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In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternating between local (sliding-window) and global (compression/selective) attention across layers, rather than using fixed patterns, enables more effective propagation of long-range dependencies and substantially boosts performance on long-sequence tasks. Meanwhile, we further refine NSA’s branches with Latent Attention that the sliding-window branch is enhanced with Multi-head Latent Attention (MLA) while compression and selective branches adopt Group-head Latent Attention (GLA). These changes reduce KV-cache memory by 50% versus NSA while improving the model&#39;s common-sense reasoning and long-text understanding capabilities. Experiments on models from 340M to 1.3B parameters (trained on 15B and 100B tokens) show our method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks.

ACL 2026

Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

In this work, we conduct a systematic analysis of Native Sparse Attention (NSA) and propose targeted improvements that enhance long-context modeling. A key insight is that alternating between local (sliding-window) and global (compression/selective) attention across layers, rather than using fixed patterns, enables more effective propagation of long-range dependencies and substantially boosts performance on long-sequence tasks. Meanwhile, we further refine NSA’s branches with Latent Attention that the sliding-window branch is enhanced with Multi-head Latent Attention (MLA) while compression and selective branches adopt Group-head Latent Attention (GLA). These changes reduce KV-cache memory by 50% versus NSA while improving the model's common-sense reasoning and long-text understanding capabilities. Experiments on models from 340M to 1.3B parameters (trained on 15B and 100B tokens) show our method matches or exceeds full attention and native sparse attention in both common-sense reasoning and long-context understanding tasks.

technical paper

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Recent advances in reinforcement learning (RL) have improved the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs). However, the widely used Group Relative Policy Optimization (GRPO) consistently suffers from entropy collapse, causing the policy to converge prematurely and lose diversity. Existing exploration methods introduce additional bias or variance during exploration, making it difficult to maintain optimization stability. We propose Unified Entropy Control for Reinforcement Learning (UEC-RL), a framework that provides targeted mechanisms for exploration and stabilization. UEC-RL activates more exploration on difficult prompts to search for potential and valuable reasoning trajectories. In parallel, a stabilizer prevents entropy from growing uncontrollably, thereby keeping training stable as the model consolidates reliable behaviors. Together, these components expand the search space when needed while maintaining robust optimization throughout training. Experiments on both LLM and VLM reasoning tasks show consistent gains over RL baselines on both Pass@1 and Pass@$k$. On Geometry3K, UEC-RL achieves a 37.9\% relative improvement over GRPO, indicating that it sustains effective exploration without compromising convergence and underscoring UEC-RL as a key for scaling RL-based reasoning in large models. Our code is available at \url{https://github.com/597358816/UEC-RL}.

Targeted Exploration via Unified Entropy Control for Reinforcement Learning

Egocentric AI agents, such as smart glasses, rely on pointing gestures to resolve referential ambiguities in natural language commands. However, despite advancements in Multimodal Large Language Models (MLLMs), current systems often fail to precisely ground the spatial semantics of pointing. Instead, they rely on spurious correlations with visual proximity or object saliency—a phenomenon we term ``Referential Hallucination.'' To address this gap, we introduce EgoPoint-Bench, a comprehensive question-answering benchmark designed to evaluate and enhance multimodal pointing reasoning in egocentric views. Comprising over 11k high-fidelity simulated and real-world samples, the benchmark spans five evaluation dimensions and three levels of referential complexity. Extensive experiments demonstrate that while state-of-the-art proprietary and open-source models struggle with egocentric pointing, models fine-tuned on our synthetic data achieve significant performance gains and robust Sim-to-Real generalization. This work highlights the importance of spatially-aware supervision and offers a scalable path toward precise egocentric AI assistants. The project website is available at \url{https://guyyyug.github.io/EgoPoint-Bench/}.

