Code Agent with Variable Memory

• Code Agent with Variable Memory (CAVM) is a class of computational architectures that dynamically allocates, adapts, and manages memory for code-centric AI systems.
• It employs a hierarchical memory structure—short-term, episodic, and semantic—with techniques like mutual information estimation and intelligent decay to optimize performance.
• CAVM enables advanced applications such as large-scale code generation, debugging, and collaborative multi-agent research, offering measurable improvements on benchmarks.

A Code Agent with Variable Memory (CAVM) is a class of computational agent architectures that dynamically allocate, adapt, and manage memory resources in code-centric artificial intelligence systems. These architectures integrate paradigms from reinforcement learning, memory-augmented LLMs, cognitive science, and storage-efficient operating systems, providing mechanisms for handling complex, long-horizon tasks such as large-scale code generation, debugging, software maintenance, and multi-modal research. The CAVM concept underpins a variety of recent research frameworks, including memory analysis for RL agents, von Neumann-inspired code generators, multi-agent code instruction models, hierarchical storage systems, and repository-aware software engineering agents.

1. Theoretical Foundations and Memory Quantification

The analysis of agent memory usage is formally grounded in mutual information–based approaches, notably the Memory Lens method (Dann et al., 2016). This method estimates the mutual information between an agent’s current action and its past behavioral history, thereby quantifying the extent to which historical data inform current decisions without dependence on specific network architectures. The relevant sequence of information quantities is:

• $M_0 = I(A_t; X_t)$
• $M_1 = I(A_t; Z_{t-1} | X_t)$
• $M_2 = I(A_t; Z_{t-2} | X_t, Z_{t-1})$
• etc., where $Z_k$ represents the (observation, action, reward) triple at step $k$.

The sum $\sum_{i=1}^{t-1} M_i$ yields a lower bound on $\log C(\pi)$, where $C(\pi)$ is the minimum memory capacity required—defined as the number of internal states needed to generate the observed policy $\pi$. This provides an implementation-independent and theoretically justified measure of minimum memory for any agent, serving as a guiding principle for designing code agents with adaptive memory.

2. Variable Memory Architectures and Hierarchical Organization

CAVM architectures draw on diverse models of memory organization, each tailored to distinct temporal and semantic requirements:

Level	Memory Type	Principal Function
STM	Short-term/Working	Immediate task context, LLM window
MTM/EM	Mid-term/Episodic	Current session context/history
LTM/SM	Long-term/Semantic	Persistent knowledge, profiles

Operating-system-inspired frameworks such as MemoryOS (Kang et al., 30 May 2025) incorporate a three-tier architecture: Short-Term Memory (STM) holds recent dialogue/code “pages”; Mid-Term Memory (MTM) groups these into topic-coherent segments using semantic similarity ($F_{score}$ combines cosine and Jaccard similarity); Long-Term Personal Memory (LPM) retains persistent user/agent traits and project knowledge, updated via a heat-driven mechanism. Memory updating follows FIFO for STM→MTM and heat-segmented paging for MTM→LPM transitions. This structure enables agents to handle ultra-long dialogues, code histories, or evolving project states while maintaining personalization and topical coherence.

3. Memory Management: Updating, Pruning, and Intelligent Decay

The challenge of "memory inflation"—where the agent's memory grows without bound—necessitates active management strategies. One approach is the “Intelligent Decay” mechanism (Xu, 27 Sep 2025), which assigns each memory entry a composite score:

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

where $R_i$ is recency (exponential decay), $E_i$ is relevance (cosine similarity to current task), and $U_i$ is a user-defined utility. Entries with low $S(M_i)$ are pruned or consolidated, often being abstracted into semantic memory. User-centric interfaces provide transparency and human-in-the-loop (HITL) capability, allowing users to pin or discard specific memory entries via a visual interface.

In reinforcement learning–driven CAVMs (e.g., MemAgent (Yu et al., 3 Jul 2025)), the memory is maintained as a fixed-length, human-inspectable token buffer updated via an overwrite strategy. The overwrite strategy is optimized via RL, specifically using a multi-conversation extension of Decoupled Advantage Policy Optimization (DAPO), ensuring the agent retains only context relevant for future actions or answers.

