Assertion Challenging

📚 Repository: smartmemory-ai/smart-memory 🐛 Issues: Report bugs or request features Code: smartmemory/reasoning/challenger.py · Class: AssertionChallenger

SmartMemory detects contradictions between new facts and existing knowledge using a multi-method detection cascade and an auto-resolution layer. Use it to flag — or correct — factually inconsistent input as it arrives.

Detection cascade

LLM → Graph → Embedding → Heuristic

Each method runs only if the previous one did not produce a high-confidence verdict.

Pipeline

flowchart TB
    A["💬 User Statement"] --> B["🔍 Memory Retrieval"]
    B --> C["🧠 LLM Analysis"]
    C --> D{"Conflict Detected?"}

    D -->|No| E["✅ Normal Processing"]
    D -->|Yes| F["⚠️ Conflict Analysis"]

    F --> F1["📊 Confidence Scoring"]
    F --> F2["🎯 Conflict Type Classification"]
    F --> F3["💡 Suggested Response"]

    F1 --> G["🤝 Gentle Challenge"]
    F2 --> G
    F3 --> G

    style A fill:#e3f2fd,stroke:#1976d2,stroke-width:2px
    style C fill:#fff3e0,stroke:#f57c00,stroke-width:2px
    style G fill:#e8f5e8,stroke:#388e3c,stroke-width:2px

Usage

# Challenge a new assertion explicitly
result = memory.challenge("Paris is the capital of Germany")

if result.has_conflicts:
    for conflict in result.conflicts:
        print(f"Contradicts: {conflict.existing_fact}")
        print(f"Method: {conflict.explanation}")  # [LLM], [Graph], [Embedding], etc.
        print(f"Confidence: {conflict.confidence}")

# Auto-challenge during ingestion (only fires for factual-looking content)
memory.ingest("The speed of light is 300,000 km/s")

# Force or skip explicitly
memory.ingest(content, auto_challenge=True)   # always challenge
memory.ingest(content, auto_challenge=False)  # never challenge

Smart triggering

Auto-challenge only runs for content that looks like a factual claim. Conversational, opinion, and ephemeral content is skipped to avoid false positives and unnecessary LLM cost.

Conflict types

Auto-resolution

Before deferring to human review, SmartMemory attempts to resolve the conflict from authoritative sources:

Wikipedia Lookup → LLM Reasoning → Grounding Check → Recency Heuristic

result = challenger.resolve_conflict(conflict, auto_resolve=True)

if result["auto_resolved"]:
    print(f"Resolved via {result['method']}: {result['evidence']}")
else:
    print("Could not auto-resolve, needs human review")

Manual resolution strategies

When auto-resolution defers, the caller picks one of:

• KEEP_EXISTING — trust the existing fact
• ACCEPT_NEW — replace with the new fact (decays old confidence)
• KEEP_BOTH — store both with a conflict marker
• DEFER — flag for human review

Behaviour notes

• Semantic, not regex. Detection uses LLM reasoning over retrieved context, not pattern matching, so it tolerates paraphrase and surface variation.
• Gentle by default. When a conflict surfaces during conversational ingest, the system suggests a gentle challenge rather than rejecting the input — appropriate for assistant-style products.
• Cost-aware. The cascade short-circuits as soon as a high-confidence verdict is reached; embedding and heuristic checks are cheap, the LLM only runs when needed.

Related

• Decision Memory — first-class decisions with their own contradict/supersede lifecycle
• Opinions & Observations — synthesis from supporting evidence (the inverse of contradiction)
• Inference Engine — broader reasoning surface that the challenger sits inside