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title: "Who cites Think you have — page 5 — 1,933 citing papers"
description: "1933 papers of the Elamur Research library cite «Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge»…"
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# Who cites «Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge»

1933 papers of our library cite it (of the 149,439 whose bibliographies we read) · 491 with a page here, the most cited first · page 5 of 5 · 401–491 · [← back to «Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge»](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/)

[← prev](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/cited-by/page/4/) [1](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/cited-by/) … [4](https://md.research.elamur.ai/free/en/papers/clark-2018-think-have-solved-question-answering/cited-by/page/4/) **5**

401. **[Balancing Knowledge Breadth and Task Depth for Effective Domain Adaptation Fine-Tuning](https://md.research.elamur.ai/free/en/papers/zhang-2026-balancing-knowledge-breadth-task-depth/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mu Zhang, Yuxiang Chu, Guangya Yu, et al.*2026*
402. **[Benchmarking Post-Training Quantization of Large Language Models under Microscaling Floating Point Formats](https://md.research.elamur.ai/free/en/papers/zhang-2026-benchmarking-post-training-quantization-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Manyi Zhang, Ji-Fu Li, Zhongao Sun, et al.*2026*
403. **[Beyond Uniform SVD: Dual-Level Optimization across Columns and Modules for LLM Compression](https://md.research.elamur.ai/free/en/papers/xv-2026-beyond-uniform-svd-dual-level/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Xv, Xian Gao, Ting Liu, et al.*2026*
404. **[Biomed-Enriched: Data-Efficient Biomedical Pretraining via Paragraph-Level Annotation](https://md.research.elamur.ai/free/en/papers/touchent-2026-biomed-enriched-data-efficient-biomedical/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rian Touchent, Nathan Godey, Éric Villemonte de la Clergerie*2026*
405. **[BWLA: Breaking the Barrier of W1AX Post-Training Quantization for LLMs](https://md.research.elamur.ai/free/en/papers/zhao-2026-bwla-breaking-barrier-w1ax-post/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhixiong Zhao, Zukang Xu, Dawei Yang*2026*
406. **[Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://md.research.elamur.ai/free/en/papers/vintar-2026-charting-european-llm-benchmarking-landscape/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Spela Vintar, Mojca Brglez, Taja Kuzman Pungeršek, et al.*2026*
407. **[COMPEL: Compensated Mixture-of-Experts Pruning with Expert-Layer distribution](https://md.research.elamur.ai/free/en/papers/yoon-2026-compel-compensated-mixture-experts-pruning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Seohee Yoon, Yong Suk Choi*2026*
408. **[Component Transfer Can Exceed Full Model Performance: Investigating Post-Trained Mixture-of-Experts](https://md.research.elamur.ai/free/en/papers/tiwari-2026-component-transfer-can-exceed-full/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Rabin Tiwari*2026*
409. **[Compressing Language Models for Specialized Domains](https://md.research.elamur.ai/free/en/papers/williams-2026-compressing-language-models-specialized-domains/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Miles Williams, George Chrysostomou, Vitor Amancio Jeronymo, et al.*2026*
410. **[Controllable Contamination Detection for Reliable LLM Evaluation with Statistical Guarantees](https://md.research.elamur.ai/free/en/papers/zhang-2026-controllable-contamination-detection-reliable-llm/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zheng Zhang, Qi Liu, Siyuan Liang, et al.*2026*
411. **[CSULoRA: Closest Safe Update Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/marchenko-2026-csulora-closest-safe-update-low/) [International Conference on Natural Language Processing and Artificial Intelligence for Cyber Security](https://md.research.elamur.ai/free/en/venues/nlpaics/)**Oleksandr Marchenko, Adelaide Danilov, Aria Nourbakhsh, et al.*2026*
412. **[Decoupling Generalization and Adaptation in Meta-Learning for Large Language Models](https://md.research.elamur.ai/free/en/papers/vetcha-2026-decoupling-generalization-adaptation-meta-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nitin Vetcha, Binqian Xu, Dianbo Liu*2026*
