6e0a586f51
Implement XIC v1.5: Symbolic Pipeline Abstraction with Glyph-Aware Transformations
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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
69c97e125a
Extend XIC v1 Engine with Symbolic Mode, 5 New Ops, GPU Path, Cognition Integration
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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
5c4bfb2dc1
Implement GlyphOS Cognitive Kernel
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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
GlyphRunner System