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Speculative decoding (SD) has proven to be an effective technique for accelerating autoregressive generation in large language models (LLMs), however its application to vision-language models (VLMs) remains relatively unexplored. We propose DREAM-S, a novel SD framework designed specifically for fast and efficient decoding in VLMs. DREAM-S leverages a neural architecture search (NAS) framework with target-aware supernet training to automatically identify both the optimal interaction strategy between the draft and target models, and the most suitable draft model architecture for the underlying hardware implementation platform. DREAM-S additionally incorporates adaptive intermediate feature distillation, guided by attention entropy, to enable efficient draft training. Experiments on a range of well-established VLMs show that DREAM-S achieves up to a 3.85$\times$ speedup compared to standard decoding approaches and significantly outperforms existing SD baselines.

ACL 2026

DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
**[Continue reading...](https://drive.google.com/file/d/1jX-qGhqVSZZCIrAnJaz798pflu5Irrdn/view?usp=sharing)**

*- Colin Cherry, Google, NAACL 2025 General Chair* 

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NAACL 2025

Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

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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. 
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Complex reasoning with Large Language Models (LLMs) demands a careful balance between accuracy and computational cost. Verification is crucial for reliability but faces trade-off: robust process-based verifiers are computationally prohibitive, while fast verifiers lack precision. We introduce flexive, a unified generative verifier designed to navigate this trade-off by dynamically allocating compute between rapid fast thinking and deliberative slow thinking. A key innovation is our training strategy: we use Group Relative Policy Optimization (GRPO) to specifically enhance the reliability of the fast mode. This targeted training generalizes effectively, elevating the slow mode to state-of-the-art open-source performance. To deploy flexive, we propose the solve-detect-verify (SDV) pipeline. Moving beyond static Best-of-N ranking, SDV employs an iterative refinement process that utilizes likelihood-based probing to detect solution completion, curtailing overthinking, and leverages flexive's feedback for targeted correction. Solve-detect-verify establishes a new open-source state-of-the-art on ProcessBench, outperforming GenPRM-32B while requiring ~2.3x fewer TFLOPS and 15x less training data. On AIME 2024, the full SDV pipeline achieves 83.3% accuracy, surpassing strong baselines while using significantly fewer tokens.

 Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative Verifier

Understanding a discourse requires tracking entities and the relations that hold between them. While Large Language Models (LLMs) perform well on relational reasoning, the mechanism by which they bind entities, relations, and attributes remains unclear. We study discourse-level relational binding and show that LLMs encode it via a Cell-based Binding Representation (CBR): a low-dimensional linear subspace in which each ``cell'' corresponds to an entity--relation index pair, and bound attributes are retrieved from the corresponding cell during inference. Using controlled multi-sentence data annotated with entity and relation indices, we identify the CBR subspace by decoding these indices from attribute-token activations with Partial Least Squares regression. Across domains and two model families, the indices are linearly decodable and form a grid-like geometry in the projected space. We further find that context-specific CBR representations are related by translation vectors in activation space, enabling cross-context transfer. Finally, activation patching shows that manipulating this subspace systematically changes relational predictions and that perturbing it disrupts performance, providing causal evidence that LLMs rely on CBR for relational binding.

Cell-Based Representation of Relational Binding in Language Models

Large Language Models have shown remarkable potential as autonomous agents, but their effectiveness in knowledge-intensive tasks remains limited by passive knowledge utilization. We introduce KARL (Knowledge-Augmented Reinforcement Learning), a framework that enables LLM agents to dynamically explore structured knowledge sources through multi-turn interactions. Unlike existing retrieval-augmented approaches, KARL empowers agents to proactively decide when and what knowledge to acquire during task execution. Our framework incorporates online reinforcement learning with curiosity-driven reward shaping, explicitly incentivizing knowledge exploration while optimizing tool-use behaviors end-to-end. Extensive evaluation across six structured knowledge benchmarks demonstrates that KARL achieves state-of-the-art performance, with our Qwen2.5-14B-based agent significantly outperforming GPT-4o, Claude-4, and o4-mini on both knowledge graph and database tasks.
Source code is available at https://github.com/THUDM/KARL.

KARL: Reinforcement Learning for LLM Agents on Multi-Turn Knowledge-Intensive Agentic Tasks

Large language models (LLMs) for code rely on subword tokenizers, such as byte-pair encoding (BPE), learned from mixed natural language text and programming language code but driven by statistics rather than grammar. As a result, semantically identical code snippets can be tokenized differently depending on superficial factors such as whitespace or identifier naming. To measure the impact of this misalignment, we introduce TokDrift, a framework that applies semantic-preserving rewrite rules to create code variants differing only in tokenization. Across nine code LLMs, including large ones with over 30B parameters, even minor formatting changes can cause substantial shifts in model behavior. Layer-wise analysis shows that the issue originates in early embeddings, where subword segmentation fails to capture grammar token boundaries. Our findings identify misaligned tokenization as a hidden obstacle to reliable code understanding and generation, highlighting the need for grammar-aware tokenization for future code LLMs.

TokDrift: When LLM Speaks in Subwords but Code Speaks in Grammar

Retrieval-augmented generation (RAG) enables large language models (LLMs) to dynamically access external information, which is powerful for answering questions over previously unseen documents. Nonetheless, they struggle with high-level conceptual understanding and holistic comprehension due to limited context windows, which constrain their ability to perform deep reasoning over long-form, domain-specific content such as full-length books. To solve this problem, knowledge graphs (KGs) have been leveraged to provide entity-centric structure and hierarchical summaries, offering more structured support for reasoning. However, existing KG-based RAG solutions remain restricted to text-only inputs and fail to leverage the complementary insights provided by other modalities such as vision. On the other hand, reasoning from visual documents requires textual, visual, and spatial cues into structured, hierarchical concepts. To address this issue, we introduce a multimodal knowledge graph-based RAG that enables cross-modal reasoning for better content understanding. Our method incorporates visual cues into the construction of knowledge graphs, the retrieval phase, and the answer generation process. Experimental results across both global and fine-grained question answering tasks show that our approach consistently outperforms existing approaches on both textual and multimodal benchmarks.