Do MLLMs Understand Pointing? Benchmarking and Enhancing Referential Reasoning in Egocentric Vision

ArkTS is the primary programming language for Huawei's HarmonyOS ecosystem, which has expanded across smartphones, tablets, and IoT devices. While large language models have demonstrated strong code generation capabilities for mainstream languages, their performance on ArkTS remains largely unexplored. To address this gap, we introduce ArkRepoBench, the first repository-level code completion benchmark for ArkTS to our knowledge, 7,519 samples from 20 official HarmonyOS repositories. The benchmark covers multiple difficulty levels and further categorizes completion instances into Single-File, Cross-File Independent, and Cross-File Dependent settings based on dependency analysis, distinguishing the mere presence of cross-file context from its actual necessity. Our experiments show that: (1) ArkTS completion consistently underperforms mainstream languages under our experimental settings, suggesting language-specific challenges associated with this emerging language; (2) open-source 7B models achieve performance comparable to close-source models; (3) cross-file context is a double-edged sword, with sparse retrieval(Jaccard) outperforming dense methods on ArkTS; and (4) HarmonyOS API documentation consistently improves performance, suggesting the benefits of domain-specific enhancements in low-resource settings.

ArkRepoBench: A Repository-Level Code Completion Benchmark for HarmonyOS Development

Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potential for such predictive tasks, yet existing approaches mainly focus on enhancing medical knowledge through distillation or RAG while relying on the model's internal ability to interpret contextual information. In this work, we present ReMedi (Reasoner for Medical Clinical Prediction), a framework for improving clinical outcome prediction from EHR. ReMedi generates rationale–answer pairs using a challenging sample regeneration mechanism for complex clinical questions, which leverages ground-truth answers as hints to enhance reasoning for further fine-tuning and preference tuning. ReMedi integrates ground-truth outcome guidance into the preference data construction loop, regenerating rationale-answer variants. By tuning on these rationale-answer pairs, the model improves its predictive performance. Experiments on multiple EHR prediction tasks demonstrate substantial gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi's effectiveness in real-world clinical prediction.

ReMedi: Reasoner for Medical Clinical Prediction

Cross-lingual transfer in NLP is often hindered by the "script barrier" where differences in writing systems inhibit transfer learning between languages. Transliteration, the process of converting the script, has emerged as a powerful technique to bridge this gap by increasing lexical overlap. This paper provides a comprehensive survey of the application of transliteration in cross-lingual NLP. We present a taxonomy of key motivations to utilize transliterations in language models, and also provide an overview of different approaches of incorporating transliterations as input. We analyze the evolution and effectiveness of these methods, discussing the critical trade-offs involved, and contextualize their need in modern LLMs. The review explores various contexts how transliteration is beneficial, including handling code-mixed text, leveraging language family relatedness, and pragmatic gains in inference efficiency. Based on this analysis, we provide concrete recommendations for researchers on selecting and implementing the most appropriate transliteration strategy based on their specific language, task, and resource constraints.

Scripts Through Time: A Survey of the Evolving Role of Transliteration in NLP

We introduce Comic Visual Question Answering (\textbf{ComicVQA}), a comics-based benchmark for evaluating MLLMs on visual reasoning. ComicVQA comprises of (i) \textbf{Missing Panel Prediction}, testing fine-grained visual grounding and (ii) \textbf{Panel Sorting}, which evaluates sequential narrative understanding. Proprietary models achieve up to 62.6% on Missing Panel Prediction and 46.4% on Panel Sorting, whereas open-source models reach only 47.7% and 26.9%, respectively. In contrast, human annotators achieve over 83% accuracy on both tasks, revealing a large gap between current models and human-level multimodal understanding in comics. Through controlled ordering ablations and a detailed error taxonomy, we show that current MLLMs rely primarily on coarse temporal cues and struggle with fine-grained visual reasoning. These findings demonstrate ComicVQA as a diagnostic benchmark for advancing multimodal visual reasoning in comics.

