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Vision-language models (VLMs) have been explored for visual programming, where they generate code to solve visual tasks. However, most prior work focuses on visual programming for productivity; it remains unclear how well current VLMs perform on education-oriented visual programming and what factors limit their performance. To bridge this gap, we introduce TURTLEAI, a benchmark containing 823 tasks curated based on real-world visual programming tasks in the Turtle Graphics domain. Solving these tasks requires models to perceive geometric patterns, reason about spatial relationships, and synthesize Python code that faithfully reproduces geometric patterns. We evaluate 20+ VLMs, including GPT-5, GPT-4o, and Qwen2-VL-72B, and find that they struggle significantly, with most achieving success rates below 30%. To address these limitations, we propose a data generation technique that requires only a small set of seed samples. Fine-tuning Qwen2-VL-72B on the resulting synthetic data yields an improvement of about 20% on real-world tasks. Our failure analysis reveals that GPT-4o struggles with spatial reasoning and precise visual replication, whereas fine-tuning primarily improves the alignment between visual reasoning and code implementation.

ACL 2026

TURTLEAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics

### 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)

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.

Large Language Models (LLMs) are increasingly deployed in human-centric applications, yet they often fail to provide substantive emotional support. While Reinforcement Learning (RL) has been utilized to enhance empathy of LLMs, existing reward models typically evaluate empathy from a single perspective, overlooking the inherently bidirectional interaction nature of empathy between the supporter and seeker as defined by Empathy Cycle theory. To address this limitation, we propose Psychology-grounded Empathetic Reward Modeling (PERM). PERM operationalizes empathy evaluation through a bidirectional decomposition: 1) Supporter perspective, assessing internal resonation and communicative expression; 2) Seeker perspective, evaluating emotional reception. Additionally, it incorporates a bystander perspective to monitor overall interaction quality. Extensive experiments on a widely-used emotional intelligence benchmark and an industrial daily conversation dataset demonstrate that PERM outperforms state-of-the-art baselines by over 10%. Furthermore, a blinded user study reveals a 70% preference for our approach, highlighting its efficacy in generating more empathetic responses.

PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models

Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing approaches still use fixed sequential agent pipelines with weak global coordination, which limits their robustness and overall performance. In this work, we propose Hierarchical Research Agent System (HiRAS), a hierarchical multi-agent framework for end-to-end paper reproduction that employs supervisory manager agents to coordinate specialised agents across fine-grained stages. We also identify limitations in the reference-free evaluation of the Paper2Code benchmark and introduce Paper2Code-Extra (P2C-Ex), a refined protocol that incorporates repository-level information and better aligns with the original reference-based metric. We conduct extensive evaluation, validating the effectiveness and robustness of our proposed methods, and observing improvements, including >10% relative performance gain above the previous state-of-the-art using open-source backbone models and significantly reduced hallucination in the evaluation. All code and data will be made publicly available.

HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution

Understanding and reasoning about entire soft-ware repositories is an essential capability for intelligent software engineering tools. While existing benchmarks such as CoSQA and CodeQA have advanced the field, they predominantly focus on small, self-contained code snippets. These setups fail to capture the complexity of real-world repositories, where effective understanding and reasoning often require navigating multiple files, understanding software architecture, and grounding answers in long-range code dependencies. In this paper, we present SWE-QA, a repository-level code question answering (QA) benchmark designed to facilitate research on automated QA systems in realistic code environments. SWE-QA involves 720 high-quality question-answer pairs spanning diverse categories, including intention understanding, cross-file reasoning, and multi-hop dependency analysis. To construct SWE-QA, we first crawled 77,100 GitHub issues from 12 popular repositories. Based on an analysis of naturally occurring developer questions extracted from these issues, we developed a two-level taxonomy of repository-level questions and constructed a set of seed questions for each category. For each category, we manually curated and validated questions and collected their corresponding answers. We evaluate six advanced LLMs on SWE-QA under various context augmentation strategies. Experimental results highlight the promise of LLMs.

SWE-QA: Can Language Models Answer Repository-level Code Questions?

