博士论文 | 可靠智能系统的优化、控制与形式化验证

TL;DR - A Stanford PhD thesis (Emiko Soroka, advised by Sanjay Lall, EE, June 2026) that brings optimization, control, statistical calibration, and formal verification into ML/LLM systems so they are checkable rather than merely capable. It matters because it offers an engineering path to reliability for agentic LLM systems in long-horizon, unlabeled-data, and safety-constrained settings.

• Ch.2 learns interpretable signal temporal logic predicates from trajectory data: predict trajectories from partial observations, compute robustness distributions of candidate atoms, use conformal quantile regression for distribution-free coverage intervals, then optimize logic expressions (genetic programming, grammatical evolution) over them; ships Satisfiability.jl, a Julia SMT interface.
• Ch.3 proposes LLM-guided clustering for unlabeled human-LLM logs: embedding k-means with over-clustering, LLM-generated cluster labels/summaries, then merging by label similarity; more stable labels across runs than a pure LLM-as-a-judge baseline on chat, code-feedback, insurance, WebShop, and KB/OS/SQL tool data.
• Ch.4 uses small fine-tuned LMs as distribution approximators for unlabeled evaluation: interaction-completion framed as sequence modeling (8B models match or beat 70B judges on task-oriented datasets), plus approximate response trees branching on high-probability alternate tokens with semantic entropy for sequence-level uncertainty; completion is ill-defined for fuzzy data like code feedback and insurance.
• Ch.5 tests LLM generation of STL specs (harder on 25x25 grids/mazes; Python code-form specs reduce syntax errors and raise semantic correctness) and "code-form planning" with executable Python plans plus a verifier — gains on path planning, multi-hop reasoning (GPT-4o/4.1, Claude Sonnet 4, Gemini 2.0 Flash Lite) and fewer off-task errors on WebMall, though the author notes some benefit comes from code comments acting as step-by-step reasoning, and checkpoint completion improved without improving final item-selection accuracy.