ae13f78c2221e3e2d61c5877fcf2d81b1ed52d98
9 Commits
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ae13f78c22 | Initial commit: 2125_GCE project | ||
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c3a826b65c |
Implement XIC v2 control flow with IF, MATCH, LOOP operations
PHASE A: Safe predicate evaluator (glyphos/control/predicate.py) - AST-based safe expression evaluation - Supports comparisons, boolean ops, attribute access - Helper function: dominant_contains() - Protected against code injection attacks PHASE B: XICContext queue helpers - enqueue_chain(label) for FIFO chain scheduling - pop_next_chain() to get next scheduled chain - jump_to(label) for immediate destination changes PHASE C: Control flow operations (xic_ops.py) - op_IF: Conditional branching with optional else - op_MATCH: Pattern matching against fused fields - op_LOOP: Iterative execution with guardrails - Added to OP_TABLE for operation dispatch PHASE D: Execution loop enhancement (xic_vm.py) - Chain queue scheduling with label matching - Total steps tracking for guardrail enforcement - max_total_steps limit across all operations - Graceful execution stop on guardrail trigger PHASE E: Comprehensive test suite (tests/test_control_flow.py) - 14 unit tests covering all operations - Predicate evaluator tests - IF/MATCH/LOOP operation tests - Queue helper and guardrail tests - All tests passing (14/14) PHASE F: Example programs - demo_control_flow_if.gx.json: IF branching example - demo_control_flow_loop.gx.json: LOOP iteration example PHASE G: Complete documentation - XIC_V2_CONTROL_FLOW_SUMMARY.md: Technical guide - XIC_V2_QUICK_REFERENCE.md: Developer quick reference - FedMart UI and integration documentation Integration points: - FedMart telemetry captures control flow steps - UI dashboard displays control branching - Symbolic pipeline predicate evaluation - 100% backward compatible with XIC v1.5 Test results: 36/36 passing (14 control flow + 12 FedMart + 10 UI) Status: Production ready |
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8f55949b11 |
Integrate XIC telemetry with FedMart (Phase 1)
Implement telemetry schema, adapter, and pipeline integration for FedMart real-time monitoring of XIC symbolic pipeline execution. ## Components ### Telemetry Schema (integrations/fedmart/telemetry_schema.json) - JSON schema defining XIC telemetry event structure - Required fields: event_type, timestamp, run_id, glyph_count, etc. - Optional: metadata, raw_payload for detailed analysis - Supports multi-glyph resonance summaries and guardrail events ### FedMart Adapter (integrations/fedmart/xic_adapter.py) - FedMartAdapter class for telemetry emission and spec registration - emit_telemetry(): normalize and forward telemetry events - register_spec_map(): push XIC specification status - Control hooks: pause_run(), throttle_run() for guardrail actions - Local mode (buffering) and remote mode (HTTP POST) - Global singleton instance via get_adapter() ### Pipeline Integration (glyphos/symbolic_pipeline.py) - Emit telemetry at end of run_symbolic_pipeline() - Captures: glyph_ids, resonance scores, execution steps, guardrails - Builds resonance_map_summary with top glyphs and averages - Optional import (graceful degradation if FedMart not available) ### Validation Suite (tests/validate_fedmart_integration.py) - 12 comprehensive tests covering all adapter functions - Tests: telemetry emission, normalization, spec registration - Tests: control actions, buffer operations, schema compliance - Tests: multi-glyph resonance tracking, guardrail event capture - All 12 tests passing ✅ ## Key Features ✅ Telemetry normalization (timestamp ISO 8601, run_id generation) ✅ Multi-glyph resonance summaries (top 5 glyphs, average resonance) ✅ Guardrail event tracking (truncation, max steps, etc.) ✅ Spec map registration for specification tracking ✅ Control actions (pause/throttle for guardrail responses) ✅ Local mode for testing, remote mode for production ✅ Schema compliance validation ✅ Graceful degradation if FedMart not available ## Testing All 12 validation tests passing: ✅ Schema validation ✅ Adapter initialization ✅ Telemetry emission (local mode) ✅ Normalization with defaults ✅ Spec map registration ✅ Control actions ✅ Pipeline telemetry integration ✅ Guardrail event capture ✅ Multi-glyph resonance tracking ✅ Buffer operations ✅ Schema compliance ✅ Empty buffer handling ## Next Steps Phase 2: UI Visualization - real-time dashboard for FedMart Phase 3: XIC v2 Control Flow - IF, MATCH, LOOP operations Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> |
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150a036604 |
Implement multi-glyph resonance system for XIC v1.5 (6 phases)
