chunks. Delta encoding exploits shared history. Relational templates may exploit repeated structure that is not byte-identical.

A fair benchmark must let each mechanism compete on workloads that expose and do not expose its strengths.

2. Representation Ladder

The benchmark recognizes a ladder: bits -> bytes -> characters -> tokens -> phrases -> physical chunks -> semantic shards -> relational templates -> artifact manifests.

These layers are analytical choices, not claims about fundamental ontology.

3. Baselines

Required baselines include raw storage/transfer, gzip or DEFLATE, Brotli, Zstandard, content-defined chunk deduplication, and a generic delta encoder. Additional systems can be added where licensing and implementation permit.

4. Relational Variants

R1: shard-reference only. R2: shard plus delta. R3: shard plus relational templates. R4: adaptive hybrid routing. R5: hybrid plus provenance requirements.

Each variant is compared against the same corpus and integrity requirements.

5. Workload Families

Workloads should include independent novel files, near-duplicate files, many versions of the same document, templated reports, source-code repositories, knowledge bases with recurring concepts, and persistent agent transcripts.

A system that wins only on synthetic repetition should not be generalized to unrelated data.

6. Canonical Preprocessing

Text normalization, Unicode handling, line endings, file metadata, compression dictionaries, chunking parameters, and version order must be frozen in the benchmark specification.

The earlier AInunnaki replication demonstrated why preprocessing differences can preserve qualitative behavior while changing raw counts.

7. Metrics

Report compressed bytes, ratio to original, unique bytes stored, network bytes transmitted, metadata overhead, encode/decode time, peak memory, CPU time, cache hit rate, dependency depth, and number of fallback transfers.

For knowledge modes also report provenance completeness and reconstruction correctness.

8. Total-Cost Score

A single headline compression ratio can conceal enormous compute cost. Define TotalCost = a*Bytes + b*Latency + c*CPU + d*Memory + e*FailurePenalty.

Weights must be disclosed and raw metrics must always remain available.

9. Shared-State Sweep

For each workload, vary receiver overlap from 0% to 100%. This reveals the threshold at which shard/reference methods begin to beat ordinary transfer.

Expected behavior: at 0% shared state, relational methods should often lose because of metadata overhead; as overlap rises, they may gain.

10. Novelty Sweep

Vary the percentage of genuinely novel data. The benchmark should measure graceful degradation as information becomes less reusable.

11. Granularity Sweep

Test multiple chunk and shard sizes. Very small units improve reuse but increase identifier and manifest overhead; very large units reduce overhead but miss partial recurrence.

The benchmark should identify workload-specific optima rather than one universal size.

12. Template Ablation

Remove relational templates while keeping shard deduplication constant. Any additional gain can then be attributed to reusable structure rather than ordinary content reuse.

13. Provenance Ablation

Measure storage and transmission with and without provenance requirements. This quantifies the real cost of traceability instead of treating metadata as free.

14. Reconstruction Stress

Delete random cached shards, corrupt manifests, alter versions, and force fallbacks. Measure recovery traffic and whether integrity checks prevent silent corruption.

15. Statistical Analysis

Use repeated runs, confidence intervals, and paired comparisons across identical inputs. Do not report only best-case examples.

Publish negative results where relational methods lose.

16. Success Criteria

The multiscale system succeeds only where it offers statistically and practically meaningful improvements under declared constraints. A result of 'Zstandard wins' is a valid outcome for a workload.

17. Generalization to Media

The same benchmark architecture can later compare audio/video codecs with higher-order shard reuse. The first stage should remain text and code because exactness and provenance are easier to inspect.

18. Conclusion

The benchmark converts the compression branch of the Shard Library into an engineering question. The winning representation is whichever reconstructs the required state at the lowest verified total cost - whether that is a conventional codec, a shard manifest, a delta, or a hybrid.

Methodological Guardrails

• Compare against strong existing baselines; never claim gains relative only to raw/uncompressed data.

• Count manifests, hashes, provenance, indices, repair traffic, and routing overhead as real cost.

• Keep logical shard boundaries separate from physical storage/chunk boundaries.

• Distinguish exact byte reconstruction from functional or semantic reconstruction.

• Treat similarity as evidence of resemblance, not proof of derivation or shared provenance.

• Publish crossover points and negative results where conventional methods win.

• Use cryptographic integrity checks for exact reconstruction experiments.

• Treat security, privacy, and access boundaries as constraints, not optional afterthoughts.

• Mappability to TSTOEAO is not validation; empirical advantage must be demonstrated.

References

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423, 623-656.

Deutsch, P. (1996). DEFLATE Compressed Data Format Specification version 1.3. RFC 1951.

Korn, D. G., MacDonald, J. P., Mogul, J. C., & Vo, K.-P. (2002). The VCDIFF Generic Differencing and Compression Data Format. RFC 3284.

Tridgetgell, A., & Mackerras, P. (1996). The rsync algorithm. Australian National University Technical Report TR-CS-96-05.

