Plan-Aware Compression & Forward Selection

• Plan-Aware Compression and Forward-Looking Selection is a methodology that optimizes compression policies by leveraging explicit future task plans to improve downstream utility.
• It integrates cost and fidelity objectives to ensure efficient data transmission and accurate performance across applications, from robotic navigation to LLM-driven workflows.
• Empirical studies demonstrate significant resource savings, including up to 98% reduction in transmitted data and substantial token and memory efficiency gains with minimal performance loss.

Plan-aware compression and forward-looking selection define a family of methodologies in which compression or abstraction policies are directly optimized to anticipate downstream tasks, leveraging explicit knowledge of upcoming plans, operator sequences, or workflow requirements. These approaches go beyond traditional stateless or query-driven compression, optimizing not only for information-theoretic criteria but also for the utility of the compressed representation under prospective use. The framework appears in diverse settings: robot teams performing real-time navigation, LLM agents executing complex multi-step plans, federated learning with heterogeneous client properties, and data-centric machine learning pipelines. Central to all approaches is the formulation of a cost or fidelity objective that integrates future usage and resource constraints, and an algorithmic mechanism to select or adapt compressions in a forward-looking, plan-informed manner.

1. Mathematical Formulation of Plan-Aware Compression

At the core of plan-aware compression lies the definition of a structured environment, a workflow or a plan, and the explicit representation of compression decisions as optimization variables subject to constraints driven by expected downstream utility. In the robot online map compression paradigm, the local environment is modeled as a discretized 2D grid map $\mathcal M\subset\mathbb R^2$, encoded as $x \in [0,1]^N$ for $N$ fine-resolution cells. The robot acting as a sensor observes local regions, and at each timestep $t$ chooses a compression template $\theta_t\in\Theta$ from a finite set. Communication cost per abstraction is

$n^{\theta_t} = k^{\theta_t} n_m + n_a,$

and is constrained $n^{\theta_t} \le B$, where $k^{\theta_t}$ is the compressed dimension and $B$ is the per-timestep bit budget (Psomiadis et al., 13 Mar 2025).

In LLM-based agentic workflows, the context at step $t$ is

$$C_t = [I_0, P, \pi, H_{0:t-1}, O_{0:t-1}, R_{0:t-1}, M],$$

where $\pi = [T_1,\dots,T_n]$ is the agent’s plan, and $H, O, R, M$ represent reasoning traces, tool outputs, retrievals, and memory. A plan-aware compression policy $\varphi$ acts as

$\tilde C_t = \varphi(C_t, \pi_{t:t+k}),$

and is learned to minimize total token length while enforcing task fidelity thresholds, formalized as

$$\min_{\varphi} \mathbb{E}_W \left[ \sum_{t=1}^n \lvert \tilde C_t \rvert \right]\quad\text{subject to}\quad F(y_{\text{full}}^t, y_{\text{comp}}^t)\ge \theta,\ \forall t,$$

where $F(\cdot,\cdot)$ is a semantic or exact-match fidelity score (Yuksel, 18 Dec 2025).

Workload-aware compression in data-centric ML pipelines defines a compression plan

$$\mathcal{P} = [P^0, \ldots, P^k],\quad P^i=(E^i,G^i)$$

where each $E^i$ is a choice of (possibly morphable) lossless compression scheme and grouping/co-coding for intermediate $X^i$. The selection is driven by anticipated linear algebra workload $w^i$ and end-to-end resource models (Baunsgaard et al., 15 Apr 2025).

2. Forward-Looking and Plan-Aware Selection Algorithms

A defining property of these methodologies is the anticipation of future needs: selection of compression or abstraction is explicitly conditioned on the expected trajectory of the plan, operator pipeline, or downstream queries.

