---
title: "Who cites Imanol Schlag — page 3 — 874 papers of our library"
description: "874 papers of the Elamur Research library cite the works of Imanol Schlag, found in their bibliographies · page 3 of 3 · 201–280; the most cited first."
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---
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# Who cites Imanol Schlag

888 citations of their works from 874 papers of our library (of the 149,439 whose bibliographies we read) · 280 with a page here, the most cited first · page 3 of 3 · 201–280 · [← Imanol Schlag](https://md.research.elamur.ai/free/en/authors/imanol-schlag/)

[← prev](https://md.research.elamur.ai/free/en/authors/imanol-schlag/cited-by/page/2/) [1](https://md.research.elamur.ai/free/en/authors/imanol-schlag/cited-by/) [2](https://md.research.elamur.ai/free/en/authors/imanol-schlag/cited-by/page/2/) **3**

201. [**mR3: Multilingual Rubric-Agnostic Reward Reasoning Models arXiv:2510.01146**David Anugraha, Shou-Yi Hung, Zilu Tang, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/anugraha-2025-mr3-multilingual-rubric-agnostic-reward/)
202. [**Ring-lite: Scalable Reasoning via C3PO-Stabilized Reinforcement Learning for LLMs arXiv:2506.14731**Ling Team, Bin Hu, Cai Chen, et al.*2025 · 5 citations*](https://md.research.elamur.ai/free/en/papers/ling-team-2025-ring-lite-scalable-reasoning/)
203. **[Teach Small Models to Reason by Curriculum Distillation](https://md.research.elamur.ai/free/en/papers/jiang-2025-teach-small-models-reason-curriculum/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wangyi Jiang, Yaojie Lu, Hongyu Lin, et al.*2025 · 5 citations*
204. **[Dynamic Sampling that Adapts: Self-Aware Iterative Data Persistent Optimization for Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/rao-2026-dynamic-sampling-that-adapts-self/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jun Rao, Xuebo Liu, Hexuan Deng, et al.*2026 · 4 citations*
205. **[Select2Reason: Efficient Instruction-Tuning Data Selection for Long-CoT Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2026-select2reason-efficient-instruction-tuning-data/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cehao Yang, Xueyuan Lin, Xiaojun Wu, et al.*2026 · 4 citations*
206. **[Sparse-RL: Breaking the Memory Wall in LLM Reinforcement Learning via Stable Sparse Rollouts](https://md.research.elamur.ai/free/en/papers/luo-2026-sparse-rl-breaking-memory-wall/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sijia Luo, Xiaokang Zhang, Yuxuan Hu, et al.*2026 · 4 citations*
207. **[Do Not Step Into the Same River Twice: Learning to Reason from Trial and Error](https://md.research.elamur.ai/free/en/papers/tang-2026-do-not-step-into-same/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenming Tang, Hsiu-Yuan Huang, Weijie Liu, et al.*2026 · 3 citations*
208. **[Estonian WinoGrande Dataset: Comparative Analysis of LLM Performance on Human and Machine Translation](https://md.research.elamur.ai/free/en/papers/ojastu-2026-estonian-winogrande-dataset-comparative-analysis/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Marii Ojastu, Hele-Andra Kuulmets, Aleksei Dorkin, et al.*2026 · 3 citations*
209. **[FLEXITOKENS: Flexible Tokenization for Evolving Language Models](https://md.research.elamur.ai/free/en/papers/owodunni-2026-flexitokens-flexible-tokenization-evolving-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abraham Toluwase Owodunni, Orevaoghene Ahia, Sachin Kumar*2026 · 3 citations*
210. **[Harnessing Negative Signals: Reinforcement Distillation from Teacher Data for LLM Reasoning](https://md.research.elamur.ai/free/en/papers/xu-2026-harnessing-negative-signals-reinforcement-distillation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuyao Xu, Cheng Peng, Jiangxuan Long, et al.*2026 · 3 citations*
211. **[Learning While Staying Curious: Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models](https://md.research.elamur.ai/free/en/papers/wang-2026-learning-while-staying-curious-entropy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hao Wang, Hao Gu, Hongming Piao, et al.*2026 · 3 citations*
212. **[MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis](https://md.research.elamur.ai/free/en/papers/yu-2026-mathagent-adversarial-evolution-constraint-graphs/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zixiong Yu, Jun Rao, Guhan Chen, et al.*2026 · 3 citations*
213. [**Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models arXiv:2605.11887**Boyi Deng, Xu Wang, Yaoning Wang, et al.*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/deng-2026-qwen-scope-turning-sparse/)
214. [**A Sovereign, Open-Source Foundation Model for German and English arXiv:2607.09424**Soofi-Team: Benedikt Droste, David Fitzek, Ruben Harle, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/droste-2026-sovereign-open-source-foundation-model/)
