Search arXiv⌕ Search

arXiv · 2609.33839

Riftbound is Turing Complete

Abstract

Riftbound: League of Legends Trading Card Game is a trading card game about capturing and holding locations in a king-of-the-hill style contest. Originally released in China in August of 2025, and later released in the United States in October of 2025, the game has been well received for its depth and complexity. In this paper we demonstrate a facet of this complexity by providing sequences of valid game states which construct Universal Turing machines within the game. Each of these machines are constructed with tournament legal decks at the time of writing and strategies assigned are directed by the game state. We also show that given an appropriate board state the machine may be constructed and the computation may be performed in one game turn.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Nathan Dalaklis, Beckett Fields. 2026-09-27. Riftbound is Turing Complete. https://arxiv.org/abs/2609.33839

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Deterministic Depth-4 PIT and Normalization

In this paper, we initiate the study of deterministic PIT for $Σ^{[k]}ΠΣΠ^{[δ]}$ circuits over fields of any characteristic, where $k$ and $δ$ are bounded. Our main result is a deterministic polynomial-time black-box PIT algorithm for $Σ^{[3]}ΠΣΠ^{[δ]}$ circuits, under the additional condition that one of the summands at the top $Σ$ gate is squarefree. Our techniques are purely algebro-geometric: they do not rely on Sylvester--Gallai-type theorems, and our PIT result holds over arbitrary fields. The core of our proof is based on the normalization of algebraic varieties. Specifically, we carry out the analysis in the integral closure of a coordinate ring, which enjoys better algebraic properties than the original ring.

cs.CC↗

Local Search with Correlated Randomness

How much does an algorithm's running-time distribution under independent randomness reveal about its behavior when independence is no longer guaranteed? We study sources satisfying $ν[w]\le DP[w]^s$ for every finite prefix $w$, where $P$ is an independent reference law, $0<s\le1$, and $D\ge1$. The constraint controls complete-prefix probabilities while allowing individual choices to be predictable, even fully determined by the past. For retry tasks, all deterministic history-dependent selectors have the same independent-source running-time law. Yet two orders have worst-case failure probabilities $1$ and $\exp[-Θ(n)]$ at the same linear deadline under the same source constraint. We identify a static priority rule that is optimal at every deadline and every $D$. For the standard local walk on a $k$-CNF with at least $r$ true literals per clause under some assignment, $k/2<r<k$, we determine the sharp source threshold $s_*$. At and above it, the expected flip count is $O_{k,r}(\min\{L^3,L/(s-s_*)\})$, where $L=h+\log D+1$, $h$ is the initial Hamming distance to that assignment, and $L/0=\infty$. The bound allows arbitrary clause overlap and history-dependent clause selection. Matching instances admit one source forcing this delay with probability one for every selector. At criticality and fixed $D$, the delay is cubic despite a linear independent-source expectation. Variable-depth prefix covers, together with classical tree max-flow/min-cut, yield an exact criterion for restoring exponential tails by restarting on the same tape. We synthesize updates and restarts for explicit finite-state processes. Under a sufficient prefix guarantee, we also obtain noisy predecessor search with error at most $η$ and expected query count polynomial in the correct leaf's depth and $\log(D/η)$, without knowing the depth or tree height.

cs.CC↗

Sample Complexity of Equivariant Reinforcement Learning

Reinforcement learning (RL) is a powerful framework for robotic control, yet its practical application is often hindered by high sample complexity. This is particularly restrictive in physical domains where interaction data is costly. While the world often exhibits geometric and physical symmetries, standard RL algorithms typically fail to exploit this structure. In this paper, we demonstrate that exploiting group symmetries significantly reduces the sample complexity of RL. Focusing on finite-horizon Markov decision processes, we find that leveraging homomorphisms induced by group symmetries significantly reduces the theoretical upper and lower bounds on the number of environment interactions required to reach an optimal return. We further extend these bounds to continuous state and action spaces, providing corresponding sample-complexity guarantees under appropriate regularity assumptions. Beyond theory, we validate our findings through controlled experiments and demonstrate the advantages of symmetry-aware policy learning on high-dimensional continuous robotic simulations. Our results show that integrating symmetry into the learning pipeline yields substantial gains in sample efficiency and performance, offering a principled path toward more data-efficient robotics.

cs.CC↗