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This paper proposes a small candidate formalizer trained on offline teacher-generated lists to bridge the interface gap between open-ended LLM workloads and fast decision models, reporting up to 52.6% task success on CALVIN-D with 2.09s end-to-end latency, compared to 11.8% without the formalizer and slower baselines like Qwen3.5-27B.
This paper explores the impact of different candidate-generation schedules on the energy consumption and performance of large language models during test-time scaling, demonstrating that larger batch sizes reduce energy use and latency.
This paper introduces a hybrid candidate generation approach for vacation rental recommendations, combining collaborative filtering and graph neural networks to improve recall by 14.8% over baseline methods.
This paper evaluates LLM rerankers in conversational recommendation systems, demonstrating that performance and stability are highly dependent on retrieval protocols, candidate pool configuration, and decoding settings.
This paper presents a training-free LLM-based candidate generation pipeline for vacation rental marketplaces, using an off-the-shelf LLM to synthesize semantic queries and dense retrieval to complement collaborative filtering, significantly improving coverage for long-tail properties while maintaining performance on well-served ones.
F-GRPO proposes a factorized group-relative policy optimization framework that unifies candidate generation and ranking in a single autoregressive LLM, addressing credit assignment issues and improving top-ranked performance across sequential recommendation and multi-hop QA benchmarks.