arXiv AI
Aug 5
Training Documents Reranker with Search Rubrics for Deep Research Agent
arXiv:2608. 03527v1 Announce Type: cross Abstract: Retrieval systems help deep research agents generate high-quality answers by providing relevant documents.
QD-Rec: Open-Ended Evolution of Recommendations via Quality-Diversity
QD-Rec is a quality–diversity approach that expands recommendation systems by generating diverse, high‑quality suggestions paired with natural‑language hypotheses. It uses a contextual bandit to select items and a foundation model to craft reasons, storing new recommendations in an archive when they are novel or superior to existing ones. Experiments on mergers & acquisitions, customer targeting, and content recommendation show that QD‑Rec achieves higher mean quality and larger archives than baseline methods.
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arXiv Machine Learning
Aug 4
MUSS: Multilevel Subset Selection for Relevance and Diversity
arXiv:2503. 11126v4 Announce Type: replace Abstract: The problem of relevant and diverse subset selection has a wide range of applications, including recommender systems and retrieval-augmented generation (RAG).
arXiv AI
Jun 3
Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation
arXiv:2606. 03866v1 Announce Type: cross Abstract: Scaling recommender systems via large language models (LLMs) has become a prominent trend in the industry.
arXiv Machine Learning
Sep 17
LIGE-GR: A Smooth Leap from Ranking to Generative Recommendation in the LLM Era
LIGE‑GR is a framework that transitions traditional ranking‑based recommender systems to a generative, listwise approach inspired by large language models. It extends existing pointwise recommendation models into a listwise generation system, enabling sequence‑level optimization without overhauling the entire infrastructure. Experiments on Instagram Reels and Facebook Video show modest gains in user time spent—1.14 % and 0.72 % respectively—while adding only slight inference overhead.
arXiv AI
Jul 3
Optimizing RAG Rerankers with LLM Feedback via Reinforcement Learning
arXiv:2604. 02091v2 Announce Type: replace-cross Abstract: Rerankers play a pivotal role in refining retrieval results for Retrieval-Augmented Generation.
arXiv Computation and Language
Oct 1
Decision-Oriented Recommendation Reranking: An Empirical Study of Jev
arXiv:2609.40241v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation q...
arXiv AI
Oct 16
QREF: Improving DeepSearch Agents by Aligning Search with Reasoning via Query Refinement and Evidence Filtering
QREF is a plug‑and‑play system that improves DeepSearch agents by refining search queries to match the current reasoning intent and filtering evidence to keep only what is relevant. The Query Refiner rewrites queries for complementary coverage across each search batch, while the Evidence Filter removes distracting retrieval results. Experiments show that QREF boosts DeepSearch performance on four benchmarks, achieving significant Pass@1 gains and dramatically reducing ineffective query batches without changing the agents’ architectures or retraining their reasoning policies.
arXiv Machine Learning
Jul 14
Diversified Multinomial Logit Contextual Bandits
arXiv:2607. 11684v1 Announce Type: cross Abstract: Existing contextual multinomial logit (MNL) bandits model relevance-driven choice but ignore the potential benefits of within-assortment diversity, while submodular/combinatorial bandits encode diversity in rewards but lack structured choice probabilities.
arXiv AI
Jun 2
SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
arXiv:2606. 00593v1 Announce Type: cross Abstract: Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge.
arXiv AI
Jul 29
MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation
arXiv:2607. 25420v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used in recommender systems, but it is often unclear how much performance can be obtained from strong pre-trained backbones alone when they are placed inside a structured recommendation pipeline.
arXiv AI
Sep 25
Learning Better Reasoning for Generative Recommendation with Semantic IDs
The paper introduces Evo-Rec, a three‑stage framework that improves generative recommendation by learning better reasoning traces for Semantic ID (SID) generation. It first aligns SIDs with textual and behavioral contexts, then selects candidate reasoning traces that improve ground‑truth item prediction, and finally refines the reasoning policy via reinforcement learning with catalog‑constrained generation and ranking‑aware feedback. Experiments on Amazon Review datasets show Evo‑Rec consistently outperforms existing discriminative, generative, and reasoning‑enhanced recommenders across all metrics.
arXiv Computation and Language
2d ago
Controlling Output Rankings in LLM-Based Search via Generative Engine Optimization
arXiv:2602.03608v2 Announce Type: replace Abstract: The way customers search for and choose products is changing with the rise of large language models (LLMs). LLM-based search, or generative engines...