Jev 1.13 scores 100/100 on a 20-task scientific choice benchmark
A video says Jev 1.13 uses non-autoregressive forward passes for discrete semantic selection, hitting 100/100 exactness across 20 experimental domains at a median 0.335s and 51x cheaper than GPT-6 Sol, and notes autoregressive models can answer correctly at the top level while corrupting intermediate states.
this is pure f*cking treasure JEV Engineering breaks the silent memory poisoning in Agent Lifespan Engineering Autonomous agent clusters decay from the Deceptive Safety Mask. Autoregressive models emit the correct top-level verdict while committing corrupted intermediate states into persistent memory buffers. Across multi-day production runs, this latent divergence accumulates into catastrophic context collapse A benchmark evaluation from arXiv:2609.24965 tests Jev 1.13 across twenty scientific choices in ten experimental domains. Resolving discrete semantic relations through non-autoregressive forward passes delivers 100/100 semantic exactness in 0.335s median latency for $0.0595 per 1,000 requests, operating 51x cheaper than GPT-6 Sol and 180x cheaper than Claude Opus 5.5 Evidence e → System 1 Gate (Jev 1.13: 0.335s) → Deterministic Derivation g → Verified Lifespan Memory S_t Four technical discoveries proving why autoregressive generation fails as an agent router: 1. The Deceptive Safety Mask (Case B002) In the historical Luria-Delbrück fluctuation benchmark, 100% of autoregressive model responses produced the correct final label (Inconsistent). But behind that passing score, models inverted culture growth relations from (1, 10) to (10, 1). Because the top-level classification matched ground truth, the corrupted numerical vector was silently saved into state memory, poisoning downstream derivations 2. The Three Levels of Correctness Real-world scientific decisions span three linked tiers: Semantic Choice r̂ = f(e, Q), Downstream Code Derivation ẑ = g(e, r̂), and Final Claim Label ŷ. Standard evaluations test only ŷ, hiding a 14% latent divergence rate where the final verdict passes by coincidence. Jev 1.13 enforces the Joint Invariant, guaranteeing 100/100 simultaneous exactness across all three tiers 3. Sub-Second Latency vs Autoregressive Token Bleed Forcing frontier models through sequential autoregressive decode loops for binary routing decisions drains compute budgets. Jev 1.13 resolves choices in 0.335s (p95: 0.442s) at $0.0595 per 1,000 requests. GPT-6 Sol requires 1.385s at $3.0470, while Claude Opus 5.5 burns 3.237s at $10.6965. Non-autoregressive forward passes eliminate decode latency entirely 4. The Anti-Aging Cluster Runtime Long-horizon architectures decouple routing from generation: Jev 1.13 executes sub-second System 1 gating, GPT-6 Sol explores candidate branches, GPT-6 Luna runs low-cost step triage ($0.20/1k), Grok 4.7 falsifies edge cases with a tripwire audit, and Claude Opus 5.5 compiles immutable code invariants. Isolating discrete choices from exploratory sampling prevents context rot and keeps agent lifespans stable across thousands of turns Autonomous reasoning cannot survive on linear token generation alone. Scaling agent lifespans requires non-autoregressive gating to lock state invariants before committing a single step to long-term memory Full breakdown of the Anti-Aging Architecture in the quoted article below ↓
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