413. **[Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs](https://md.research.elamur.ai/free/en/papers/nakamura-2026-demystifying-mixed-outcomes-self-training/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yusuke Nakamura, Hirokazu Kiyomaru, Chaoran Liu, et al.*2026*
414. **[Diagnosing Translated Benchmarks: An Automated Quality Assurance Study of the EU20 Benchmark Suite](https://md.research.elamur.ai/free/en/papers/thellmann-2026-diagnosing-translated-benchmarks-automated-quality/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Klaudia Thellmann, Bernhard Stadler, Michael Färber*2026*
415. **[Disentangling the Effects of Unlearning in Measuring Parametric Faithfulness of Chain-of-Thought](https://md.research.elamur.ai/free/en/papers/mitsuhashi-2026-disentangling-effects-unlearning-measuring-parametric/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ryo Mitsuhashi, Gaku Morio, Ayana Niwa, et al.*2026*
416. **[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*
417. **[Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression](https://md.research.elamur.ai/free/en/papers/sun-2026-dual-activation-weight-sparsity-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luoyang Sun, Guangyan Li, Cheng Deng, et al.*2026*
418. **[EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models](https://md.research.elamur.ai/free/en/papers/xing-2026-efficientllm-unified-pruning-aware-pretraining/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xingrun Xing, Zheng Liu, Shitao Xiao, et al.*2026*
419. **[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*
420. **[EvoMD-LLM: Learning the Language of Species Evolution in Reactive Molecular Dynamics](https://md.research.elamur.ai/free/en/papers/tang-2026-evomd-llm-learning-language-species/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichen Tang, Zhengzheng Dang, Yulin Chen, et al.*2026*
421. **[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*
422. **[G-LoRA: Global-Local Decoupled Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/xiong-2026-g-lora-global-local-decoupled/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiahao Xiong, Yihong Huang, Yihe Liu, et al.*2026*
423. **[GAST: Gradient-aligned Sparse Tuning of Large Language Models with Data-layer Selection](https://md.research.elamur.ai/free/en/papers/yao-2026-gast-gradient-aligned-sparse-tuning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Kai Yao, Zhenghan Song, Kaixin Wu, et al.*2026*
424. **[GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-grace-generative-approach-better-confidence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhaohan Zhang, Ziquan Liu, Ioannis Patras*2026*
425. **[GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-grasprune-global-gating-budgeted-structured/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyang Wang, Jiangfeng Xiao, Chuan Xiao, et al.*2026*
426. **[GRASS: Gradient-based Adaptive Layer-wise Importance Sampling for Memory-efficient Large Language Model Fine-tuning](https://md.research.elamur.ai/free/en/papers/tian-2026-grass-gradient-based-adaptive-layer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaiyuan Tian, Linbo Qiao, Yu Tang, et al.*2026*
427. **[Grounded Concreteness: Human-Like Concreteness Sensitivity in Vision–Language Models](https://md.research.elamur.ai/free/en/papers/roy-2026-grounded-concreteness-human-like-concreteness/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Aryan Roy, Zekun Wang, Christopher J. MacLellan*2026*
428. [**HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model**Noam Kayzer, Dan Revital, Ori Bar Joseph, et al.*2026*](https://md.research.elamur.ai/free/en/papers/kayzer-2026-hebatron-hebrew-specialized-open-weight/)
429. **[How Can Synthetic Data Improve Multilingual Language Model Pretraining? A Data Quality Perspective](https://md.research.elamur.ai/free/en/papers/zhu-2026-how-can-synthetic-data-improve/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tongyao Zhu, Qian Liu, Chang Ma, et al.*2026*
430. **[ImF: Embedding an Implicit Fingerprint in Your Large Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-imf-embedding-implicit-fingerprint-your/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaxuan Wu, Wanli Peng, Hang fu, et al.*2026*
431. **[Interpretable Safety Alignment via SAE-Constructed Low-Rank Subspace Adaptation](https://md.research.elamur.ai/free/en/papers/wang-2026-interpretable-safety-alignment-sae-constructed/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dianyun Wang, Qingsen Ma, Yuhu Shang, et al.*2026*
