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Author SHA1 Message Date
GlyphRunner System bce6b6fa37 Implement XIC v1.5 glyph resonance awareness upgrade (Phase 3-4) 
This commit completes the comprehensive glyph resonance awareness upgrade
with queryable resonance metrics, new instruction, and formal specification.

## Changes

### Phase 3: New GET_GLYPH_RESONANCE Instruction
- Added op_GET_GLYPH_RESONANCE to xic_ops.py for querying glyph resonance data
- Supports metrics: report, global, dominant, weight, lineage, contributor, frequency, grammar
- Results printed with [XIC-RESONANCE] prefix and stored in ctx._state
- Handles both full pipeline result (preferred) and fallback to resonance_metrics dict
- Updated OP_TABLE to include 10th operation

### Phase 4: Formal Specification & Demo

#### XIC_SEMANTICS_v1_5.md Updates
- Added comprehensive "Glyph Resonance Structure" section documenting:
  - FusedSymbol dataclass with summary, glyph_ids, resonance_map
  - GlyphResonanceMap with resonances dict and utility methods
  - GlyphResonanceMetrics (weight, lineage_score, contributor_score, frequency_score, grammar_score)
  - Example JSON structure from LAIN cognition
- Added "GET_GLYPH_RESONANCE" instruction semantics with:
  - Signature and preconditions/postconditions
  - Metric table describing all query types
  - Detailed side effects and remarks
  - Data access patterns

#### New Demo Program
- Created programs/demo_glyph_resonance.gx.json
- Two-chain demonstration:
  - Chain 1: compression_theory glyph with report, global, dominant, weight queries
  - Chain 2: neural_dynamics glyph with individual metric queries (lineage, contributor, frequency, grammar)
- Full instrumentation with CHAIN markers and LOG statements

#### Comprehensive Report
- Created XIC_GLYPH_RESONANCE_REPORT.md documenting:
  - Executive summary of resonance awareness upgrade
  - Detailed explanation of all components
  - Architecture and data flow diagrams
  - All 10 validation test results
  - Usage examples and design decisions
  - Backward compatibility guarantees
  - Future extensibility notes

## Implementation Details

### Enhanced Data Structures (glyphos/symbolic_pipeline.py)
- GlyphResonanceMetrics: 5-dimensional resonance scoring
- GlyphResonanceMap: with get_glyph_resonance(), get_top_glyphs(), get_average_resonance()
- FusedSymbol.from_lain_result(): parses LAIN output structure

### Glyph Resonance Utilities
- extract_glyph_resonances(): extract per-glyph metrics from pipeline result
- get_dominant_glyphs(n): rank glyphs by weight
- format_glyph_resonance_report(): human-readable resonance output

### Enhanced CALL_GLYPH
- Now stores comprehensive resonance data in ctx._state["glyph_{glyph_id}"]
- Captures output_text, fused_symbol, resonance_metrics, global_resonance_score, steps
- Also stores full SymbolicPipelineResult for direct access

### New op_GET_GLYPH_RESONANCE
- Query stored resonance metrics with flexible metric selection
- Integrates with symbolic_pipeline utilities for full introspection
- Prints results and stores in ctx._state for programmatic access

## Exports (glyphos/__init__.py)
- GlyphResonanceMetrics
- GlyphResonanceMap
- extract_glyph_resonances
- get_dominant_glyphs
- format_glyph_resonance_report

## Testing
All 10 validation tests pass:
✅ GlyphResonanceMetrics instantiation
✅ GlyphResonanceMap methods (get_glyph_resonance, get_top_glyphs, get_average_resonance)
✅ FusedSymbol.from_lain_result() parsing
✅ extract_glyph_resonances() functionality
✅ get_dominant_glyphs() ranking
✅ format_glyph_resonance_report() generation
✅ OP_TABLE has GET_GLYPH_RESONANCE
✅ op_GET_GLYPH_RESONANCE callable
✅ demo_glyph_resonance.gx.json valid
✅ All exports available from glyphos