MegaRAG: Multimodal Knowledge Graph-Based Retrieval Augmented Generation

Large language models have seen widespread adoption, yet they remain vulnerable to multi-turn jailbreak attacks, threatening their safe deployment. This has led to the task of training automated multi-turn attackers to probe model safety vulnerabilities. However, existing approaches typically rely on turn-level optimization, which is insufficient for learning long-term attack strategies. To bridge this gap, we formulate this task as a multi-turn reinforcement learning problem, directly optimizing the harmfulness of the final-turn response as the outcome reward. To address the sparse supervision of the outcome reward, we introduce TROJail, which employs two process rewards to evaluate the utility of intermediate prompts and integrate them into advantage estimation. These rewards (1) penalize overly harmful prompts that trigger the model’s refusal mechanism, and (2) encourage steering the semantic relevance of responses toward the targeted harmful content. Experimental results show improved attack success rates across multiple models and benchmarks, highlighting the effectiveness of our approach. The code is available at \url{https://anonymous.4open.science/r/TROJail}. \textcolor{red}{Warning: This paper contains examples of harmful content.}

TROJail: Trajectory-Level Optimization for Multi-Turn Large Language Model Jailbreaks with Process Rewards

We introduce MinerU2.5, a 1.2B-parameter document parsing vision-language model that achieves state-of-the-art recognition accuracy while maintaining exceptional computational efficiency. Our approach employs a coarse-to-fine, two-stage parsing strategy that decouples global layout analysis from local content recognition. In the first stage, the model performs efficient layout analysis on downsampled images to identify structural elements, circumventing the computational overhead of processing high-resolution inputs. In the second stage, guided by the global layout, it performs targeted content recognition on native-resolution crops extracted from the original image, preserving fine-grained details in dense text, complex formulas, and tables. To support this strategy, we developed a comprehensive data engine that generates diverse, large-scale training corpora for both pretraining and fine-tuning. Ultimately, MinerU2.5 demonstrates strong document parsing ability, achieving state-of-the-art performance on multiple benchmarks, surpassing both general-purpose and domain-specific models across various recognition tasks, while maintaining significantly lower computational overhead.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing

Misinformation on social media undermines informed decision-making and public trust. Prebunking offers a proactive complement by helping users recognize manipulation tactics before they encounter them in the wild. We present **CritiSense**, a mobile media-literacy app that builds these skills through short, interactive challenges with instant feedback. It is the **first** multilingual (supporting nine languages) and modular platform, designed for rapid updates across topics and domains. We report a usability study with 93 users: 83.9% expressed overall satisfaction and 90.1% rated the app as easy to use. Qualitative feedback indicates that **CritiSense** helps improve digital literacy skills. Overall, it provides a multilingual prebunking platform and a testbed for measuring the impact of microlearning on misinformation resilience. Over 3+ months, we have reached 300+ active users. It is freely available to all users on the Apple [App Store](https://apps.apple.com/us/app/critisense/id6749675792) and [Google Play Store](https://play.google.com/store/apps/details?id=com.critisense&hl=en). **Demo Video:** [https://shorturl.at/CDcdc](https://shorturl.at/CDcdc)

G-D1 CritiSense: Critical Digital Literacy and Resilience Against Misinformation

Large language models (LLMs) enable scalable content generation for personalized learning, but reliability and pedagogical alignment remain open challenges. We present PathBuilder, a web-based system that integrates expert-validated assessment, retrieval-augmented generation (RAG), and an LLM-as-a-Judge validation loop within a closed instructional pipeline. The system uses a 17,758-item curriculum-aligned question bank, including 1,018 expert-approved LLM-generated items, to construct diagnostic and post-tests for fine-grained learner profiling. In a real-world deployment with 179 registered users (75 matched learners), PathBuilder achieved a mean absolute gain of 37.9 percentage points, Hake’s normalized gain of 0.760, and a large effect size (Cohen’s d = 0.98). A controlled study of the judge mechanism showed consistent high-quality instructional outputs with a 100\% threshold pass rate. These results demonstrate that structured curriculum alignment combined with retrieval grounding and automated validation can support reliable LLM-based personalization in deployed learning systems. A live demonstration of PathBuilder is available at https://demo.pathbuilderedu.com.

G-D2 PathBuilder: A Quality-Controlled LLM System for Personalized Learning Pathways

Material claims in papers, patents, etc., often involve physical feasibility (e.g., stability under conditions, property consistency), not just textual feasibility. Yet most claim verifiers operate over language, therefore producing ungrounded judgments. On the other hand, direct
first-principles verification (e.g., density functional theory, DFT) is inflexible and hard to invoke from underspecified free-form claims.
Therefore, we introduce **PhyVer**, a **phy**sics-grounded material claim **ver**ification system that bridges this gap by translating claims
into multi-fidelity physical evidence and interpretable verdicts. To support human-in-the-loop inspection, we present an interactive web interface that visualizes the instantiated structure, optimization trajectories, DFT summaries, and the final decision. On expert-labeled claims, **PhyVer** improves agreement with experts over text-only GPT-5.1, reducing MAE from 1.54 to 1.20 and Signed MAE from
0.95 to 0.82, and increasing Accuracy@±1 from 50% to 70%.

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