发表机构
Ericsson Ireland（爱立信爱尔兰）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出任务充分收缩概念，通过任务预定义的源缩减保留完整下游问题，在有限动作集和二次损失下给出精确刻画，支持异构机器按需交换信息。
AI 中文摘要
一个声明的任务有时可以在下游编码器、码本、速率、失真目标或优化器被选择之前，认证一个缩减的源。本文研究这种缩减何时保留完整的下游问题族，这一性质被称为任务充分收缩。缩减的源在后续工作点被选择之前由任务固定。精确收缩允许后续问题在该源上求解，其结果与保留完整源时相同。对于具有固定可能动作集和固定损失的机器，本文通过仅当每个可用动作在两种源中具有相同遗憾时合并状态，来识别一个消费者特定的源。对于有限动作集，用此缩减源替换更丰富的源可保留完整的一步速率-遗憾曲线，即使缩减是在失真目标被选择之前固定的。第二个结果给出了仿射可行动作集上二次损失的精确刻画：典型缩减源是投影到可行动作可以不同的方向上。在固定能量预算下，这变为居中负载，而仅保留最优注水动作则过于粗糙。先前的信息瓶颈、语义速率-失真和目标导向量化结果随后被用于区分精确、架构条件、近似、失败和修正的收缩。该框架提出了一种方式，使异构机器能够交换接收任务所需的内容，而无需首先对齐其完整内部表示。
英文摘要
A declared task can sometimes certify a reduced source before a downstream encoder, codebook, rate, distortion target, or optimizer is chosen. This paper studies when one such reduction preserves the complete downstream problem family, a property termed Task-Sufficient Contraction. The reduced source is fixed by the task before the later operating point is selected. An exact contraction allows the later problem to be solved on that source with the same result as if the full source had been retained. For a machine with a fixed set of possible actions and a fixed loss, the paper identifies a consumer-specific source by merging states only when every available action has the same regret in both. For finite action sets, replacing the richer source by this reduced source preserves the complete one-step rate-regret curve, even though the reduction is fixed before the distortion target is chosen. A second result gives an exact characterization for quadratic loss on affine feasible-action sets: the canonical reduced source is the projection onto the directions in which feasible actions can differ. Under a fixed energy budget, this becomes centered load, while retaining only the optimal water-filled action is too coarse. Earlier Information Bottleneck, semantic rate-distortion, and goal-oriented quantization results are then used to distinguish exact, architecture-conditioned, approximate, failed, and corrected contractions. The framework suggests a way for heterogeneous machines to exchange what a receiving task needs without first aligning their full internal representations.
Comments19 pages, 1 figure, 1 table. An earlier version was first posted on Zenodo in September 2026 (v1: doi:10.5281/zenodo.22834532; all versions: doi:10.5281/zenodo.22834531). Companion paper on task-relative information contracts: doi:10.5281/zenodo.22819849
