---
title: "Who cites Efficient Solutions For An Intriguing — 7 citing papers"
description: "7 papers of the Elamur Research library cite «Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean…"
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---
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# Who cites «Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly»

7 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly»](https://md.research.elamur.ai/free/en/papers/hosseini-2025-efficient-solutions-intriguing-failure-llms/)

1. **[MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://md.research.elamur.ai/free/en/papers/liu-2025-mdcure-scalable-pipeline-multi-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabrielle Kaili-May Liu, Bowen Shi, Avi Caciularu, et al.*2025 · 11 citations*
2. **[SepSeq: A Training-Free Framework for Long Numerical Sequence Processing in LLMs](https://md.research.elamur.ai/free/en/papers/sun-2026-sepseq-training-free-framework-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jie Sun, Yu Liu, Lu Han, et al.*2026 · 1 citation*
3. **[Beyond the Context Window: Scaling Agentic RL via End-to-end Optimized Context Compression](https://md.research.elamur.ai/free/en/papers/lu-2026-beyond-context-window-scaling-agentic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Miao Lu, Weiwei Sun, Weihua Du, et al.*2026*

4 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/hosseini-2025-efficient-solutions-intriguing-failure-llms/) — Efficient Solutions For An Intriguing Failure of LLMs: Long Context Window Does Not Mean LLMs Can Analyze Long Sequences Flawlessly