SpecLogician - AI Framework for Formal Specification Synthesis

Problem Statement

The Challenge of Formal Specifications

Real-world systems face critical challenges in maintaining formal specifications

Rapid System Evolution

Most real-world systems evolve faster than their formal documentation or contracts, leading to ambiguity, regressions, and subtle bugs.

Expertise Gap

Writing correct formal specifications requires deep expertise, and teams rarely have the capacity to keep specs aligned with rapidly changing codebases.

Informal Artifacts

Execution traces, logs, example-driven tests, and domain knowledge remain informal and cannot be checked, proven, or reasoned about symbolically.

Hidden Violations

Without continuous specification analysis, subtle contract violations and invariant breakages accumulate unnoticed until they cause failures.

LLM Limitations

LLMs alone will not solve this - statistical models generate plausible specs but not necessarily sound ones; without symbolic checking, these errors remain hidden.

Introducing Imandra SpecLogician

SpecLogician ingests execution traces, logs, tests, domain rules, and code structure to infer pre/postconditions, invariants, and behavioral contracts—capturing what the system actually does rather than what developers assume.

LLMs propose candidate specs; symbolic reasoning (via ImandraX) checks them for soundness, consistency, and completeness. Wrong or incomplete pieces are automatically refined using counterexamples.
With a symbolic backend, SpecLogician proves properties, detects invariant violations, and highlights spec drift. Engineers get concrete counterexamples, test inputs, and minimal failing traces.
As code, tests, or inputs change, SpecLogician re-synthesizes and re-checks specifications automatically—ensuring specs remain aligned with real behavior and catching regressions early.
By linking code, observed behavior, and formal reasoning, SpecLogician turns specifications into a living artifact—enabling trustworthy verification pipelines, automated documentation, and safer system evolution.

Advantages

Advantages for the Post-LLM World

SpecLogician brings unique advantages to formal specification synthesis

Local Reasoning

Each scenario is a small symbolic composition over named predicates and transitions, so LLMs reason over dozens of symbols instead of trying to track a massive, monolithic state space.

Incremental Grounding

Logs, tests, docs, and incidents map directly into new scenarios and predicates, allowing the formal model to grow organically with the system rather than requiring expensive global redesigns.

Vocabulary-Based Composition

LLMs operate on stable names (order_size_exceeds_limit, reject_order) rather than raw formal code, which dramatically improves reliability, reuse, and cross-scenario consistency.

Continuous Scalability

Adding a new scenario composes with the existing model instantly, avoiding the "rebuild and re-understand everything" cycle typical of traditional monolithic formal specs.

Process

SpecLogician: A Typical Process

From observation to proven specification in six iterative steps

Observe & Extract

Collect code structure, execution traces, logs, and tests as behavioral evidence.

Update the Model

AI proposes initial IML types, invariants, and contracts based on observed behavior.

Check the Logic

Symbolic reasoning checks the draft for soundness; proofs succeed or counterexamples are produced.

Refine Specification

Users edit the IML (or describe changes in natural language), strengthening or adjusting properties.

Re-Check & Iterate

ImandraX verifies updates; SpecLogician re-synthesizes missing parts and highlights inconsistencies.

Converge on Proven Spec

The final model is machine-checked, documented, and ready for regression analysis.

Get Started with SpecLogician

Start synthesizing formal specifications from your code, tests, and logs today.

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Welcome to SpecLogician