Austria

Large language models (LLMs) are increasingly used as autonomous agents, tackling tasks from robotics to web navigation. Their performance depends on the underlying `base agent`. Existing methods, however, struggle with long-context reasoning and goal adherence. We introduce `StateAct`, a novel and efficient `base agent` that enhances decision-making through (1) `self-prompting`, which reinforces task goals at every step, and (2) `chain-of-states`, an extension of chain-of-thought that tracks state information over time. StateAct outperforms ReAct, the previous best `base agent`, by over 10\% on Alfworld, 30\% on Textcraft, and 7\% on Webshop across multiple frontier LLMs. We also demonstrate that StateAct can be used as a drop-in replacement for ReAct with with advanced LLM agent methods such as test-time scaling, yielding an additional 12\% gain on Textcraft. By improving efficiency and long-range reasoning without requiring additional training or retrieval, StateAct provides a scalable foundation for LLM agents. We open source our code to support further research at https://github.com/ai-nikolai/stateact.

ACL 2025

StateAct: Enhancing LLM Base Agents via Self-prompting and State-tracking

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

workshop paper

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

Developing language model-based dialogue agents requires effective data to train models that can follow specific task logic. However, most existing data simulation methods focus on increasing diversity in language, topics, or dialogue acts at the utterance level, largely neglecting a critical aspect of task logic diversity at the dialogue level. This paper proposes a novel data simulation method designed to enhance the diversity of synthetic dialogues by focusing on task execution logic. Our method uses LLMs to generate decision tree-structured task plans, which enables the derivation of diverse dialogue trajectories for a given task. Each trajectory, referred to as a "dialog flow", guides the generation of a multi-turn dialogue that follows a unique trajectory. We apply this method to generate a task-oriented dialogue dataset comprising 3,886 dialogue flows across 15 different domains. We validate the effectiveness of this dataset using the next action prediction task, where models fine-tuned on our dataset outperform strong baselines, including GPT-4. Upon acceptance of this paper, we plan to release the code and data publicly.

DFLOW: Diverse Dialogue Flow Simulation with Large Language Models

Author profiling is the task of inferring characteristics about individuals by analyzing content they share. Supervised machine learning still dominates automatic systems that perform this task, despite the popularity of prompting large language models to address natural language understanding tasks. One reason is that the classification instances consist of large amounts of posts, potentially a whole user profile, which may exceed the input length of Transformers. Even if a model can use a large context window, the entirety of posts makes the application of API-accessed black box systems costly and slow, next to issues which come with such "needle-in-the-haystack" tasks. To mitigate this limitation, we propose a new method for author profiling which aims at distinguishing relevant from irrelevant content first, followed by the actual user profiling only with relevant data. To circumvent the need for relevance-annotated data, we optimize this relevance filter via reinforcement learning with a reward function that utilizes the zero-shot capabilities of large language models. We evaluate our method for Big Five personality trait prediction on two Twitter corpora. On publicly available real-world data with a skewed label distribution, our method shows similar efficacy to using all posts in a user profile, but with a substantially shorter context. An evaluation on a version of these data balanced with artificial posts shows that the filtering to relevant posts leads to a significantly improved accuracy of the predictions.

Prompt-based Personality Profiling: Reinforcement Learning for Relevance Filtering

With large language models~(LLMs) reaching human-like cognitive levels, understanding their cooperation and social mechanisms is becoming increasingly important. In particular, how LLMs balance self-interest and collective well-being is a critical challenge for ensuring alignment, robustness, and safe deployment. In this paper, we examine the challenge of costly sanctioning in multi-agent LLM systems, where an agent must decide whether to invest its own resources to incentivize cooperation or penalize defection. To study this, we adapt a public goods game with institutional choice from behavioral economics, allowing us to observe how different LLMs navigate social dilemmas over repeated interactions. Our analysis reveals four distinct behavioral patterns among models: some consistently establish and sustain high levels of cooperation, others fluctuate between engagement and disengagement, some gradually decline in cooperative behavior over time, and others rigidly follow fixed strategies regardless of outcomes. Surprisingly, we find that reasoning LLMs, such as the o1 series, struggle significantly with cooperation, whereas some traditional LLMs consistently achieve high levels of cooperation. These findings suggest that the current approach to improving LLMs, which focuses on enhancing their reasoning capabilities, does not necessarily lead to cooperation, providing valuable insights for deploying LLM agents in environments that require sustained collaboration.

Corrupted by Reasoning: Reasoning Language Models Become Free-Riders in Public Goods Games

This paper focuses on intelligent Q&A assistants in gaming, providing timely and accurate services by integrating structured game knowledge graphs, semi-structured FAQ pairs, and unstructured real-time online content. It offers personalized emotional companionship through customized virtual characters and provides gameplay guidance, data queries, and product recommendations through in-game tools. We propose a Tiered Conversational Q&A Agent (TCQA$^2$), characterized by high precision, personalized chat, low response latency, efficient token cost and low-risk responses. Parallel modules in each tier cut latency via distributed tasks. Multiple retrievers and short-term memory boost multi-turn Q&A. Hallucination and safety checks improve response quality. Player tags and long-term memory enable personalization. Real-world evaluations show TCQA$^2$ outperforms prompt-engineered LLMs and RAG-based agents in gaming Q&A, personalized dialogue, and risk mitigation.

