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This paper investigates the capabilities of LLM agents in designing methods for complex AI tasks by comparing their performance and algorithmic designs to those of human counterparts. The authors derive task-specific algorithmic design spaces from human methods and map both human- and agent-designed methods into these spaces to quantify differences. Results indicate that while agents can occasionally achieve or exceed human state-of-the-art performance, they predominantly operate within the confines of human-derived algorithmic frameworks, suggesting a reliance on existing designs rather than true innovation.
Current AI agents can match human performance in some tasks, but they largely recycle existing human-designed algorithms rather than creating novel solutions.
Recent advances in LLM agents have made them increasingly capable of designing methods for complex AI tasks. This raises two central questions about agent-designed methods relative to human-designed methods: how well they perform, and how different their algorithmic designs are. To study these questions, this paper introduces an analysis that derives task-specific algorithmic design spaces from human-designed methods, maps both human- and agent-designed methods into these spaces, and quantifies their algorithmic differences at the module level. Widely used LLM agents are evaluated on a suite of representative, open-ended AI tasks spanning multiple modalities, and the methods they design are analyzed in terms of both task performance and algorithmic differences from human-designed methods. Experimental results show that current agents can occasionally match or surpass human state-of-the-art (SOTA) performance (10/72 configurations), but such success does not generalize reliably across tasks or agents. Moreover, 96.8% of agent-designed methods fall within human-derived algorithmic design spaces, largely recombining algorithmic choices found in human-designed methods, while nearly half exactly match an existing human algorithmic design. Taken together, these findings suggest that although current agents can occasionally match or surpass human SOTA performance, their algorithmic designs remain within human-derived algorithmic design spaces, reflecting the reuse and recombination of algorithmic choices.
