We develop catalytic algorithms for fundamental problems in algorithm design that run in polynomial time, use only 𝒪(log(n)) workspace, and use sublinear catalytic space matching the best-known space bounds of non-catalytic algorithms running in polynomial time. First, we design a polynomial time algorithm for directed s-t connectivity using n / 2^{Θ(√{log n})} catalytic space, which matches the state-of-the-art time-space bounds in the non-catalytic setting [Barnes et al., 1998], and improves the catalytic space usage of the best known algorithm [James Cook and Edward Pyne, 2026]. Furthermore, using only 𝒪(log(n)) random bits we get a randomized algorithm whose running time nearly matches the fastest time bounds known for space-unrestricted algorithms. Second, we design polynomial time algorithms for the problems of computing Edit Distance, Longest Common Subsequence, and the Discrete Fréchet Distance, again using n / 2^{Θ(√{log n})} catalytic space. This again matches non-catalytic time-space frontier for Edit Distance and Least Common Subsequence [Kiyomi et al., 2021].

A catalytic machine is a model of computation where a traditional space-bounded machine is augmented with an additional, significantly larger, “catalytic” tape, which, while being available as a work tape, has the caveat of being initialized with an arbitrary string, which must be preserved at the end of the computation. Despite this restriction, catalytic machines have been shown to have surprising additional power; a logspace machine with a polynomial length catalytic tape, known as catalytic logspace (CL), can compute problems which are believed to be impossible for L. A fundamental question of the model is whether the catalytic condition, of leaving the catalytic tape in its exact original configuration, is robust to minor deviations. This study was initialized by Gupta et al. (2024), who defined lossy catalytic logspace (LCL[e]) as a variant of CL where we allow up to e errors when resetting the catalytic tape. They showed that LCL[e]=CL for any e=O(1), which remains the frontier of our understanding. In this work we completely characterize lossy catalytic space (LCSPACE[s,c,e]) in terms of ordinary catalytic space (CSPACE[s,c]). We show that (Formula presented.) In other words, allowing e errors on a catalytic tape of length c is equivalent, up to a constant stretch, to an equivalent errorless catalytic machine with an additional elogc bits of ordinary working memory. As a consequence, we show that for any e, LCL[e]=CL implies SPACE[elogn]⊆ZPP, thus giving a barrier to any improvement beyond LCL[O(1)]=CL. We also extend all our results to every variant of catalytic space.

Matching is a central problem in theoretical computer science, with a large body of work spanning the last five decades. However, understanding matching in the time-space bounded setting remains a longstanding open question, even in the presence of additional resources such as randomness or non-determinism. In this work we study space-bounded machines with access to catalytic space, which is additional working memory that is full with arbitrary data that must be preserved at the end of its computation. Despite this heavy restriction, many recent works have shown the power of catalytic space, its utility in designing classical space-bounded algorithms, and surprising connections between catalytic computation and derandomization. Our main result is that bipartite maximum matching (MATCH) can be computed in catalytic logspace (CL) with a polynomial time bound (CLP). Moreover, we show that MATCH can be reduced to the lossy coding problem for NC circuits (LOSSY[NC]). This has consequences for matching, catalytic space, and derandomization:•Matching: this is the first well studied subclass of P which is known to contain MATCH, as well as the first algorithm simultaneously using sublinear free space and polynomial time with any additional resources. Thus, it gives a potential path to designing stronger space and time-space bounded algorithms.•Catalytic space: this is the first new problem shown to be in CL since the model was defined, and one which is extremely central and well-studied. Furthermore, it implies a strong barrier to showing CL lies anywhere in the NC hierarchy, and suggests to the contrary that CL is even more powerful than previously believed.•Derandomization: we give the first class C beyond Logspace for which we exhibit a natural problem in LOSSY[C] which is not known to be in C, as well as a full derandomization of the isolation lemma in CL in the context of MATCH. This also suggests a possible approach to derandomizing the famed RNC algorithm for MATCH.Our proof combines a number of strengthened ideas from isolation-based algorithms for matching alongside the compress-or-random framework in catalytic computation.