4. Multi-Agent and Collaborative Memory Mechanisms

CAVMs often operate in multi-agent or collaborative settings to maximize cross-lingual or cross-domain knowledge transfer. A canonical example is the multilingual instruction tuning framework (Yang et al., 11 Feb 2025), which employs multiple language-specific agents ($A = \{p, o, m, r\}$: profile, operations, memory, reflection) each equipped with variable-sized generation memory. These agents engage in centralized or parallel collaborative protocols, share generation histories, reflect on successes and failures, and jointly create high-quality, cross-lingual datasets. This "generation memory" acts as a priority queue, with continual updates and pruning based on similarity thresholds and performance metrics.

Hierarchical collaborative systems (Zhang et al., 27 Jul 2025) introduce a further separation:

• Individual memory: agent-specific experience
• Collective memory: globally shared, evaluated knowledge
• Buffer pool: transient memory subjected to multi-indicator evaluation (value error, rarity)

Memory items are filtered based on criteria such as $\vert\delta_t\vert > \theta_{value}$ (where $\delta_t$ is the value error) or $R(m_i) > \theta_{rare}$ (rarity threshold), with adaptive pruning to avoid overload.

5. Application Domains and Performance

CAVM architectures have demonstrated tangible benefits across several domains:

• Code Generation: Systems such as L2MAC (Holt et al., 2023) employ a von Neumann–style instruction registry $\mathcal{I}$ and file store $\mathcal{D}$, managed by a control unit coordinating LLM agents. Each instruction is executed in sequence, memory is read/written for precise context, and code output is validated via syntactic and semantic checks. Reported metrics include "Features %" (the proportion of user-specified features implemented, pass@1 in HumanEval benchmarks), with L2MAC achieving a state-of-the-art 90.2% Pass@1.
• Repository-Aware Localization: Augmentation with episodic (commit history) and semantic (module summaries) memory components (Wang et al., 1 Oct 2025) enables file-level bug localization accuracy improvements on SWE-bench-verified/live, shifting agents toward more hypothesis-driven exploration.
• Long-Horizon Task Management: In low-code/no-code settings (Xu, 27 Sep 2025), hybrid memory (working/episodic/semantic) combined with intelligent decay yields better task completion rates and contextual consistency compared to sliding window or naive RAG approaches.
• Financial Deep Research: The FinSight multi-agent framework (Jin et al., 19 Oct 2025) unifies data, tools, and agent states into a single variable space $\mathcal{V}$, enabling iterative, code-driven research and report generation with professional-grade visualization. Experimental comparisons show substantial gains over previous research systems in factual accuracy and presentation quality.

System	Domain	Notable Performance
L2MAC	Codegen	90.2% HumanEval Pass@1
RepoMem-augmented	SW Eng.	Acc@5 ↑ on SWE-bench
MemoryOS	Dialogue	F1 ↑ 49.11%, BLEU-1 ↑ 46.18%
FinSight	Financial	Quality ↑ vs. GPT-5/Deep Research

6. Challenges and Future Directions

Despite progress, challenges remain. Estimating mutual information and memory requirements is nontrivial in continuous, high-dimensional, or non-Markovian domains (Dann et al., 2016). Dynamic memory resizing and adaptive consolidation strategies require further research, especially for scaling CAVMs to massive, evolving codebases or multi-modal environments. Research directions include incorporating variational mutual information estimators for continuous spaces, integrating causal intervention for probing memory usage, and developing principled meta-strategies for memory reliance and optimization across tasks (Dann et al., 2016, Xu, 27 Sep 2025).

A plausible implication is that future CAVMs will combine robust foundations in information theory, cognitive-inspired memory architectures, transparent user interfaces, and RL-driven content selection, resulting in agents with human-expert–like adaptability and long-term learning capabilities. These architectures are poised to play a central role in the next generation of autonomous software engineering, knowledge work, and multi-modal reasoning systems.