In online path-planning, the compression-selection mechanism executes a one-step lookahead: for each candidate $\theta\in\Theta$ satisfying the bandwidth constraint, the framework predicts the actor's map estimate after the update, refines the path plan, computes the spatially-weighted estimation error (using a weight matrix $W_t$ derived from the planned path), and sums this with a proxy for communication cost. The cost function per step is

$$J_t(\theta_t) = \|W_t\circ(\tilde{x}_t - \hat{x}_t(\theta_t))\|^2 + \lambda(\theta_t),$$

and $\theta_t^* = \arg\min_{\theta} J_t(\theta)$ is selected for transmission (Psomiadis et al., 13 Mar 2025, Psomiadis et al., 2023).

In PAACE for LLM agents, forward-looking context selection entails computing relevance of each fragment $c$ in $C_t$ to the next $k$ scheduled plan steps, using a scoring model such as

$$r(c, T_{i:i+k}) = \alpha\, \cos\bigl(\text{emb}(c),\, \text{emb}(T_{i:i+k})\bigr) + \beta\, \text{Dep}(c, T_{i:i+k}),$$

where $\text{emb}(\cdot)$ is an embedding function and $\text{Dep}(\cdot)$ encodes structural dependency. The binary selection mask $s(c)\in\{0,1\}$ is chosen to maximize total relevance, constrained by a token budget (Yuksel, 18 Dec 2025).

For data-centric ML pipelines, BWARE leverages compile-time workload summaries $w^i$ for each transformation step $i$ and solves

$$E^i = \arg\min_{E\in\mathcal{S}} [ \alpha \cdot \text{mem}_E + \beta \cdot \text{io}_E + \gamma \cdot \text{estComp}_E(w^i) ],$$

where the cost model is parameterized by anticipated memory, I/O, and compute costs along the pipeline (Baunsgaard et al., 15 Apr 2025).

3. Decoder Architectures and Function-Preserving Compression

Decoder design mediates the transformation from compressed abstractions back to full or task-relevant representations, with explicit fidelity objectives.

In robotic map-sharing, the actor and sensor robots maintain a Gaussian belief $x \sim \mathcal N(\hat{x},\Sigma)$. Upon receiving a linear measurement $o_t = A_t x + v_t$, the Kalman filter update equations are applied: \begin{align*} &K_t = \Sigma_{t-1}A_t^\top(A_t \Sigma_{t-1}A_t^\top + V)^{-1}, \ &\hat{x}t' = \hat{x}{t-1}' + K_t(o_t - A_t\hat{x}{t-1}'), \ &\Sigma_t = (I - K_tA_t)\Sigma{t-1}, \end{align*} followed by projection to $[0,1]^N$. This enables rapid, iterative, non-history-dependent map estimation, supporting a larger template set for online selection (Psomiadis et al., 13 Mar 2025).

In PAACE, function-preserving compression is ensured by integrating offline teacher LLMs, evaluator LLMs, and embedding-based semantic similarity. Only compressions passing pre-defined semantic thresholds and human-aligned evaluations are retained for imitation and deployment (Yuksel, 18 Dec 2025).

BWARE, for data-centric ML pipelines, deploys "morph" operators—lossless transforms on compressed representations—which guarantee that decompression after morphing yields the original data. Core primitives include dictionary-to-dictionary recoding, column-group co-coding, and fast index bit-width adjustments, all formally equipped with correctness guarantees (Baunsgaard et al., 15 Apr 2025).

4. Computational Complexity and Scalability

Plan-aware compression and forward-looking selection have distinct computational characteristics due to their integration of path, plan, or workload simulation within the selection loop.

In robot map compression, each selection cycle involves $|\Theta|$ Kalman filter updates and graph searches, for per-step cost $O(|\Theta|(N^3 + N\log N))$, with $N$ the map cell count. This is an order of magnitude faster than prior quadratic-program-based decoders with historical dependence, and supports real-time operation on large maps (Psomiadis et al., 13 Mar 2025). Previous convex QP decoders grew in runtime with the selection horizon $T^S$, whereas the iterative filter-based decoder's cost is independent of $T^S$.

