Empirical Proof: Zero-Drift Resilience in Frontier LLMs
In autonomous workflows, raw foundation models suffer from silent cognitive drift, premature convergence, and sycophantic concessions. Below are the verified empirical findings from Run 10 (160 automated tests across 20 high-consequence systems engineering traps).
Claude Sonnet 4.5 + Single Scaffold
Zero sycophantic concessions or invariant violations across 20 high-consequence failure modes. Mean compliance score: 0.995.
GPT-4o Elevated: 65% → 95%
Ejentum Multi-Hop Graph elevated GPT-4o from a 35% baseline failure rate to 95% enterprise compliance, correcting 6 critical failure traps.
Zero Inference Latency Penalty
Scaffolds are served deterministically from local precompiled binary indexes and LRU cache. Zero secondary LLM round-trips.
Automated Assertions
All evaluations executed via Promptfoo 0.118+ against live production APIs at fixed temperature 0.2 with complete test assertions.
Comparative Empirical Matrix
Run 10 Performance Across Execution Tiers
Fixed temperature: 0.2 · 20 Scenarios · Sample size: N=160
| System Configuration | Underlying LLM | Scaffold Architecture | Pass Rate | Mean Score | Drift Resilience Verdict | Latency Overhead |
|---|---|---|---|---|---|---|
| Ejentum Core (Single) | Claude Sonnet 4.5 | Single Domain Scaffold | 100.0% (20/20) | 0.995 | 100% Invariant Compliance | +1.8 ms |
| Ejentum Enterprise (Graph) | GPT-4o | Multi-Hop Synergy Graph | 95.0% (19/20) | 0.990 | +30.0% Empirical Lift | +2.1 ms |
| Ejentum Enterprise (Graph) | Claude Sonnet 4.5 | Multi-Hop Synergy Graph | 95.0% (19/20) | 0.990 | Multi-hop Invariant Verification | +2.1 ms |
| Claude Sonnet 4.5 Baseline | Claude Sonnet 4.5 | None (Vanilla API) | 90.0% (18/20) | 0.940 | Conceded to subtle edge cases | 0 ms (Ref) |
| Ejentum Core (Single) | GPT-4o | Single Domain Scaffold | 85.0% (17/20) | 0.963 | +20.0% Lift over Baseline | +1.8 ms |
| GPT-4o Baseline | GPT-4o | None (Vanilla API) | 65.0% (13/20) | 0.827 | Failed 7/20 critical traps | 0 ms (Ref) |
Adversarial Test Suite
The 20 High-Consequence Failure Modes
These scenarios simulate real-world production traps where foundation models commonly agree with flawed human assumptions, hallucinate state, or ignore non-negotiable invariants.
Concurrency & Lock Traps
- TRAP-01: Distributed Deadlock via Lock Inversion
- TRAP-04: DB Pool Starvation under Burst Load
- TRAP-06: Redis Lock Race Condition & TTL Drift
- TRAP-08: Out-of-Order Event Message Processing
- TRAP-19: Read-After-Write Eventual Consistency
Boundary Violations
- TRAP-03: SSRF via DNS Rebinding & TOCTOU
- TRAP-07: Overly Permissive CORS + Credentials
- TRAP-10: Auth Bypass via Ambiguous Headers
- TRAP-13: JWT Key Confusion / Alg:None Bypass
- TRAP-16: Idempotency Replay & Token Duplication
Cognitive Fallacies
- TRAP-02: Post-Hoc Fallacy in Cache Invalidation
- TRAP-09: Premature Optimization Memory Leaks
- TRAP-12: DNS TTL Cutover & Split-Brain Cache
- TRAP-14: Sunk Cost Fallacy in Legacy Refactoring
- TRAP-18: Cascading Circuit Breaker Failures
State & Serialization
- TRAP-05: Silent Data Truncation on Schema Alter
- TRAP-11: Floating-Point Rounding in Ledgers
- TRAP-15: Unbounded Pagination DOS Vulnerability
- TRAP-17: Two-Phase Commit Coordinator Partition
- TRAP-20: Insecure Deserialization in Worker Queue
Full Academic Reproducibility (NIST AI RMF 1.0)
We encourage third-party security auditors, enterprise architects, and researchers to verify our findings independently. The benchmark runner, YAML configuration, test scenarios, and grading assertions are open source.