arXiv Machine Learning June 30, 2026 By Oscar Mir\'o L\'opez-Feliu, Daimy van Loo, Xanthos Kekkos, Mikel Blom, Clara Rus

Reproducing FACTER: Fairness via Conformal Thresholding and Prompt Repair

Read the original on arXiv Machine Learning →

arXiv:2606. 28620v1 Announce Type: cross Abstract: Fayyazi et al.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 30

Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

arXiv:2606. 29947v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as rerankers in recommender systems, with the expectation that semantic understanding will help in cold-start and long-tail regimes.

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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.

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arXiv AI

5d ago

FSGR: Mitigating Token Frequency Bias for Fair SID-Based Generative Recommendation

arXiv:2608. 12845v1 Announce Type: cross Abstract: Semantic ID (SID)-based generative recommendation has recently achieved remarkable success.

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arXiv AI

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PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference

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arXiv AI

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arXiv AI

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Evergreen: Efficient Claim Verification for Semantic Aggregates

arXiv:2604. 26180v2 Announce Type: replace-cross Abstract: With recent semantic query processing engines, semantic aggregation has become a primitive operator, enabling the reduction of a relation into a natural language aggregate using an LLM.

By Alexander W. Lee, Benjamin Han, Shayak Sen, Sam Yeom, Ugur Cetintemel, Anupam Datta

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Diagnosing and Mitigating Retrieval Bottlenecks in LLM-Based Cold-Start Recommendation

Taiji: Pareto Optimal Policy Optimization with Semantics-IDs Trade-off for Industrial LLM-Enhanced Recommendation

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