BACKBONE MODELS

Text-to-Code Generation with Modality-relative Pre-training (2024.eacl-long)

Challenge:	Recent trends in learning monolingual and multilingual sentence embeddings are based on contrastive learning (CL) among an anchor, one positive and multiple negative instances.
Approach:	They propose to leverage multiple positives to improve learning of multilingual sentence embeddings by using an anchor, one positive, and multiple negative instances.
Outcome:	The proposed approach improves retrieval, semantic similarity, and classification performance on unseen languages.

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Fenia Christopoulou, Guchun Zhang, Gerasimos Lampouras

Beyond Coarse Labels: Fine-Grained Problem Augmentation and Multi-Dimensional Feedback for Emotional Support Conversation (2025.findings-emnlp)

Challenge:	Large pre-trained language models have been applied to programming language tasks with great success, often through further pre-training of a strictly-natural language model.
Approach:	They propose to map programming language modalities into the same embedding space by separating embeddable spaces between modality and modality-relative training objectives.
Outcome:	The proposed model can be adapted and represented differently depending on which modality they belong to and to the ultimate benefit of the downstream task.

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Yuanchen Shi, Jiawang Hao, Fang Kong

Dialogue Systems & Conversational Agents

Challenge:	Existing ESC datasets often use coarse-grained problem categories, limiting models’ ability to address users’ complex, overlapping challenges.
Approach:	They propose a generalizable fine-grained problem enhancement method that augments problem types, user scenarios, and profiles, enabling the construction of richer and more diverse ESC corpora.
Outcome:	The proposed method improves both automatic and human evaluation metrics across different models.

Relation-Specific Attentions over Entity Mentions for Enhanced Document-Level Relation Extraction (2022.naacl-main)

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Jiaxin Yu, Deqing Yang, Shuyu Tian

Information Extraction & Text Mining

Challenge:	Existing document-level relation extraction methods do not distinguish between mention-level features and entity-level feature . document-based methods are more challenging because of multiple mentions of entities.
Approach:	They propose a method which selectively attentions different entity mentions with respect to candidate relations and performs relation-specific representations of entities.
Outcome:	The proposed method improves relation-specific representations of entities on two benchmark datasets.

INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025.acl-long)

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Haohang Li, Yupeng Cao, Yangyang Yu, Shashidhar Reddy Javaji, Zhiyang Deng, Yueru He, Yuechen Jiang, Zining Zhu, K.p. Subbalakshmi, Jimin Huang, Lingfei Qian, Xueqing Peng, Jordan W. Suchow, Qianqian Xie

Towards End-to-End Open Conversational Machine Reading (2023.findings-eacl)

Challenge:	Recent advances have underscored the potential of large language model (LLM)-based agents in financial decision-making.
Approach:	They propose to evaluate LLM agents using 13 different LLMs as backbone models across various market environments and tasks.
Outcome:	The proposed framework assesses the reasoning and decision-making capabilities of 13 different LLMs across various market environments and tasks.

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Sizhe Zhou, Siru Ouyang, Zhuosheng Zhang, Hai Zhao

Natural Language Interfaces

Challenge:	Existing approaches to the problem of open-retrieval conversational machine reading (OR-CMR) use two separate modules to approach the problem's two successive sub-tasks.
Approach:	They propose to model OR-CMR as a unified text-to-text task in a fully end-to end style and propose to use a text-based approach to solve the problem.
Outcome:	Experiments on the ShARC and OR-ShARC dataset show that the proposed framework can generalize to different backbone models.

KnowAgent: Knowledge-Augmented Planning for LLM-Based Agents (2025.findings-naacl)

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Yuqi Zhu, Shuofei Qiao, Yixin Ou, Shumin Deng, Shiwei Lyu, Yue Shen, Lei Liang, Jinjie Gu, Huajun Chen, Ningyu Zhang

Bridging the Code Gap: A Joint Learning Framework across Medical Coding Systems (2024.lrec-main)

Challenge:	Large Language Models (LLMs) fail to effectively guide the planning trajectories during task solving and result in planning hallucinations.
Approach:	They propose a novel approach to enhance the planning capabilities of large language models by incorporating explicit action knowledge.
Outcome:	The proposed approach can achieve comparable or superior performance to existing baselines on HotpotQA and ALFWorld.

