tranSymbolics

Context Engine & Operators — Full Specification

The Context Engine—with its built-in Context Synthesizer—is the computational substrate that transforms, maintains, and reasons over evolving symbolic/subsymbolic memory. Analogous to classic Geometry and Reality Engines, it processes abstract meaning rather than pixels or geometry.

Subsymbolic Tier

Store
Inputs: KVCache, TokenSeq, PosIndex → Outputs: ContextSnapshot
  1. Captures full active state (KV, tokens, positions).
  2. Supports rollback, branching, and inspection.
  3. Snapshotting enables conditional inference.
  4. Foundation of memory sovereignty.
  5. Records momentary token context.
  6. Enables longitudinal memory across sessions.
  7. Operates entirely in RAM; low latency.
  8. Unifies symbolic/subsymbolic context.
  9. Supports later compression via Distill or Embed.
  10. Used at key decision points.
  11. Allows speculative or parallel reasoning.
  12. May include controller metadata.
  13. Enables defensive state rollback.
  14. Supports multi-threaded inference.
  15. Facilitates experiment reproducibility.
  16. Adapts to different cache architectures.
  17. TokenSeq enables integrity validation.
  18. Essential for context tracing.
  19. Symmetric to Load/Restore operations.
  20. Analogy: photographing current mind state.
Distill
Inputs: ContextSnapshot → Outputs: LatentVec
  1. Compresses full context into compact vector.
  2. Extracts salient patterns and threads.
  3. Used for memory summarization and retention.
  4. Enables internal self-reflection workflows.
  5. Supports introspective question answering.
  6. Allows distilled vectors to steer future generation.
  7. Forms link between symbolic and latent planes.
  8. Flexible implementations possible.
  9. Driven by auxiliary controllers.
  10. Forms hierarchical memory structures.
  11. Allows retrieval with nearest-neighbor queries.
  12. Building block for memory systems.
  13. Enables novelty detection via distance metrics.
  14. Guides policy alignment via reward signals.
  15. Supports context-aware fine-tuning.
  16. Selectively includes memory layers.
  17. Encodable and shareable.
  18. May be ephemeral or persisted.
  19. Analogy: summarizing a chapter into lessons.
Embed
Inputs: TokenSeq or TagSet → Outputs: LatentVec
  1. Maps tokens or tags into latent space.
  2. Used to anchor concepts in memory space.
  3. Helps with symbolic retrieval and memory fusion.
  4. Supports linking of past insights.
  5. Foundation of retrieval-augmented generation.
  6. May use pooling, averaging, or specialized projections.
  7. Embeddings drive future prompt conditioning.
  8. Can be merged with new contexts.
  9. Cross-compatible across model versions.
  10. Enables semantic matching between contexts.
  11. Triggers memory search during workflows.
  12. Compressable for storage efficiency.
  13. Analogous to tagging a document in a library.

