# Self-Knowledge Checkpoint

# Local Evaluation and Self-Knowledge Checkpoint for DoD Responses

This section outlines how Membria's DoD agent determines the optimal path to answering a user query. It uses a local Self-Knowledge Checkpoint module to decide whether the query can be answered from internal knowledge, from a local RAG system, or requires external DoD inference. All reasoning is performed locally using a lightweight reward model (TinyRM), a local SLM, and optional use of the Knowledge Cache Graph (KCG).

# Objective

Efficiently select the most accurate and useful answer while minimizing unnecessary computation and external calls. The Self-Knowledge Checkpoint module ensures that retrieval and generation are invoked only when needed.

# Architecture Overview

User Query
   │
   ▼
Self-Knowledge Checkpoint (SLM)
   │
   ├─ LOCAL → SLM Answer Generation
   │
   ├─ CACHE → Query Knowledge Cache Graph (KCG)
   │
   ├─ LOCAL RAG → Local Retrieval-Augmented Generation
   │
   └─ DOD → Distillation-on-Demand (Big LLMs)
             │
             ▼
     Receive 3–4 LLM Answers
             ▼
   TinyRM Scoring + Optional SLM Tie-Break
             ▼
   Best Answer → Output + Optional Caching in KCG

# Components

Self-Knowledge Checkpoint
A local SLM that decides whether to answer using built-in knowledge, the cache, a local RAG system, or to escalate to external distillation. This step prevents unnecessary computation and improves response time.
Local RAG Module
If needed, a local vector database (e.g., FAISS, Qdrant) is queried using the input prompt to provide retrieved context for the SLM to use in answer generation.
TinyRM Scoring
A small reward model evaluates answers for quality and relevance based on the original query.
SLM Tie-Breaker
If scores are inconclusive, the SLM selects the most accurate answer with reasoning.
Caching
Final verified answers may be stored in KCG for future reuse.

# Pseudocode Example

decision = self_knowledge_checkpoint(query)

if decision == "LOCAL":
    return slm_answer(query)
elif decision == "CACHE":
    context = query_kcg(query)
    return slm_with_context(query, context)
elif decision == "LOCAL_RAG":
    context = query_local_vectordb(query)
    return slm_with_context(query, context)
elif decision == "DOD":
    answers = call_big_llms(query)
    scores = [tinyrm_score(query, a) for a in answers]
    if max(scores) - sorted(scores)[-2] < 0.1:
        return slm_compare_all(query, answers)
    else:
        return answers[argmax(scores)]

# Total Latency Estimate

Step	Estimated Delay
Self-Knowledge Checkpoint (SLM)	100–300 ms
TinyRM Scoring	200–400 ms
SLM Tie-break Reasoning	300–600 ms
KCG Retrieval (if used)	200–500 ms
Local RAG Retrieval	150–400 ms
DoD Request (optional, external)	800–2000 ms
Output & Caching	50–100 ms

# Overall Delay Summary

Local only with cache or RAG: 0.8–1.6 seconds
With external DoD inference: 2.5–5 seconds
Fully local with fallback: 1.0–1.8 seconds

# Benefits

Privacy: All routing logic is local and intelligent.
Speed: Avoids unnecessary retrieval or external calls.
Quality: Scales from built-in knowledge to local RAG to global distillation.
Learning: Answer paths and outputs improve the cache over time.

This modular architecture allows Membria agents to act with contextual intelligence and progressive autonomy while maintaining trust and performance across environments.