BudgetMem: Memory Budgets in Computation

Updated 7 February 2026

BudgetMem is a class of approaches that enforce explicit memory budgets in computing, enabling efficient trade-offs between resource use and task performance.
It employs adaptive algorithms, selective memory policies, and dynamic tiered routing to optimize memory allocation in areas such as long-context LLMs, streaming systems, and cloud computing.
Empirical studies demonstrate that BudgetMem reduces memory footprint with minimal accuracy loss while cutting operational costs by up to 87% across varied applications.

BudgetMem encompasses a class of methodologies, algorithms, and system-level solutions for enforcing, optimizing, and adapting to explicit memory budgets in computational systems. In both algorithmic theory and applied machine learning, BudgetMem frameworks enable resource-efficient computation by learning or reasoning about what information or state to retain, discard, or process—subject to strict constraints on memory usage—without compromising task utility or accuracy. The term applies across parallel programming, memory-augmented LLMs, agent memory architectures, recommender system embeddings, streaming write policies, KV-cache compression, and serverless function scheduling, unified by an explicit, algorithmically or empirically validated performance–cost frontier.

1. Fundamental Concepts of BudgetMem

The core principle of BudgetMem is to enforce a deterministic or adaptive bound on memory usage, optimizing for a specified task objective (accuracy, latency, utility) under this constraint. This is implemented via memory write policies, modular architectures with explicit cost-performance tiering, dynamically-learned compression or selection mechanisms, or combinatorial optimization of resource placement. BudgetMem frameworks go beyond ad hoc or static allocation by employing explicit selection, gating, routing, or budget bracketing algorithms that allow precise trade-offs and quantifiable adherence to the given memory regime. The enforcement of strict budget adherence distinguishes BudgetMem from generic “memory-efficient” designs.

2. BudgetMem in Long-Context LLMs

In language modeling, BudgetMem architectures select which context fragments to store and use for downstream inference, maximizing question-answering (QA) performance while minimizing retained memory (Alla et al., 7 Nov 2025). The system components are:

Selective memory policy: For a sequence of candidate document chunks $\{c_i\}$ , salience is assessed via a feature vector (entity density, TF-IDF, discourse markers, position bias, number density). A sigmoid-activated linear or learned gating function yields selection scores $s_i = \sigma(w^\top f_i + b)$ .
Budget enforcement: For budget $B$ (number of chunks/tokens to keep), the top- $B$ scored chunks are retained; others are discarded. Selection can be streaming, ensuring $\sum_i g_i \leq B$ with a min-heap procedure.
Retrieval and use: At inference, a lightweight BM25 index operates over the stored subset; top- $k$ relevant chunks are retrieved and provided as LLM prompt input.
Empirical results: On documents up to 10K tokens, BudgetMem achieves $>72\%$ reduction in external memory with only $1\%$ F1 loss versus full-memory RAG. Gains grow with document length; 30–40\% budget suffices for minimal accuracy degradation (Alla et al., 7 Nov 2025).

3. Query-Aware Tiered Routing in Agent Memory

BudgetMem is implemented for runtime agent memory by structuring memory processing as a modular pipeline, with each stage offering three budget tiers (Low/Mid/High). A query-aware router, trained via reinforcement learning (policy $\pi_\theta$ ), dynamically selects the tier for each module, conditioned on query, input, and module identity (Zhang et al., 5 Feb 2026). Tiering can be realized along three axes:

Implementation tiering: Varying module complexity, e.g., symbolic → BERT → LLM-based scoring/extraction.
Reasoning tiering: Varying inference pattern, e.g., direct → chain-of-thought → reflection-based generation.
Capacity tiering: Varying model backbone size, e.g., 3B → 8B → 70B parameters.

Key findings indicate that BudgetMem surpasses prior memory architectures across performance regimes and enables explicit control of performance–cost boundaries. Tiering axes serve complementary needs: implementation/capacity for broad dynamic range, reasoning for fine-grained adjustment.

