vsf-rsi — Recursive Self-Improvement for VSM systems

Don't ask the language model to improve itself. Give the improvement a substrate.

vsf-rsi is a cybernetic feedback loop that observes, evaluates, and improves socratic-engine predicates and trees through four levels of recursive self-improvement (RSI).

The system is deliberately bounded. It does not try to make the AI "smarter" unboundedly. It gives the system a formal structure in which improvements can be proposed, executed, validated, and rolled back — all within VSM safety constraints.

Status: v0.1.7 — production-ready with error recovery, logging, convergence checks, and pattern decay. 63 tests passing.

The bet

LLMs are excellent at proposing solutions. They are less reliable when they must implicitly improve their own evaluation criteria while simultaneously maintaining safety guarantees.

The bet here is simple:

Let the model propose improvements. Let a deterministic substrate validate and execute them. The model does not certify its own improvements.

flowchart LR
    A["LLM<br/>propose improvement"] --> B["RSI Observer<br/>capture + discriminate"]
    B --> C["Resolution<br/>drift / inject / generate / evolve"]
    C --> D["SocraticEngine<br/>re-evaluate"]
    D --> E["Metrics<br/>did it improve?"]
    E --> A

This creates a division of labour:

The central boundary is:

The LLM proposes. The substrate validates. The LLM does not self-certify.

What it is

At its core, vsf-rsi provides:

• Observer — wraps SocraticEngine.evaluate() to capture every evaluation event
• Bridge — feeds events to rsi_metrics for tracking
• Discriminator — classifies errors as BLOCKING or STRUCTURAL
• Resolver — applies the appropriate improvement level
• Scenario Memory — records and matches past corrections
• Predicate Generator — creates new predicates from error patterns (with syntax validation)
• Genetic Algorithm — evolves populations of predicates (with convergence checks)
• Pattern Detector — identifies recurring errors (with time-based decay)
• Error Recovery — system continues with degraded functionality on failures
• Logging — structured logging for debugging and monitoring

The four levels of RSI

flowchart TD
    subgraph L1["L1: Parameter Drift"]
        A1["adjust thresholds<br/>(autonomous)"]
    end
    
    subgraph L2["L2: Capability Extension"]
        A2["inject predicate wrappers<br/>(autonomous)"]
    end
    
    subgraph L3["L3: Predicate Generation"]
        A3["generate new predicates<br/>(human approval)"]
    end
    
    subgraph L4["L4: Genetic Evolution"]
        A4["evolve predicate populations<br/>(human approval)"]
    end
    
    L1 -->|"if drift fails"| L2
    L2 -->|"if pattern detected"| L3
    L3 -->|"if enough data"| L4

Architecture

flowchart TD
    subgraph SocraticEngine["Socratic Engine"]
        SE["evaluate(tree, ctx)"]
        PD["predicates"]
    end
    
    subgraph RSIObserver["RSI Observer"]
        OB["wrapper: evaluate()"]
        BR["bridge: track_classification()"]
        DI["discriminate: BLOCKING / STRUCTURAL"]
        RE["resolve: L1 / L2 / L3 / L4"]
    end
    
    subgraph RSIModules["RSI Modules"]
        MET["rsi_metrics<br/>track + aggregate"]
        FL["rsi_feedback_loop<br/>adjust thresholds"]
        SM["scenario_memory<br/>match + record"]
        PG["rsi_predicate_generator<br/>create predicates"]
        GA["rsi_genetic_algorithm<br/>evolve populations"]
    end
    
    subgraph VSMConstraints["VSM Safety"]
        A0["A0: purpose inalienable"]
        A5["A5: autonomy ceiling"]
        R17["R17: destructive consent"]
    end
    
    SE -->|"capture"| OB
    OB -->|"feed"| BR
    BR -->|"store"| MET
    MET -->|"analyze"| DI
    DI -->|"classify"| RE
    RE -->|"L1"| FL
    RE -->|"L2"| OB
    RE -->|"L3"| PG
    RE -->|"L4"| GA
    FL -->|"adjust"| PD
    SM -->|"match"| RE
    SM -->|"record"| RE
    A0 -.->|"constrain"| RE
    A5 -.->|"constrain"| RE
    R17 -.->|"constrain"| RE

