Scalable Code Planning Engine (SCOPE)

• SCOPE is a planning framework that synthesizes executable solver code, decoupling reasoning from execution for efficient multi-constraint planning and code generation.
• It employs persistent repository planning graphs to encode dependencies and structure, ensuring deterministic and scalable automation of code synthesis tasks.
• Empirical results highlight SCOPE’s state-of-the-art performance with near-linear scaling, lower inference cost, and significant latency reductions.

The Scalable COde Planning Engine (SCOPE) represents a paradigm shift in software and multi-constraint planning workflows: it reframes planning as code synthesis and execution, decoupling reasoning from execution to achieve efficiency, determinism, and scalability. SCOPE, as developed in the context of both repository-level code generation and combinatorial planning, leverages unified, reusable solver functions or persistent graph structures. This enables consistent handling of complex dependencies and the reliable satisfaction of constraints, leading to state-of-the-art performance across planning, code generation, and repository-level automation tasks (Luo et al., 19 Sep 2025, Deik et al., 14 Jan 2026, Bairi et al., 2023).

1. Formal Problem Definitions and Planning Models

SCOPE targets problems where plans—possibly large codebases or combinatorial solutions—must be synthesized subject to multiple, sometimes conflicting, constraints. In multi-constraint domains, define:

• $\mathcal{C} = \{c_1, \dots, c_n\}$: constraints, each $c_i$ a predicate on plans,
• $\mathcal{P}$: space of candidate plans,
• $I$: input instance,
• Each plan $P \in \mathcal{P}$ incurs constraint violation $\ell_i(P) \geq 0$,
• $$L(I, P) = \sum_{i=1}^n \ell_i(P) + \lambda V(P; \mathcal{C})$$, with $V(P; \mathcal{C}) = \sum_{i=1}^n \ell_i(P)$.

The objective is to compute a plan $P^* = \arg\min_{P \in \mathcal{P}} L(I, P)$, subject to hard constraints in $\mathcal{C}$. In repository-level code generation, the “plan” is a complete, consistent repository: a global graph or chain of edit obligations transforming the initial state into a target satisfying a correctness oracle (e.g., build passes, tests succeed) (Deik et al., 14 Jan 2026, Luo et al., 19 Sep 2025, Bairi et al., 2023).

2. Architectural Principles: Reasoning/Execution Separation and Graph Representation

SCOPE architectures are unified by the strict separation of query-specific reasoning from generic execution:

• Reasoning: Natural language intent and constraints are parsed into formal parameterizations (e.g., combination parameters, constraint sets, feature subtrees).
• Execution: Deterministic, reusable solver functions—instantiated once per domain—process parameters and execute synthetic plans without further LLM interaction.

In repository generation, the Repository Planning Graph (RPG) formalism $G = (V, E)$ encodes the persistent blueprint for the codebase:

• $V = C \cup F \cup D \cup M$ where
  • $C$: proposal-level capability nodes,
  • $F$: file-level (folder/file) nodes,
  • $D$: data-flow nodes,
  • $M$: implementation-level (class/function) nodes.
• $E = E_h \cup E_d \cup E_o$ representing hierarchical, data-flow, and ordering edges.

This unified graph supports coherent long-horizon planning, encoding both “what to build” and “how to build it” in a persistent structure. Natural language plans, which are fragile under complex dependencies, are supplanted by explicit graph semantics (Luo et al., 19 Sep 2025).

3. Algorithmic Pipeline and Function Synthesis

The SCOPE engine follows a multi-stage pipeline, parameterized by context:

In Multi-Constraint Planning (Deik et al., 14 Jan 2026)

1. Query-Specific Reasoning (Stage I):
   • Parse input and example solutions into structured parameters $(\mathcal{C}, \mathcal{K}, \mathcal{S})$, where $\mathcal{C}$ are combination parameters and $\mathcal{K}$ are constraint parameters.
   • Optimization agents remove redundancy and promote symmetry in parameter space.
2. Generic Solver Generation (Stage II):
   • Synthesize three Python functions:
     • combinations_func(data): Enumerates candidate plans.
     • plan_func(candidates, constraints): Filters for admissible plans.
     • deliver_func(solution): Formats the solution as natural language.
   • These functions are fixed (and reusable) after induction, amortizing LLM cost.
3. Inference (Stage III):
   • For each new query, only the input parsing (single LLM call) incurs cost; plan synthesis and execution run locally in constant time per query.

In Repository-Level Planning (Luo et al., 19 Sep 2025, Bairi et al., 2023)

• Proposal-Level Planning: Select proposal-level capabilities via feature tree traversal and LLM-guided subtree selection, balancing semantic relevance (exploit) and diversity (explore).
• Implementation-Level Refinement: Attach folder/file structure, data-flow, and concrete implementation nodes; encode ordering, interface, and dependency information in the RPG.
• Graph-Guided Code Generation: Topologically traverse the RPG, generating code stubs and unit tests; employ test-driven development (TDD) loops with graph-based localization tools for efficient debugging and correction.