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Geunyeong Jeong, Seokwon Jeong, Juoh Sun, Harksoo Kim

Challenge:	Existing methods for automating medical coding focus on a single coding system . however, there are still challenges to overcome in coding.
Approach:	They propose a joint learning framework for Across Medical coding systems which jointly learns different coding system through multi-task learning.
Outcome:	The proposed framework improves the performance of the MIMIC-IV ICD-9 and MIMICIV I CD-10 datasets.

Adversarial Mixing Policy for Relaxing Locally Linear Constraints in Mixup (2021.emnlp-main)

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Guang Liu, Yuzhao Mao, Huang Hailong, Gao Weiguo, Li Xuan

Information Extraction & Text Mining

Challenge:	Experiments on five text classification benchmarks and five backbone models have shown that our methods reduce the error rate over Mixup variants in a significant margin (up to 31.3%), especially in low-resource conditions (upto 17.5%).
Approach:	They propose to add a small adversarial perturbation to the mixing coefficients rather than the examples to relax locally linear constraints.
Outcome:	Experiments on five text classification benchmarks and five backbone models show that the proposed methods reduce the error rate over Mixup variants by 31.3%, especially in low-resource conditions.

Think Before You Speak: Cultivating Communication Skills of Large Language Models via Inner Monologue (2024.findings-naacl)

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Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

Transductive Learning for Textual Few-Shot Classification in API-based Embedding Models (2023.emnlp-main)

Challenge:	emergence of large language models (LLMs) improves capabilities of dialogue systems . but they lack communication skills, which make them more like information seeking tools .
Approach:	They propose to empower LLMs with communication skills through inner monologues . they use a benchmark to evaluate the dialogue generation ability of the model .
Outcome:	The proposed model outperforms the baselines in the evaluation of communication skills.

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Pierre Colombo, Victor Pellegrain, Malik Boudiaf, Myriam Tami, Victor Storchan, Ismail Ayed, Pablo Piantanida

FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data (2025.naacl-long)

Challenge:	Proprietary and closed APIs are impacting the practical applications of natural language processing.
Approach:	They propose a scenario where a pre-trained model is served through a gated API . they propose 'transductive inference' that leverages statistics of unlabelled data .
Outcome:	The proposed model performs a few-shot classification task with unlabelled data using a gated API . the proposed model can be used to perform the task with a handful of classes .

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Deren Lei, Yaxi Li, Siyao Li, Mengya Hu, Rui Xu, Ken Archer, Mingyu Wang, Emily Ching, Alex Deng

FS-Researcher: Test-Time Scaling for Long-Horizon Research Tasks with File-System-Based Agents (2026.acl-long)

Challenge:	Prior research on training grounded factuality classification models to detect hallucinations in large language models (LLMs) has relied on public natural language inference (NLI) data and synthetic data.
Approach:	They propose a method that leverages multi-hop reasoning on context graphs extracted from documents to generate complex multi-level claims without relying on LLMs to decide data labels.
Outcome:	The proposed model outperforms GPT-4-o on the LLM-Aggrefact benchmark with much smaller model size.

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Chiwei Zhu, Benfeng Xu, Mingxuan Du, Shaohan Wang, Xiaorui Wang, Zhendong Mao, Yongdong Zhang

Self-Reasoning Language Models: Unfold Hidden Reasoning Chains with Few Reasoning Catalyst (2025.findings-acl)

Challenge:	Long trajectories in deep research often exceed model context limits, compressing token budgets for both evidence collection and report writing.
Approach:	They propose a file-system-based framework that scales deep research beyond context window . a Context Builder agent acts as a librarian and a Report Writer agent composes the final report .
Outcome:	Experiments on two open-ended benchmarks show that FS-Researcher achieves state-of-the-art report quality across different backbone models.

