mlsanityData to 7 Oct 2026

On Task Scope and Information Retention in Source Coding

arXiv 2609.37575 ↗First posted 29 Sep 2026cs.IT cs.LG eess.IVcited by 0

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Abstract (arXiv)

We argue that dividing codec design into Coding for Machines (CfM) and Coding for Humans (CfH) is a misleading distinction for deciding what information a codec may discard. Receiver identity does not determine admissible information loss. The required rate depends on task scope, including the predictions to support, their losses and tolerated risks, the encoder observation, and the permitted decoding procedures. Notably, a machine task may have a higher minimum rate than a restricted human decision. Rate savings on selected machine tasks apply only to the stated requirements, not to an intrinsic ordering by receiver type. We extend source and feature coding to finite task families, derive when restricting the encoder observation preserves the minimum rate, and show that equality between source and split-feature coding rates can no longer hold as the task scope expands.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 31 of its 43 references that are in the map. First posted on arXiv 29 Sep 2026.

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31 of 43 references.

References · 31

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19 more
  1. 2021Scalable Image Coding for Humans and Machinescited by 65
  2. 2021Lossy Compression for Lossless Predictioncited by 51
  3. 2021Supervised Compression for Resource-Constrained Edge Computing Systemscited by 31
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  9. 2024FOOL: Addressing the Downlink Bottleneck in Satellite Computing with Neural Feature Compressioncited by 6
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  13. 2026Model-Aware Rate-Distortion Limits for Task-Oriented Source Codingcited by 2
  14. 2026Task-Aware Answer Preservation under Audio Compression for Large Audio Language Modelscited by 2
  15. 2025Machines Serve Human: A Novel Variable Human-machine Collaborative Compression Frameworkcited by 1
  16. 2026Lossy Common Information in a Learnable Gray-Wyner Networkcited by 1
  17. 2026Source-Side Sufficiency for the Information Bottleneck: Exact Reduction and Finite-Block Equivalencecited by 1
  18. 2026Bayes-Sufficient Representations in Supervised Learningcited by 1
  19. 2026UniTAC: Universal Task-Aware Compression via Weighted Distortion Measurescited by 1

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