CodeMEM: Deterministic Code Memory Framework

• CodeMEM is a paradigm that formalizes structured, code-centric memory in machine learning to ensure deterministic and reproducible code generation.
• It employs dynamic multi-call protocols and versioned code banks to manage tool discovery and context efficiently, reducing overhead in multi-turn workflows.
• The framework also formalizes memorization detection and membership inference to mitigate privacy risks and unauthorized data reuse in model outputs.

CodeMEM is a general term for architectures and formal methodologies that introduce explicit, code-centric, or structured memory representations in machine learning systems, particularly in code-generating LLMs, agentic workflows, and repository-level code generation. Its primary goals are to achieve deterministic, reproducible behavior, mitigate probabilistic instability, enable robust code reuse, and manage the evolving context in complex multi-step or multi-turn code automation tasks. The CodeMEM concept has been instantiated in several technical lines: as agentic procedural memory via dynamic tool discovery and code banks (Gaurav et al., 17 Dec 2025, Gaurav et al., 23 Dec 2025), as AST-guided adaptive memory for repository-level LLM collaborations (Wang et al., 6 Jan 2026), and as a mechanism for formalizing and quantifying memorization or unauthorized data use in model outputs (Karmakar et al., 2022, Nie et al., 2024).

1. Architectural Foundations: Procedural Memory and Agent Reproducibility

CodeMEM was originally developed to systematize agentic procedural memory in tool-using LLM-based agents. Early code agents like CodeAct and ReAct allowed agents to use an unbounded Python action space but suffered from three fundamental deficiencies:

• Limited Tool Discovery: Naïve approaches required shipping definitions of all possible tools into each agent prompt—causing the context window to grow linearly with the number of tools and producing inefficiency.
• Probabilistic Instability: For identical tasks in identical environments, probabilistic LLMs could generate different trajectories or code solutions, undermining reproducibility.
• Context Inefficiency: Feedback and earlier logic would roll out of context in long or multi-step workflows.

The CodeMEM framework addresses these issues by decomposing agentic logic into two tightly coupled components (Gaurav et al., 17 Dec 2025):

• Dynamic Multi-Call Protocol (MCP): This module maintains access to a large, dynamically discovered tool registry. Instead of loading all tool definitions a priori, the agent uses search_functions and load_functions to bring just-in-time tool stubs or schemas into context, keeping the prompt size constant and the planning space modular (O(1) context cost).
• Procedural Memory as Code: All logic validated by sandbox trials is persisted in a versioned bank of Python functions. Each function (called a "skill") is indexed, versioned, and loaded into both the LLM context and runtime for future reuse. Registering skills permanently freezes user-validated code and guarantees deterministic, reproducible invocation.

This deterministic architecture ensures that given same input, same code, and same stored logic, the agent will always produce the same output, bypassing the stochastic sampling of LLMs in workflow execution (Gaurav et al., 17 Dec 2025).

2. Dynamic Memory Management for Repository-Level Code Generation

In large, evolving codebases, repository-level code generation faces persistent context-management and forgetting challenges. The CodeMEM architecture proposed by (Wang et al., 6 Jan 2026) introduces an AST-guided, structured-memory approach with two principal modules:

• Code Context Memory: This dynamic memory tracks only the relevant code blocks (functions, classes) needed for each generation request. Context blocks are keyed by signature, comments, attributes, or method lists, and blocks are retained or discarded based on AST-based analysis of API dependencies. The memory is updated according to LLM-prompted policies (ADD or KEEP), with pruning of irrelevant context after each code generation round.
• Session Memory: This captures user–LLM multi-turn interaction history at the code-edit level, recording instructions, generated code, AST-level diffs, and LLM-generated summaries. Links are maintained between session memory blocks with similar instructions. Explicit detection and correction of "forgetting" is performed by analyzing AST diff conflicts between current and previous versions—enabling the model to avoid reverting or contradicting prior correct changes.

This architecture yields state-of-the-art performance on instruction-following and iterative code benchmarks, with 12% improvement in instruction accuracy and over 50% reduction in forgetting compared to natural-language-centric memory approaches (Wang et al., 6 Jan 2026).

3. Workflow Synthesis, Structural Bottlenecks, and Skill Hardening

Advancing beyond deterministic replay, CodeMEM-based workflow agents must autonomously "synthesize" procedural memory—constructing robust, production-grade skills from scratch (Gaurav et al., 23 Dec 2025). The process is structured as a multi-stage scientific methodology:

1. Discovery Gap: Efficient tool selection from a large registry (size N ~ 10³–10⁵) via O(1) semantic search and just-in-time loading, leveraging MCP.
2. Verification Gap: Ensuring that the schemas and API response shapes are grounded via mandatory probe calls and sample-based verification.
3. Decomposition Gap: Planning multi-stage workflows using "Linear State Anchoring": decomposing the workflow into ordered, status-tracked steps ("todos") that preserve state and enable modular progress.
4. Scaling Gap: Guaranteeing that workflows scale from prototype to production using async concurrency, checkpoint persistence, and idempotency guards.