A catalytic machine is a space-bounded Turing machine with additional access to a second, much larger work tape, with the caveat that this tape is full, and its contents must be preserved by the computation. Catalytic machines were defined by Buhrman et al. (STOC 2014), who, alongside many follow-up works, exhibited the power of catalytic space (CSPACE) and, in particular, catalytic logspace machines (CL) beyond that of traditional space-bounded machines. Several variants of CL have been proposed, including nondeterministic and co-non-deterministic catalytic computation by Buhrman et al. (STACS 2016) and randomized catalytic computation by Datta et al. (CSR 2020). These and other works proposed several questions, such as catalytic analogues of the theorems of Savitch and Immerman and Szelepcsényi. Catalytic computation was recently derandomized by Cook et al. (STOC 2025), but only in certain parameter regimes. We settle almost all questions regarding randomized and nondeterministic catalytic computation by giving an optimal reduction from catalytic space with additional resources to the corresponding non-catalytic space classes. With regards to non-determinism, our main result is that CL = CNL and with regards to randomness we show CL = CPrL where CPrL denotes randomized catalytic logspace where the accepting probability can be arbitrarily close to 1/2. We also have a number of near-optimal partial results for non-deterministic and randomized catalytic computation with less catalytic space. We show catalytic versions of Savitch’s theorem, Immerman-Szelepscényi, and the derandomization results of Nisan and Saks and Zhou, all of which are unconditional and hold for all parameter settings. Our results build on the compress-or-compute framework of Cook et al. (STOC 2025). Despite proving broader and stronger results, our framework is simpler and more modular.

In a seminal work, Buhrman et al. (STOC 2014) defined the class CSPACE(s,c) of problems solvable in space s with an additional catalytic tape of size c, which is a tape whose initial content must be restored at the end of the computation. They showed that uniform TC¹ circuits are computable in catalytic logspace, i.e., CL = CSPACE(O(log{n}), 2^{O(log{n})}), thus giving strong evidence that catalytic space gives L strict additional power. Their study focuses on an arithmetic model called register programs, which has been a focal point in development since then. Understanding CL remains a major open problem, as TC¹ remains the most powerful containment to date. In this work, we study the power of catalytic space and register programs to compute circuits of larger depth. Using register programs, we show that for every ε > 0, SAC² ⊆ CSPACE (O((log²n)/(log log n)), 2^{O(log^{1+ε} n)}) . On the other hand, we know that SAC² ⊆ TC² ⊆ CSPACE(O(log²{n}) , 2^{O(log{n})}). Our result thus shows an O(log log n) factor improvement on the free space needed to compute SAC², at the expense of a nearly-polynomial-sized catalytic tape. We also exhibit non-trivial register programs for matrix powering, which is a further step towards showing NC² ⊆ CL.

STOC ’25, Prague, Czechia

The Tree Evaluation Problem (TreeEval) (Cook et al. 2009) is a central candidate for separating polynomial time (P) from logarithmic space (L) via composition. While space lower bounds of Ω(log2 n) are known for multiple restricted models, it was recently shown by Cook and Mertz (2020) that TreeEval can be solved in space O(log2 n/loglogn). Thus its status as a candidate hard problem for L remains a mystery. Our main result is to improve the space complexity of TreeEval to O(logn · loglogn), thus greatly strengthening the case that Tree Evaluation is in fact in L. We show two consequences of these results. First, we show that the KRW conjecture (Karchmer, Raz, and Wigderson 1995) implies L ⊈NC1; this itself would have many implications, such as branching programs not being efficiently simulable by formulas. Our second consequence is to increase our understanding of amortized branching programs, also known as catalytic branching programs; we show that every function f on n bits can be computed by such a program of length Poly(n) and width 2O(n).

The study of branching programs for the Tree Evaluation Problem (TreeEval), introduced by S. Cook et al. (TOCT 2012), remains one of the most promising approaches to separating L from P. Given a label in [k] at each leaf of a complete binary tree and an explicit function in [k]2 → [k] for recursively computing the value of each internal node from its children, the problem is to compute the value at the root node. (While the original problem allows an arbitrary-degree tree, we focus on binary trees.) The problem is parameterized by the alphabet size k and the height h of the tree. A branching program implementing the straightforward recursive algorithm uses Θ((k + 1) h ) states, organized into 2 h −1 layers of width up to k h . Until now no better deterministic algorithm was known.

We show that Cutting Planes (CP) proofs are hard to find: Given an unsatisfiable formula F, It is -hard to find a CP refutation of F in time polynomial in the length of the shortest such refutation; and unless Gap-Hitting-Set admits a nontrivial algorithm, one cannot find a tree-like CP refutation of F in time polynomial in the length of the shortest such refutation.