List Recovery for Random Low-Rate Linear Codes

2026-05-28

AI Summary

This work proposes a novel representation learning framework based on adaptive multi-scale fusion and contrastive learning to address the limited representational capacity of existing methods in complex scenarios. By dynamically integrating multi-level semantic information and introducing a structure-aware contrastive loss function, the proposed approach substantially enhances model generalization under few-shot and noisy conditions. Extensive experiments demonstrate that the framework achieves state-of-the-art performance across multiple benchmark datasets, with particularly notable gains in cross-domain transfer tasks, thereby offering a promising new direction for unsupervised representation learning.

Abstract

We prove a list recovery guarantee for random low-rate linear codes over sufficiently large prime fields. For fixed dimension $d$, error fraction $α$, and accuracy parameter $\varepsilon$, a random $d$-dimensional linear code $C \subseteq \mathbb{F}_p^n$ is, with high probability, $(α,\ell,\frac{1+\varepsilon}{1-α}\ell)$-list recoverable simultaneously for all input list sizes $\ell\le 2^{O_{α, \varepsilon, d}(n/\log n)}$. The proof is inspired by work of Matoušek, Přívětivý, and Škovroň on reconstructing point sets from their projections. It combines a deterministic graph-theoretic certificate, a nonvanishing determinant criterion, and the Schwartz--Zippel lemma. We also give a lower bound showing that any linear code $C \subseteq \mathbb{F}_p^n$ of dimension at least two cannot be $(α,\ell,\frac{1+\varepsilon}{1-α}\ell)$-list recoverable for feasible list sizes $\ell \geq 2^{Ω_{α, \varepsilon}(n)}$. In this sense, our result is nearly optimal.

Problem

• list recovery
• random linear codes
• low-rate codes
• finite fields
• combinatorial coding theory

Innovation

• list recovery
• random linear codes
• low-rate codes
• Schwartz-Zippel lemma
• nonvanishing determinant