Anti-Slop Validation Framework

“AI slop” refers to outputs that are syntactically plausible but semantically hollow, physically infeasible, or epistemically unchecked — outputs that look like answers but are not engineering solutions. HIIE treats slop prevention as an architectural first-class concern, not a post-hoc filter.

Pillar 1: Recursive Validation with Divergence Enforcement

Each domain output undergoes iterative self-critique and cross-agent challenge. If cosine similarity between successive iterations exceeds the convergence threshold τ = 0.92, the system forces a divergence prompt — injecting a contrastive perspective that challenges the current reasoning path. This prevents circular loops where agents reinforce each other's errors.

Stack overflow protection is enforced via a hard recursion depth limit Dmax = 8, after which the current best output is escalated to Alice with a flag rather than continuing to recurse.

Pillar 2: Human Perspective Injection

At defined checkpoints, HIIE generates a layperson summary and a domain-expert critique of every technical output. These are required inputs to the Feasibility Manager's scoring function:

Fscore = w1 · Stechnical + w2 · Sphysical + w3 · Scommercial + w4 · Sethical

Where w1=0.35, w2=0.30, w3=0.20, w4=0.15. Outputs with Fscore < 65 are rejected and returned to the agent team for rework. Outputs with Fscore ∈ [65, 80) are flagged for Alice's attention. Outputs with Fscore ≥ 80 proceed to the Ethics Board.

Pillar 3: Physical Ground-Truth Validation

Every design claim must be anchored to a verifiable physical reference:

  • Material properties sourced from NIST, Matweb, or ASM International — not generated from model weights
  • Electrical designs validated against PySpice simulation before proceeding
  • Mechanical designs stress-tested in FreeCAD Python API before BOM generation
  • Chemistry outputs cross-referenced against PubChem and ChemRxiv before synthesis feasibility is asserted

Anti-Slop Guarantee

HIIE will not produce a Bill of Materials that references components that do not exist, a schematic that violates Kirchhoff's laws, a material specification that contradicts published NIST data, or a patent claim that describes physics that cannot occur. If simulation or database validation fails, the output is blocked — not papered over.

Preparation Steps for Anti-Slop Implementation

  1. Embed ground-truth retrieval at every domain module entry point
  2. Implement the cosine similarity divergence check (τ = 0.92) in the Celery task wrapper
  3. Deploy the Feasibility Manager as a separate model instance — independence is required for genuine critique
  4. Configure Dmax = 8 and automatic escalation in the FastAPI orchestration layer
  5. Require layperson and expert summaries at every approval gate
  6. Log all validation failures with full reasoning traces to the immutable audit log