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Electronic Colloquium on Computational Complexity

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TR26-074 | 12th May 2026
Rohan Goyal, Venkatesan Guruswami

Improved analysis of list-decodability of random linear codes: It’s all about counting constraints

List-decoding and list recovery ask how much corruption or uncertainty a code can tolerate while still keeping the number of plausible codewords small. For large alphabet codes, the ultimate benchmark for list-decoding is the ($\epsilon$-relaxed) generalized Singleton bound, which targets list-of-$L$ decoding radius with rate $R$ up to radius $\frac{L}{L+1}(1-R-\epsilon)$. ... more >>>


TR26-073 | 11th May 2026
Vishwas Bhargava, Leonard Schulman, Shiri Sivan

An Algorithmic Proof of Kruskal’s Tensor Decomposition Theorem

A famous theorem of Kruskal gives the simplest and arguably most fundamental criterion under which a tensor is guaranteed a unique minimum-rank decomposition. Kruskal's condition requires that the sum of the Kruskal ranks $\{k_i\}_{i=1}^m$ of the components satisfies $\sum_{i \in [m]} k_i \ge 2r + m - 1$, where $r$ ... more >>>


TR26-072 | 7th May 2026
Robert Andrews, Abhibhav Garg

An Improved Construction of Variety-Evasive Subspace Families

We study the question of explicitly constructing variety-evasive subspace families, a pseudorandom primitive introduced by Guo (Computational Complexity 2024) that generalizes both hitting sets and lossless rank condensers. Roughly speaking, a variety-evasive subspace family $\mathcal{H}$ is a collection of subspaces such that for every algebraic variety $V$ in a fixed ... more >>>



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