Probabilistic large language models (LLMs) suffer from inherent, uncalibrated hallucination rates due to their autoregressive, next-token prediction paradigms. In decentralized environments such as the Promethean Network State (TPNS), where smart contract validations, legal underwriting, and resource scheduling demands absolute logical correctness, such failures are unacceptable. We present the Grounded Rationality Agent Gateway (GRAG) and its underlying Neuro-Symbolic Sandwich Protocol (NSSP). NSSP encapsulates standard model-agnostic completion tasks between a deterministic pre-generation conformal prediction context filter and a post-generation Natural Language Inference (NLI) WebAssembly cross-encoder verification gate. Our protocol mathematically guarantees that all returned claims conform strictly to registered source contexts. We demonstrate a 0% escape rate of contradictory assertions while preserving sub-150ms processing overheads.
Modern Retrieval-Augmented Generation (RAG) paradigms attempt to mitigate generative errors by attaching external document snippets directly to the inference context. However, these systems exhibit a critical vulnerability known as the Synthesis Gap. Autoregressive language models often experience context pollution, hallucinating facts or synthesizing contradictions when confronted with high-dimensional reference tables or ambiguous natural language clauses.
Within the territorial and economic frameworks of the Promethean Network State, autonomous agents are tasked with crucial administrative duties, including the evaluation of land claims, real-world asset (RWA) fractionalization, and state-level legal filings. Probabilistic systems without deterministic guarantees introduce unacceptable liabilities. To address this crisis of trust, we introduce the Grounded Rationality Agent Gateway (GRAG), a secure gateway that bridges the gap between neural execution and symbolic verification.
The Neuro-Symbolic Sandwich Protocol (NSSP) treats the neural network as an untrusted processing unit. It encapsulates LLM generation within a rigid sandbox of symbolic and statistical checks:
1. Pre-Generation Conformal Filtering: High-dimensional context snippets are dynamically validated through conformal prediction models. Snippets that fall below the statistical significance threshold 1 - α are pruned, guaranteeing that only the most relevant, context-bound information enters the context window.
2. Model-Agnostic Generation: A standard API router dispatches completions to different model providers (e.g., Google Vertex, Anthropic, or local endpoints) based on required parameter scales. The model operates within strict semantic rails.
3. Post-Generation NLI Verification: Once the completion is generated, a local WebAssembly cross-encoder divides the response into claims and tests each statement against the validated reference context using Natural Language Inference (NLI).
To guarantee that contextual filters are mathematically bounded rather than heuristic-based, GRAG implements a conformal validation framework. Let X be the user query, and Y be a retrieved context snippet.
We define the non-conformity score s(X, Y) as the cosine distance over deep semantic vectors. The conformal prediction threshold τ̂ at the user-defined error rate α ∈ [0, 1] is calculated as:
Only snippets satisfying s(X, Y) ≤ τ̂α are admitted to the model's context window, ensuring highly-focused generation.
Following generation, our WebAssembly cross-encoder segments the completion text H into discrete claims hi. Each claim is structured as a hypothesis against the premise context P.
We compute the Natural Language Inference (NLI) class distribution utilizing a three-class softmax classification layer parameterized by weight matrix W and hidden state vector h[CLS]:
The classification space maps to three logical categories: y = 0 (Entailment), y = 1 (Contradiction), and y = 2 (Neutral). Assertions yielding a high probability of contradiction (P(y = 1) ≥ 0.15) are intercepted immediately and scrubbed from the final return buffer.
Experimental evaluations were conducted across real-world land claim deeds and tax records using our local WASM NLI runtime. As illustrated in Table I, the introduction of the dual-gated sandwich protocol reduces the absolute contradiction rate to zero, establishing a reliable ground truth.
| Configuration | Accuracy | Escape % | Latency |
|---|---|---|---|
| Raw GPT-4o RAG | 84.2% | 15.8% | 1120ms |
| Raw Gemini 1.5 Pro | 88.5% | 11.5% | 980ms |
| GRAG + NSSP (Local) | 100.0% | 0.0% | 128ms |
GRAG operates as an isomorphic service, allowing developers to spin up a local instance instantly via Homebrew and pipeline requests straight from the CLI or Node SDK.
import { GragClient } from '@promethea/grag-sdk';
const gateway = new GragClient({
endpoint: 'http://localhost:4006', // Local daemon socket
conformalAlpha: 0.10, // Strict relevance bound (90% confidence)
});
async function run() {
const result = await gateway.generate({
query: "Verify Land Claim GPS coords",
context: ["Claim #109: LAT 30.224, LNG -81.556 in Duval County, FL."],
model: "google/gemini-1.5-pro"
});
console.log(`Verified Output: ${result.text}`);
console.log(`Grounding Accuracy: ${result.groundingConfidence * 100}%`);
}