PUDA enables effective promotion of unpopular target items in black-box LLM sequential recommenders by using evolutionary LLM refinement to infer hidden prompts, training a surrogate model, and combining adversarial text revision with surrogate-generated poisoning sequences.
See: Strategic exploration and exploitation for cohesive in-context prompt optimization
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FitText embeds memetic evolutionary retrieval inside the agent's reasoning loop to iteratively refine pseudo-tool descriptions, raising retrieval rank from 8.81 to 2.78 on ToolRet and pass rate to 0.73 on StableToolBench.
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Prompt-Unknown Promotion Attacks against LLM-based Sequential Recommender Systems
PUDA enables effective promotion of unpopular target items in black-box LLM sequential recommenders by using evolutionary LLM refinement to infer hidden prompts, training a surrogate model, and combining adversarial text revision with surrogate-generated poisoning sequences.
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FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
FitText embeds memetic evolutionary retrieval inside the agent's reasoning loop to iteratively refine pseudo-tool descriptions, raising retrieval rank from 8.81 to 2.78 on ToolRet and pass rate to 0.73 on StableToolBench.