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Hongru Wang, Deng Cai, Wanjun Zhong, Shijue Huang, Jeff Z. Pan, Zeming Liu, Kam-Fai Wong

NG-Router: Graph-Supervised Multi-Agent Collaboration for Nutrition Question Answering (2026.eacl-long)

Challenge:	Recent studies have demonstrated that inference-time scaling increases performance of Large Language Models (LLMs) in various reasoning tasks such as mathematics and complex question answering by increasing the length of Chain-of-Thought (CoT).
Approach:	They propose a model which synthesizes longer CoT data and iteratively improves performance through self-training by incorporating a few demonstration examples.
Outcome:	The proposed model achieves an average improvement of more than +2.5 points across five reasoning tasks: MMLU, GSM8K, ARC-C, HellaSwag, and BBH on two backbone models.

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Kaiwen Shi, Zheyuan Zhang, Zhengqing Yuan, Keerthiram Murugesan, Vincent Galassi, Chuxu Zhang, Yanfang Ye

Structured Data in NLP

Challenge:	Existing methods for nutrition question answering face limited reasoning capacity and contextual overload . poor dietary patterns are associated with more than 11 million deaths in 2017 .
Approach:	They propose a framework that enables supervised multi-agent collaboration for nutritional QA.
Outcome:	The proposed framework outperforms single-agent and ensemble baselines in multi-agency reasoning tasks.

GuiLoMo: Allocating Experts and Ranks for LoRA-MoE via Bilevel Optimization with GuidedSelection Vectors (2025.findings-emnlp)

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Xinrong Chen, Hengyuan Zhang, Yingmin Qiu, Xiao Liang, Ziyue Li, Guanyu Wang, Weiping Li, Tong Mo, Hayden Kwok-Hay So, Ngai Wong

Green & Sustainable NLP

Challenge:	Low-Rank Adaptation (LoRA) methods are efficient for a large language model with reduced computational costs.
Approach:	They propose a layer-wise expert numbers and ranks allocation strategy with GuidedSelection Vectors.
Outcome:	The proposed method achieves superior or comparable performance to all baselines on three backbone models.

Key ingredients for effective zero-shot cross-lingual knowledge transfer in generative tasks (2024.naacl-long)

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Nadezhda Chirkova, Vassilina Nikoulina

Comments as Natural Logic Pivots: Improve Code Generation via Comment Perspective (2024.findings-acl)

Challenge:	Existing studies have focused on zero-shot cross-lingual transfer . mBERT, mBART and mT5 provide high-quality representations for texts in various languages .
Approach:	They propose to use mBART and NLLB-200 to finetune a multilingual pretrained language model on input-output pairs in one language and use it to make task predictions for inputs in other languages.
Outcome:	The proposed approach significantly reduces generation in the wrong language with full finetuning and can be competitive in some cases.

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Yijie Chen, Yijin Liu, Fandong Meng, Yufeng Chen, Jinan Xu, Jie Zhou

Text Generation

Challenge:	Existing studies decompose complex tasks into intermediate steps by prompting strategies, such as Chain-of-Thought and its variants.
Approach:	They propose to use code comments as natural logic pivot between natural language and code language to boost the code generation ability of code LLMs.
Outcome:	The proposed method significantly improves the code pass rate on humanEval and MBPP, while the robustness of the logical comment decoding strategy is higher than the Chain-of-thoughts prompting.

Phrase-aware Unsupervised Constituency Parsing (2022.acl-long)

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Xiaotao Gu, Yikang Shen, Jiaming Shen, Jingbo Shang, Jiawei Han

Low-Resource NLP

Challenge:	Recent studies have achieved inspiring success in unsupervised grammar induction using masked language modeling (MLM) as the proxy task.
Approach:	They propose to regularize the parser with phrases extracted by an unsupervised phrase tagger to help the LM model quickly manage low-level structures.
Outcome:	The proposed method improves the identification of high-level structures using phrase-guided masking.

Empowering Backbone Models for Visual Text Generation with Input Granularity Control and Glyph-Aware Training (2024.emnlp-main)

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Wenbo Li, Guohao Li, Zhibin Lan, Xue Xu, Wanru Zhuang, Jiachen Liu, Xinyan Xiao, Jinsong Su

Visual Data in NLP

Challenge:	Existing text-to-image models struggle to generate images with legible visual texts . current models lack support for Chinese texts, misspelling, and lack of diversity .
Approach:	They propose to empower backbone models to generate visual texts in Chinese and English . they propose to augment conventional training objective with glyph-aware training losses .
Outcome:	The proposed methods can generate visual texts in English and Chinese while maintaining image generation quality.