Large language models (LLMs) exhibit cultural biases, yet existing benchmarks rely on closed-form, domain-specific questionnaires. We introduce FRAMENET-CULTURES, a benchmark for evaluating cultural alignment in LLMs based on Fillmore-style frame semantics. Using the EveryCulture encyclopedia, we construct a lexicon of 18 cultural frames (e.g., greeting,child-rearing) across 20 countries, treating it as a structured reference for comparison rather than a definitive representation of contemporary societies. For each frame, we prompt five major LLMs—ChatGPT-5, Gemini-2.5-Flash, Mistral-Large, Qwen-3-Max, DeepSeek-V3.2—three times to generate open-ended instantiations, which are manually annotated and binarized. We measure alignment with country- and continent-level profiles using normalized Hamming distance, and validate cultural recognizability through human evaluation of generated dialogues. Under culture-neutral prompting, outputs align most closely with European profiles, followed by Asian and American ones, indicating a consistent cross-model pattern. With culture-specific prompting, models shift toward the target regions, aligning most strongly with Africa for Ethiopia and with Asia for India. FRAMENET-CULTURES is the first open-ended benchmark for cultural alignment relying on frame semantics. Data, prompts, and annotations are publicly available at https://github.com/neda-jamshidi/FrameNet-Cultures.

FrameNet-Cultures: A Benchmark for Evaluating LLMs via Cross-Cultural Frame Semantics

While large language models (LLMs) show promise in literary translation, Shijing (The Book of Songs) serves as a rigorous yet under-explored testbed for testing their limits, given its linguistic antiquity and complex poetic constraints. Automated evaluation in this domain is currently hindered by a scarcity of multilingual resources and the inadequacy of existing metrics in capturing both semantic fidelity and aesthetic quality. In this paper, we bridge these gaps by curating a Shijing parallel corpus with line-by-line Chinese-English-German alignments, together with a fine-grained lexical knowledge base (KB) for archaic expressions. Based on these resources, we propose a hybrid evaluation framework that integrates knowledge-driven, rule-based, and LLM-as-judge metrics. Experimental results show that our framework achieves significantly higher human correlation than traditional metrics and demonstrates high statistical stability. By applying this framework to evaluate representative LLMs, we reveal that while top-tier models like Gemini-2.5-Pro and DeepSeek-3.1 show potential, achieving semantic precision and aesthetic sophistication—particularly in lower-resource directions like German—remains a persistent challenge. Our code, lexical KB, and corpus reconstruction protocols are available at https://github.com/ML-KULeuven/ShijingLLMTrans.

From Shijing to English and German: Resources and Evaluation for LLM Translation of Early Chinese Poetry

Visual token pruning has emerged as a pivotal strategy to alleviate the computational bottleneck in Multimodal Large Language Models (MLLMs), yet it frequently compromises the integrity of visual understanding in pursuit of efficiency. Existing methods face a fundamental tension: vision-centric approaches are susceptible to the attention sink phenomenon and operate in a query-agnostic manner, whereas text-guided methods often create an overly narrow focus, discarding essential background context and failing on ambiguous queries. In this paper, we propose CrisPrune, a training-free and model-agnostic method that reconciles efficiency with understanding by integrating visual saliency and text relevance. Specifically, we introduce intrinsic visual saliency with robust normalization to identify information-rich regions characterized by significant visual features. Simultaneously, we design dual-source text relevance to synergize explicit instruction alignment with implicit scene priors. Finally, we reformulate the selection process using a Determinantal Point Process (DPP) to balance token quality and spatial diversity. Extensive experiments demonstrate that CrisPrune significantly outperforms state-of-the-art methods. On LLaVA-NeXT, it achieves a 13 $\times$ decrease in FLOPs while maintaining 97% of the original performance with 94.4% of visual tokens pruned, effectively bridging the gap between efficiency and holistic understanding.

CrisPrune: Combining Contextual Relevance and Intrinsic Saliency for Efficient Visual Token Pruning in MLLMs

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.

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

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.

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PERM: Psychology-grounded Empathetic Reward Modeling for Large Language Models

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