arXiv:2503.10954v2 Announce Type: replace-cross Abstract: Large Language Model (LLM) agents can solve *any* computational problem *without* an algorithm in a runtime *independent* of the computational complexity of that problem. Instead of specifying precisely how to solve problem instance using *programming*, we ask an LLM to solve the problem instance using *prompting*. Outputs are sampled from a distribution rather than generated procedurally. In this vision paper, we explore the challenges and opportunities of this new form of computation and observe that its capabilities and limits *canno
The paper from arXiv cs.AI, published in 2026, posits a fundamental shift in computational paradigms, highlighting advancements in LLM capabilities.
This concept suggests a move from algorithmic programming to large language model prompting, potentially rendering traditional computational complexity irrelevant for many problems.
The method of problem-solving fundamentally changes from explicit instruction to emergent sampling, altering the landscape of software development and problem-solving.
- · LLM developers
- · Prompt engineering specialists
- · Industries with complex, ill-defined problems
- · AI compute providers
- · Traditional software developers reliant on algorithmic precision
- · Legacy programming languages and frameworks
- · Education systems focused solely on procedural programming
- · Workflows optimized for linear, deterministic processes
Companies will re-evaluate their software development methodologies and invest heavily in LLM integration.
A new class of 'computational problem-solvers' will emerge, skilled in framing problems for LLMs rather than writing code.
The very definition of 'computation' and its limits will be re-examined, leading to potential philosophical and scientific shifts.
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Read at arXiv cs.AI