Contract2Plan: Bridging Contracts and Plans

• Contract2Plan is a comprehensive methodology that transforms formal contract representations into executable, correct-by-construction plans.
• It combines contract content retrieval, schema mapping, and compliance verification to support domains such as procurement, security, robotics, and multiagent systems.
• Empirical evaluations show that Contract2Plan reduces constraint violations and enables dynamic refinement for reliable, evidence-grounded planning.

Contract2Plan encompasses a family of methodologies integrating the structuring, analysis, and compliance of contracts with the generation of executable plans. It provides a bridge between formal contract representations—whether legal, behavioral, security, or operational—and the synthesis of correct-by-construction plans or action protocols across domains including procurement optimization, security auditing, cyber-physical coordination, and distributed multiagent systems. Contract2Plan frameworks combine retrieval/extraction of relevant contract content, formalization into schema or algebraic models, incorporation of verification or compliance gates, and domain-specific plan synthesis pipelines. The following sections elaborate the architectural paradigms, core formal models, system algorithms, guarantees, limitations, and representative instantiations of Contract2Plan.

1. Foundational Architectures and Pipeline Designs

Contract2Plan is broadly characterized by a sequence of contract-grounded processing stages. Across domains, these typically include:

1. Retrieval and Provenance Clause or term evidence is retrieved from heterogeneous sources (contracts, supplier documents, codebases) using sparse and dense retrieval augmented by versioning, filtering, and reranking. Each extracted datum is linked to a provenance span for verification (Agarwal, 7 Jan 2026).
2. Contractual Structuring Unstructured content is mapped to a domain-specific schema: e.g., constraint fields for MOQs, lead times, substitution policies (operations), behavior types for multiagent sessions, or LTL specifications for mission contracts (Agarwal, 7 Jan 2026, Mallozzi et al., 2022, Shukla et al., 2018).
3. Extraction and Constraint Normalization LLMs or fine-tuned models transform retrieved content into typed fields, with unit harmonization, reference resolution, and enforcement of non-default field justifications via evidence tags (Agarwal, 7 Jan 2026, Wei et al., 21 May 2025).
4. Compilation and Plan Synthesis The extracted representations are compiled to planning artifacts: MILP or integer programs (operations), dependency graphs or action schemas (CPS), mission goals (robotics), or audit step plans (security) (Agarwal, 7 Jan 2026, Shukla et al., 2018, Wei et al., 21 May 2025).
5. Compliance Checking and Repair Before execution, a compliance verifier systematically enforces domain-specific constraints—grounding, eligibility, consistency, cross-document reconciliation—and, where necessary, refrains from action or repairs plans (Agarwal, 7 Jan 2026, Mallozzi et al., 2022).
6. Iterative or Dynamic Refinement Some workflows support ongoing refinement: dynamic plan update in response to findings (security), or algebraic repair/quotients in mission synthesis if initial refinement fails (Wei et al., 21 May 2025, Mallozzi et al., 2022).

This paradigm mandates a "verification-before-action" principle, ensuring that all emitted plans provably respect extracted contract terms and domain constraints.

2. Formal Contract Models and Algebraic Operations

Formalism underlies all Contract2Plan engines, providing soundness, compositionality, and actionable semantics.

2.1 Assume–Guarantee Contracts and Algebra (Mission Planning)

Contracts are defined as pairs $\mathcal{C} = (A, G)$ with $A$ the assumption language (typically as LTL or $\omega$-regular sets over environment traces) and $G$ the guarantee language (system's commitments if $A$ holds) (Mallozzi et al., 2022).

Key algebraic structures:

• Composition $(\parallel)$: Synchronous requirement $G_\parallel = G_1 \cap G_2$, $$A_\parallel = (A_1 \cap A_2) \cup \overline{G_1 \cap G_2}$$
• Quotient: $\mathcal{C}' / \mathcal{C}_1$ yields the most permissive $\mathcal{C}_Q$ so that $$\mathcal{C}_1 \parallel \mathcal{C}_Q \preceq \mathcal{C}'$$
• Merge and Separation: Minimal extension or relaxation required to make a contract attainable from components
• Refinement $(\preceq)$: $(A_1, G_1) \preceq (A_2, G_2)$ iff $A_1 \supseteq A_2$ and $G_1 \subseteq G_2$

All operations have explicit closed forms in LTL or contract algebra, enabling automated checking and repair.

2.2 Constraint Classes and Merging (Operational Contracts)

Clauses are classed as (Agarwal, 7 Jan 2026):

• Class A: Monotone feasibility (e.g., MOQs, lead times), supporting conservative merge (max/min restrictiveness)
• Class B: Eligibility (tiers, price breaks), encoded as hard constraints or selectors
• Class C: Non-monotone or exception-heavy (rebates, penalties) requiring abstention/human confirmation

Guaranteed conservative feasibility for Class A is established by Theorem 1 in (Agarwal, 7 Jan 2026).

3. Core Algorithms and Verification Gates

Contract2Plan instantiates domain-specific plan-generation and verification pipelines, with key shared algorithmic stages:

• Contract Extraction and Schema Validation Schema-enforced LLM extraction, with evidence cross-validation and normalization (Agarwal, 7 Jan 2026).
• MILP and Dependency Graph Compilation Extraction populates constraint sets for multi-echelon, BOM-coupled procurement MILPs (Agarwal, 7 Jan 2026) or partial-order action DAGs (Shukla et al., 2018).
• Solver-Based Verification Gate A compliance verifier applies layered checks: schema/unit correctness, provenance match, cross-document consistency (with conservative merge for monotone constraints), and global feasibility check via MILP solver (Agarwal, 7 Jan 2026). If any layer fails, targeted repair (or abstention/human-in-the-loop) is triggered.
• Dynamic Planning and Repair Loop In security auditing and plan-execute frameworks (Wei et al., 21 May 2025), planning and execution steps are iterated, with feedback from execution and evidence retrieval leading to refinement, extension, or reprioritization of plan steps.
• Contractual Choreography Synthesis In multiagent settings, local behavioral contracts are synthesized into a global type via matching and compliance checks, with session fidelity guaranteed for honest participants (Lange et al., 2013).