ComicVQA: A Benchmark for Visual Reasoning in Multimodal LLMs

Understanding climate change requires reasoning over complex causal networks. Yet, existing causal discovery datasets predominantly capture explicit, direct causal relations. We introduce ClimateCause, a manually expert-annotated dataset of higher-order causal structures from science-for-policy climate reports, including implicit and nested causality. Cause-effect expressions are normalized and disentangled into individual causal relations to facilitate graph construction, with unique annotations for cause-effect correlation, relation type, and spatiotemporal context. We further demonstrate ClimateCause's value for quantifying readability based on the semantic complexity of causal graphs underlying a statement. Finally, large language model benchmarking on correlation inference and causal chain reasoning highlights the latter as a key challenge.

ClimateCause: Complex and Implicit Causal Structures in Climate Reports

Steerable pluralistic alignment aims to enable large language models (LLMs) to reliably adhere to diverse and potentially conflicting human values, particularly when target objectives involve multi-dimensional, compositional values. Current methods largely rely on prompt engineering or reasoning-time guidance, which often results in fragile and non‑persistent control once prompts are perturbed or omitted. In this work, we study value‑controllable alignment through discrete condition vectors and propose Verifiable‑reward‑Routed LoRA—a parameter‑efficient mixture‑of‑experts LoRA framework enhanced with conditioned gating. This gating mechanism dynamically directs the flow among multiple LoRA experts based on an input value or moral vector. To ensure that such routing leads to semantically compliant outputs, we formulate post‑training as a reinforcement learning problem with verifiable rewards. We further introduce a conditional consistency reward, computed by an external model‑based verifier implemented as a lightweight discriminator, and optimize the adapter parameters using GRPO. Experiments on the Touché23‑valueEval (value alignment) and MIC (moral alignment) benchmarks, using two 8‑billion‑parameter backbones, show that our method consistently outperforms prompt‑based steering and multi‑task PEFT baselines. It attains the highest overall controllability across micro‑F1, macro‑F1, and Jaccard metrics—a conclusion further reinforced by human pairwise evaluations.

V-RoLoRA: RLVR-Driven MoE Routing for Steerable Pluralistic Alignment

The complexity of recent natural language classification models led to interest in developing methods for improving the performance of explainable models (e.g. Logistic Regression). Existing methods focus on clustering word embeddings to discover fine-grained contextual features that can be used to train a linear model. While those methods help reduce the gap in performance between black-box models and explainable models, they are based on discovering a large number of features, and this affects interpretability. In this work, we propose a model that leverages Large Language Models (LLMs) and clustering algorithms to discover a compact set of interpretable features. The proposed model first uses GPT-4o mini to extract rationales (i.e. phrases which explain an item's label) from labeled text, and then clusters those rationales to obtain a compact, interpretable feature space. Across 3 Style Classification tasks, the resulting features achieve comparable performance to word-cluster baselines on most tasks, while reducing the number of features by 85–99%. These results highlight the potential of LLMs to improve the compactness of explainable AI models.

LLM-induced Rationales for More Compact Explainable Style Classification Models

Mobile GUI agents show promise in automating tasks but face significant generalization challenges in long-tail scenarios. While learning from few-shot demonstrations is an emerging solution, its progress is hindered by two critical gaps: the lack of a comprehensive benchmark for systematic evaluation on mobile devices, and the absence of a systematic framework designed to learn from demonstrations in this domain. To address these gaps, we introduce \textbf{LearnGUI}, the first comprehensive benchmark designed for studying demonstration-based learning in mobile agents, comprising 2,252 offline and 101 online tasks. We further develop \textbf{LearnAct}, a modular agent framework engineered to systematically extract, retrieve, and leverage knowledge from visual demonstrations. Extensive evaluations across six backbone models validate our approach: LearnAct achieves dramatic improvements for general-purpose models (e.g., Gemini-2.5-Pro: 38.5\%→58.9\%) and specialized models alike (e.g., UI-TARS-7B-SFT's online success rate: 18.1\%→32.8\%), demonstrating consistent gains across model architectures. Our work provides a robust benchmark and a systematic framework, paving the way for more adaptable and practical mobile agents. Our code and data are publicly available at \url{https://lgy0404.github.io/LearnAct/}.

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Targeted Exploration via Unified Entropy Control for Reinforcement Learning

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