Hallucination Diversity-Aware Active Learning for Text Summarization (2024.naacl-long)

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Yu Xia, Xu Liu, Tong Yu, Sungchul Kim, Ryan Rossi, Anup Rao, Tung Mai, Shuai Li

Responsible & Trustworthy NLP

Challenge:	Existing methods for alleviating hallucinations require costly human annotations . Existing approaches focus on a specific type of hallucinism, which limits their effectiveness .
Approach:	They propose a method to detect hallucinations from errors in semantic frame, discourse and content verifiability in LLM summarization using HAllucination Diversity-Aware Sampling.
Outcome:	The proposed framework reduces the need for costly human annotations to correct hallucinations in LLM outputs.

GhostBERT: Generate More Features with Cheap Operations for BERT (2021.acl-long)

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Zhiqi Huang, Lu Hou, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu

Retrieval-Generation Alignment for End-to-End Task-Oriented Dialogue System (2023.emnlp-main)

Challenge:	Existing studies show that some parameters in pre-trained language models can be pruned away without severe accuracy degradation.
Approach:	They propose a method which generates more features with very cheap operations from the remaining features and can be applied to unpruned BERT models to enhance their performance.
Outcome:	Empirical results on the GLUE benchmark on three backbone models (i.e., BERT, RoBERTa and ELECTRA) verify the efficacy of the proposed method.

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Weizhou Shen, Yingqi Gao, Canbin Huang, Fanqi Wan, Xiaojun Quan, Wei Bi

Natural Language Interfaces

Challenge:	generative models struggle to distinguish subtle differences among retrieved knowledge records, resulting in suboptimal quality of generated responses.
Approach:	They propose to use maximum marginal likelihood to train a perceptive retriever by utilizing signals from response generation for supervision.
Outcome:	The proposed approach improves on three task-oriented dialogue datasets using T5 and ChatGPT as the backbone models.

SimOAP: Improve Coherence and Consistency in Persona-based Dialogue Generation via Over-sampling and Post-evaluation (2023.acl-long)

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Junkai Zhou, Liang Pang, Huawei Shen, Xueqi Cheng

Dialogue Systems & Conversational Agents

Challenge:	Existing work on large-scale corpora-based language models is limited and hard to generalize to all types of pre-trained language models.
Approach:	They propose a two-stage SimOAP strategy that over-samples and post-evaluates large-scale responses from existing models and selects a good response based on multiple evaluation metrics.
Outcome:	The proposed strategy outperforms baseline and automatic evaluation strategies in both automatic and human evaluations.

Adaptive Tool Use in Large Language Models with Meta-Cognition Trigger (2025.acl-long)

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Wenjun Li, Dexun Li, Kuicai Dong, Cong Zhang, Hao Zhang, Weiwen Liu, Yasheng Wang, Ruiming Tang, Yong Liu

Attention-Enhancing Backdoor Attacks Against BERT-based Models (2023.findings-emnlp)

Challenge:	Existing research expands the tool arrays of large language models (LLMs), but the necessity of using these tools is often overlooked, leading to indiscriminate tool invocation.
Approach:	They propose a meta-cognition proxy proxy for LLMs self-assessment of their capabilities, reflecting the model’s awareness of its own limitations.
Outcome:	The proposed strategy is fine-tuned-free and costs minimal.

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Weimin Lyu, Songzhu Zheng, Lu Pang, Haibin Ling, Chao Chen

Robustness in NLP

Challenge:	Existing textual backdoor attacks focus on generating stealthy triggers or modifying model weights.
Approach:	They propose a Trojan Attention Loss (TAL) which enhances the Trojan behavior by directly manipulating attention patterns.
Outcome:	The proposed method improves the effectiveness of the backdoor attacks on different backbone models and tasks.

