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Interleaved-Modal Chain-of-Thought (I-MCoT) advances vision-language reasoning, such as Visual Question Answering (VQA). This paradigm integrates specially selected visual evidence from the input image into the context of Vision-Language Models (VLMs), enabling them to ground their reasoning logic in these details. Accordingly, the efficacy of an I-MCoT framework relies on identifying *what* to see (evidence selection) and *when* to see it (triggering of insertions). However, existing methods fall short in both aspects. First, for selection, they rely on attention signals, which are unreliable---particularly under severe granularity imbalance between the brief textual query and the informative image. Second, for triggering, they adopt static triggers, which fail to capture the VLMs&#39; dynamic needs for visual evidence. To this end, we propose a novel I-MCoT framework, **A**ctive **I**nformation-driven **M**ulti-modal **C**hain-**o**f-**T**hought (**AIM-CoT**), which aims to improve both evidence selection and insertion triggering via: (1) **Context-enhanced Attention-map Generation (CAG)** to mitigate granularity imbalance via textual context enhancement; (2) **Active Visual Probing (AVP)** to proactively select the most informative evidence via an information foraging process; and (3) **Dynamic Attention-shift Trigger (DAT)** to precisely activate insertions when VLM&#39;s attention shifts from text to visual context. Experiments across three benchmarks and four backbones demonstrate AIM-CoT&#39;s consistent superiority. Our code is available at https://anonymous.4open.science/r/AIMCoT.

ACL 2026

AIM-CoT: Active Information-driven Multimodal Chain-of-Thought for Vision-Language Reasoning

Interleaved-Modal Chain-of-Thought (I-MCoT) advances vision-language reasoning, such as Visual Question Answering (VQA). This paradigm integrates specially selected visual evidence from the input image into the context of Vision-Language Models (VLMs), enabling them to ground their reasoning logic in these details. Accordingly, the efficacy of an I-MCoT framework relies on identifying *what* to see (evidence selection) and *when* to see it (triggering of insertions). However, existing methods fall short in both aspects. First, for selection, they rely on attention signals, which are unreliable---particularly under severe granularity imbalance between the brief textual query and the informative image. Second, for triggering, they adopt static triggers, which fail to capture the VLMs' dynamic needs for visual evidence. To this end, we propose a novel I-MCoT framework, **A**ctive **I**nformation-driven **M**ulti-modal **C**hain-**o**f-**T**hought (**AIM-CoT**), which aims to improve both evidence selection and insertion triggering via: (1) **Context-enhanced Attention-map Generation (CAG)** to mitigate granularity imbalance via textual context enhancement; (2) **Active Visual Probing (AVP)** to proactively select the most informative evidence via an information foraging process; and (3) **Dynamic Attention-shift Trigger (DAT)** to precisely activate insertions when VLM's attention shifts from text to visual context. Experiments across three benchmarks and four backbones demonstrate AIM-CoT's consistent superiority. Our code is available at https://anonymous.4open.science/r/AIMCoT.

poster

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. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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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.

Medical Multimodal Large Language Models (Med-MLLMs) require egocentric clinical intent understanding for real-world deployment, yet existing benchmarks fail to evaluate this critical capability. We introduce MedGaze-Bench, the first benchmark leveraging clinician gaze as a Cognitive Cursor to assess intent understanding across surgery, emergency simulation, and diagnostic interpretation. Our benchmark addresses three fundamental challenges: visual homogeneity of anatomical structures, strict temporal-causal dependencies in clinical workflows, and implicit adherence to safety protocols. We propose a Three-Dimensional Clinical Intent Framework evaluating: (1) Spatial Intent—discriminating precise targets amid visual noise, (2) Temporal Intent—inferring causal rationale through retrospective and prospective reasoning, and (3) Standard Intent—verifying protocol compliance through safety checks. Beyond accuracy metrics, we introduce Trap QA mechanisms to stress-test clinical reliability by penalizing hallucinations and cognitive sycophancy. Experiments reveal current MLLMs struggle with egocentric intent due to over-reliance on global features, leading to fabricated observations and uncritical acceptance of invalid instructions.

Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models

Large language models (LLMs) have demonstrated strong capabilities in complex reasoning tasks, yet their autoregressive generation paradigm makes it computationally expensive to explore diverse reasoning paths. In contrast, diffusion language models (DLMs) adopt a parallel, non-autoregressive generation mechanism that enables the efficient production of diverse candidate outputs. Motivated by this complementarity, we explore a collaborative reasoning framework that combines diffusion-based generation with autoregressive evaluation. Specifically, we leverage DLMs to efficiently generate diverse intermediate reasoning thoughts, and employ LLMs as evaluators to assess and select candidates based on their plausibility and correctness. By decoupling proposal generation from evaluation, our framework exploits the strengths of both models: efficient exploration from diffusion models and causally grounded assessment from autoregressive models, which naturally aligns with the divergent-convergent thinking framework in cognitive psychology. Experiments across various mathematical and logical reasoning benchmarks demonstrate that our framework improves inference efficiency while maintaining competitive or superior reasoning accuracy, laying the groundwork for building efficient reasoning architectures. Our code is open-source at https://anonymous.4open.science/r/Diffuse-Thinking-EC60.

Diffuse Thinking: Exploring Diffusion Language Models as Efficient Thought Proposers for Reasoning

Graph structures are increasingly adopted in dialog memory systems, motivated by their success in retrieval-augmented generation and the associative nature of human memory. However, empirical findings on their effectiveness remain inconsistent, making it unclear which design choices truly matter. In this work, we present an experimental and system-oriented analysis of long-term dialog memory architectures. We formalize a unified framework that decomposes dialog memory systems into core components and supports both graph-based and non-graph approaches. Under this framework, we conduct controlled, stage-wise experiments on LongMemEval and HaluMem, comparing common design choices in memory representation, organization and maintenance, as well as indexing and retrieval. Our results show that underlying implementation details—often insufficiently specified in prior work—have a substantial impact on performance, and we identify stable, reliable strong baselines to support fair comparison and practical deployment. Code are available at https://github.com/AvatarMemory/UnifiedMem.

Does Memory Need Graphs? A Unified Framework and Empirical Analysis for Long-Term Dialog Memory

We introduce Parallel Coordinated Reasoning (PaCoRe), a training-and-inference framework designed to overcome a central limitation of contemporary language models: their inability to scale test-time compute (TTC) far beyond sequential reasoning under a fixed context window. PaCoRe departs from the traditional sequential paradigm by driving TTC through massive parallel exploration coordinated via a message-passing architecture in multiple rounds. Each round launches many parallel reasoning trajectories, compacts their findings into context-bounded messages, and synthesizes these messages to guide the next round and ultimately produce the final answer. Trained end-to-end with large-scale, outcome-based reinforcement learning, the model masters the synthesis abilities required by PaCoRe and scales to multi-million-token effective TTC without exceeding context limits. The approach yields strong improvements across diverse domains and notably pushes reasoning beyond frontier systems in mathematics: an 8B model reaches 94.5% on HMMT 2025, surpassing GPT-5’s 93.2% by scaling effective TTC to roughly two million tokens. We open-source model checkpoints, training data, and the full inference pipeline to accelerate follow-up work.

PaCoRe: Learning to Scale Test-Time Compute with Parallel Coordinated Reasoning

Chain-of-Thought (CoT) reasoning has driven recent gains of large language models (LLMs) on reasoning-intensive tasks by externalizing intermediate steps. However, excessive or redundant reasoning --- so-called overthinking --- can increase inference costs and lead LLMs toward incorrect conclusions. In this paper, we present $\textbf{REFRAIN}$ ($\underline{REF}$lective-$\underline{R}$edundancy for $\underline{A}$daptive $\underline{IN}$ference), a training-free framework that adaptively determines when to stop reasoning to mitigate overthinking. REFRAIN integrates a two-stage stop discriminator to identify reflective yet redundant reasoning and a sliding-window Upper Confidence Bound (SW-UCB) multi-armed bandit controller to dynamically adjust stopping thresholds according to problem difficulty without supervision or fine-tuning. Across four representative benchmarks and two model families, REFRAIN reduces token usage by 20-55% while maintaining or improving accuracy compared to standard CoT prompting. Extensive ablation and robustness analyses demonstrate its stability across models, scorers, and prompt variations. In summary, our findings highlight when-to-stop as a new and practical axis of test-time scaling --- enabling models to reason not just more, but just enough.

Stop When Enough: Adaptive Early-Stopping for Chain-of-Thought Reasoning

Large Language Models (LLMs) have revolutionized various fields, yet their training efficiency is heavily reliant on effective data curation. While data selection has been widely studied, the strategic data organization for enhanced training remains an underexplored area, particularly since current LLMs are often trained for only one or a few epochs. This paper systematically explores the influence of data organization on LLM training by reusing pre-computed sample-level scores originally generated for data efficiency, thereby incurring minimal additional computational overhead. We identify and formalize four key guidances for optimizing data organization: Boundary Sharpening, Cyclic Scheduling, Curriculum Continuity, and Local Diversity. Guided by them, we introduce two novel data ordering methods termed STR and SAW. Extensive experiments across different model scales and data sizes, encompassing both pre-training and SFT stages, validate the effectiveness of our summarized guidances. They also demonstrate the robustness of our proposed data ordering methods in enhancing the stability and performance of LLM training.

