Adam Benenson

A general-purpose portfolio-intelligence engine. Bayesian view translation (entropy pooling over Black-Litterman), instrumented-PCA factor decomposition, RMT-cleaned covariance (Bouchaud RIE, Ledoit-Wolf NLS), sparse-cardinality mean-CVaR optimization, graphical-matching pair selection, and an immutable decision ledger. Every result must survive a deflation-aware validation battery — combinatorially-purged cross-validation, deflated Sharpe, probability of backtest overfitting — before it is allowed to claim anything. In production in a live long/short equity consulting engagement, driving book analytics, hedge construction, and optimization counterfactuals.

A recursively self-learning alpha compounding engine for prediction markets. Hypotheses enter as replay-scored candidates, survive shadow and paper validation, earn size through bounded live review, and get clawed back when evidence degrades. Every fill updates a market factor graph; the graph reshapes candidate selection; better candidates generate more evidence.

Ambient AI layer for the browser that resurfaces relevant information before you search. The problem is structural: knowledge workers lose hours daily not to hard problems but to context reconstruction — finding the thing you already found, rebuilding the thread you already held. Most AI tools respond to prompts; Forethink anticipates context, proactively surfacing connections across your entire digital history. All processing happens on-device, with no data extraction or centralized indexing. Privacy isn't a policy — it's the substrate.

A permissioned research-intelligence platform for professional services firms — domain ontologies spanning legal matters and investment research (firms, strategies, factors, themes). Captures and reasons over a firm's research, matter, and client history through Email/Drive change-data-capture into a temporal-hypergraph knowledge graph with tri-temporal indexing (event, effective, ingest time). Specialized agentic workflows for retrieval, drafting, compliance, and escalation sit on top. Every retrieval and agent action passes through scoped ACLs with information-barrier enforcement and a full audit plane.

A personal digital twin — built to reconstruct and extend my own causal reasoning (synthesizing Pearl's causal hierarchy with Rubin's potential outcomes), belief structure, and intellectual trajectory. Ingests arbitrary data: conversations, documents, reading history, behavioral traces, structured profiles. Extracts and consolidates claims across ten psychological dimensions; maintains a 24,000-episode store, a 50-node calibrated belief graph, and a queryable knowledge graph. Context engineering is formalized as a Markov Decision Process (DT-MDP) with a contrastive IRL retrieval policy. The system runs continuously, improving its own models between sessions. It also orchestrates structured agentic execution across the lab's other systems — augmenting my day-to-day work and accelerating Forethink's development end-to-end.

Compile governance, don't narrate it. Agentic AI rules should produce typed semantic kernels whose obligations are checked, not evaluated like behavior. The forcing move: reached scarce peer-relative decisive stages cannot jointly satisfy consistency, solidarity, and cross-claimant monotonicity (Arrow-Young-Thomson tradition). The kernel target is a product object — five runtime obligations (certifiable, governance-observable, corrigible, compositionally safe, non-vacuous), social-choice graph diagnostics, a spectral scaling witness, an explicit bridge. A machine-checked Lean 4 development paired with a Rust audit pipeline; end-to-end source walks of five deployed agent harnesses (incl. Codex, Claude Agent SDK, OpenClaw) produce machine-checked rejection certificates that surface real classifier failures rather than theoretical possibilities. Zero sorry, admit, or first-party axiom under the verification gate. A chatbot can be behaviorally aligned. An institution has to be legitimate.

Led the product, engineering, data, and design departments at an enterprise analytics company serving hedge funds, sovereign wealth funds, pensions, and family offices — 140+ institutional investors managing $3T+ in assets. Full org ownership from strategy through execution; member of the executive team.

Analyst on a $2B+ systematic equity book — Sharpe above 2 sustained through the 2008–2009 regime shift. Promoted to PM and managed own $200M+ portfolio in parallel.