LLM code modernizers produce semantic drift in 39.7% of legacy-Python-2 cases and endorse 31.7% of those drifts in self-review, with rates varying widely across models but not tracking capability.
Environment-in-the-loop: rethinking code migration with LLM-based agents
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A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.
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Code as Agent Harness
A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed open challenges.