OpenAI claim summary for CS-163
computer sciencemathematicsTop Open Problemscontributionliterature_reportClaim: literature report
Summary of a claim published in openai/math; not a claim by this platform.
The result supplies the conclusion in this problem with its displayed hypotheses and quantifiers. The broader statements and applications in the manuscript are kept separate from the particular question above. The supporting result is manuscript 1 (family 138; 2026-09-26).
Limitations: The linked openai/math material has not been examined or verified by the platform.
Manuscript
Contents
Low-space worst-case Subset Sum at half-exponential time
OpenAI's claim
OpenAI presents the following claims relevant to this question, with manuscript numbers following paper order in Sources. In manuscript 1 (family 138; 2026-09-26), the asserted result is that worst-case Subset Sum has a one-sided randomized solver in $\operatorname{poly}(n)2^{n/2}$ time and $O(2^{n/5})$ ordinary writable words on every execution.Mathematical statement of the claimed result
Manuscript 1.
The input comprises positive integers $a_1,\ldots,a_n$ and a nonnegative integer $t$, with $n\ge2$; the question is whether $\sum_{i\in I}a_i=t$ for some subset $I$. Define \[ b=\max\{1,\lceil\log_2(1+\max(t,a_1,\ldots,a_n))\rceil\},\qquad w=\lceil4(n+b+\log_2(n+2))\rceil. \] The model has read-only random-access input and counted writable $w$-bit words, unit-cost word arithmetic and addressing, and unit-cost fresh random words; all stored randomness and preprocessing are charged. A single uniform algorithm has no false positive and accepts each positive instance with probability at least $2/3$. For every fixed integer $c\ge1$ there exist $A_c,k_c,N_c$ such that $n\ge N_c$ and $b\le n^c$ imply, on every random execution, \[ T\le A_c n^{k_c}2^{n/2},\qquad S\le A_c(n+b+2)^{15}2^{0.199n} \] in operations and writable words, respectively. The algorithm itself does not depend on $c$.Outline of the argument
For manuscript 1 (family 138; 2026-09-26), distinct-sum counts and entropy estimates select between two trials. Compact block dictionaries generate larger representation lists through small-state streams, while bounded random-prime sampling and exact word operations preserve the time, storage, and one-sided guarantees.Formal verification
No Lean formalization is listed at this snapshot for manuscript 1 (family 138; 2026-09-26).This site has not executed the Lean code. Described statement coverage is not an independent proof check.
Status caveat
The manuscripts were produced by an internal OpenAI model. OpenAI cautions that results without formalization can contain mistakes. This site has not independently checked the proofs; the label records the scope of OpenAI's claim.Sources
- OpenAI, A Low-Space Algorithm for Worst-Case Subset Sum, 2026-09-26; citation key
OAI:A-Low-Space-Algorithm-for-Worst-Case-Subset-Sum-September-26-2026. PDF (GitHub); PDF (direct); Manuscript page and citation; Latest version; Statement source: thm:main. - OpenAI catalogue entry.