215. **[ALBA: A European Portuguese Benchmark for Evaluating Language and Linguistic Dimensions in Generative LLMs](https://md.research.elamur.ai/free/en/papers/vieira-2026-alba-european-portuguese-benchmark-evaluating/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Inês Vieira, Inês Calvo, Iago Paulo, et al.*2026 · 2 citations*
216. **[PHEB: An European Portuguese High School-Level LLM Benchmark](https://md.research.elamur.ai/free/en/papers/tavares-2026-pheb-european-portuguese-high-school/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Diogo C. Tavares, Rafael Ferreira, Afonso Simplício, et al.*2026 · 2 citations*
217. **[Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR](https://md.research.elamur.ai/free/en/papers/xu-2026-prune-as-you-generate-online/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haobo Xu, Sirui Chen, Ruizhong Qiu, et al.*2026 · 2 citations*
218. **[AMALIA: A Fully Open Large Language Model for European Portuguese](https://md.research.elamur.ai/free/en/papers/simplicio-2026-amalia-fully-open-large-language/) [International Conference on Computational Processing of Portuguese](https://md.research.elamur.ai/free/en/venues/propor/)**Afonso Simplício, Gonçalo Vinagre, Miguel Moura Ramos, et al.*2026 · 1 citation*
219. **[Compatibility-Aware Dynamic Fine-Tuning for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-compatibility-aware-dynamic-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yucheng Zhou, Junwei Sheng, Qianning Wang, et al.*2026 · 1 citation*
220. **[Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training](https://md.research.elamur.ai/free/en/papers/yang-2026-data-mixing-agent-learning-re/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kailai Yang, Xiao Liu, Lei Ji, et al.*2026 · 1 citation*
221. **[Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/yu-2026-easy-samples-are-all-you/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhiyin Yu, Bo Zhang, Qibin Hou, et al.*2026 · 1 citation*
222. **[Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection](https://md.research.elamur.ai/free/en/papers/stacey-2026-improving-ood-performance-closed-source/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Joe Stacey, Lisa Alazraki, Aran Ubhi, et al.*2026 · 1 citation*
223. **[Incentivizing Strong Reasoning from Weak Supervision](https://md.research.elamur.ai/free/en/papers/yuan-2026-incentivizing-strong-reasoning-weak-supervision/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yige Yuan, Teng Xiao, Shuchang Tao, et al.*2026 · 1 citation*
224. **[LightReasoner: Can Small Language Models Teach Large Language Models Reasoning?](https://md.research.elamur.ai/free/en/papers/wang-2026-lightreasoner-can-small-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyuan Wang, Yankai Chen, Zhonghang Li, et al.*2026 · 1 citation*
225. [**MÖVE: A Holistic LLM Benchmark for the German Public Sector arXiv:2606.13111**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-m-ove-holistic-llm-benchmark/)
226. **[No Shortcuts to Culture: Indonesian Multi-hop Question Answering for Complex Cultural Understanding](https://md.research.elamur.ai/free/en/papers/permadi-2026-no-shortcuts-culture-indonesian-multi/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Vynska Amalia Permadi, Xingwei Tan, Nafise Sadat Moosavi, et al.*2026 · 1 citation*
227. **[Orchestrating Tokens and Sequences: Dynamic Hybrid Policy Optimization for RLVR](https://md.research.elamur.ai/free/en/papers/min-2026-orchestrating-tokens-sequences-dynamic-hybrid/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zijun Min, Bingshuai Liu, Ante Wang, et al.*2026 · 1 citation*
228. **[Rectified Sparse Attention for Efficient Long-Sequence Generation](https://md.research.elamur.ai/free/en/papers/sun-2026-rectified-sparse-attention-efficient-long/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yutao Sun, Tianzhu Ye, Li Dong, et al.*2026 · 1 citation*
229. **[StateX: Enhancing RNN Recall via Post-training State Expansion](https://md.research.elamur.ai/free/en/papers/shen-2026-statex-enhancing-rnn-recall-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.*2026 · 1 citation*
230. **[Towards Stable and Effective Reinforcement Learning for Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/zhang-2026-stable-effective-reinforcement-learning-mixture/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Di Zhang, Xun Wu, Shaohan Huang, et al.*2026 · 1 citation*