Alakuijala, J., & Szabadka, Z. (2016). Brotli Compressed Data Format. RFC 7932.

Collet, Y., & Kucherawy, M. (2018). Zstandard Compression and the application/zstd media type. RFC 8878.

Xia, W., Jiang, H., Feng, D., Douglis, F., Shilane, P., Hua, Y., Fu, M., Zhang, Y., & Zhou, Y. (2016). FastCDC: a Fast and Efficient Content-Defined Chunking Approach for Data Deduplication. USENIX Annual Technical Conference.

Swygert, J. (2026). 200 From Language to Computation: Linguistics, Punctuation, Mathematics, and Programming as a Unified Relational Architecture in Large Language Models. Ivory Tower Publishing.

Swygert, J. (2026). 203 The Shard as a Relational Unit: Deconstruction, Reconstruction, and Knowledge Architecture. Ivory Tower Publishing.

Swygert, J. (2026). 204 The Statistical Shard Library: Measuring Retrieval, Reuse, Frequency, and Relational Importance. Ivory Tower Publishing.

Swygert, J. (2026). 205 Symbolic Fingerprints: Provenance, Reuse Detection, and Plagiarism Prevention through Relational Analysis. Ivory Tower Publishing.

Swygert, J. (2026). 206 Relational Reconstruction: Building Novel Outputs from Provenance-Aware Knowledge Shards. Ivory Tower Publishing.

Swygert, J. (2026). 207 The Relational Intelligence Engine: From Language Structure to Adaptive Machine Reasoning. Ivory Tower Publishing.

Swygert, J. (2026). 208 The Empirical Relational Benchmark: A Hybrid Character, Word, Shard, and Provenance Test Architecture for Language Models and Knowledge Systems. Ivory Tower Publishing.

Shard Delta Encoding: Reconstructing Large Information States from Minimal Change: A Secretary Suite Project

Shard Delta Encoding: Reconstructing Large Information States from Minimal Change: A Secretary Suite Project 

DOI: [To be assigned]

John Swygert

August 25, 2026

Abstract

This paper develops Shard Delta Encoding as a specific mechanism within the Relational Codec. Conventional delta encoding represents one byte sequence as changes relative to another. Shard Delta Encoding generalizes the idea to structured knowledge states while preserving exact physical reconstruction where required. It distinguishes content deltas, order deltas, relational-edge deltas, metadata deltas, provenance deltas, and deletion or supersession events. A receiving system that already possesses a base shard graph can reconstruct a new document, project state, codebase, or knowledge package from a compact sequence of verified changes. The paper defines manifests, base-state negotiation, version graphs, minimal-delta selection, checkpointing, conflict handling, integrity validation, and garbage-collection requirements. It also establishes a strict fallback principle: if structural delta overhead exceeds ordinary compression or creates unacceptable dependency depth, the system must choose a simpler representation. The objective is measurable reduction of stored and transmitted data, not conceptual elegance.

Governing principle: minimize total storage or transmission cost subject to explicit reconstruction, integrity, provenance, and reliability requirements.

1. Delta as Difference

If receiver state K_t is known and target state is K_(t+1), it is wasteful to retransmit every unchanged component.

Define Delta_t = Difference(K_t,K_(t+1)). Reconstruction is K_(t+1)=Apply(K_t,Delta_t). The engineering question is how to represent Difference at the most economical valid scale.

2. From Byte Delta to Shard Delta

Byte differencing operates on physical sequences. Shard differencing operates on identified logical units and their relations. The two can coexist: shard-level delta chooses what changed; byte-level delta can encode the changed payload itself.

3. Delta Classes

A complete shard-delta system should distinguish several change classes rather than treating every modification as replacement: content, order, boundary, relation, metadata, provenance, deletion, and supersession.

Separating these dimensions prevents a small structural change from forcing wholesale retransmission of content.

4. Content Delta

A shard's payload changes while its logical identity persists. The system can store a binary or token-level delta plus a new integrity hash.

5. Order Delta

The same shards may appear in a different sequence. Instead of resending content, transmit a permutation or ordering instruction.

For n shared shards, a compact permutation can be far smaller than resending the shards, particularly when changes are local.

6. Relational-Edge Delta

A knowledge graph may change because A now qualifies B, C supersedes D, or E is newly derived from F. These are graph edits rather than text edits.

Representing them explicitly preserves semantics and may require only a few identifiers and edge types.

7. Metadata and Provenance Delta

Dates, confidence, authority, access scope, source citation, and lineage can change without changing content. These changes should be encoded independently so that content blocks remain deduplicated.

8. Deletion, Tombstones, and Supersession

Deletion is information. A receiver must distinguish absent because unknown from absent because intentionally removed.

Tombstones and supersession edges preserve the history needed for synchronization and provenance.

9. Base-State Negotiation

A delta is valid only relative to a known base. Sender and receiver exchange compact state summaries - manifest IDs, version IDs, or Merkle-style roots - and select the newest verified common ancestor.