432. **[JudgeAgent: Beyond Static Benchmarks for Knowledge-Driven and Dynamic LLM Evaluation](https://md.research.elamur.ai/free/en/papers/shi-2026-judgeagent-beyond-static-benchmarks-knowledge/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Shi, Xuhui Jiang, Chengjin Xu, et al.*2026*
433. **[KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-knowledgeberg-evaluating-systematic-knowledge-coverage/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiao Zhang, Qianru Meng, Yongjian Chen, et al.*2026*
434. **[LaCo: Layer-wise Compensation for Pruned Large Language Models](https://md.research.elamur.ai/free/en/papers/liu-2026-laco-layer-wise-compensation-pruned/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yingen Liu, Fan Wu, Panxuyan, et al.*2026*
435. **[LaCoMSA: Language-Consistency Multilingual Self-Alignment with Latent Representation Rewarding](https://md.research.elamur.ai/free/en/papers/tran-2026-lacomsa-language-consistency-multilingual-self/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Khanh-Tung Tran, Barry O'Sullivan, Hoang D. Nguyen*2026*
436. **[Large-Scale Diverse Synthesis for Mid-Training](https://md.research.elamur.ai/free/en/papers/zhang-2026-large-scale-diverse-synthesis-mid/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xuemiao Zhang, Chengying Tu, Can Ren, et al.*2026*
437. **[Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models](https://md.research.elamur.ai/free/en/papers/pham-2026-layer-wise-high-impact-parameter/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Cuong Pham, Anh Dung Hoang, Cuong C. Nguyen, et al.*2026*
438. **[LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation](https://md.research.elamur.ai/free/en/papers/song-2026-lbllm-lightweight-binarization-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Siqing Song, Chuang Wang, Yong Lang, et al.*2026*
439. **[LeLoRA: Learnable Low-Rank Adaptation of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-lelora-learnable-low-rank-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoling Zhou, Mingjie Zhang, Zhemg Lee, et al.*2026*
440. **[LEPO: Latent Reasoning Policy Optimization for Large Language Models](https://md.research.elamur.ai/free/en/papers/zhou-2026-lepo-latent-reasoning-policy-optimization/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuyan Zhou, Jiarui Yu, Hande Dong, et al.*2026*
441. **[Linear Probes Detect Task Format, Not Reasoning Mode in Language Model Hidden States](https://md.research.elamur.ai/free/en/papers/sahoo-2026-linear-probes-detect-task-format/) [Workshop on Trustworthy NLP](https://md.research.elamur.ai/free/en/venues/trustnlp/)**Subramanyam Sahoo, Vinija Jain, Aman Chadha, et al.*2026*
442. **[LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points](https://md.research.elamur.ai/free/en/papers/zhang-2026-linkqa-synthesizing-diverse-qa-multiple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xuemiao Zhang, Can Ren, Chengying Tu, et al.*2026*
443. **[LLEOT: A Privacy-Enhancing Offsite Tuning Framework via Loss Landscape Elevation](https://md.research.elamur.ai/free/en/papers/zhong-2026-lleot-privacy-enhancing-offsite-tuning/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jin Zhong, Jinglin Liang, Tongtong Yang, et al.*2026*
444. **[Localized Low-Rank Adaptation within Clustered Parameter Subspaces](https://md.research.elamur.ai/free/en/papers/xiong-2026-localized-low-rank-adaptation-within/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiahao Xiong, Yihe Liu, Xianming Hu, et al.*2026*
445. **[Map-of-Actions: Deliberate Reasoning over Multi-Labeled Graphs](https://md.research.elamur.ai/free/en/papers/ni-2026-map-actions-deliberate-reasoning-over/) [Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era](https://md.research.elamur.ai/free/en/venues/surgellm/)**Wuguang Ni, Kai Yang*2026*
446. **[MASS-RAG: Multi-Agent Synthesis Retrieval-Augmented Generation](https://md.research.elamur.ai/free/en/papers/xiao-2026-mass-rag-multi-agent-synthesis/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingchen Xiao, Heyan Huang, Runheng Liu, et al.*2026*
447. **[MDP-GRPO: Stabilized Group Relative Policy Optimization for Multi-Constraint Instruction Following](https://md.research.elamur.ai/free/en/papers/zarchi-2026-mdp-grpo-stabilized-group-relative/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohammad Mahdi Salmani-Zarchi, Zahra Rahimi, Heshaam Faili, et al.*2026*