Quick start

from vsf_rsi import RSIObserver, RSIMode
from socratic_engine import SocraticEngine

# Create engine with predicates
engine = SocraticEngine()

@engine.register("my_predicate")
def my_predicate(ctx, threshold=0.5, **kw):
    val = ctx.get("value", 0.0)
    from socratic_engine.engine import PredicateResult, Truth
    return PredicateResult(
        truth=Truth.TRUE if val > threshold else Truth.FALSE,
        certified=True,
        evidence={"value": val},
        source="my_predicate",
    )

# Create observer
observer = RSIObserver(engine, mode=RSIMode.CAPABILITY.value)

# Evaluate a tree
tree = {"predicate": "my_predicate", "args": ["$ctx", 0.5]}
ctx = {"task_id": "example", "value": 0.3, "input_value": 0.3}
event = observer.evaluate(tree, ctx)

# Check what happened
print(f"Error: {event.is_error}")
print(f"Class: {event.error_class}")

# Run pattern analysis
patterns = observer.analyze_patterns()
print(f"Patterns: {len(patterns)}")

# Generate predicates from patterns
actions = observer.generate_predicates(min_occurrences=3)
print(f"Generated: {len(actions)}")

Integration with socratic-engine

vsf-rsi depends on socratic-engine as its recursive evaluation substrate. The integration is via the observer wrapper:

sequenceDiagram
    participant User
    participant Observer
    participant Engine
    participant Metrics
    participant Resolver
    
    User->>Observer: evaluate(tree, ctx)
    Observer->>Engine: original evaluate()
    Engine-->>Observer: result
    Observer->>Metrics: track_classification(event)
    Observer->>Observer: discriminate(error)
    
    alt error detected
        Observer->>Resolver: resolve_error(event, ctx)
        Resolver->>Resolver: try L1 (parameter drift)
        alt L1 fails
            Resolver->>Resolver: try L2 (inject predicate)
        end
        Resolver-->>Observer: action
    end
    
    Observer-->>User: event + actions

Safety model

The VSM safety constraints are enforced at every level:

flowchart TD
    subgraph Autonomous["Autonomous (no approval)"]
        L1["L1: parameter_drift<br/>adjust thresholds"]
        L2["L2: inject_predicate<br/>create wrappers"]
    end
    
    subgraph HumanApproval["Human Approval Required"]
        L3["L3: generate_predicate<br/>create new predicates"]
        L4["L4: evolve_forest<br/>genetic evolution"]
    end
    
    subgraph NeverAutonomous["Never Autonomous"]
        A0["A0: purpose changes"]
        A5["A5: autonomy ceiling"]
        R17["R17: destructive actions"]
    end
    
    L1 -->|"validate before keeping"| L1
    L2 -->|"validate before keeping"| L2
    L3 -->|"require approval"| L3
    L4 -->|"require approval"| L4

Key safety properties:

• A0/A0.1: Purpose is inalienable — RSI cannot change system purpose
• A5: Autonomy ceiling — L1 is autonomous, L2+ requires validation
• R17: Destructive actions always require explicit human consent
• R4: Trust requires independent check — RSI cannot self-certify

Roadmap

See ROADMAP.md for detailed version history and future plans.

v0.1.7 — Hardened (current)

• Type validation for non-numeric inputs (BUG-001)
• Threshold drift bounds with MIN/MAX constants (BUG-002)
• Memory leak prevention with circular buffer (BUG-003)
• is_error computed property (BUG-004)
• load_thresholds path flexibility (BUG-005)
• Structured logging configuration (DEBT-001)
• Error recovery throughout pipeline (DEBT-002)
• Duplicate latency tracking removed (DEBT-003)
• Genetic algorithm convergence checks (DEBT-004)
• Pattern decay for stale patterns (DEBT-005)
• Generated code validation (DEBT-006)
• Version conflict detection (DEBT-007)
• 63 tests passing

v0.2.0 — Validation

• 50 real evaluations processed
• 1 threshold adjustment applied
• End-to-end test: generate → evaluate → evolve → measurable improvement
• Integration with state-canon-mcp

v0.3.0 — Production

• Dashboard for observation
• Cross-validation
• Overfitting detection
• PyPI publication

License

Apache-2.0