Code Context Memory Systems

• Code Context Memory is a framework that preserves, manages, and retrieves structured code data across iterative interactions with LLMs and software tools.
• It employs multi-tier architectures—including ephemeral, episodic, and semantic layers—to update, compress, and prune code elements dynamically.
• Advanced retrieval algorithms using AST cues and semantic embeddings improve instruction following and reduce errors in iterative code generation tasks.

Code Context Memory refers to a class of representational and algorithmic systems for preserving, managing, and retrieving structured code-relevant information across multi-turn or long-running interactions with LLMs, agent frameworks, or language-model-enhanced software tools. The critical function of such memory is to prevent context drift, semantic forgetting, repetitive error reintroduction, and token explosion during iterative code editing, generation, or reasoning tasks, whether in repository-scale software engineering, agent planning, or dynamic low-code/no-code development. The following sections detail mechanisms, architectures, algorithms, experimental metrics, and best practices for state-of-the-art code context memory, referencing implementations in CodeMEM, Cat, MOSS, InfLLM, CCM, PTM, and hybrid LCNC agent systems.

1. Architectural Paradigms for Code Context Memory

Modern code context memory frameworks utilize hierarchical and adaptive multi-tier architectures that integrate ephemeral, episodic, and semantic layers, often driven by AST-derived code units or semantic embeddings.

• CodeMEM architecture (Wang et al., 6 Jan 2026) maintains a dynamic key–value store of AST-extracted code blocks, each comprising function or class signatures, attributes, and method lists. At interaction round $t$, the memory state $\mathcal{M}_t$ formalizes code context as a set of blocks:

$$\mathcal{M}_t = \{ m_1, m_2, ..., m_{|\mathcal{M}_t|} \}$$

where keys are interface tuples, values are full implementations.

• Hybrid LCNC agent memory (Xu, 27 Sep 2025) separates Working Memory (immediate context), Episodic Memory (vector DB of code events), and Semantic Memory (distilled knowledge graph of project conventions). Intelligent Decay operates between tiers to prevent inflation.
• Cat context workspace (Liu et al., 26 Dec 2025) formalizes agent working state as:

$$C(t) = (\text{Task Prompt}\; Q, \; \text{Long-term Condensed Memory}\; M(t), \; \text{High-fidelity Recent Interactions}\; I^{(k)}(t))$$

enabling explicit action-triggered compression of prior trajectory.

• MOSS Python OS simulation (Zhu et al., 2024) preserves code context by constructing and replaying PyContext objects that serialize runtime namespace changes, ensuring prompt-level WYSIWYG state across agent turns.

2. Mechanisms for Context Update, Compression, and Pruning

Efficient code context memory requires continual update, selective retention, and budget-aware compression. Several approaches are employed:

• AST-guided update and add/keep selection (Wang et al., 6 Jan 2026): Each instruction $I_t$ triggers LLM scoring over block keys for $\texttt{ADD}$ or $\texttt{KEEP}$. Relevant blocks are retrieved and memory is updated.
• Policy-based pruning: CodeMEM's Selector prunes blocks not intersecting with the current code's API calls:

$$\hat{\mathcal{M}_t} = \{ m_i \mid \mathcal{A}(m_i) \cap \mathcal{A}(C_t)_{\text{ext}} \neq \varnothing \}$$

maintaining interface validity.

• Intelligent Decay scoring (Xu, 27 Sep 2025): Episodic entries $M_i$ are assigned composite scores:

$S(M_i) = \alpha R_i + \beta E_i + \gamma U_i$

where $R_i$ is exponential recency, $E_i$ is contextual relevance via cosine similarity, and $U_i$ is user-specified utility; entries below threshold $\theta_\text{decay}$ are pruned or consolidated into semantic summaries.

• Action-triggered compression (Liu et al., 26 Dec 2025): CAT agents invoke a dedicated context tool with signature $$context(Q, I^{(k)}(t), H_{\text{history}}) \to M(t)$$, executing LLM-driven compression only at semantically significant milestones, as learned through trajectory-level supervision.
• Online compressed context memory (CCM) (Kim et al., 2023): Key/Value attention context is continually compressed into a compact token via conditional LoRA adapters and merged into fixed-size memory, achieving 5–10× reduction in memory consumption with <1% performance loss.

3. Retrieval and Adaptation Algorithms

Code context memory systems deploy retrieval and adaptation algorithms, often relying on semantic embedding similarity or structured code interfaces.