## Backward Compatibility
- Zero breaking changes
- All XIC v1 and v1.5 programs work unchanged
- New resonance features are additive
- Existing instruction signatures preserved
- Compressed mode execution unaffected

Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
 2026-05-21 02:21:44 -04:00
GlyphRunner System 6e0a586f51 Implement XIC v1.5: Symbolic Pipeline Abstraction with Glyph-Aware Transformations 
Implements all phases of the symbolic pipeline extension:

**Phase 1: Symbolic Pipeline Abstraction**
- Created glyphos/symbolic_pipeline.py with:
  - SymbolicStep: tracks individual pipeline steps (name, kind, payload, context)
  - SymbolicPipelineResult: complete pipeline execution result (steps, output_text, fused_symbol)
  - run_symbolic_pipeline(prompt, context, glyph_id): high-level pipeline entrypoint
- Integrated with glyphos/__init__.py exports

**Phase 2: Glyph-Aware Transformations**
- Updated glyphos/cognitive_kernel.py:
  - run_symbolic_prompt() now thin wrapper around pipeline
  - Maintains backward compatibility
- Updated xic_ops.py operations:
  - op_RUN_PROMPT: uses pipeline in symbolic mode
  - op_STREAM: uses pipeline with line-by-line output
  - op_CALL_GLYPH: routes through pipeline with explicit glyph_id parameter
- Context propagation: glyph_id automatically injected into LAIN context

**Phase 3: XIC Instruction Semantics v1.5**
- Created XIC_SEMANTICS_v1_5.md:
  - Formal specification of all 9 XIC instructions
  - Complete semantics: preconditions, postconditions, side effects
  - Symbolic vs compressed behavior for each op
  - Context model and pipeline semantics
  - Execution paths (compressed vs symbolic)
  - Backward compatibility guarantees

**Phase 4: Demo Program & Validation**
- Created programs/demo_symbolic_pipeline.gx.json
  - Demonstrates symbolic pipeline with glyph-aware cognition
  - Uses CALL_GLYPH, RUN_PROMPT, SET_CONTEXT, CHAIN, LOG
- All 7 validation tests pass:
  ✅ Pipeline module imports
  ✅ Pipeline execution
  ✅ Glyph-aware transformations
  ✅ Demo program
  ✅ CALL_GLYPH result storage
  ✅ Backward compatibility
  ✅ run_symbolic_prompt() wrapper

**Phase 5: Final Report**
- Created XIC_SYMBOLIC_PIPELINE_REPORT.md
  - Architecture and module hierarchy
  - Integration points and data flow
  - Design decisions and rationale
  - Usage examples

Key Features:
- Step-level introspection: full SymbolicPipelineResult with step history
- Glyph-aware: explicit glyph_id routing through LAIN kernel
- Formal semantics: complete specification for tool builders
- Backward compatible: all v1 programs work unchanged
- No breaking changes: compressed execution path untouched

Constraints Met:
✅ No GPU code
✅ No XIC v2 binary container
✅ No .gx format changes
✅ Full backward compatibility
 2026-05-21 01:27:49 -04:00
GlyphRunner System 69c97e125a Extend XIC v1 Engine with Symbolic Mode, 5 New Ops, GPU Path, Cognition Integration 
New instructions:
- STREAM: Line-by-line execution and output
- CHAIN: Named execution boundaries
- CALL_GLYPH: Invoke glyph-aware cognition
- SET_CONTEXT: Set symbolic/cognitive context metadata
- LOG: Structured logging

Symbolic execution mode:
- SET_MODE "symbolic" routes prompts through LAIN 8-lane cognition pipeline
- run_symbolic_prompt() compresses prompt, builds manifest, executes via execute_symbolic()
- Full integration with glyphos/cognitive_kernel.py