448. **[Memory Dial: A Training Framework for Controllable Memorization in Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-memory-dial-training-framework-controllable/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiangbo Zhang, Ali Emami*2026*
449. **[Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO](https://md.research.elamur.ai/free/en/papers/zheng-2026-mitigating-selection-bias-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jinquan Zheng, Jia Yuan, Jiacheng Yao, et al.*2026*
450. **[ModularMoE: Fast LLM Customization with Parameter-Sharing Mixture-of-Experts for Low-Resource Settings](https://md.research.elamur.ai/free/en/papers/liu-2026-modularmoe-fast-llm-customization-parameter/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jiaxing Liu, Qi Qi, Haifeng Sun, et al.*2026*
451. **[Multimodal LLMs Do Not Compose Skills Optimally across Modalities](https://md.research.elamur.ai/free/en/papers/ontalvilla-2026-multimodal-llms-do-not-compose/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Paula Ontalvilla, Aitor Ormazabal, Gorka Azkune*2026*
452. **[Offline Preference Optimization via Maximum Marginal Likelihood Estimation](https://md.research.elamur.ai/free/en/papers/najafi-2026-offline-preference-optimization-maximum-marginal/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Saeed Najafi, Alona Fyshe*2026*
453. **[On the Editability of Delta Parameters in Post-Trained Models](https://md.research.elamur.ai/free/en/papers/tang-2026-editability-delta-parameters-post-trained/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qiaoyu Tang, Le Yu, Bowen Yu, et al.*2026*
454. **[Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow](https://md.research.elamur.ai/free/en/papers/zhong-2026-parallelism-generation-order-masked-diffusion/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yangyang Zhong, Yanmei Gu, Zhengqing Zang, et al.*2026*
455. **[PE-QAT: Parameter-Efficient Quantization-Aware Training for Large Language Models](https://md.research.elamur.ai/free/en/papers/mishra-2026-pe-qat-parameter-efficient-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shresth Mishra*2026*
456. **[Position Encoding with Random Float Sampling Enhances Length Generalization of Transformers](https://md.research.elamur.ai/free/en/papers/shimizu-2026-position-encoding-random-float-sampling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Atsushi Shimizu, Shohei Taniguchi, Yutaka Matsuo*2026*
457. **[Profiling-Free Mixed-Precision Quantization for MoE LLMs via Fuzzy Rule Interpolation](https://md.research.elamur.ai/free/en/papers/qi-2026-profiling-free-mixed-precision-quantization/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Huachen Qi, Ruiyu Zhuo, Bowen Shi, et al.*2026*
458. **[RAG in the Wild: On the (In)effectiveness of LLMs with Mixture-of-Knowledge Retrieval Augmentation](https://md.research.elamur.ai/free/en/papers/xu-2026-rag-wild-effectiveness-llms-mixture/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Ran Xu, Yuchen Zhuang, Yue Yu, et al.*2026*
459. **[RanLoRA: Residual-aware Nonlinear Low-Rank Adaptation](https://md.research.elamur.ai/free/en/papers/luo-2026-ranlora-residual-aware-nonlinear-low/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xu Luo, Yongbin Liu, Chunping Ouyang, et al.*2026*
460. **[ReasonAny: Incorporating Reasoning Capability to Any Model via Simple and Effective Model Merging](https://md.research.elamur.ai/free/en/papers/yang-2026-reasonany-incorporating-reasoning-capability-any/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junyao Yang, Chen Qian, Wen Shen, et al.*2026*
461. **[Rethinking Data Mixing from the Perspective of Large Language Models](https://md.research.elamur.ai/free/en/papers/xu-2026-rethinking-data-mixing-perspective-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanjian Xu, Tianze Sun, Changwei Xu, et al.*2026*
462. **[River-LLM: Large Language Model Seamless Exit Based on KV Share](https://md.research.elamur.ai/free/en/papers/shen-2026-river-llm-large-language-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yingtao Shen, An Zou*2026*
463. **[SAMoRA: Semantic-Aware Mixture of LoRA Experts for Task-Adaptive Learning](https://md.research.elamur.ai/free/en/papers/shi-2026-samora-semantic-aware-mixture-lora/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Boyan Shi, Wei Chen, Shuyuan Zhao, et al.*2026*
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