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2125_GCE/XIC_V2_QUICK_REFERENCE.md
GlyphRunner System 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
2026-05-21 03:40:39 -04:00

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# XIC v2 Control Flow - Quick Reference
## Operations Summary
### IF - Conditional Branching
```
IF <predicate> <then_label> [<else_label>]
```
**What it does**: Evaluates a predicate and branches to different chains
**When to use**: Decision points based on resonance scores, glyph presence, etc.
```json
{"op": "IF", "args": ["fused.global_resonance_score > 0.8", "analysis_deep", "analysis_simple"]}
```
### MATCH - Pattern Matching
```
MATCH <path> <pattern> <then_label>
```
**What it does**: Checks if a pattern matches a fused symbol field
**When to use**: Looking for specific glyphs in resonance map
```json
{"op": "MATCH", "args": ["fused.glyph_ids", "glyph://entropy", "entropy_found"]}
```
### LOOP - Iterative Execution
```
LOOP <predicate> <body_label> [max_iter]
```
**What it does**: Repeatedly runs a chain while predicate is true
**When to use**: Iterative refinement, convergence detection
```json
{"op": "LOOP", "args": ["fused.global_resonance_score > 0.6", "refine_step", 5]}
```
---
## Predicate Syntax
### Fields
Access fused symbol fields with dot notation:
```
fused.global_resonance_score # float 0.0-1.0
fused.glyph_ids # list of strings
fused.glyph_count # integer
```
### Operators
```
> Greater than
< Less than
>= Greater or equal
<= Less or equal
== Equal
!= Not equal
and Boolean AND
or Boolean OR
not Boolean NOT
```
### Examples
```
fused.global_resonance_score > 0.8
fused.global_resonance_score > 0.8 and fused.glyph_count > 1
fused.global_resonance_score <= 0.3 or fused.glyph_count < 2
not (fused.global_resonance_score < 0.5)
```
### Helper Functions
```
dominant_contains('glyph://id') # Check if glyph in dominant list
```
Example:
```
dominant_contains('glyph://entropy')
dominant_contains('glyph://compression') and fused.global_resonance_score > 0.7
```
---
## Complete Program Example
```json
{
"magic": "GXIC1",
"version": 1,
"entrypoint": "main",
"symbols": {
"main": 0,
"loop_body": 7,
"high_path": 11,
"low_path": 14,
"end": 16
},
"instructions": [
{"op": "SET_MODE", "args": ["symbolic"]},
{"op": "SET_CONTEXT", "args": ["domain", "analysis"]},
{"op": "PUSH_GLYPH_CONTEXT", "args": ["glyph://a"]},
{"op": "PUSH_GLYPH_CONTEXT", "args": ["glyph://b"]},
{"op": "LOOP", "args": ["fused.global_resonance_score > 0.5", "loop_body", 3]},
{"op": "CHAIN", "args": ["loop_body"]},
{"op": "RUN_PROMPT", "args": ["Refine the analysis"]},
{"op": "IF", "args": ["fused.global_resonance_score > 0.8", "high_path", "low_path"]},
{"op": "CHAIN", "args": ["high_path"]},
{"op": "LOG", "args": ["High resonance detected"]},
{"op": "RUN_PROMPT", "args": ["Detailed analysis"]},
{"op": "CHAIN", "args": ["end"]},
{"op": "CHAIN", "args": ["low_path"]},
{"op": "LOG", "args": ["Lower resonance - trying different approach"]},
{"op": "RUN_PROMPT", "args": ["Alternative analysis"]},
{"op": "CHAIN", "args": ["end"]},
{"op": "CHAIN", "args": ["end"]},
{"op": "LOG", "args": ["Control flow complete"]}
]
}
```
---
## Parameters
Set limits with `SET_PARAM`:
```json
{"op": "SET_PARAM", "args": ["max_loop_iterations", 5]}
{"op": "SET_PARAM", "args": ["max_total_steps", 100]}
```
**Default Values:**
- `max_loop_iterations`: 50
- `max_total_steps`: 1000
---
## Testing Your Control Flow
```python
from xic_loader import XICProgram
from xic_vm import run_xic_program
# Load your program
prog = XICProgram.from_json_file("your_program.gx.json")
# Execute
ctx = run_xic_program(prog)
# Check results
print(ctx._state.get("control_steps")) # Control decisions made
print(ctx._state.get("guardrails")) # Guardrails triggered
print(ctx._state.get("symbolic_steps")) # All execution steps
```
---
## Troubleshooting
### "Chain 'xyz' not found"
- Make sure you have a `CHAIN` instruction with the label name
- Check spelling exactly matches
### "Predicate evaluation error"
- Check syntax: `fused.field_name` (not `fused['field_name']`)
- Verify field exists in fused symbol
- Test with simpler predicate first
### "Guardrail triggered"
- Loop exceeded max iterations: increase `max_loop_iterations`
- Total steps exceeded: increase `max_total_steps`
- Check predicate doesn't always evaluate true
### Control flow not executing
- Verify `CHAIN` labels match between ops and chain names
- Check execution with `ctx._state["symbolic_steps"]`
- Enable `LOG` ops to trace execution path
---
## Performance Tips
1. **Keep predicates simple** - Complex boolean logic slows evaluation
2. **Set reasonable loop limits** - High max_loop_iterations can timeout
3. **Use MATCH for frequent checks** - Simpler than IF with complex predicates
4. **Monitor total_steps** - Long programs may hit max_total_steps
---
## Integration with FedMart
Control flow steps automatically:
- Appear in telemetry events
- Display in dashboard timeline
- Contribute to symbolic steps tracking
- Trigger guardrail alerts when limits hit
---
## Next Steps
1. Review example programs:
- `programs/demo_control_flow_if.gx.json`
- `programs/demo_control_flow_loop.gx.json`
2. Check test suite:
- `tests/test_control_flow.py`
3. Read full documentation:
- `XIC_V2_CONTROL_FLOW_SUMMARY.md`
---
**XIC v2 Control Flow - Ready to Use**