[Paper] Lumos: Let there be Language Model System Certification

Source: arXiv - 2512.02966v1

Overview

The paper presents Lumos, the first formal framework that lets engineers specify and certify the behavior of large language model (LLM)‑based systems. By treating prompts as probabilistic graphs, Lumos gives developers a programmable way to describe complex input distributions—and then automatically verify that the model meets safety, reliability, or performance guarantees across those distributions.

Key Contributions

• A new DSL for LMS specifications – an imperative, probabilistic programming language built on graph abstractions that can generate i.i.d. prompts.
• Hybrid semantics (operational + denotational) that give a mathematically rigorous meaning to specification programs.
• Integration with statistical certifiers enabling automated, quantitative certification for arbitrary prompt distributions.
• Expressiveness – with a handful of composable constructs, Lumos can encode existing relational, temporal, and safety specifications, and it can define novel properties (e.g., vision‑language safety for autonomous driving).
• Empirical case study – applying Lumos to a state‑of‑the‑art vision‑language model (Qwen‑VL) reveals a >90 % failure probability in right‑turn, rainy‑weather scenarios, exposing a concrete safety risk.
• Failure‑case generation – specification programs can be used to automatically locate concrete inputs that trigger violations, aiding debugging and model hardening.

Methodology

1. Prompt Graph Modeling – Developers describe the space of possible prompts as a directed graph where nodes represent atomic pieces (text snippets, images, sensor readings) and edges capture logical or temporal relationships.
2. Probabilistic Sampling – Lumos executes the DSL to randomly sample sub‑graphs, producing concrete prompts that follow the intended distribution (e.g., “any rainy‑weather image followed by a navigation query”).
3. Specification Writing – Using a small set of language constructs (conditionals, loops, assertions), users encode desired properties such as “the model must never suggest a left turn when the road is blocked.”
4. Certification Engine – The sampled prompts are fed to the target LMS; statistical hypothesis tests (e.g., concentration bounds, PAC‑style guarantees) evaluate whether the observed behavior satisfies the specification with high confidence.
5. Hybrid Semantics – The authors define both an operational view (step‑by‑step execution of the DSL) and a denotational view (mathematical mapping from graphs to probability distributions), proving they coincide. This ensures that the certification results are sound with respect to the written specification.

Results & Findings

• Expressiveness demo – Lumos successfully re‑implemented several published LLM safety specs (prompt injection resistance, factual consistency) using fewer than 30 lines of code each.
• Vision‑language safety – In a simulated autonomous‑driving benchmark, Qwen‑VL generated unsafe navigation instructions (e.g., “turn left into oncoming traffic”) with ≥ 90 % probability under right‑turn, rainy‑weather prompts.
• Failure‑case extraction – The same Lumos program that certified the safety property also produced concrete image‑text pairs that triggered the failure, enabling targeted model debugging.
• Performance – Certification runs required on the order of a few thousand sampled prompts per property, completing in minutes on a single GPU, showing the approach is practical for iterative development cycles.

Practical Implications

• Safety‑by‑design pipelines – Teams building LLM‑powered assistants, chatbots, or multimodal agents can embed Lumos specifications directly into CI/CD, automatically rejecting model releases that don’t meet certified thresholds.
• Regulatory compliance – As governments begin to require demonstrable safety guarantees for AI systems, Lumos offers a provable, auditable artifact that regulators can inspect.
• Rapid threat‑model updates – Because specifications are modular graph programs, security teams can quickly add new prompt patterns (e.g., emerging phishing templates) without rewriting large test suites.
• Debugging aid – The failure‑case generation capability turns abstract statistical failures into concrete inputs, accelerating data collection for fine‑tuning or reinforcement‑learning‑from‑human‑feedback (RLHF).
• Cross‑modal verification – By handling vision‑language prompts, Lumos opens the door to certifying safety of autonomous‑driving stacks, robotics controllers, and AR/VR assistants that rely on multimodal LMs.

Limitations & Future Work

• Scalability of sampling – Extremely large or highly constrained prompt graphs may require prohibitive numbers of samples to achieve tight statistical guarantees.
• Model‑agnostic assumptions – The current certifiers treat the LMS as a black box; integrating gradient‑based or internal‑state information could yield tighter bounds.
• Specification ergonomics – Writing graph‑based DSL programs still demands a learning curve; the authors suggest future work on higher‑level libraries or visual editors.
• Dynamic environments – Extending Lumos to certify models that interact continuously with changing environments (e.g., closed‑loop robotics) remains an open challenge.

Lumos marks a significant step toward turning AI safety from an after‑thought into a programmable, testable component of the software development lifecycle. For developers eager to ship trustworthy LLM‑driven products, the framework offers a concrete, mathematically grounded toolbox to do just that.

Authors

• Isha Chaudhary
• Vedaant Jain
• Avaljot Singh
• Kavya Sachdeva
• Sayan Ranu
• Gagandeep Singh

Paper Information

• arXiv ID: 2512.02966v1
• Categories: cs.PL, cs.AI, cs.MA
• Published: December 2, 2025
• PDF: Download PDF