231. **[When AI Writes, Whose Voice Remains? Quantifying Cultural Marker Erasure Across World English Varieties in Large Language Models](https://md.research.elamur.ai/free/en/papers/navneet-2026-ai-writes-whose-voice-remains/) [Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA)](https://md.research.elamur.ai/free/en/venues/extended-abstracts-of-the-chi-conference-on-human-factors-in-computing-systems/)**Satyam Kumar Navneet, Joydeep Chandra, Yong Zhang*2026 · 1 citation*
232. **[AraEval: An Arabic Multi-Task Evaluation Suite for Large Language Models](https://md.research.elamur.ai/free/en/papers/althnian-2025-araeval-arabic-multi-task-evaluation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alhanoof Althnian, Norah A. Alzahrani, Shaykhah Z. Alsubaie, et al.*2025 · 1 citation*
233. **[A Survey of Multimodal Mathematical Reasoning: From Perception, Alignment to Reasoning](https://md.research.elamur.ai/free/en/papers/yang-2026-survey-multimodal-mathematical-reasoning-perception/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianyu Yang, Sihong Wu, Yilun Zhao, et al.*2026*
234. **[AAPO: Enhancing the Reasoning Capabilities of LLMs with Advantage Margin](https://md.research.elamur.ai/free/en/papers/xiong-2026-aapo-enhancing-reasoning-capabilities-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jian Xiong, Jingbo Zhou, Jingyong Ye, et al.*2026*
235. **[Adapting Vision-Language Models for E-commerce Understanding at Scale](https://md.research.elamur.ai/free/en/papers/nulli-2026-adapting-vision-language-models-e/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Matteo Nulli, Vladimir Orshulevich, Tala Bazazo, et al.*2026*
236. **[Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations](https://md.research.elamur.ai/free/en/papers/zuo-2026-adaptive-test-time-compute-allocation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bowen Zuo, Dongruo Zhou, Yinglun Zhu*2026*
237. **[Are the LLMs Capable of Maintaining at Least the Language Genus?](https://md.research.elamur.ai/free/en/papers/mitrovic-2026-are-llms-capable-maintaining-least/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Sandra Mitrović, David Kletz, Ljiljana Dolamic, et al.*2026*
238. **[Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks](https://md.research.elamur.ai/free/en/papers/shelat-2026-beyond-memorization-testing-llm-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shlok Shelat, Jay Raval, Souvik Roy, et al.*2026*
239. **[Breaking the Impasse: Dual-Scale Evolutionary Policy Training for Social Language Agents](https://md.research.elamur.ai/free/en/papers/wang-2026-breaking-impasse-dual-scale-evolutionary/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minzheng Wang, Run Luo, Yanbo Wang, et al.*2026*
240. **[Calibrated Speculative Decoding: Frequency-Guided Candidate Selection for Efficient Inference](https://md.research.elamur.ai/free/en/papers/zhouxuwen-2026-calibrated-speculative-decoding-frequency-guided/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhouxuwen, Fangxin Liu, Chao Wang, et al.*2026*
241. **[Calibration-Aware Policy Optimization for Reasoning LLMs](https://md.research.elamur.ai/free/en/papers/wang-2026-calibration-aware-policy-optimization-reasoning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziqi Wang, Xingzhou Lou, Meiqi Wu, et al.*2026*
242. **[Can Large Language Models Replace Statistical Software?](https://md.research.elamur.ai/free/en/papers/staudt-2026-can-large-language-models-replace/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Yves Staudt*2026*
243. **[CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/schlesinger-2026-claro-controlled-attribute-driven-reasoning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Oded Schlesinger, Young Kyung Kim, J. Matias Di Martino, et al.*2026*
244. **[CoTEvol: Self-Evolving Chain-of-Thoughts for Data Synthesis in Mathematical Reasoning](https://md.research.elamur.ai/free/en/papers/wang-2026-cotevol-self-evolving-chain-thoughts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhuo Wang, Zhuo Zhang, Yafu Li, et al.*2026*
245. **[Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains](https://md.research.elamur.ai/free/en/papers/su-2026-crossing-reward-bridge-expanding-reinforcement/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yi Su, Dian Yu, Linfeng Song, et al.*2026*
246. **[Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory](https://md.research.elamur.ai/free/en/papers/nejadgholi-2026-defining-cultural-capabilities-ai-evaluation/) [Workshop on Evaluating Evaluations (EvalEval)](https://md.research.elamur.ai/free/en/venues/evaleval/)**Isar Nejadgholi, Masoud Kianpour, Krishnapriya Vishnubhotla, et al.*2026*
247. **[Distributional Clarity: The Hidden Driver of RL-Friendliness in Large Language Models](https://md.research.elamur.ai/free/en/papers/sun-2026-distributional-clarity-hidden-driver-rl/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shaoning Sun, Mingzhu Cai, Huang He, et al.*2026*