If no useful common base exists, use a full checkpoint.

10. Version Graphs

Linear version chains are simple but can create long reconstruction paths. Branching systems require a version graph with explicit parentage.

The encoder may choose a delta against the nearest shared ancestor rather than merely the immediately previous version.

11. Minimal Delta Is Not Always Minimal Cost

The smallest patch in bytes may be expensive to apply or depend on a deep chain. A cost-aware selection should consider DeltaCost = Bits + ApplyCost + DependencyDepth + FailureRisk.

Periodic checkpoints cap dependency depth.

12. Exactness and Integrity

Every exact-mode reconstruction terminates in a target hash check. A valid relational graph is not sufficient if the required artifact is byte-exact.

Per-shard hashes identify the failing region and permit targeted retransmission.

13. Conflict Handling

Distributed systems may modify the same shard independently. Automatic merges must be restricted to relation classes for which composition is safe.

Otherwise the system records parallel versions and requires policy or human resolution. Provenance must not be destroyed to force convergence.

14. Storage Savings

A versioned document repository can store one base plus deltas rather than complete copies. Highly edited shards can be periodically re-based; unchanged shards remain shared.

The benchmark should report physical bytes, metadata overhead, reconstruction time, and average chain depth.

15. Transmission Savings

For synchronized endpoints, network payload can consist of only changed shards, graph edits, and metadata deltas. The receiver reconstructs the full target state locally.

The strongest expected gains are in persistent workspaces, code repositories, collaborative documents, agent memory, and repeated media projects with substantial overlap.

16. TSTOEAO Interpretation

Delta encoding expresses correction explicitly: a prior state is moved toward a target state through a bounded set of changes whose cost can be measured.

That mapping is useful only if it improves routing, error localization, or empirical prediction.

17. Conclusion

Shard Delta Encoding turns the Shard Library from a static knowledge store into a version-aware reconstruction system. Its governing principle is simple: preserve the shared state, encode only what changed, and checkpoint whenever the relational shortcut costs more than it saves.

Methodological Guardrails

• Compare against strong existing baselines; never claim gains relative only to raw/uncompressed data.

• Count manifests, hashes, provenance, indices, repair traffic, and routing overhead as real cost.

• Keep logical shard boundaries separate from physical storage/chunk boundaries.

• Distinguish exact byte reconstruction from functional or semantic reconstruction.

• Treat similarity as evidence of resemblance, not proof of derivation or shared provenance.

• Publish crossover points and negative results where conventional methods win.

• Use cryptographic integrity checks for exact reconstruction experiments.

• Treat security, privacy, and access boundaries as constraints, not optional afterthoughts.

• Mappability to TSTOEAO is not validation; empirical advantage must be demonstrated.

References

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423, 623-656.

Deutsch, P. (1996). DEFLATE Compressed Data Format Specification version 1.3. RFC 1951.

Korn, D. G., MacDonald, J. P., Mogul, J. C., & Vo, K.-P. (2002). The VCDIFF Generic Differencing and Compression Data Format. RFC 3284.

Tridgetgell, A., & Mackerras, P. (1996). The rsync algorithm. Australian National University Technical Report TR-CS-96-05.

Alakuijala, J., & Szabadka, Z. (2016). Brotli Compressed Data Format. RFC 7932.

Collet, Y., & Kucherawy, M. (2018). Zstandard Compression and the application/zstd media type. RFC 8878.

Xia, W., Jiang, H., Feng, D., Douglis, F., Shilane, P., Hua, Y., Fu, M., Zhang, Y., & Zhou, Y. (2016). FastCDC: a Fast and Efficient Content-Defined Chunking Approach for Data Deduplication. USENIX Annual Technical Conference.

Swygert, J. (2026). 200 From Language to Computation: Linguistics, Punctuation, Mathematics, and Programming as a Unified Relational Architecture in Large Language Models. Ivory Tower Publishing.

Swygert, J. (2026). 203 The Shard as a Relational Unit: Deconstruction, Reconstruction, and Knowledge Architecture. Ivory Tower Publishing.

Swygert, J. (2026). 204 The Statistical Shard Library: Measuring Retrieval, Reuse, Frequency, and Relational Importance. Ivory Tower Publishing.

Swygert, J. (2026). 205 Symbolic Fingerprints: Provenance, Reuse Detection, and Plagiarism Prevention through Relational Analysis. Ivory Tower Publishing.

Swygert, J. (2026). 206 Relational Reconstruction: Building Novel Outputs from Provenance-Aware Knowledge Shards. Ivory Tower Publishing.

Swygert, J. (2026). 207 The Relational Intelligence Engine: From Language Structure to Adaptive Machine Reasoning. Ivory Tower Publishing.

Swygert, J. (2026). 208 The Empirical Relational Benchmark: A Hybrid Character, Word, Shard, and Provenance Test Architecture for Language Models and Knowledge Systems. Ivory Tower Publishing.