ReCUT: Balancing Reasoning Length and Accuracy in LLMs via Stepwise Trails and Preference Optimization (2025.findings-emnlp)

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Zhensheng Jin, Xinze Li, Yifan Ji, Chunyi Peng, Zhenghao Liu, Qi Shi, Yukun Yan, Shuo Wang, Furong Peng, Ge Yu

Skill-Based Few-Shot Selection for In-Context Learning (2023.emnlp-main)

Challenge:	Existing methods to train LLMs suffer from overthinking, leading to lengthy reasoning traces . Existing approaches to train large language models suffer from this problem .
Approach:	They propose a method to combine multiple reasoning chains for training LLMs . they use stepwise exploration and long-short switched sampling to evaluate reasoning paths .
Outcome:	The proposed method reduces reasoning lengths by approximately 30-50% . it also maintains or improves reasoning accuracy compared to baselines .

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Shengnan An, Bo Zhou, Zeqi Lin, Qiang Fu, Bei Chen, Nanning Zheng, Weizhu Chen, Jian-Guang Lou

Generative Table Pre-training Empowers Models for Tabular Prediction (2023.emnlp-main)

Challenge:	Existing methods based on pre-trained embeddings can be easily biased by surface features that are not important for the target task.
Approach:	They propose a skill-based few-shot selection method for in-context learning . it generates skill-specific descriptions for each test case and candidate example .
Outcome:	The proposed method significantly outperforms existing methods in five cross-domain semantic parsing datasets and six backbone models.

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Tianping Zhang, Shaowen Wang, Shuicheng Yan, Li Jian, Qian Liu

NeuReasoner: Towards Explainable, Controllable, and Unified Reasoning via Mixture-of-Neurons (2026.acl-long)

Challenge:	Existing methods to use table pre-training to boost tabular prediction performance remain open . a bachelor's degree earns less than 50K, and a generative LM can be used to unify tasks via one LM.
Approach:	They propose a method that leverages table pre-training to empower tabular prediction models.
Outcome:	The proposed method outperforms baseline models on 12 datasets and can be easily combined with various backbone models.

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Haonan Dong, Kehan Jiang, Haoran Ye, Wenhao Zhu, Zhaolu Kang, Guojie Song

Responsible & Trustworthy NLP

Challenge:	Existing Large Reasoning Models (LRMs) lack explainability and controllability . Existing models target isolated levels without unification, while relying on RL .
Approach:	They propose an explainable, controllable, and unified reasoning framework driven by MoN.
Outcome:	The proposed framework achieves performance gains of 27.0% while reducing token consumption by 19.6% 63.3%.

Beyond Meta-Reasoning: Metacognitive Consolidation for Self-Improving LLM Reasoning (2026.acl-long)

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Ziqing Zhuang, Linhai Zhang, Jiasheng Si, Deyu Zhou, Yulan He

Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation (2025.acl-long)

Challenge:	Existing approaches for improving LLM reasoning remain episodic and lack reusable meta-reasoning skills.
Approach:	They propose a framework that consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.
Outcome:	The proposed framework consolidates metacognitive experience from past reasoning episodes into reusable knowledge that improves future meta-reasoning.

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Deokhyung Kang, Jeonghun Cho, Yejin Jeon, Sunbin Jang, Minsub Lee, Jawoon Cho, Gary Lee

Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning (2026.findings-acl)

Challenge:	Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and widespread usage in various domains.
Approach:	They propose to train VPLs from user instructions using large language models . they propose to use retrieval-augmented fine-tuning to leverage repetitive use of subroutines .
Outcome:	The proposed method outperforms prompting-based methods for LD generation accuracy even with smaller backbone models.

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Yuyang Hu, Jiongnan Liu, Jiejun Tan, Yutao Zhu, Zhicheng Dou

FoE: Forest of Errors Makes the First Solution the Best in Large Reasoning Models (2026.acl-long)

Challenge:	Existing methods for storing and retrieving memory are limited by shallow semantic retrieval.
Approach:	They propose a memory mechanism that organizes and retrieves past experiences to support decision-making.
Outcome:	Experiments on LoCoMo and NarrativeQA show that CompassMem improves retrieval and reasoning performance across multiple backbone models.

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Kehan Jiang, Haonan Dong, Zhaolu Kang, Zhengzhou Zhu, Guojie Song

Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for LLM Agents (2026.acl-long)

Challenge:	Recent Large Reasoning Models (LRMs) have demonstrated remarkable success in complex reasoning tasks.
Approach:	They propose a self-guided efficient reasoning framework that reduces FoE by pruning subs.
Outcome:	The proposed model outperforms eight competitive baselines while reducing token consumption by 37.7% 70.4%.