• Procedural memory retrieval (Kagaya et al., 29 Sep 2025): For robot code adaptation, successful (instruction, code) pairs are indexed by fixed-length text embeddings; new instructions query memory via cosine similarity, retrieving top-$k$ contextually relevant examples for adaptation via LLM prompts matched to the target environment.
• Block-level memory selection (InfLLM) (Xiao et al., 2024): Evicted KV pairs are chunked into blocks, each represented by high-attention tokens. Upon new code input $X$, relevant blocks are selected via dot-product scores with current queries. Selected blocks augment attention cache to enable long-range dependency resolution.
• Resonant retrieval (PTM) (Houichime et al., 23 Dec 2025): Infinite context is encoded as a trajectory on an ergodic manifold; retrieval reconstructs history by solving for the vanished impulse in toroidal geometry, then fusing geometric and semantic priors for probabilistic token recovery.

4. Session Memory, Forgetting Detection, and Consistency Enforcement

Long-horizon code sessions require persistent memory of past edits and explicit forgetting mitigation to avoid regression.

• Code Session Memory (Wang et al., 6 Jan 2026) records per-turn edits ($I$, generated code $\mathcal{C}$, AST diff $\Delta\mathcal{C}$, and notes), identifying potentially forgotten fixes by detecting structural conflicts via AST diff intersection:

$$\text{Conf}(\Delta^t, \Delta^i) = (\Delta^t_{\text{del}} \cap \Delta^i_{\text{add}}) \cup (\Delta^t_{\text{add}} \cap \Delta^i_{\text{del}})$$

Triggered blocks are prepended to prompts, ensuring LLM reconsiders prior corrections.

• MOSS runtime isolation (Zhu et al., 2024): Each agent turn computes its own delta in namespace changes, reifies edits only to relevant global context, and replays state via PyContext serialization, ensuring local variable isolation and cumulative consistency.

5. Empirical Benchmarks and Comparative Metrics

Implementation efficacy is established via extensive benchmarks evaluating instruction following, code generation, forgetting ratio, context usage, and runtime efficiency.

Method	Instruction Acc. (CodeIF-Bench)	Conversation Acc.	Pass@1 (CoderEval)	Tokens/Round
Full-Ctx BM25	41.1%	38.4%	50.9%	131.8k
MemGPT	39.1%	38.2%	49.1%	31.0k
A-Mem	41.1%	37.8%	46.5%	358.5k
CodeMEM	46.1%	42.8%	55.7%	107.8k
ReAct-32B			40.2%	~65k
SWE-Compressor			57.6%	~35k

• CodeMEM yields +12.2% IA and +11.5% CA gain over full-context retrievers, and reduces rounds by 2–3 with competitive token usage (Wang et al., 6 Jan 2026).
• CAT Swe-compressor outperforms static compression and ReAct agents on SWE-Bench-Verified, achieving stable, bounded token usage and better pass rates as a function of context budget per round (Liu et al., 26 Dec 2025).
• LCNC hybrid memory systems attain completion rates of 92.5% vs. 65.2% for sliding window, with lower contradiction and token cost (Xu, 27 Sep 2025).

6. Hardware and Systems Considerations

Recent advances include memory-side context injection, enabling device-level attribution of memory requests to code regions.

• Metadata injection at the memory address level (Roberts, 21 Aug 2025): Programmer-visible context (function execution markers, object allocation events) is encoded directly into physical memory addresses via bit-level packets in DRAM reads. Decoding requires only address trace monitoring, yielding zero overhead, 100% recovery, and <0.1% throughput impact. This enables telemetry, dynamic scheduling, and targeted optimization of memory resources for code-active regions.

7. Design Principles and Best Practices

Extracted from these systems are several architectural guidelines for robust code context memory in AI assistants:

• Modularity: Separate working, episodic, and semantic memory; avoid monolithic designs (Xu, 27 Sep 2025).
• Active pruning and condensation: Score and condense memory continuously, combining recency, relevance, and user input (Wang et al., 6 Jan 2026).
• User and tool-driven control: Allow explicit context compression as an agent action, enabling memory management aligned with logical stage boundaries (Liu et al., 26 Dec 2025).
• AST/structure-aware organization: Leverage code block, signature, and API-level features for higher precision memory and retrieval (Wang et al., 6 Jan 2026, Xiao et al., 2024).
• Budget-aware summarization: Optimize context similarity and compression under token constraints (Liu et al., 26 Dec 2025).
• Session-level consistency and forgetting mitigation: Track and reconcile sequential edits via AST conflicts and instruction similarity (Wang et al., 6 Jan 2026).
• Adaptation for hardware/systems: Integrate program context into device telemetry for fine-grained, real-time optimization (Roberts, 21 Aug 2025).
• Compression for scalability: Employ block-level, LoRA-based, or geometric/spectral compression schemes to support infinite or repository-scale context (Houichime et al., 23 Dec 2025, Kim et al., 2023).

In conclusion, code context memory is an evolving domain, synthesizing code structure analysis, adaptive neural memory mechanisms, episodic-to-semantic consolidation, hardware-level context visibility, and trajectory-aligned compression strategies to enable durable, efficient, and contextually coherent code interaction for LLM agents and next-generation developer tools.