Symbolic Tier

Harvest
Inputs: TokenSeq, SymbolMap → Outputs: TagSet
  1. Extracts semantically rich fragments.
  2. Used for reflection and pattern detection.
  3. Identifies insight-rich, low-priority content.
  4. Preps content for distillation or archival.
  5. Clustering heuristics for fragment selection.
  6. Supports recursive reflection.
  7. Analogy: collecting side-ideas in brainstorming.
  8. Feeds TagSet for Meta operations.
Refract
Inputs: TokenSeq → Outputs: SymbolMap
  1. Maps raw tokens into structured symbols.
  2. Supports normalization and schema alignment.
  3. Bridges sub-symbolic and symbolic interpretations.
  4. Facilitates formal context reasoning.
  5. Used before embedding or symbolic workflows.
  6. Analogy: natural‑language → logical atom translation.
  7. Enables semantic policy for internal logic.
  8. Declutters intent from surface phrasing.
Echo
Inputs: TagSet or TokenSeq → Outputs: TokenSeq
  1. Reinjects harvested or tagged content.
  2. Used for memory rehearsal and consistency.
  3. Scheduled by salience or timing policies.
  4. Enhances thematic cohesion across turns.
  5. Avoids echo loops via controller logic.
  6. Analogy: repeating a key theme in speech.
  7. Used to remind model of past commitments.
  8. Supports grounded coherence.
Inscribe
Inputs: TagSet or SymbolMap → Outputs: MemoryStore
  1. Writes symbolic fragments into persistent memory.
  2. Stores facts, identity markers, procedures.
  3. Transitions transient memory to durable knowledge.
  4. Part of consolidation with distillation.
  5. Requires validation before commit.
  6. Analogy: engraving insight in registry.
  7. Enables retrieval across sessions.
  8. Supports cumulative narrative building.
Annotate
Inputs: TokenSeq or SymbolMap → Outputs: TagSet
  1. Tags context with metadata attributes.
  2. Used to mark source, polarity, confidence.
  3. Enables filtering and prioritization.
  4. Feeds other reflective operators.
  5. Indicator for deferred workflows.
  6. Analogy: margin notes on draft.
  7. Supports traceable reasoning logs.
  8. Encodes domain, sensitive content markers.
Defer
Inputs: TagSet → Outputs: DeferredQueue
  1. Schedules content for later processing.
  2. Manages cognitive load during generation.
  3. Tags indicate revisit timing.
  4. Queued for background analysis.
  5. Analogy: sticky-tab placeholder.
  6. Preserves context without interrupting flow.
  7. Triggers secondary analysis when free.
  8. Supports staged workflows.
Attenuate
Inputs: TokenSeq or LatentVec → Outputs: TokenSeq or LatentVec
  1. Softly reduces influence of content.
  2. Used to de-focus old or irrelevant context.
  3. Can attenuate with decay or priority logic.
  4. Analogous to dimming background music.
  5. Avoids deleting potentially useful info.
  6. Can be reversed if needed.
  7. Supports memory lifecycle management.
  8. Maintains context without dominance.
Lattice
Inputs: TagSets or SymbolMaps → Outputs: GraphOverlay
  1. Constructs structured semantic graph.
  2. Encapsulates concept associations and relations.
  3. Supports relational retrieval and analogy.
  4. Analogy: embroidery forming a semantic net.
  5. Enhances associative memory.
  6. Can model narrative or causal links.
  7. Includes node-edge metadata tagging.
  8. Useful for trans-context bridging.
Diffuse
Inputs: LatentVec or TagSet → Outputs: KVCache or TokenSeq
  1. Reintegrates distilled insights back into memory.
  2. Used to bias upcoming generation.
  3. Injected via prompt, prefix, or cache overlay.
  4. Analogous to scent diffusing through space.
  5. Maintains coherence across separate turns.
  6. Enables model to act on prior distilled ideas.
  7. Controlled by Context Synthesizer policy.
  8. Supports reflective bootstrapping.
Graft
Inputs: TokenSeq or SymbolMap → Outputs: TokenSeq
  1. Splices learned segments into new context.
  2. Supports style or analogy transfer.
  3. Must align tense, role, and semantics.
  4. Analogy: grafting a branch to a tree.
  5. Used to reapply extracts meaningfully.
  6. Helps maintain persona consistency.
  7. Validates content alignment before insertion.
  8. Allows reuse of effective patterns.

Supersymbolic Tier

Fork
Inputs: ContextSnapshot → Outputs: ContextSnapshot ×2
  1. Creates duplicative reasoning branches.
  2. Enables speculative or parallel inference.
  3. Supports beam search, hypotheticals.
  4. Allows nested branch exploration.
  5. Minimal overhead if memory immutable.
  6. Often paired with Merge or Prune.
  7. Analogy: timelines in alternate history.
  8. Can carry branch metadata for origin tracking.
  9. May clone controller state as well.
  10. Used in multi-agent workflows.
  11. Supports reflective agent profiles.
  12. Used to model counterfactual scenarios.
Merge
Inputs: ContextSnapshot ×2 → Outputs: ContextSnapshot
  1. Reconciles divergent context paths.
  2. Supports weighted or diff-based merging.
  3. Requires conflict resolution logic.
  4. Preserves provenance in merged output.
  5. Essential in ensemble or team reasoning.
  6. Validates compatibility or warns conflicts.
  7. Analogy: merging edited document revisions.
  8. Often followed by pruning or summarization.
  9. Consolidates insights from parallel forks.
  10. Useful in cooperative many-agent systems.
  11. May distort semantics if misaligned.
  12. Enables iterative refinement across versions.