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Yifei Li, Weidong Guo, Lingling Zhang, Rongman Xu, Muye Huang, Hui Liu, Lijiao Xu, Yu Xu, Jun Liu

Challenge:	Existing benchmarks and evaluation protocols focus on surface-level factual recall.
Approach:	They propose a benchmark for assessing cognitive memory under cue–trigger semantic disconnect.
Outcome:	The proposed framework reveals failures not captured by existing benchmarks.

SegDRE: A Salient Entity Guided Approach to Document-Level Relation Extraction (2026.findings-acl)

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Xing Yang, Chengxiang Tan

Information Extraction & Text Mining

Challenge:	Existing models struggle to address two major bottlenecks in Document-level Relation Extraction: extreme class imbalance and complexity of multi-hop reasoning.
Approach:	They propose a method that decouples the extraction space into dense and sparse scenarios.
Outcome:	The proposed approach yields consistent improvements over various backbone models and achieves advanced performance compared to existing enhancement methods.

Enhance Multimodal Consistency and Coherence for Text-Image Plan Generation (2025.findings-acl)

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Xiaoxin Lu, Ranran Haoran Zhang, Yusen Zhang, Rui Zhang

Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)

Challenge:	Existing studies on textual plan generation only focus on LLMs, enabling applications in robotics, virtual assistants, and instruc.
Approach:	They propose a framework that generates and refines text-image plans step-by-step . they collect a new benchmark consisting of 1,100 tasks and their text- image pair solutions covering 11 daily topics.
Outcome:	The proposed framework generates and refines text-image plans step-by-step and improves on existing models.

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Kai Guo, Harry Shomer, Shenglai Zeng, Haoyu Han, Yu Wang, Jiliang Tang

Bone Soups: A Seek-and-Soup Model Merging Approach for Controllable Multi-Objective Generation (2025.acl-long)

Challenge:	Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning.
Approach:	They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning.
Outcome:	Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models.

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Guofu Xie, Xiao Zhang, Ting Yao, Yunsheng Shi

Green & Sustainable NLP

Challenge:	Existing approaches focus on merging language models tuned on single objectives . existing approaches ignore the impacts of competing objectives on model tuning .
Approach:	They propose a model merging approach that seeks a series of backbone models and merges them according to user preferences.
Outcome:	The proposed approach exhibits strong controllability and Pareto optimality in controllable multi-objective generation.

GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment (2026.acl-long)

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Jiwei Tang, Zhicheng Zhang, Shunlong Wu, Jingheng Ye, Lichen Bai, Zitai Wang, Tingwei Lu, Lin Hai, Yiming Zhao, Hai-Tao Zheng, Hong-Gee Kim

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation (2026.acl-long)

Challenge:	Large Language Models (LLMs) have achieved remarkable performance across NLP tasks . however, in long-context scenarios, they face high computational cost and information redundancy.
Approach:	They propose an encoder-decoder context compression framework that generates a compact sequence of soft tokens for downstream tasks.
Outcome:	Experiments show that GMSA outperforms baselines on multiple long-context question answering and summarization benchmarks while maintaining low end-to-end latency.

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Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi

Machine Translation

Challenge:	Multi-domain machine translation (MDMT) is a unique challenge due to varying levels of linguistic complexity across domains.
Approach:	They propose a resource-rational framework that learns to modulate inference between intuitive and deliberate reasoning.
Outcome:	Evaluated on 15 benchmarks spanning in-domain and out-of-domain settings, as well as 3 seen and 59 unseen languages, with ablations across three backbone models, TwT-7B and Twt-14B outperform much larger SOTA reasoning models in translation quality, while reducing token usage by 32–60%.

LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark (2026.findings-acl)

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Guangyi Liu, Pengxiang Zhao, Liang Liu, Zhiming Chen, Yuxiang Chai, Yaozhen Liang, WenHao Wang, Siheng Chen, Zhengxi Lu, Shuai Ren, Hao Wang, Shibo He, Yong Liu, Wenchao Meng