If your goal is reliable answers on real workloads (enterprise QA, doc analysis, math/coding with tools), you don’t need a frontier-scale LLM. A Small Language Model (SML, ~0.5–1B params) paired with a logic verifier, orchestrated through an Agent-to-Agent (A2A) workflow, and backed by an engineered RAG stack with strong verifiers can reach GPT-4-level outcomes on many tasks—at a small fraction of the cost. You’ll still lag on tool-resistant, long-horizon reasoning (e.g., HLE-style puzzles), but for most business cases this architecture is the highest ROI.
Summary Architcture:
[User/Task]
|
[Planner (SML)] –decides subgoals & budgets–> [Retriever]
| / \
| [BM25] [Dense]
| \ /
| [Re-ranker]
| |
[Solver (SML)] <— evidence & tools — [Evidence Store / Tools] | \ (code exec, math, SQL) | \__________ candidates / traces __________/ | | [Critic (SML)] –logic/NLI checks–> [Strong Verifiers: NLI, Math, Code, SQL, Consistency]
| |
+—-(replan / escalate)—————+
|
[Selector] –weighted vote & abstain–> [Final Answer + Citations + Confidence]
Intuition on why this Architecture works:
Why this architecture now
• Scaling laws still favor bigger models—but returns on generalization per dollar are flattening for non-frontier teams.
• Externalizing knowledge (RAG) and verifying outputs (logic/math/code/tabular verifiers) beats trying to “store the world” in parameters.
• Adaptive workflows (A2A) use thinking tokens only where they pay off, controlling latency and spend.
Key ideas
- SML: handles grammar, composition, and orchestration.
- Logic model/verifier: small NLI/consistency model catches unsupported or contradictory claims.
- A2A: Planner → Solver → Critic with a budget policy (how many steps, how many self-consistency samples k, when to call tools).
- Engineered RAG: hybrid retrieval, cross-encoder re-ranking, semantic chunking, MMR diversity, freshness gates.
- Strong verifiers: symbolic math/units; code execution; table/SQL evaluation; NLI/entailment for faithfulness.
What “good” looks like (expected performance ranges)
| Task (setting) | SML + Logic + A2A + Engineered RAG + Verifiers | GPT-4-class (reference) |
|---|---|---|
| Enterprise QA (doc-grounded) | 85–95% EM on curated evals | 80–90% |
| Open-domain QA (RAG allowed) | 75–85% EM | 75–85% |
| Coding w/ execution tests | pass@1: 55–65% (mid-diff sets) | 55–70% |
| Math w/ solver | 65–80% (e.g., GSM8K-style) | 70–85% |
| MMLU-Open (RAG/tools allowed) | 60–68% | ~65–72% |
| MMLU-Strict (no tools) | 52–58% | ~70% |
| HLE (reasoning, tools allowed) | 12–16% | ~18–24% |
Because our architecture is RAG dependent, we beat hallucination bench marks hands down, and because it is doc-grounded, the result quality beats great LLMs.
Components that matter (and why)
- Planner (SML)
- Decomposes task → subgoals; assigns thought budget BBB and self-consistency kDecomposes task → subgoals; assigns thought budget BBB and self-consistency k.
- Generates disjoint retrieval queries to maximize evidence diversity.
- Retriever + Re-ranker
- Hybrid (BM25 + dense) → cross-encoder re-ranker (≤110M) → MMR diversity.
- Chunking 512–800 tokens with 64–96 overlap; semantic tiling for tables.
- Solver (SML)
- Produces grounded hypotheses citing spans, calls tools (math, code, SQL).
- Keeps chains short and auditable (“program-of-thought” style).
- Critic (SML) + Strong Verifiers
- NLI/entailment: evidence ⇒ claim, contradiction detection.
- Math/units: symbolic checks (e.g., SymPy) + unit algebra.
- Code: sandbox run + unit tests.
- Tabular/SQL: query-then-answer; compare result vs natural-language claim.
- Logic consistency across steps/hypotheses.
- Feeds scores into selection and the RL reward.
- Selector & Abstain
- Verifier-weighted voting over k traces; abstain/route-up under uncertainty to protect precision.
Training & cost envelope (order-of-magnitude)
- SML pretrain / continue-pretrain (0.5–1B params)
- 50–100B high-quality tokens for grammar/semantics/structure.
- Cost: ~$50k–$200k on efficient H100 clusters (high utilization).
- SFT + RL (“budget-aware thinking”)
- Few-hundred-k curated traces; RL with reward = faithfulness + success + consistency − compute.
- Cost: ~$10k–$60k.
- Verifiers & retrieval
- Train NLI and re-ranker (small models): $5k–$20k.
- RAG infra (100M docs, FAISS/HNSW + re-ranker): $10k–$50k/yr.
Total v1: ~$70k–$300k to reach strong, production-ready performance on evidence-grounded workloads (ex-data licensing).
KPIs you should track
- Faithfulness@k (claimed spans entail answer).
- Consistency@k (agreement across traces).
- Answer-Change Rate when masking top evidence (leak check).
- Exact Match / F1 on QA sets; pass@k on coding; unit-correct on math.
- Cost/answer (tokens + retrieval + verifier passes).
- Abstain rate (and business impact).
Common failure modes & mitigations
- Retriever myopia → diversify queries; enforce lexical/semantic diversity; MMR.
- Hallucinated synthesis → hard faithfulness gate (NLI must pass; show spans).
- Math/coding errors → require successful tool execution; retry with minimal temperature.
- Latency spikes → budget-aware RL; escalate locally (per subgoal), not globally.
- Over-abstain → calibrate thresholds per domain; route to a larger model only when ROI is clear.
When to scale the core
If you need better tool-resistant reasoning (HLE-style) without frontier costs, lift the core to ~3B params (keep the same stack). Expect step-changes on MMLU-Strict (+3–6 pts) and HLE (+3–4 pts), with modest inference cost increases.
Conclusion
For most real applications, the winning move is workflow engineering—not parameter bloat. A SML + logic verifier + A2A orchestration + engineered RAG + strong verifiers delivers GPT-4-class outcomes where it matters: evidence-grounded, auditable, and cost-controlled answers.
There is no single engineering recipe for all problems, good engineering matters, our proprietary trained models help accelerate projects and cut costs.