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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&#39;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.

ACL 2026

LLM-induced Rationales for More Compact Explainable Style Classification Models

### 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)

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.

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.

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/}.

LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark

Multimodal Large Language Models (MLLMs) have achieved remarkable progress but continue to struggle with geometric reasoning, primarily due to the perception bottleneck regarding fine-grained visual elements. While formal languages have aided plane geometry understanding, solid geometry which requires spatial understanding remains largely unexplored. In this paper, we address this challenge by designing a unified formal language that integrates plane and solid geometry, comprehensively covering geometric structures and semantic relations. We construct GDP-29K, a large-scale dataset comprising 20k plane and 9k solid geometry samples collected from diverse real-world sources, each paired with its ground-truth formal description. We propose a training paradigm combining Supervised Fine-Tuning with Reinforcement Learning via Verifiable Rewards, which effectively enforces syntactic correctness and geometric consistency. Experiments show that our approach achieves state-of-the-art parsing performance. Furthermore, we demonstrate that our parsed formal descriptions serve as a critical cognitive scaffold, significantly boosting MLLMs' capabilities for downstream geometry reasoning tasks.

Geoparsing: Diagram Parsing for Plane and Solid Geometry with a Unified Formal Language

Large language models (LLMs) have demonstrated strong performance on formal language tasks, yet whether this reflects genuine symbolic reasoning or pattern matching on familiar constructions remains unclear. We introduce a benchmark for deterministic finite automata (DFA) construction from regular languages, comprising factual knowledge questions, seen construction problems from public sources, and two types of unseen problems: hand-crafted instances with multiple interacting constraints and systematically generated problems via Arden's theorem. Models achieve perfect accuracy on factual questions and 84-90% on seen tasks. However, accuracy drops sharply on unseen problems (by 30-64%), with failures stemming from systematic misinterpretation of language constraints, incorrect handling of Kleene-star semantics, and a failure to preserve global consistency. We evaluate a three-stage hint protocol that enables correction of shallow errors but does not reliably resolve globally inconsistent or structurally flawed automata. Our analysis across multiple prompting strategies (direct, Chain-of-Thought, Tree-of-Thought) reveals that errors persist regardless of prompting approach, exposing a fundamental gap between LLMs' ability to generate syntactically plausible DFAs and their capacity for semantically correct formal reasoning.

Beyond Memorization: Testing LLM Reasoning on Unseen Theory of Computation Tasks

Large language model (LLM) agents increasingly rely on accumulated memory to solve long-horizon decision-making tasks. However, most existing approaches store memory in fixed representations and reuse it at a single or implicit level of abstraction, which limits generalization and often leads to negative transfer when distribution shift. This paper proposes the Meta-Cognitive Memory Abstraction method (MCMA), which treats memory abstraction as a learnable cognitive skill rather than a fixed design choice. MCMA decouples task execution from memory management by combining a frozen task model with a learned memory copilot. The memory copilot is trained using direct preference optimization; it determines how experience should be structured, abstracted, and reused. Memories are further organized into a hierarchy of abstraction levels, enabling selective reuse based on task similarity. When no memory is transferable, MCMA transfers the ability to abstract and manage memory by transferring the memory copilot. Experiments on ALFWorld, ScienceWorld, and BabyAI demonstrate substantial improvements in performance, out-of-distribution generalization, and cross-task transfer over several baselines.

Learning How to Remember: A Meta-Cognitive Management Method for Structured and Transferable Agent Memory

Reinforcement learning (RL) offers a principled way to enhance the reasoning capabilities of large language models, yet its effectiveness hinges on training signals that remain informative as models evolve. 
In practice, RL progress often slows when task difficulty becomes poorly aligned with model capability or when training is dominated by a narrow set of recurring problem patterns.
To jointly address these issues, we propose \textbf{SCALER} (\textbf{S}ynthetic s\textbf{C}alable \textbf{A}daptive \textbf{L}earning \textbf{E}nvironment for \textbf{R}easoning), a framework that sustains effective learning signals through adaptive environment design.
SCALER introduces a scalable synthesis pipeline that converts real-world programming problems into verifiable reasoning environments with controllable difficulty and unbounded instance generation, enabling RL training beyond finite datasets while preserving strong correctness guarantees. Building on this, SCALER further employs an adaptive multi-environment RL strategy that dynamically adjusts instance difficulty and curates the active set of environments to track the model’s capability frontier and maintain distributional diversity. This co-adaptation prevents reward sparsity, mitigates overfitting to narrow task patterns, and supports sustained improvement throughout training. Extensive experiments show that SCALER consistently outperforms other RL baselines across diverse reasoning benchmarks and exhibits more stable, long-horizon training dynamics.

