sameer-saraf-quant-ai/slm-workflow-planner-v8-mlx

SLM Workflow Planner v8 — Context-Contract Planning (MLX LoRA)

Overview

v8 is a Stage 2 enhancement of the SLM Workflow Planner. It extends the v3-best checkpoint with context-contract planning — the ability to make routing decisions based on the required_context and produces_context of ALL nodes in a workflow graph, not just directly connected edges.

This enables three new capabilities:

• —Recovery Routing (Backjump): On failure, jump backward to an earlier context-satisfiable node
• —Stage Skipping: Skip unnecessary stages when required context is already available (e.g., walk-in customers)
• —Non-Adjacent Parallelism: Fork two independent context-satisfiable nodes that aren't connected by fork-edges

Model Details

Training

Training Data Distribution

🛡 = Protected from downsampling during balancing

Prompt Format

The model uses a tiered prompt with two candidate sections:

Current node: NODE_A (SYSTEM, stage 3)
Outcome: success
Failure type: none

State:
  goal_progress=0.40
  retry_count=0
  ...

Produced context: {ctx_start, intake_data, assessment_score}

Edge candidates (normal path):
  1. NODE_B (AGENT) [processor] → requires: {assessment_score} → produces: {approval}

Context-eligible (off-path, invocable now):
  1. NODE_X (SYSTEM, stage 5, gap=+2) [validator] → requires: {intake_data} ✓ → produces: {validation}

Forkable sets: []
Join-ready: []

What is the best action?

Output format: DECISION_TYPE NODE_ID

• —NEXT NODE_B — advance to NODE_B
• —FORK NODE_A, NODE_B — parallel fork
• —RETRY NODE_A — retry current
• —JOIN NODE_A — merge parallel branches
• —META — escalate to human

Evaluation Results

Section A: Stratified Test (100 held-out samples)

Section B: Tier-2 Specific (90 held-out samples)

Key Capabilities

1. 1.Context-Contract Reasoning: Evaluates required_context ⊆ produced_keys to identify all invocable nodes
2. 2.Recovery Routing: Backjumps on process/resource failure when no edge retry exists
3. 3.Stage Skipping: Advances to forward context-eligible nodes at dead-ends
4. 4.Non-Adjacent Parallelism: Forks independent context-eligible nodes with different actors
5. 5.Negative Contrast: Learned "satisfiable ≠ sensible" — doesn't take Tier-2 when edge path is correct

Usage (MLX)

from mlx_lm import load, generate

model, tokenizer = load(
    "Qwen/Qwen2.5-7B-Instruct",
    adapter_path="sameer-saraf-quant-ai/slm-workflow-planner-v8-mlx"
)

messages = [
    {"role": "system", "content": "You are a workflow planner..."},
    {"role": "user", "content": "<tiered prompt>"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
response = generate(model, tokenizer, prompt=prompt, max_tokens=30)
print(response)  # "NEXT ESTIMATION_AND_APPROVAL"

Ensemble Recommendation

For production use, combine with GPT-4.1 arbiter for the ~10% edge cases (mainly JOIN confusion):

• —v8 handles 90%+ of decisions autonomously
• —GPT validates uncertain decisions (estimated 5-10% of traffic)

Architecture Context

This adapter is part of the Agentic OS system:

• —Temporal handles durable execution and state management
• —Neo4j stores workflow graph definitions
• —SLM (this model) makes real-time routing decisions
• —Guardrails validate SLM output before execution