Complete end-to-end multi-glyph resonance enabling simultaneous analysis of multiple glyphs with cross-glyph resonance metrics, guardrails, and comprehensive telemetry. ## Phase 1: XIC Layer - Context Accumulation ### XICContext Enhancement - Added glyph_contexts: list field for accumulating glyph IDs ### New Operations - PUSH_GLYPH_CONTEXT: accumulate glyph with guardrail enforcement - CLEAR_GLYPH_CONTEXT: reset context for new analysis chains ### Enhanced Existing Operations - CALL_GLYPH: detects populated glyph_contexts, passes glyph_ids to pipeline - RUN_PROMPT: supports multi-glyph context via glyph_ids parameter - STREAM: supports multi-glyph context via glyph_ids parameter ### Guardrail Integration - max_resonance_glyphs (default 10, configurable) - enable_resonance_guardrails (default True) - Enforced at PUSH_GLYPH_CONTEXT to prevent exceeding limit ## Phase 2: Symbolic Pipeline - Multi-Glyph Support ### Extended Signature - run_symbolic_pipeline now accepts glyph_ids parameter - Multi-glyph mode detection and routing - glyph_ids takes precedence over glyph_id if both provided ### Multi-Glyph Processing - SymbolicStep(kind="multi_glyph_resonance") for glyph_ids - SymbolicStep(kind="guardrail") when truncation needed - Guardrail enforcement with pipeline-level truncation to max_resonance_glyphs ### Null-Safety Fixes - extract_glyph_resonances: handles None resonance_map - get_dominant_glyphs: handles None resonance_map - format_glyph_resonance_report: handles None resonance_map ## Phase 3: LAIN Cognitive Kernel - Resonance Computation ### New Method: compute_multi_glyph_resonance - Takes glyph_ids list and execution result - Computes 5-dimensional metrics per glyph: - weight: relative importance [0.0, 1.0] - lineage_score: symbolic ancestry [0.0, 1.0] - contributor_score: contribution to fusion [0.0, 1.0] - frequency_score: occurrence frequency [0.0, 1.0] - grammar_score: structural alignment [0.0, 1.0] - Returns global_resonance_score as weighted average ### Enhanced execute_symbolic - Detects context["glyph_ids"] for multi-glyph mode - Post-processes LAIN result via compute_multi_glyph_resonance - Merges multi-glyph metrics into fused_symbol - Maintains backward compatibility (single-glyph unaffected) ## Phase 4: Guardrails & Telemetry ### Guardrail Enforcement - PUSH_GLYPH_CONTEXT rejects pushes exceeding max_resonance_glyphs - run_symbolic_pipeline truncates glyph_ids if needed - Guardrail step recorded in pipeline with reason message ### Telemetry Collection - ctx._state["last_resonance_stats"] stores: - glyph_count: number of glyphs processed - global_resonance_score: weighted average [0.0, 1.0] - guardrails_triggered: list of guardrail messages - timestamp: execution time ## Phase 5: Validation Suite ### 12 Comprehensive Tests (all passing) 1. New operations in OP_TABLE 2. XICContext.glyph_contexts field 3. PUSH_GLYPH_CONTEXT accumulation 4. CLEAR_GLYPH_CONTEXT reset 5. Guardrail enforcement on PUSH 6. run_symbolic_pipeline signature 7. compute_multi_glyph_resonance method 8. Multi-glyph resonance structure 9. execute_symbolic multi-glyph processing 10. Single-glyph backward compatibility 11. Demo programs validity 12. Multi-glyph demo structure ### Test File: test_multi_glyph_resonance.py - Unit tests for all components - Integration tests for data flow - Backward compatibility validation - Mock-based testing for isolated units ## Phase 6: Documentation ### Updated XIC_SEMANTICS_v1_5.md - Added PUSH_GLYPH_CONTEXT instruction semantics - Added CLEAR_GLYPH_CONTEXT instruction semantics - Added comprehensive Multi-Glyph Resonance section with: - Context accumulation model diagram - Complete workflow documentation - Guardrail specifications - Telemetry format definition - Three-glyph analysis example with JSON/Python output ### Created demo_multi_glyph_resonance.gx.json - Two-chain demonstration program - Chain 1: 3-glyph analysis (compression, entropy, information) - Chain 2: 4-glyph analysis (cognition, language, symbol, meaning) - Shows complete resonance query pipeline - Demonstrates context clearing and reset ### Created XIC_MULTI_GLYPH_RESONANCE_REPORT.md - Comprehensive implementation documentation - All 6 phases detailed with code examples - Architecture overview and data flow diagrams - Design decisions with rationale - Backward compatibility guarantees - Usage examples (CLI, JSON, programmatic) - Future enhancement suggestions ## Key Features ✅ Explicit context accumulation (PUSH_GLYPH_CONTEXT) ✅ Automatic multi-glyph detection in CALL_GLYPH/RUN_PROMPT/STREAM ✅ Guardrails prevent exceeding max_resonance_glyphs ✅ Telemetry tracking for analytics ✅ Full backward compatibility maintained ✅ Single-glyph mode unaffected ✅ Comprehensive validation suite (12/12 tests passing) ✅ Complete formal specification updates ✅ Demo program showcase ## Backward Compatibility - All XIC v1 programs work unchanged - Single-glyph CALL_GLYPH still works identically - Empty glyph_contexts → single-glyph behavior - .gx binary format unchanged - No breaking changes to APIs Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com> |
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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>
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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 |
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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 |
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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. |
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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. |