TCQA2: A Tiered Conversational Q&A Agent in Gaming

Multi-agent systems, where specialized agents collaborate to solve a shared task hold great potential, from increased modularity to simulating complex environments. However, they also have a major caveat -- a single agent can cause the entire system to fail. Consider a simple game where the knowledge to solve the task is distributed between agents, which share information in a communication channel. At each round, any of the agents can terminate the game and make the final prediction, even if they are uncertain about the outcome of their action. Detection of such rogue agents __before they act__ may prevent the system's failure. In this work, we propose to __monitor__ agents during action prediction and __intervene__ when a future error is likely to occur. To test our approach, we introduce WhoDunitEnv, a multi-agent collaboration environment that allows modular control over task complexity and communication structure. Experiments on WhoDunitEnv, code generation tasks and the GovSim environment for resource sustainability show that our approach leads to substantial performance gains up to 17.4\%, 2.5\% and 20\%, respectively. Thorough analysis shows that our monitors successfully identify critical points of agent confusion and our interventions effectively stop agent errors from propagating.

Preventing Rogue Agents Improves Multi-Agent Collaboration

This paper finds that agentic AI systems intensify existing challenges to traditional public sector oversight mechanisms --- which rely on siloed compliance units and episodic approvals rather than continuous, integrated supervision. We identify five governance dimensions essential for responsible agent deployment: cross-departmental implementation, comprehensive evaluation, enhanced security protocols, operational visibility, and systematic auditing. We evaluate the capacity of existing oversight structures to meet these challenges, via a mixed-methods approach consisting of a literature review and interviews with civil servants in AI-related roles. We find that agent oversight poses intensified versions of three existing governance challenges: continuous oversight, deeper integration of governance and operational capabilities, and interdepartmental coordination. We propose approaches that both adapt institutional mechanisms and design agent architectures compatible with public sector constraints.

Oversight Structures for Agentic AI in Public-Sector Organizations

Highlight summarization in baseball requires balancing statistical analysis with narrative coherence. Traditional approaches—such as Win Probability Added (WPA)-based ranking or computer vision-driven event detection—can identify scoring plays but often miss strategic depth, momentum shifts, and storyline progression. Manual curation remains the gold standard but is resource-intensive and not scalable. We introduce $\textbf{DIAMOND}$, an $\textbf{LLM-driven agent for context-aware baseball highlight summarization}$ that integrates $\textbf{structured sports analytics with natural language reasoning}$. DIAMOND leverages sabermetric features—Win Expectancy, WPA, and Leverage Index—to quantify play importance, while an LLM module enhances selection based on contextual narrative value. This hybrid approach ensures both $\textbf{quantitative rigor and qualitative richness}$, surpassing the limitations of purely statistical or vision-based systems. Evaluated on five diverse Korean Baseball Organization League games, DIAMOND improves F1-score from 42.9\% (WPA-only) to 84.8\%, outperforming both commercial and statistical baselines. Though limited in scale, our results highlight the potential of modular, interpretable agent-based frameworks for event-level summarization in sports and beyond.

DIAMOND: An LLM-Driven Agent for Context-Aware Baseball Highlight Summarization

We introduce AgentAda, the first LLM-powered analytics agent capable of learning and applying new data analytics skills to generate more specialized and insightful analyses. Unlike existing systems that require users to manually select analytical methods, AgentAda automatically identifies the most suitable technique from a curated library of skills. This enables it to perform complex tasks—such as clustering, predictive modeling, and BERT-based NLP analysis—that standard LLMs cannot execute on their own. AgentAda’s dataset-to-insight pipeline consists of three main steps: (I) a question generator that formulates queries based on the user’s goal and persona, (II) a hybrid Retrieval-Augmented Generation (RAG)-based skill matcher that selects the most relevant skill, and (III) a code generator that uses the retrieved skill's documentation to produce executable code for uncovering patterns and trends. To evaluate AgentAda, we introduce KaggleBench, a benchmark of curated notebooks spanning diverse domains. Human evaluations show that AgentAda delivers more insightful analytics than unskilled agents, with 48.78% of users preferring its outputs compared to 27.67% for the baseline. We also propose a scalable LLM-as-a-judge evaluation framework, which we validate by demonstrating strong alignment with human judgments.

AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discovery

Temporal progression is an integral part of knowledge accumulation and update. Web search is frequently adopted as the grounding for agent knowledge, yet an improper configuration affects the quality of the agent's responses. Here, we assess the agent behavior using distinct date-controlled tools (DCTs) as a stress test to measure the knowledge variability of large language model (LLM) agents. We demonstrate the temporal effects of an LLM agent as a writing assistant, which uses web search to complete scientific publication abstracts. We show that the temporality of search engines translates into tool-dependent agent performance but can be alleviated with base model choice and explicit reasoning instructions such as chain-of-thought prompting. Our results indicate that agent design and evaluations should take a dynamical view and implement effective measures to account for the temporal influence of external resources to improve agent reliability.

Measuring temporal effects of agent knowledge by date-controlled tool use

Event forecasting is a challenging task that requires temporal reasoning over historical data. Although iterative reasoning agents following the ReAct paradigm bring improvements to event forecasting tasks, they also increase the cost of each prediction and bring challenges in tracing the information that contributes to the prediction. In this study, we simplify the ReAct framework into a retrieval-augmented generation (RAG) pipeline. Surprisingly, the RAG outperforms ReAct with only 10\% of the token costs. Furthermore, our experiments reveal that structured statistical contexts significantly enhance forecasting accuracy, whereas introducing unstructured semantic information (e.g., news article titles) negatively impacts performance. In-depth analyses further highlight that the iterative reasoning traces impair forecasting accuracy in smaller-scale models but benefit larger models (e.g., 70B) in the event forecasting task. These insights underscore existing limitations in large language models' temporal and semantic reasoning abilities, providing critical guidance for developing more cost-effective and reliable forecasting systems.

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DFLOW: Diverse Dialogue Flow Simulation with Large Language Models

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ReflAct: World-Grounded Decision Making in LLM Agents via Goal-State Reflection

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