4. Security, Trust, and Adversarial Models

Contract2Plan approaches explicitly reason about adversarial threat models and trust boundaries:

• Immutability and On-Chain Enforcement In cyber-physical and blockchain-integrated CPS, plan and action contracts, once deployed on-chain, are immutable. Execution ordering, dependency checks, and validation are enforced in smart contracts, preventing out-of-order or fake completions (Shukla et al., 2018).
• Oracle Integrity and Authentication Trusted oracles (e.g., Oraclize, with SGX proofs) are used to bind precondition/effect predicates to real-world observations, defending against agent manipulation (Shukla et al., 2018).
• Honesty and Progress in Choreographies With contract-oriented CO₂ systems, the calculus allows pinpointing of participant culpability, automatic exclusion, and session progress provided honesty assumptions hold (Lange et al., 2013).
• Abstention and Human-Gate for Non-Monotone Conflicts For operational and legal clauses without monotonicity or where evidence does not cover the most restrictive case, plan emission is blocked unless explicitly confirmed (Agarwal, 7 Jan 2026).

A common pattern is the reliance on verifiable, auditable execution states, with bounded risk exposure even in presence of faults or adversaries.

5. Performance, Empirical Results, and Complexity

Several empirical studies have established the performance advantages and tractability boundaries of Contract2Plan pipelines:

• Micro-Benchmarks in Procurement Planning Synthetic experiments (500 instances, $T=5$) on single-item replenishment with extracted MOQ/lead times demonstrate that "extraction-only" planning results in $16.6\%$ MOQ-violation instances and mean regret $\sim$5.40% of cost. In contrast, Contract2Plan with compliance gates always preserves feasibility for monotone constraints, at the cost of human-in-the-loop for certain ambiguous cases (Agarwal, 7 Jan 2026).
• Security Auditing Accuracy SmartAuditFlow demonstrates 100% top-max accuracy on a standard vulnerability set, MRR=0.80, and 41.2% comprehensive vulnerability coverage with retrieval-augmented planning outperforming fixed-workflow LLM approaches (Wei et al., 21 May 2025).
• Multiagent and CPS Overhead On-chain execution for multiagent plan enforcement incurs gas and block-time overhead, with Plan_SC and Act_SC deployment totaling 1–3M gas and per-action costs scaling at 100–200k gas; throughput is typically 1–2 actions per block (Shukla et al., 2018).
• Computational Complexity Retrieval and extraction are typically linear in corpus size, plan compilation is polynomial in constraint or DAG parameters, while MILP feasibility and LTL contract synthesis remain worst-case exponential in instance size but are tractable for bounded horizons/components (Mallozzi et al., 2022, Agarwal, 7 Jan 2026).

6. Limitations and Future Research Directions

A number of open challenges delimit current Contract2Plan instantiations:

• Clause Coverage Guarantee Conservative feasibility hinges on complete retrieval of the strictest constraint for each monotone type; omission undermines safety (Agarwal, 7 Jan 2026).
• Resolution of Non-Monotone Constraints Exceptions, nested carve-outs, or penalty clauses (Class C) require human oversight, constraining automation in complex or ambiguous documents (Agarwal, 7 Jan 2026).
• Scalability Large BOMs, deep planning horizons, or complex LTL contracts press against the limits of current solvers; decomposition and rolling horizon algorithms are active areas (Agarwal, 7 Jan 2026, Mallozzi et al., 2022).
• Integration of Stochasticity and Richer Clauses Robust/stochastic versions of planning, and automatic encoding of complex price/rebate logic, represent ongoing work.
• Active Learning and Precedent Mining Reducing abstention rates for Class C by mining past resolutions or encoding structured exception logic is a proposed direction (Agarwal, 7 Jan 2026).

A plausible implication is that, as retrieval, extraction, and solver technologies mature and further formal coverage guarantees are established, Contract2Plan will increasingly support full automation of contract-grounded planning in complex, adversarial, and evidence-critical domains.

7. Domain Instantiations and Comparative Table

Contract2Plan is instantiated in several key application areas, each tailoring the foundational pattern as summarized below:

Domain	Core Model	Plan Synthesis Target
BOM-Aware Procurement (Agarwal, 7 Jan 2026)	Schema+MILP (monotone+other)	Feasible orders / inventory
Smart Contract Auditing (Wei et al., 21 May 2025)	Code+static+RAG/LLM loop	Audit task plan w/ findings
Multiagent CPS (Shukla et al., 2018)	DAG dependencies, on-chain SC	Lockstep distributed exec
Mission Planning (Mallozzi et al., 2022)	Assume–guarantee LTL algebra	Finite-state controllers
Choreography Synthesis (Lange et al., 2013)	Behavioral contracts, CO₂ calc.	Session global type/protocol

Each instantiation leverages the general Contract2Plan recipe: extraction/structuring of contract terms, formal model compilation, rigorous verification/gating, and dynamic plan/control generation under evidence-grounded compliance.