 Demystifying Data Organization for Enhanced LLM Training

Large language models (LLMs) trained on massive text corpora may inadvertently memorize sensitive or copyrighted content, motivating the need for more targeted unlearning. Selective LLM unlearning focuses on identifying token-level or span-level unlearning targets within a text, rather than treating entire sequences as unlearning targets. However, many existing selective approaches depend on external supervision to identify unlearning targets, which may misalign unlearning objectives with the model's internal behavior. In this paper, we propose a selective span-level unlearning method that is grounded entirely in model-intrinsic information. Our method first estimates token-level importance scores by contrasting gradient information induced by forget and retain datasets, identifying tokens that disproportionately contribute to information targeted for unlearning. These token-level importance scores are then used as anchors to identify coherent span-level unlearning targets via a self-consistency–based generation process, allowing the model to determine stable spans based on its own predictions. Experiments on two LLM unlearning benchmarks show that our approach achieves comparable unlearning performance while substantially better preserving retained knowledge.

Selective Span-Level Unlearning for Large Language Models

Process Reward Models (PRMs) have emerged as a powerful tool for providing step-level feedback when evaluating the reasoning of Large Language Models (LLMs), which frequently produce chains of thought (CoTs) containing errors even when the final answer is correct. However, existing PRM datasets remain expensive to construct, prone to annotation errors, and predominantly limited to the mathematical domain.
This work introduces a novel and scalable approach to PRM dataset generation based on planning logical problems expressed in the Planning Domain Definition Language (PDDL). 
Using this method, we generate a corpus of approximately one million reasoning steps across various PDDL domains and use it to train PRMs. Experimental results show that augmenting widely-used PRM training datasets with PDDL-derived data yields substantial improvements in both mathematical and non-mathematical reasoning, as demonstrated across multiple benchmarks.
These findings indicate that planning problems constitute a scalable and effective resource for generating robust, precise, and fine-grained training data for PRMs, going beyond the classical mathematical sources that dominate this field.

 Process Reward Models Meet Planning: Generating Precise and Scalable Datasets for Step-Level Rewards

Large language models (LLMs) excel at general programming but struggle with domain-specific software development. This gap motivates research into domain specialization methods that enable LLMs to learn and utilize domain knowledge and data. However, existing domain-specific code benchmarks focus on assessing what knowledge LLMs possess rather than how they acquire and apply new knowledge, lacking explicit knowledge corpora for developing domain specialization methods. To this end, we present KOCO-bench, a novel benchmark designed for evaluating domain specialization methods in real-world software development. KOCO-bench contains 6 emerging domains with 11 software frameworks and 25 projects, featuring curated knowledge corpora alongside multi-granularity evaluation tasks including domain code generation (from function-level to project-level with rigorous test suites) and domain knowledge understanding (via multiple-choice Q&A). Unlike previous benchmarks that only provide test sets for direct evaluation, KOCO-bench requires acquiring and applying diverse domain knowledge (APIs, rules, constraints, etc.) from the corpora to solve evaluation tasks. Our evaluations reveal that KOCO-bench poses significant challenges to state-of-the-art LLMs. Even with domain specialization methods (e.g., SFT, RAG, kNN-LM) applied, improvements remain marginal. Best-performing coding agent, Claude Code, achieves only 34.2%, highlighting the urgent need for more effective domain specialization methods. We release KOCO-bench, evaluation code, and baselines to advance further research at https://github.com/jiangxxxue/KOCO-bench.

KoCo-Bench: Can Large Language Models Leverage Domain Knowledge in Software Development?

GUI agents that interact with graphical interfaces on behalf of users are a promising direction for practical AI assistants, yet training them is hindered by scarce suitable environments. We present InfiniteWeb, a system that automatically generates functional web environments at scale for GUI agent training. While LLMs perform well on generating a single webpage, building a realistic and functional website with many interconnected pages faces challenges. We address these challenges through unified specification, task-centric test-driven development, and combining website seed variation with reference design images. Our system also generates verifiable task evaluators enabling dense reward signals for reinforcement learning. Experiments show that our system surpasses commercial coding agents at realistic website construction, and GUI agents trained on our generated environments achieve significant performance improvements on OSWorld and Online-Mind2Web, demonstrating the effectiveness of the proposed system.

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Benchmarking Egocentric Clinical Intent Understanding Capability for Medical Multimodal Large Language Models

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