GPU-accelerated path:
- xic_extensions/gpu_runtime.py: has_gpu() probes torch.cuda, run_on_gpu() executes
- SET_PARAM "use_gpu" true enables GPU (auto-fallback to CPU if unavailable)
- No required GPU dependencies; system works equally on CPU

Demo programs:
- demo_symbolic.gx.json: Shows symbolic mode through LAIN pipeline
- demo_gpu.gx.json: Shows GPU mode with CPU fallback

Backward compatibility:
- All 4 original ops unchanged; 5 new ops added to OP_TABLE
- xic_vm.py, xic_executor.py: No changes (pure dispatcher pattern holds)
- demo_chat.gx.json: Still executes identically
- All existing GlyphRunner commands: Unchanged behavior

Architecture:
- Lazy imports prevent circular dependencies (xic_ops, glyphos, xic_extensions)
- Clean separation: XIC is client of cognition layer
- Zero breaking changes; additive extension only
- No XIC v2 binary format; all within v1 JSON+.gx architecture

Validation:
- 10 integration tests: all passing
- Backward compat verified with original demo
- Symbolic and GPU modes tested end-to-end
- No external dependencies required (GPU optional)

Co-contributors: LAIN cognition engine, gx_compiler GSZ3, glyphos event system
 2026-05-21 01:19:40 -04:00
GlyphRunner System 9792449157 Implement GlyphOS Event System 
Add lightweight, in-process event bus with Cognitive Kernel integration:

New Components:
- glyphos/events.py: EventBus class + functional API
  * EventBus: publish/subscribe pattern with history
  * Event type definitions (EventType literal)
  * Singleton: get_event_bus(), emit(), on()
  * History filtering and limits
  * Graceful handler error handling

- tests/test_events.py: Comprehensive test suite (16 tests, 100% pass)
  * EventBus subscription/publishing/history
  * Global singleton behavior
  * Functional API (on, emit, get_event_bus)
  * Kernel integration tests
  * Cognition event emission tests

Modified:
- glyphos/cognitive_kernel.py: Event emissions at key points
  * kernel.warmup.completed: After warmup() completes
  * cognition.started: At start of execute_gx()
  * cognition.completed: After execute_gx() completes
  * glyph.resonance.updated: When glyph resonance present

- glyphos/__init__.py: Export events module

Test Results:
- Registry tests:          12/12 ✅
- Bridge tests:            10/10 ✅
- Kernel tests:             8/8  ✅
- Event system tests:      16/16 ✅ (NEW)
- Integration tests:        6/6  ✅
- Total:                   52/52 ✅

No breaking changes - all 36 existing tests still pass.
 2026-05-20 18:11:25 -04:00
GlyphRunner System 5c4bfb2dc1 Implement GlyphOS Cognitive Kernel 
Add a system service layer on top of LAIN cognition and Supercharged Glyph Registry:

Components:
- glyphos/cognitive_kernel.py: CognitiveKernel class + functional API
  * CognitiveKernel: Main orchestrator with execute_gx(), execute_symbolic()
  * Result accessors: get_last_result(), get_last_trace(), get_last_fused_symbol()
  * get_kernel(): Singleton kernel instance
  * run_gx(): Convenience function for global kernel
  * kernel_status(): Status introspection

- glyphos/__init__.py: Package initialization

- tests/test_cognitive_kernel.py: Comprehensive test suite (8 tests, 100% pass)
  * Kernel initialization and warmup
  * GX execution and result validation
  * Result accessor methods
  * Singleton pattern
  * Functional API

- COGNITIVE_KERNEL.md: Complete documentation

Test Results:
- 12 registry tests ✅
- 10 glyph bridge tests ✅
- 6 integration suites ✅
- 8 cognitive kernel tests ✅
- Total: 36 tests, 0 failures

No breaking changes - all existing tests pass.
 2026-05-20 18:03:25 -04:00