United States

We propose a corpus-dependent alternative to byte encoding that learns fixed-length atomic codes for characters directly from text, which we refer to as Latom (Learned Atom-based Encoding).
We instantiate this framework by training an HMM on N-repeated character sequences to estimate &quot;atom&quot; posteriors, followed by a Hungarian assignment yielding a globally optimal one-to-one character-code mapping.
Across 14 languages, the encodings improve intrinsic metrics, including token counts after subword tokenization and bigram perplexity, with appropriate code lengths.
On Amazon Reviews in six languages, Latom improves text classification accuracy and reduces decoding errors in language model generation.
Overall, these results demonstrate that character encodings can be learned from corpus statistics while remaining reversible and compatible with standard tokenization pipelines.

ACL 2026

Corpus-Dependent Subcharacter Encoding via HMM-Guided Code Assignment

### 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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ACL 2025

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

We propose a corpus-dependent alternative to byte encoding that learns fixed-length atomic codes for characters directly from text, which we refer to as Latom (Learned Atom-based Encoding).
We instantiate this framework by training an HMM on N-repeated character sequences to estimate "atom" posteriors, followed by a Hungarian assignment yielding a globally optimal one-to-one character-code mapping.
Across 14 languages, the encodings improve intrinsic metrics, including token counts after subword tokenization and bigram perplexity, with appropriate code lengths.
On Amazon Reviews in six languages, Latom improves text classification accuracy and reduces decoding errors in language model generation.
Overall, these results demonstrate that character encodings can be learned from corpus statistics while remaining reversible and compatible with standard tokenization pipelines.

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 Model (LLM) based Chinese Grammatical Error Correction (CGEC) systems face two critical challenges: general-purpose models lack specialized linguistic priors for subtle grammatical distinctions, and Supervised Fine-Tuning (SFT) with Maximum Likelihood Estimation fails to optimize for precision-focused metrics, leading to systematic over-correction. We propose CSRP, a three-stage framework that progressively builds correction capability through Continual Pre-training (CPT) on 5.9M balanced samples to internalize domain knowledge, Chain-of-Thought SFT with explicit error reasoning for diagnostic transparency, and Group Relative Policy Optimization with a novel Efficiency-Aware Reward that explicitly penalizes unnecessary edits. On the NACGEC benchmark, CSRP achieves state-of-the-art performance with 50.99 $F_{0.5}$ and 57.17 precision, substantially outperforming previous best results while effectively mitigating the over-correction bias inherent in MLE-trained models. Our method also advances CSCD spelling correction to 59.61 F1, surpassing GPT-4 by 5.20 points. Comprehensive ablation studies demonstrate that the RL alignment stage contributes a 8\% relative gain over the SFT baseline, and that this gain is orthogonal to the contribution of large-scale CPT, validating that explicit optimization for edit efficiency is essential for high-quality grammatical error correction. Our code is available at https://github.com/TW-NLP/ChineseErrorCorrector.

CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

The transformer is the most popular neural architecture for language modeling. The cornerstone of the transformer is its global attention mechanism, which lets the model aggregate information from all preceding tokens before generating the next token. One common variant of attention is called local attention, which restricts each token to aggregating information from a bounded window of predecessors, reducing the quadratic cost of global attention to linear. Although this restriction is usually motivated by efficiency, it has also been found to improve model quality, a phenomenon that has so far lacked a satisfactory explanation. We provide a formal account of this phenomenon in terms of recognizer expressivity. It has been shown that fixed-precision transformers with global attention correspond to a fragment of linear temporal logic containing a single past operator. We additionally prove that adding local attention introduces a second temporal operator, strictly enlarging the class of recognizable regular languages. Moreover, global and local attention are expressively complementary: neither subsumes the other, and combining them yields the richest fragment. Experiments on formal language recognition and natural language modeling corroborate the theory, showing that hybrid global--local transformers outperform their global-only counterparts.

Characterizing the Expressivity of Local Attention in Transformers

Laughter is a complex social signal that conveys communicative intent beyond amusement. While prior work has focused on isolated laughter analysis tasks, a comprehensive understanding of laughter in real-world scenarios remains underexplored. We introduce SMILE-Next, a dataset for real-world laughter understanding with multimodal textual representations and question–answer annotations across three tasks: laughter detection, laughter type classification, and laughter reasoning. Building on this dataset, we propose a laughter expert LLM that leverages disentangled multimodal textual cues, together with a Mixture-of-Laugh-Experts framework and laughter-specific self-instruction for task-adaptive specialization. Experimental results show that the combination of our proposed components substantially outperforms multimodal LLM baselines, advancing robust real-world laughter understanding.

SMILE-Next: Teaching Large Language Models to Detect, Classify, and Reason about Laughter

Reinforcement learning (RL) has catalyzed the emergence of Large Reasoning Models (LRMs) that have pushed reasoning capabilities to new heights. While their performance has garnered significant excitement, exploring the internal mechanisms driving these behaviors has become an equally critical research frontier. This paper provides a comprehensive survey of the mechanistic understanding of LRMs, organizing recent findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. By synthesizing these insights, we aim to bridge the gap between black-box performance and mechanistic transparency. Finally, we discuss under-explored challenges to outline a roadmap for future mechanistic studies, including the need for applied interpretability, improved methodologies, and a unified theoretical framework.

Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures

The rapid evolution of text-to-image generation has blurred the perceptual boundary between natural and synthetic imagery. However, it remains questionable whether the statistical structure of generated visual content mirrors the information density of the physical visual world. Drawing upon principles from statisti-cal linguistics, this study investigates the visual language of generative models through the lens of Zipfian dynamics. By analyzing a large-scale corpus of real and synthetic images, we uncover a fundamental divergence between visual syntax and semantics. We find that while generative models have successfully replicated the low-level physics of light, their high-level texture vocabulary exhibits distinct statistical signatures. Our analysis reveals a spectrum of entropy, identifying architectural fingerprints unique to each model. Furthermore, we investigate the relationship between generated images and prompt complexity, and find that increasing the semantic specificity of text prompts systematically degrades the statistical realism of the generated output.

A Zipfian Analysis of Visual Token Distributions for AI-Generated Images

Vision foundation models produce features that generalize across visual domains without fine-tuning, yet naively transferring labels through these feature spaces fails under large distribution shifts.
We propose SAOT Semantically Aware Optimal Transport), which learns a transport cost within a fused unbalanced optimal transport formulation for dense label transfer from frozen vision transformer features to new domains.
\method{} combines a learnable appearance metric with semantic class-prototype priors, unbalanced transport for partial matching under distribution shift, and a block-sparse solver for tractable inference.
We pair this with a two-stage decoder: an MLP trained on SAOT pseudo-labels, then refined via EMA-teacher self-training with class-balanced sampling.
On GTA5$\to$Cityscapes with frozen DINOv2 ViT-L/14 features, SAOT+Decoder reaches 25.7\% mIoU, a \textbf{3.8$\times$} improvement over nearest-neighbor transfer (6.7\%), without any backbone adaptation.
Per-class results show large gains on spatially coherent classes (road 90.3\%, car 76.2\%, building 71.5\%), demonstrating that learned semantic transport costs capture domain-invariant structure even under severe synthetic-to-real shifts. On VOC train$\to$val with frozen ViT-B/16 features, the full pipeline reaches 47.5\% mIoU, indicating that the approach extends beyond synthetic-to-real adaptation.

Semantically Aware Optimal Transport for Dense Label Transfer

Vision-language model (VLM)-powered agents are increasingly enabling new forms of automation across various human tasks. While prior work has primarily focused on well-defined problems with explicit goals, the capabilities of agents in creative graphic design, where goals are inherently open-ended and subjective, remain largely underexplored. To bridge this gap, we introduce GraphicWeaver, a planning benchmark for graphic design comprising 1,079 diverse user queries and associated images spanning four design categories. Comprehensive experiments with six models reveal that current VLM-based agents struggle to handle such complex planning tasks, which require taking into account both explicit design constraints specified in queries and implicit commonsense design principles. We attribute these failures to challenges in (1) retrieving appropriate parameters for tool usage, (2) understanding spatial relationships across design components, and (3) coordinating dependencies across agents. We envision GraphicWeaver as a challenging yet valuable testbed for advancing VLM agent planning in creative design contexts.

GraphicWeaver: Benchmarking Agentic Planning for Graphic Design Generation

Spatial embodied intelligence under partial observability requires agents to actively acquire missing information rather than passively consume complete observations. While multimodal foundation models excel at passive perception and reasoning, their ability to support self-directed exploration for building and maintaining coherent spatial beliefs remains understudied. We propose \tos, defined as an agent’s ability to construct, revise, and exploit a spatial belief through active exploration under partial observability. We implement \tos as a benchmark in textual and visual environments, where the goal is curiosity-driven exploration to build a complete and accurate spatial belief. A key innovation is spatial belief probing, which prompts agents to externalize their internal spatial belief as a cognitive map at each step, enabling direct measurement of belief quality. Evaluating state-of-the-art models on downstream tasks reveals three bottlenecks: (1) the \textbf{Active-Passive Gap}, where performance drops when agents must autonomously gather information (e.g., \textsc{GPT-5.2}: $57.1{\to}46.0$); (2) \textbf{Inefficiency}, with redundant and unsystematic exploration; and (3) unstable global beliefs, where spatial knowledge degrades over time. A false-belief paradigm further reveals \textbf{Belief Inertia}, especially severe in vision-based models

Theory of Space: Active Exploration and Spatial Belief Probing for Foundation Models

Vision--Language Models (VLMs) have shown strong performance on multimodal reasoning tasks, yet most evaluations focus on short videos and assume unconstrained computational resources. In industrial settings such as pharmaceutical content understanding, practitioners must process long-form videos under strict GPU, latency, and cost constraints, where many existing approaches fail to scale. In this work, we present an industrial GenAI framework that processes over 200{,}000 PDFs, 25{,}326 videos across eight formats (e.g., MP4, M4V, etc.), and 888 multilingual audio files in more than 20 languages. Our study makes three contributions: (i) an industrial large-scale architecture for multimodal reasoning in pharmaceutical domains; (ii) empirical analysis of over 40 VLMs on two leading benchmarks (Video-MME and MMBench) and proprietary dataset of 25{,}326 videos across 14 disease areas; and (iii) four findings relevant to long-form video reasoning: the role of multimodality, attention mechanism trade-offs, temporal reasoning limits, and challenges of video splitting under GPU constraints. Results show 3--8$\times$ efficiency gains with SDPA attention on commodity GPUs, multimodality improving up to 8/12 task domains (especially length-dependent tasks), and clear bottlenecks in temporal alignment and keyframe detection across open- and closed-source VLMs. Rather than proposing a new "A+B" model, this paper characterizes practical limits, trade-offs, and failure patterns of current VLMs under realistic deployment constraints, and provide actionable guidance for both researchers and practitioners designing scalable multimodal systems for long-form video understanding in industrial domains.

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CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

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CSRP: Chain-of-Thought Reasoning for Chinese Text Correction via Reinforcement Learning with Efficiency-Aware Rewards

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Retrofitting Large Language Models with Dynamic Tokenization

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