> **Jason Stiltner — Research Engineer | Multi-Agent Coordination, Verifiable Behavior** > Verification-centered research engineer: multi-agent coordination, verifiable behavior, scalable oversight. Controlled evaluation decides what ships. Based in Nashville. > > Source: https://jasonstiltner.com/ --- # Jason Stiltner Research Engineer Staff Engineer at GSV AI Labs Research applied inside a production engineering practice: multi-agent coordination, verifiable behavior, scalable oversight — in service of systems that ship, not the other way around. Staff Engineer at GSV AI Labs, the AI division of private equity firm Greater Sum Ventures — building CharlieIQ · Shipped production AI at HCA Healthcare, the largest US hospital system · Accenture Automation CoE [jason@jasonstiltner.com](mailto:jason@jasonstiltner.com) · [GitHub](https://github.com/jstiltner) · [LinkedIn](https://linkedin.com/in/jasonlstiltner) ## Focus Verification-centered empirical research: AI-accelerated experimentation, controlled evaluation, production deployment. Pre-linguistic coordination: how agents cooperate without shared language. Verifiable behavior grounded in observable actions rather than stated intentions. ## Corpus ### [Grounded Commitment Learning](https://jasonstiltner.com/projects/grounded-commitment-learning/) Multi-agent coordination through verifiable behavioral contracts. Agents commit to observable behaviors rather than inferred mental states—enabling external verification without access to internal representations. Grounded in Hart-Moore incomplete contracts theory (Nobel Prize in Economics, 2016). 40.4% hold-up reduction (95% CI: \[37.2%, 43.5%\]) · r = -0.972 punishment paradox, p < 0.001 ### [The Archive That Cites Itself](https://jasonstiltner.com/writing/archive-that-cites-itself/) Persistent AI advisors, the people they model, and the limits of “more context”—why a user model should preserve the history of its claims rather than a polished conclusion. ### [Chat with the Research](https://jasonstiltner.com/projects/chat-with-the-research/) Grounded RAG chatbot over this site, red-team hardened. Published eval-gate numbers including the bars still unmet, and the real defects the harness found and root-caused—rate limiter, citation injection, retrieval crowding. ### [Collaborative Nested Learning](https://jasonstiltner.com/projects/collaborative-nested-learning/) Extension of Google Research’s nested optimization: 5 timescales with 9 bidirectional knowledge bridges. Addresses catastrophic interference where fast learning degrades slow-learned representations, via normalization constraints that preserve component distinctiveness during optimization. +89% accuracy at high regularization, where baseline collapses ### [Aegis](https://jasonstiltner.com/projects/aegis/) Systems-architecture layer beneath agent frameworks: durability, verification, and policy—not orchestration. Event-sourced state for resume/replay from any checkpoint, a tool gateway enforcing policy at invocation time, and GCL commitments as first-class objects with explicit failure modes. 303 tests passing Single-node only. No performance benchmarks yet, and no production deployment. [Full corpus — 14 entries, filterable by facet →](https://jasonstiltner.com/corpus/) ## Methods 1. Empirical research. AI-accelerated hypothesis generation and experimental iteration, gated by controlled evaluation, statistical validation, and reproducible evidence. Model-generated explanations are hypotheses, not evidence. 2. Mechanistic validation. Ablations and interventions that separate predictive success from causal explanation. The punishment paradox is re-derived by CI on every push; CNL's bridge ablation weekly, at full scale — [both with the run behind them](https://jasonstiltner.com/corpus/reproducibility/). 3. Formal foundations. Mathematical proofs where applicable. Convergence guarantees, conservation laws, contraction mappings. 4. Executable research. Research artifacts built as software: automated evaluation, reproducible experiments, CI, test coverage, inspectable results. 5. Production systems. Research shaped by the constraints of systems that ship. Observability, failure recovery, deployment — GCP, Terraform, Docker, HIPAA-compliant architectures, multi-provider routing, edge inference. ## Background Path here: language, then automation, then ML. M.A. Université de Paris VII (French-language graduate program) Littérature, Langues, et Civilisations des Pays Anglophones [More →](https://jasonstiltner.com/about/)