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Synchronizing Probabilities in Model-Driven Lossless Compression

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
data compressionmodel-driven predictionnon-determinism
Abstract:

It is well-known in the field of lossless data compression that probabilistic next-symbol prediction can be used to compress sequences of symbols. Deep neural networks are able to capture rich dependencies in data, offering a powerful means of estimating these probabilities and hence an avenue towards more effective compression algorithms. However, both compressor and decompressor must have exactly matching predictions; even small non-deterministic differences (which often happen with learned models due to hardware, software, or computation order) can lead to cascading decoding failures. In this paper, we formalize the problem of prediction mismatch in model-driven compression, and introduce Probability Matching Interval Coding (PMATIC), a model-agnostic algorithm that tolerates bounded prediction mismatch with low overhead. PMATIC works with the predicted probabilities, making it compatible as a drop-in replacement for the arithmetic encoder in model-driven compression tools. We show theoretical correctness and performance bounds for PMATIC, and validate these results on text data. These results confirm that, when paired an advanced prediction model, PMATIC is robust to prediction mismatch while achieving compression rates that out-perform standard modern compression tools.

Disclaimer
This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
If you have any questions, please contact: mingzhang23@m.fudan.edu.cn

Overview

Overall Novelty Assessment

The paper introduces PMATIC, an algorithm designed to tolerate bounded prediction mismatch in model-driven lossless compression, alongside a formalization of the mismatch problem itself. Within the taxonomy, it occupies the 'Interval Coding with Probability Matching' leaf under 'Prediction Mismatch Tolerance Mechanisms'. Notably, this leaf contains only the original paper—no sibling papers were identified in the taxonomy. This positioning suggests the work addresses a relatively sparse research direction: while prediction-based compression is well-established, explicit mechanisms for handling encoder-decoder probability discrepancies appear underexplored in the examined literature.

The taxonomy reveals two main branches: mismatch tolerance mechanisms and domain-specific compression methods. The original paper sits in the former, which focuses on algorithmic robustness when predictions diverge. The neighboring 'Large Language Model Output Compression' leaf (one paper) represents domain-specific applications that may encounter similar mismatch issues but do not explicitly address tolerance mechanisms. The taxonomy's scope notes clarify that standard arithmetic coding without mismatch handling belongs outside this branch, emphasizing that PMATIC's contribution lies in extending interval coding to accommodate bounded discrepancies—a boundary that appears sparsely populated in the current taxonomy structure.

Across three identified contributions, the literature search examined 24 candidate papers total, with no refutable pairs found. The formalization of prediction mismatch examined 10 candidates (0 refutable), the PMATIC algorithm examined 4 candidates (0 refutable), and the theoretical bounds examined 10 candidates (0 refutable). These statistics indicate that among the limited set of semantically similar papers reviewed, none provided overlapping prior work on mismatch-tolerant interval coding. The absence of refutations across all contributions, combined with the sparse taxonomy leaf, suggests the work occupies a relatively novel intersection of probabilistic compression and robustness to model discrepancies.

Given the limited search scope (24 candidates from top-K semantic retrieval), this analysis captures nearby work but cannot claim exhaustive coverage of the compression literature. The taxonomy structure and contribution-level statistics consistently point toward a sparse research area, though a broader survey might reveal related work in adjacent fields (e.g., distributed source coding, robust arithmetic coding) not captured by the semantic search. The findings reflect what is visible within the examined candidate set, not a definitive statement on absolute novelty across all compression research.

Taxonomy

Core-task Taxonomy Papers
1
3
Claimed Contributions
24
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: lossless compression with probabilistic next-symbol prediction under model mismatch. This field addresses the challenge of achieving efficient compression when the probability model used by the encoder and decoder may not perfectly match the true data distribution or each other. The taxonomy organizes work into two main branches. The first, Prediction Mismatch Tolerance Mechanisms, focuses on algorithmic strategies that maintain correctness and efficiency even when predictions are imperfect—such as interval coding schemes that adapt to probability mismatches or methods that synchronize encoder and decoder beliefs. The second branch, Domain-Specific Prediction-Based Compression, explores how prediction-based compression can be tailored to particular data types (text, images, structured data), often leveraging domain knowledge or specialized models to improve compression rates despite inherent model limitations. Within Prediction Mismatch Tolerance Mechanisms, a particularly active line of work examines how to robustly encode symbols using interval or arithmetic coding when probability estimates are uncertain or evolving. Synchronizing Probabilities[0] sits squarely in this area, specifically within the Interval Coding with Probability Matching subfield, where it addresses how encoder and decoder can maintain consistent probability distributions to avoid decoding errors. Meanwhile, Domain-Specific approaches such as LLM Text Compression[1] illustrate how large language models can serve as powerful predictors for text data, though they must contend with the same mismatch issues when model outputs diverge from actual symbol frequencies. The central tension across these branches is balancing the expressiveness of rich predictive models against the need for reliable, synchronized compression—a trade-off that Synchronizing Probabilities[0] directly engages by proposing mechanisms to keep probabilistic beliefs aligned during the coding process.

Claimed Contributions

Formalization of prediction mismatch problem in model-driven compression

The authors formally define the prediction mismatch problem that arises when encoder and decoder use the same model but obtain slightly different probability predictions due to non-determinism, which can cause cascading decoding failures in arithmetic coding-based compression.

10 retrieved papers
PMATIC algorithm for mismatch-tolerant compression

The authors propose PMATIC, a drop-in replacement for arithmetic coding that quantizes probabilities into bins and uses helper bits to ensure encoder-decoder agreement on probability distributions despite bounded prediction mismatch.

4 retrieved papers
Theoretical correctness and performance bounds for PMATIC

The authors establish formal guarantees showing PMATIC correctly decodes when conditional total variation distance is bounded, and derive theoretical bounds on the compression overhead required to achieve mismatch robustness.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Within the taxonomy built over the current TopK core-task papers, the original paper is assigned to a leaf with no direct siblings and no cousin branches under the same grandparent topic. In this retrieved landscape, it appears structurally isolated, which is one partial signal of novelty, but still constrained by search coverage and taxonomy granularity.

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Formalization of prediction mismatch problem in model-driven compression

The authors formally define the prediction mismatch problem that arises when encoder and decoder use the same model but obtain slightly different probability predictions due to non-determinism, which can cause cascading decoding failures in arithmetic coding-based compression.

Contribution

PMATIC algorithm for mismatch-tolerant compression

The authors propose PMATIC, a drop-in replacement for arithmetic coding that quantizes probabilities into bins and uses helper bits to ensure encoder-decoder agreement on probability distributions despite bounded prediction mismatch.

Contribution

Theoretical correctness and performance bounds for PMATIC

The authors establish formal guarantees showing PMATIC correctly decodes when conditional total variation distance is bounded, and derive theoretical bounds on the compression overhead required to achieve mismatch robustness.

Table of Contents