CodePlan, a related system, employs incremental dependency and change-impact analysis alongside an adaptive plan graph of edit obligations, integrating LLM calls per affected code fragment while maintaining consistency globally (Bairi et al., 2023).

4. Scalability, Efficiency, and Empirical Performance

SCOPE achieves:

• Linear/near-linear scaling: In ZeroRepo, planned features, leaf nodes, and total LOC grow near-linearly with planning iterations, unlike baselines, which stagnate early (Luo et al., 19 Sep 2025).
• Latency and cost control: In combinatorial planning, SCOPE limits inference LLM tokens to input parsing only; all generation and filtering are local, yielding constant token cost and significant wall-time reductions (e.g., 3 s per query vs 14 s for CoT; 1.4x cheaper) (Deik et al., 14 Jan 2026).
• Repository synthesis at scale: ZeroRepo produces codebases averaging 36 KLOC (3.9$\times$ larger than Claude Code; $\approx$64$\times$ other baselines), attains 81.5% coverage and 69.7% pass rate—27.3 and 35.8 percentage points above the strongest baseline (Luo et al., 19 Sep 2025).
• Adaptation to complex, interdependent edits: CodePlan passes validity checks and attains 100% match in 5/6 real-world C# repositories, demonstrating superior accuracy in large-scale automated code transformations (Bairi et al., 2023).

Summary benchmark comparison (TravelPlanner, GPT-4o) (Deik et al., 14 Jan 2026):

Method	Success Rate	Inference Cost (\$)	Latency (s)
SCOPE	93.1%	0.005	3
CoT	31.5%	0.007	14

5. Advantages, Limitations, and Future Directions

Key strengths of SCOPE:

• Robustness: Plans are executed deterministically as code, eliminating stochastic failures typical of natural language reasoning chains.
• Amortized LLM cost: LLM calls for solver induction become a fixed overhead per domain; per-query cost becomes minimal.
• Scalability: Constraints and plan complexity scale with local compute, not LLM wall-time or token usage.
• Consistent, interpretable plans: Persistent graphs and synthetic code structures promote modularity, enable localization, and mitigate drift and ambiguity.

Limitations:

• Leveraging SCOPE requires sufficiently strong code synthesis capabilities from the underlying LLM (closed-source GPT/Gemini or similar).
• Domain transfer entails re-induction of solver code or RPG construction.
• Current instantiations focus on single-domain induction and unit-test-driven correctness; complex cross-domain or few-shot refinements remain open.

Future work outlined includes meta-template learning for cross-domain transfer, open-source adaptation to LLaMA-style models, and dynamic, few-shot refinement for tighter constraint coverage (Deik et al., 14 Jan 2026).

6. Comparison with Other Paradigms and Broader Context

Compared to alternative paradigms:

• Chain-of-Thought (CoT), Tree-of-Thought (ToT), EvoAgent: SCOPE achieves much higher success rates, lower cost, and orders-of-magnitude lower latency as problem complexity increases. In planning with 10 cities, SCOPE remains ≥90% success, while comparative methods rapidly degrade (Deik et al., 14 Jan 2026).
• Natural-language planning and reactive approaches: Reliance on natural language introduces ambiguity and scaling limits. Explicit graph-based or code-driven planning (RPG, CodePlan) achieves systematic growth, modularity, and reliability (Luo et al., 19 Sep 2025, Bairi et al., 2023).
• Dependency- and change-impact-driven code automation: SCOPE and its relatives (e.g., CodePlan) differ fundamentally from local LLM-only edit approaches by employing global dependency graphs, static analysis, and adaptive scheduling of obligations for comprehensive and minimal edit plans (Bairi et al., 2023).

A plausible implication is that the SCOPE family—encompassing RPG-driven synthesis, code-driven combinatorial planning, and dependency-guided code automation—sets the foundation for scalable, reliable, and modular planning in code and combinatorial domains, surpassing prior free-form or solely LLM-driven methods.

7. Significance and Impact

SCOPE establishes a new operational substrate for planning across both code and general combinatorial tasks, leveraging explicit representations (persistent graphs or code) and separating reasoning from execution. Empirical results demonstrate superior scaling, correctness, and efficiency over prior baselines in both code repository generation and general multi-constraint domains (Luo et al., 19 Sep 2025, Deik et al., 14 Jan 2026, Bairi et al., 2023). This suggests that unified planning engines based on deterministically executed solver code or persistent blueprints will play a central role in automated software synthesis, large-scale codebase maintenance, and constraint-based planning workflows.