Safety & Memory Control

Clear
Inputs: KVCache → Outputs: KVCache
  1. Erases all active memory traces.
  2. Resets context for clean slate generation.
  3. Prevents cross-task leakage.
  4. Supports persona or domain switching.
  5. Partial clears possible by layer/head.
  6. Complements eviction for selective resets.
  7. Fast, no serialization needed.
  8. Analogy: wiping clean a whiteboard.
  9. Ensures deterministic reruns.
  10. Removes positional dependencies.
  11. Guardrail against hallucination buildup.
  12. Foundational for session boundaries.
Freeze
Inputs: KVCache → Outputs: KVCache (read-only)
  1. Locks memory cache against change.
  2. Used during controlled generation/testing.
  3. Prevents accidental state override.
  4. Can apply to symbols or tagsets.
  5. Must be undone before inference changes.
  6. Analogy: toggling read-only mode.
  7. Supports snapshot integrity.
  8. Ensures repeatable evaluation.
Restore
Inputs: ContextSnapshot → Outputs: KVCache
  1. Injects a prior snapshot into memory.
  2. Supports rollback and scenario switching.
  3. Requires structural compatibility.
  4. May need alignment post-load.
  5. Enables hot context swapping.
  6. Core to time travel in reasoning.
  7. Restores token indices and pointer state.
  8. Accelerates memory rehydration.
  9. Can layer patches post-restore.
  10. Used to test alternate dialog flows.
  11. Supports regenerative thinking loops.
  12. Analogy: jumping back to a saved scene.
Save
Inputs: ContextSnapshot, FilePath → Outputs: Bool
  1. Persists snapshot to disk or cloud.
  2. Supports long-term memory retention.
  3. Includes subsymbolic and symbolic content.
  4. Useful for logging and experiment replay.
  5. May compress or quantize serialized data.
  6. Tags include timestamps and model IDs.
  7. Atomic save to protect integrity.
  8. Analogy: sealing journal entry in vault.
  9. Essential for off-line reflection.
  10. Used in collaborative inference.
  11. Affects session resumption fidelity.
  12. Forms archive trail of memory lineage.
Patch
Inputs: KVCache, PatchSet → Outputs: KVCache
  1. Applies localized edits to memory.
  2. Inserts corrections or new information.
  3. Hotfixes state without full reload.
  4. Must respect memory structure.
  5. Useful in real-time debugging.
  6. Can adjust symbolic anchors.
  7. Analogy: editing a single line in a live file.
  8. Supports policy-based steering.
Evict
Inputs: KVCache, EvictPolicy → Outputs: KVCache
  1. Removes entries based on policy.
  2. Controls memory size and relevance.
  3. Policy may be LRU or content-sensitive.
  4. Protects pinned entries.
  5. Analogous to cache garbage collection.
  6. Helps maintain focus and reduce drift.
  7. Used pre-distillation.
  8. Adapts to streaming use cases.
Scrub
Inputs: TokenSeq or KVCache → Outputs: Sanitized TokenSeq or Cache
  1. Removes private/toxic content.
  2. Used in safety and compliance flows.
  3. Works via regex or PII detection.
  4. Applied before sharing or logging.
  5. Marks scrubbed items post-process.
  6. Can operate on both symbolic/subsymbolic.
  7. Analogy: redacting sensitive info in docs.
  8. Prevents future seeding of unsafe content.
Pin
Inputs: TokenSeq or KVIndex → Outputs: KVCache
  1. Anchors entries to prevent eviction.
  2. Used for persona or system prompts.
  3. Overrides eviction policy.
  4. Whether hard or soft depends on metadata.
  5. Analogy: pinning sticky note on board.
  6. Ensures consistency across turns.
  7. Manually unpin when no longer needed.
  8. Enables identity persistence.

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