This pipeline systematically drives each new skill through a loop of hypothesis, probe, decomposition, code synthesis, and hardening, producing reusable, deterministic, and robust procedural memory (Gaurav et al., 23 Dec 2025).

4. Formalizations of Memorization, Membership, and Memory Risks

The term CodeMEM also refers to formal models quantifying memorization in code LLMs, encompassing two main classes:

• Behavioral Memorization Detection: A model is defined to "memorize" a prompt–solution pair if it reproduces, with high similarity, code seen during training, even under prompt mutations or truncations. Metrics include token-level Jaccard overlap, edit distance, and mutation success rates (MSR)—empirically revealing overfitting and privacy leakage in otherwise high-performing models (Karmakar et al., 2022).
• Membership Inference: Code Membership Inference (CMI) is the task of determining whether a code fragment was in a model's training data. Techniques include layerwise signal extraction, shadow modeling, logistic ensembling, and calibration based on code perturbations or code–comment proximity, yielding high-accuracy membership decisions in both white-box and gray-box threat models (Zhang et al., 2023).

A practical implication of these formalizations is the identification of risks related to privacy leakage, intellectual property rights, and overestimation of genuine synthesis abilities in LLMs.

5. Experimental Results and Practical Impact

Across several instantiations, CodeMEM architectures deliver substantial gains in correctness, workflow efficiency, and reproducibility:

Agent/Model	Correctness	Avg Calls	P50 Latency	Tokens
Gemini 3 Full (Gaurav et al., 17 Dec 2025)	96%	7.0	100 s	2.02 M
Claude 4.5 Sonnet	79%	7.0	71 s	2.61 M
GPT-5 Chat	68%	2.8	14.8 s	0.49 M

CodeMEM outperforms standard JSON-tool agent baselines by 20% absolute in multistep success rates and reduces context/token costs as workflow complexity grows (Gaurav et al., 17 Dec 2025).

AST-guided CodeMEM improves instruction accuracy by 12.2%, conversation accuracy by 11.5%, and halves instruction forgetting rates in iterative code tasks (Wang et al., 6 Jan 2026).

DeSec, a CodeMEM-instantiated method for secret extraction, achieves 44.74% plausible secret rate and extracts up to 5× more real secrets than prompt-engineering or beam search alone (Nie et al., 2024).

In workflow synthesis, CodeMEM agents autonomously construct production-ready orchestration modules with verified concurrency, persistence, and robustness, operating reliably on large-scale, real-world integration tasks (Gaurav et al., 23 Dec 2025).

6. Limitations, Generalizability, and Future Directions

Several limitations and directions emerge from CodeMEM research:

• Current procedural memory synthesis methods depend on LLM capabilities for correct tool selection and workflow decomposition; hallucinated tool schemas and missed dependencies remain possible failure modes (Gaurav et al., 23 Dec 2025).
• AST-guided memory systems are evaluated primarily on Python and on instruction-following or bug-fixing benchmarks. Extension to polyglot repositories, higher-order workflow logic, or low-resource domains is pending (Wang et al., 6 Jan 2026).
• Token-level secret extraction and membership inference depend on the representativeness of proxy models and calibration sets; linear classifiers may underfit more complex memory patterns (Nie et al., 2024, Zhang et al., 2023).
• Real-world privacy risks hinge on deduplication and cleaning of pretraining sets, which are an ongoing area of research and engineering.
• Integration with symbolic analysis, memory leak detectors, and other static verification tools is an avenue for strengthening runtime memory safety and compliance monitoring.

Potential extensions include glueing code procedural memory banks to dynamic or hybrid feedback analyzers, as well as continued work on universal, language-agnostic structural memory systems with robust “forgetting detectors,” audit trails, and workflow introspection.

7. Broader Implications

The CodeMEM paradigm transforms LLMs from stochastic improvisers into reproducible, auditable, and safe workflow architects. By aligning dynamic tooling, deterministic code banks, and AST-structured memory, CodeMEM closes the reproducibility gap in agentic workflows and large-scale code generation, while also exposing and enabling measurement of privacy and intellectual property risks associated with memorization. Its modular, code-based methodology provides a foundation for scalable, maintainable, and reliable automation platforms, code assistants, and compliance-driven AI systems (Gaurav et al., 17 Dec 2025, Wang et al., 6 Jan 2026, Gaurav et al., 23 Dec 2025, Nie et al., 2024, Karmakar et al., 2022, Zhang et al., 2023).