248. **[Don't Tell the Answer, Truly Guide the Reasoning During RL Rollouts](https://md.research.elamur.ai/free/en/papers/wang-2026-don-t-tell-answer-truly/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xinyi Wang, Jinyi Han, Zishang Jiang, et al.*2026*
249. **[Efficient KL Divergence Estimation via Truncated Top-K Integration for Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-efficient-kl-divergence-estimation-truncated/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xinyuan Wang, Luozhijie Jin, Bo Wang, et al.*2026*
250. **[EIFFEL: a novel benchmark to measure bias of English heavy training on French idiomatic expressions](https://md.research.elamur.ai/free/en/papers/noel-2026-eiffel-novel-benchmark-measure-bias/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Charlotte Noel, Nicholas Asher, Olivier Gouvert, et al.*2026*
251. **[FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization](https://md.research.elamur.ai/free/en/papers/xiao-2026-faq-mitigating-quantization-error-regenerating/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Haiyang Xiao, Weiqing Li, Jinyue Guo, et al.*2026*
252. **[GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language Models](https://md.research.elamur.ai/free/en/papers/ruan-2026-gift-guided-fine-tuning-transfer/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiwen Ruan, Yichao Du, Jianjie Zheng, et al.*2026*
253. **[GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning](https://md.research.elamur.ai/free/en/papers/xie-2026-gr1-reinforcement-enhanced-llm-geoscience/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yule Xie, Jiaxin Ding, Cheng Deng, et al.*2026*
254. **[Graph-Augmented LLMs for Swiss MP Ideology Prediction](https://md.research.elamur.ai/free/en/papers/yuan-2026-graph-augmented-llms-swiss-mp/) [Edition of the Swiss Text Analytics Conference](https://md.research.elamur.ai/free/en/venues/swisstext/)**Yifei Yuan, Luis Salamanca, Sophia Schlosser, et al.*2026*
255. **[Improved Policy Optimization for Mixture-of-Experts Models: Importance Sampling and Rewarding from an Expert-Centric Perspective](https://md.research.elamur.ai/free/en/papers/qian-2026-improved-policy-optimization-mixture-experts/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yining Qian, Jinpeng Li, Fei Mi, et al.*2026*
256. **[InstructDiff: Domain-Adaptive Data Selection via Contrastive Entropy for Efficient LLM Fine-Tuning](https://md.research.elamur.ai/free/en/papers/su-2026-instructdiff-domain-adaptive-data-selection/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyou Su, He Zhu, Xiao Luo, et al.*2026*
257. **[Is Biomedical Specialization Still Worth It? Insights from Domain-Adaptive Language Modelling with a New French Health Corpus](https://md.research.elamur.ai/free/en/papers/mannion-2026-is-biomedical-specialization-still-worth/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Aidan Mannion, Cécile Macaire, Armand Violle, et al.*2026*
258. **[Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions](https://md.research.elamur.ai/free/en/papers/zhao-2026-large-language-models-are-bad/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Minda Zhao, Yilun Du, Mengyu Wang*2026*
259. **[Learning from Cognition: Enhancing RL Efficiency for LLM Reasoning via Hierarchical Metacognitive Decomposition and Refinement](https://md.research.elamur.ai/free/en/papers/sun-2026-learning-cognition-enhancing-rl-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zexu Sun, Yongcheng Zeng, Erxue Min, et al.*2026*
260. **[Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://md.research.elamur.ai/free/en/papers/xueyun-2026-learning-mistakes-negative-reasoning-samples/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tian Xueyun, MingHua Ma, Bingbing Xu, et al.*2026*
261. [**LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering arXiv:2607.02763**Nina Hosseini-Kivanani, Marco Matassoni, Alessio Brutti*2026*](https://md.research.elamur.ai/free/en/papers/hosseini-kivanani-2026-luxsqa-ask-me-luxembourgish-tts/)
262. **[Multi-lingual Functional Evaluation for Large Language Models](https://md.research.elamur.ai/free/en/papers/ojewale-2026-multi-lingual-functional-evaluation-large/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Victor Ojewale, Inioluwa Deborah Raji, Suresh Venkatasubramanian*2026*
263. **[Open Problems Solved by LLMs? A Survey of Verifiable Mathematical Discovery](https://md.research.elamur.ai/free/en/papers/tzachristas-2026-open-problems-solved-llms-survey/) [Big Picture v2: Crafting a Research Narrative](https://md.research.elamur.ai/free/en/venues/bigpicture/)**Ioannis Tzachristas, Georgios Tzachristas, Aifen Sui*2026*
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