arXiv AI June 2, 2026 By Denica Kjorvezir, Marko Djukanovi\'c, Ana Gjorgjevikj, Gjorgjina Cenikj, Tome Eftimov

Consistent and Distinctive: LLM Benchmark Efficiency via Maximum Independent Set Prompt Selection on Similarity Graphs

arXiv:2606. 01400v1 Announce Type: cross Abstract: Evaluating large language models (LLMs) across comprehensive benchmarks is expensive and time-consuming.

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

arXiv AI

Jul 14

Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks

arXiv:2607. 09739v1 Announce Type: new Abstract: We study LLM benchmark coreset selection: selecting a small subset of prompts over multiple benchmarks whose induced model scores and rankings approximate those obtained from the full benchmark suite.

By Jihan Yao, Gantavya Bhatt, Arnav Das, Peter Jin, Ke Bao, Qiaolin Yu, Khushi Bhardwaj, Chang Su, Jialei Wang, Yikai Zhu, Sugam Devare, Damon Mosk-Aoyama, Zhen Dong, Venkat Krishna Srinivasan, Yineng Zhang, Oleksii Kuchaiev, Jiantao Jiao, Banghua Zhu, Jeff Bilmes

llms rag benchmarks

arXiv Machine Learning

Aug 7

Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

arXiv:2608. 05724v1 Announce Type: cross Abstract: Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on sparse global corpus statistics.

By Sriram Loganathan, Gokul Anand, Aung Bo Bo, Yourui Shao, William B. Andreopoulos

rag nlp efficiency

arXiv Machine Learning

Jun 5

The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models

arXiv:2606. 05169v1 Announce Type: new Abstract: We give a stereological theory of LLM benchmark coverage.

By Jason Z Wang

llms benchmarks

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.

By Zhe Dong (University of Maine at Presque Isle), Fang Qin (Stanford University), Manish Shah (Independent Researcher), Yicheng Wang (Independent Researcher)

llms rag benchmarks

Hugging Face Trending Papers

Aug 6

Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

Sparse word embedding pipelines can avoid dense co-occurrence matrix materialization, dense factorization, and gradient training while still relying on sparse global corpus statistics. This paper studies Random Indexing (RI) vectors refined by weighted averaging on a sparse Positive Pointwise Mutual Information (PPMI) graph.

rag nlp efficiency

arXiv AI

Jun 26

The Capability Frontier: Benchmarks Miss 82% of Model Performance

arXiv:2606. 26836v1 Announce Type: new Abstract: Existing benchmarks typically report accuracy for a single model on a single run.

By Bradley Fowler, Ryan Smith, Daniel Thi Graviet, William Myers, Joshua Greaves, Narmeen Fatimah Oozeer, Ant\'ia Garc\'ia, Philip Quirke, Amirali Abdullah, Fazl Barez, Shriyash Kaustubh Upadhyay

llms agents benchmarks

Related stories

Coresets Before Score Sets: Evaluation-Unsupervised Prompt Subset Selection for LLM Benchmarks

Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

The Evaluation Blind Spot: A Stereological Theory of Benchmark Coverage for Large Language Models

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

Sparse Mutual Information Graph Averaging for Improving Random Indexing Embeddings

The Capability Frontier: Benchmarks Miss 82% of Model Performance