SCALER: Synthetic Scalable Adaptive Learning Environment for Reasoning

Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational benchmarks, which simulate real-world customer-centric issue resolution scenarios, these agents frequently fail due to the cascading effects of incorrect decision-making. These challenges are particularly pronounced for open-source LLMs with smaller parameter sizes, limited context windows, and constrained inference budgets, which contribute to increased error accumulation in agentic settings. To tackle these challenges, we present the **Failure-Aware Meta-Agentic (FAMA)** framework. FAMA operates in two stages: first, it analyzes failure trajectories from baseline agents to identify the most prevalent errors; second, it employs an orchestration mechanism that activates a minimal subset of specialized agents tailored to address these failures by injecting a targeted context for the tool-use agent before the decision-making step. Experiments across open-source LLMs demonstrate performance gains up to **27%** across evaluation modes over standard baselines. These results highlight that targeted curation of context through specialized agents to address common failures is a valuable design principle for building reliable, multi-turn tool-use LLM agents that simulate real-world conversational scenarios.

FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments

Role-playing has garnered rising attention as it provides a strong foundation for human-machine interaction and facilitates sociological research. 
However, current work is confined to textual modalities, neglecting speech, which plays a predominant role in daily life, thus limiting genuine role-playing. 
To bridge this gap, we conceptualize and benchmark speech role-playing through ActorMindBench, and we present a corresponding reasoning framework, called ActorMind. Specifically, (1) Speech Role-Playing enables models to deliver spontaneous responses with personalized verbal traits based on their role, the scene, and spoken dialogue. (2) ActorMindBench is a hierarchical benchmark comprises Utterance-Level content with 7,653 utterances, Scene-Level content with 313 scenes, and Role-Level content with 6 roles. (3) ActorMind is an off-the-shelf, multi-agent, chain-of-though style reasoning framework that emulates how human actors perform in theaters. Concretely, ActorMind first reads its assigned role description via Eye Agent, then comprehends emotional cues within contextual spoken dialogues through Ear Agent. Subsequently, Brain Agent generates a descriptive emotional state, and finally, Mouth Agent delivers the scripts infused with corresponding emotion state. Experimental results demonstrate the effectiveness of ActorMind in enhancing speech role-playing. The project page is available at https://github.com/OzymandiasChen/ActorMind.

ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing

Existing work on value alignment typically characterizes value relations statically, ignoring how alignment interventions—such as prompting, fine-tuning, or preference optimization—reshape the broader value system. 
In practice, aligning a target value can implicitly shift other values, creating value trade-offs that remain largely unmeasured.
We introduce the VAT, a framework that quantifies value trade-offs by measuring how alignment-induced changes propagate across interconnected values relative to achieved on-target gain. VAT captures the system-level dynamics of value expression under alignment intervention, enabling evaluation of both intended improvements and unintended side effects.
Using a controlled scenario–action dataset grounded in Schwartz value theory, we collect paired pre–post normative judgments and analyze alignment effects across models, values, and interventions. Results show that alignment often produces uneven and structured co-movement among values, revealing systematic trade-offs between target and non-target values. These effects are largely invisible under conventional target-only evaluation, but become evident via VAT, highlighting process-level alignment risks and offering new insights into the dynamic nature of value alignment in LLMs.
Dataset and code are open-sourced.

VALUE ALIGNMENT TAX: Measuring Value Trade-offs in LLM Alignment

Recent automated transcription systems have focused on end-to-end orthographic approaches driven by deep neural networks and sequence-to-sequence transformers. Growing public interest in transcription at the phonemic or phonetic level has led to re-purposing these systems to segment and identify phones, the basic sounds which comprise human speech. However, they miss the mark on a fundamental component of time-series analysis, namely time. For linguistic applications which require high fidelity in the temporal domain, the loss of timing information is untenable. Our work proposes a deadline-bounded expectation maximization (EM) algorithm with a novel initialization method to estimate formants, i.e., salient speech frequencies, for enhanced phonetic segmentation. Based on the concept of spectral gravity, i.e., treating spectral energy as mass attenuated by the square of frequency distance across the spectrum, our technique outperforms the recent state of the art on key clustering metrics, generating reasonable alignments across multiple languages with no a priori training.

Spectral Gravity Formant Estimation for Phonetic Segmentation

We reformulate explanation quality assessment as a ranking problem rather than a generation problem. Instead of optimizing models to produce a single “best” explanation token-by-token, we train reward models to discriminate among multiple candidate explanations and learn their relative quality. Concretely, we construct per-instance candidate sets with graded quality levels and train listwise and pairwise ranking models (ListNet, LambdaRank, RankNet) to preserve ordinal structure and avoid score compression typical of pointwise regression or binary preference objectives. We observe three findings: First, ranking losses consistently outperform regression on score separation across all domains tested. Second, the optimal ranking loss depends on data characteristics: listwise objectives excel with well-separated quality tiers, while pairwise methods are more robust to noisy natural annotations. Third, when trained on carefully curated and well-structured data, small encoder models can match models that are orders of magnitude larger, suggesting that data quality matters more than model scale. Finally, when used as rewards in policy optimization, ranking-based scores enable stable convergence in settings where regression-based rewards fail entirely. Code and data are available at: https://github.com/Tankiit/PPO_Learning_to_rank

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LearnAct: Few-Shot Mobile GUI Agent with a Unified Demonstration Benchmark

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