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FutureFill: Fast Generation from Convolutional Sequence Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
convolutional modelsfast inference
Abstract:

We address the challenge of efficient auto-regressive generation in sequence prediction models by introducing FutureFill—a general-purpose fast generation method for any sequence prediction algorithm based on convolutional operators. FutureFill reduces generation time from quadratic to quasilinear in the context length. Moreover, when generating from a prompt, it requires a prefill cache whose size grows only with the number of tokens to be generated—often much smaller than the caches required by standard convolutional or attention‐based models. We validate our theoretical claims with language modeling experiments and demonstrate substantial efficiency gains when generating from a deep convolutional sequence prediction model.

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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.
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Overview

Overall Novelty Assessment

The paper introduces FutureFill, a method to accelerate auto-regressive generation from convolutional sequence models by reducing complexity from quadratic to quasilinear in context length. It resides in the 'Parallel and Blockwise Decoding Strategies' leaf of the taxonomy, which contains only two papers total. This leaf sits within the broader 'Efficient Generation and Decoding Methods' branch, indicating a relatively sparse research direction focused specifically on overcoming sequential generation bottlenecks through parallel prediction schemes.

The taxonomy reveals neighboring work in 'Adaptive Inference Strategies' (three papers on early termination and confidence-based stopping) and 'Low-Complexity and Low-Latency Architectures' (two papers on parameter-efficient designs). FutureFill diverges from adaptive methods by targeting fixed-complexity blockwise generation rather than dynamic stopping criteria. The broader 'Core Convolutional Sequence-to-Sequence Architectures' branch (six papers across three leaves) establishes foundational designs, while FutureFill addresses inference-time optimization rather than base architecture innovation. The taxonomy's scope explicitly excludes domain-specific applications and coding-theoretic sequential decoding, clarifying that this work targets general-purpose neural sequence generation.

Among thirty candidates examined, the analysis identifies one refutable candidate for the core FutureFill method (ten candidates examined), while the two algorithmic variants—Epoched-FutureFill and Continuous-FutureFill—show no clear refutations among ten candidates each. The single sibling paper in the same taxonomy leaf represents the most directly comparable prior work on blockwise parallel decoding. The limited search scope means these statistics reflect top-ranked semantic matches rather than exhaustive coverage, suggesting the core contribution has at least one overlapping predecessor within the examined set, while the specific algorithmic trade-offs appear less explored.

Given the sparse taxonomy leaf and limited literature search, FutureFill appears to address a recognized but under-explored problem space. The presence of one refutable candidate for the main contribution indicates some prior work on blockwise generation exists, though the algorithmic variants show fewer direct precedents among examined papers. The analysis covers top-thirty semantic matches and does not claim exhaustive field coverage, leaving open whether additional related work exists beyond this scope.

Taxonomy

Core-task Taxonomy Papers
26
3
Claimed Contributions
30
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: efficient auto-regressive generation from convolutional sequence models. The field encompasses several distinct branches that address different facets of this challenge. Core Convolutional Sequence-to-Sequence Architectures explore foundational designs such as fully convolutional networks for sequence learning (Convolutional Sequence Learning[5]) and specialized temporal structures (Time-Depth Separable[8], Hierarchical Autoregressive[7]). Efficient Generation and Decoding Methods focus on accelerating inference through parallel and blockwise strategies (Blockwise Parallel Decoding[6]), which reduce the sequential bottleneck inherent in auto-regressive generation. Meanwhile, Sequential Decoding in Convolutional Coding Theory and Polarization-Adjusted Convolutional (PAC) Codes branches draw from information theory, examining decoding algorithms for error-correcting codes (Sequential Decoding[17], Fast List PAC[15], PAC List Decoders[22]) that share structural similarities with sequence generation. Domain-Specific Applications demonstrate how convolutional sequence models are adapted to tasks ranging from speech enhancement (Low-Latency Speech Enhancement[18]) to medical signal processing (Arrhythmia Detection[4], Seizure Detection[12]) and natural language correction (Chinese Grammar Correction[3]). A particularly active line of work centers on reducing the computational cost of generating long sequences token-by-token. Blockwise Parallel Decoding[6] exemplifies efforts to predict multiple positions simultaneously, trading off some model flexibility for substantial speed gains. FutureFill[0] sits squarely within this Parallel and Blockwise Decoding Strategies cluster, proposing mechanisms to fill future tokens in blocks rather than strictly left-to-right. Compared to earlier convolutional sequence-to-sequence frameworks like Convolutional Sequence Learning[5], which established the viability of purely convolutional architectures, FutureFill[0] emphasizes inference-time efficiency and parallelism. Its approach contrasts with hierarchical or multi-scale methods (Hierarchical Autoregressive[7]) that decompose generation into coarse-to-fine stages, instead focusing on direct blockwise prediction. This positioning reflects broader tensions in the field between maintaining generation quality, preserving model simplicity, and achieving low-latency deployment—a balance that remains an open question as convolutional models compete with transformer-based alternatives.

Claimed Contributions

FutureFill method for fast generation from convolutional sequence models

The authors propose FutureFill, a general method that reduces auto-regressive generation time in convolutional sequence models from quadratic to quasilinear complexity relative to context length. The method applies to any convolution-based sequence prediction algorithm.

10 retrieved papers
Can Refute
Epoched-FutureFill algorithm with runtime-memory trade-off

The authors develop Epoched-FutureFill, an algorithmic variant that offers a flexible trade-off between computational complexity and memory usage, achieving O(L^(3/2)√log L) runtime with O(√L log L) memory when generating L tokens from scratch.

10 retrieved papers
Continuous-FutureFill algorithm for quasilinear generation

The authors introduce Continuous-FutureFill, which achieves quasilinear O(L log^2 L) total generation time with O(L) memory for generating L tokens from scratch, and O(L log L + K log^2 K) time with O(K) cache when generating K tokens from a prompt of length L.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

FutureFill method for fast generation from convolutional sequence models

The authors propose FutureFill, a general method that reduces auto-regressive generation time in convolutional sequence models from quadratic to quasilinear complexity relative to context length. The method applies to any convolution-based sequence prediction algorithm.

Contribution

Epoched-FutureFill algorithm with runtime-memory trade-off

The authors develop Epoched-FutureFill, an algorithmic variant that offers a flexible trade-off between computational complexity and memory usage, achieving O(L^(3/2)√log L) runtime with O(√L log L) memory when generating L tokens from scratch.

Contribution

Continuous-FutureFill algorithm for quasilinear generation

The authors introduce Continuous-FutureFill, which achieves quasilinear O(L log^2 L) total generation time with O(L) memory for generating L tokens from scratch, and O(L log L + K log^2 K) time with O(K) cache when generating K tokens from a prompt of length L.

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