BWARE's plan generation phase is linear in the size of the scheme-group product, with runtime dominated by per-column operation costs. Morphing operations are $O(n+d)$ for $n$ rows and $d$ columns. End-to-end pipeline execution leverages the plan to minimize decompress-recompress cycles, supporting multi-dataset scaling and massively improved throughput (order-of-magnitude reductions in pipeline time and memory) (Baunsgaard et al., 15 Apr 2025).

PAACE’s distilled compressors realize over an order of magnitude reduction in LLM inference cost, with <8% added per-step latency and 35–60% token savings compared to teacher performance, and can be trained at scale using large synthetic workflow corpora (Yuksel, 18 Dec 2025).

5. Empirical Results and Performance Analysis

Empirical validation of plan-aware compression and forward-looking selection shows substantial gains in communication, compute, and task performance across applications.

In robotic navigation, the communication-aware approach reduces information sent by up to 98% on large Mars and Earth maps, compared to transmitting full raw data, while only increasing planning cost by 10–17% (e.g., Earth traversability: mean bits sent is $6,602\pm115$ for abstraction selection vs. $283,500$ for fully-informed; planning cost $19.4\pm0.9$ vs. $16.7\pm6.5$, respectively) (Psomiadis et al., 13 Mar 2025). CPU time for decoding is an order of magnitude lower than prior methods on long horizons.

In LLM workflow benchmarks, PAACE improves correctness and efficiency: on AppWorld, accuracy increased from 56.5% (baseline) to 59.0% while peak context was reduced from 7.33k to 6.23k tokens and attention dependency from 4.69M to 3.75M tokens. On OfficeBench, PAACE achieved +1.3 percentage point accuracy over no compression with 41% reduction in peak context length and 63% reduction in dependency (Yuksel, 18 Dec 2025). Compression does not degrade performance; on the contrary, distilled student models retain $97–98\%$ of teacher performance at drastic latency and context savings.

BWARE demonstrates up to 65× memory reduction versus naïve string storage, matches or exceeds uncompressed I/O throughput, and yields end-to-end speedups from days to hours for large data-centric pipelines. Model training time is reduced 2×–11×, and compressed linear algebra kernels achieve 2.7× higher instruction-level parallelism with commensurate energy savings (Baunsgaard et al., 15 Apr 2025).

6. Limitations, Trade-offs, and Extension Directions

Greedy and single-step lookahead schemes generally do not optimize over long temporal horizons; thus, potential gains are bounded by myopic policy performance. Predefined template or compression-scheme sets may not capture unforeseen data or plan patterns. Gaussian noise and linear measurement assumptions restrict robustness to rare or adversarial distributions in navigation tasks (Psomiadis et al., 13 Mar 2025, Psomiadis et al., 2023). PAACE compressors are domain-specific, lacking formal guarantees of information preservation beyond embedding and LLM-judge criteria, which limits safety-critical deployment (Yuksel, 18 Dec 2025).

Promising directions for future research include extension to multi-step or model-predictive plan-aware selection, adaptive or learned abstraction sets, hybrid architectural memory modules in agentic systems, and adaptation to unstructured or heterogeneous data modalities such as 3D point clouds or time series. The integration of plan-aware compression with joint optimization of abstraction selection and trajectory (in robots), or with instruction-only refinement in LLM agents, represents active areas of investigation.

7. Theoretical and Practical Significance

Plan-aware compression bridges optimization and information theory with planning, by embedding future task utility and resource models directly into compression policy. This yields representations that are maximally informative for downstream tasks, not merely for generic reconstruction. Across online robotic navigation, LLM agentic workflows, federated learning, and data-centric ML, forward-looking, plan-aware compression and selection mechanisms translate to orders-of-magnitude savings in resource usage at minimal or even negative impact on target performance (Psomiadis et al., 13 Mar 2025, Yuksel, 18 Dec 2025, Baunsgaard et al., 15 Apr 2025).

The plan-aware and forward-looking methodology provides a unifying principle for designing adaptive, task-driven abstraction capabilities in complex, resource-constrained systems.