4. BudgetMem Write Policies in Streaming and Storage

Strictly enforcing a memory budget in streaming external memory systems requires explicit write, merge, and evict policies (Cham, 31 Jan 2026). Formally, at each timestep $t$ :

$\sum_{i\in M_t} w_i + \sum_{j\in\Delta_t} \delta_j \leq B$

where $w_i$ is the size of stored item $i$ , $\delta_j$ is the size of delta-merge $j$ , and $B$ is the byte budget. Policy classes include:

Priority-threshold: Only admit steps with score above a threshold.
Window-evict: Expire oldest items to admit new inputs.
Merge-aggressive: Merge semantically similar updates to compact the stored footprint.

Metrics include F1 (precision/recall for critical events), utilization, and regret vs. an oracle. Experiments show priority-threshold and merge-aggressive policies are optimal under low and moderate budgets, respectively, with byte-accurate cost accounting and hard policy enforcement (Cham, 31 Jan 2026).

5. Memory Budgeting in Parallel and Hybrid Systems

Algorithmic BudgetMem solutions address the p-processor memory high-water mark (MHWM) in parallel fork-join programs (Kaler et al., 2019). The goal is to compute the worst-case heap usage over all legal schedules on $p$ processors or, given threshold $M$ , guarantee if the MHWM is above $M/2$ or below $M$ . Two algorithmic approaches are:

Exact MHWM computation: $O(T_1p)$ time, $O(pD)$ space; dynamic programming over a DAG, recursively composing series/parallel subgraphs and propagating allocated memory profiles.
Approximate thresholding: $O(T_1)$ time/space; robust antichain analysis yields a factor-2 bracketing of the true MHWM.

Combined in a BudgetMem workflow: first bracket with the approximate method, then refine with the exact method as needed. Allows deterministic memory bounding for parallel jobs, generalizing to rapid development and reliable production deployment (Kaler et al., 2019).

Hybrid main-memory systems under energy constraints use BudgetMem-style object placement and migration, formulated as a binary ILP to minimize latency within both capacity and total energy bounds (Kim et al., 2020). Static placement (eMPlan) assigns heap objects to DRAM or NVM to meet a specified budget. Dynamic adjustment (eMDyn) migrates objects at runtime in response to budget changes while minimizing migration cost.

6. Budgeted Selection in Model Parameters and KV-Cache Compression

In model architectures, BudgetMem principles appear in embedding table design (Qu et al., 2023) and inference-time KV-cache compression (Tang et al., 3 Sep 2025, Ni et al., 24 Feb 2025):

Budgeted Embedding Table (BET): A set-based action policy selects embedding sizes for all users and items to strictly enforce a global parameter budget. Table-level sampling and fitness prediction (based on set representation learning) ensure constraint compliance and efficient search. BET achieves state-of-the-art recall/ndcg as compared to RL and pruning baselines, guaranteeing hard budget adherence (Qu et al., 2023).
KV-Cache Compression: GVote (Tang et al., 3 Sep 2025) and DBudgetKV (Ni et al., 24 Feb 2025) eliminate the need for manually specified fixed budgets, using either future-query Monte-Carlo sampling or attention-based metrics to dynamically adjust the retained key-value pairs in LLM inference, always ensuring minimal loss and maximally pruned cache.

7. Adaptive and Learned Memory Budgeting in Cloud Computing

BudgetMem in serverless (FaaS) environments is realized via input-aware learned models for dynamic memory configuration (Agarwal et al., 2024). MemFigLess profiles function input–resource–performance relationships offline and uses a multi-output Random Forest Regressor to predict the smallest feasible memory allocation for each incoming invocation, minimizing both runtime and cost while enforcing strict resource and latency compliance. Empirical deployments on AWS Lambda achieve up to 82\% resource savings and 87\% cost reductions relative to conventional methods (Agarwal et al., 2024).

BudgetMem methodologies bring explicit, algorithmic, and learnable control over memory allocation, persistence, and utilization, optimizing relevant utility objectives under rigorously enforced constraints across a spectrum of computational domains from deep learning to distributed systems. Recent work integrates budgeted memory control into RL-based agent memory, embedding compression, long-context LLM deployment, and dynamic hybrid memory management, advancing both theory and practice of resource-bounded computation.