Austria

The rapid proliferation of heterogeneous data in government and industry presents increasing challenges for users seeking to retrieve actionable insights across both structured and unstructured sources. To address this, this paper presents InfoTech Assistant, a novel multimodal conversational framework that enables natural language interaction with both semantic document retrieval and structured database querying. The system integrates Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) and schema-aware Text-to-SQL capabilities, enabling dual-mode processing of user input for unstructured explanations and relational analytics. The architecture features a modular, locally deployed backend built with Flask and optimized for Graphics Processor Unit (GPU) acceleration, supporting low latency, privacy preserving inference. User queries are dynamically routed through an intent-aware processing pipeline, leveraging sentence embeddings, schema metadata, and prompt engineering strategies. A pilot deployment using infrastructure datasets from the Federal Highway Administration (FHWA) InfoHighway portal demonstrates the system’s effectiveness in real-world domain-specific retrieval. The assistant ingests FHWA technology documents and National Bridge Inventory (NBI) text records, tables, and images organized in a hybrid schema supporting both semantic and SQL-driven interaction. Evaluation results show 95\% accuracy in RAG-based semantic tasks and 88.6\% success in translating natural language into executable SQL queries. These findings underscore the potential of hybrid LLM-based agents for scalable, secure knowledge access in critical public-sector and industrial applications.

ACL 2025

A Conversational Agent Framework for Multimodal Knowledge Retrieval: A Case Study in FHWA InfoHighway Web Portal Queries

### 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

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

Recent works have demonstrated that incorporating search during inference can significantly improve reasoning capabilities of language agents. Some approaches may make use of the ground truth or rely on model's own generated feedback. The search algorithm uses this feedback to then produce values that will update its criterion for exploring and exploiting various reasoning paths. In this study, we investigate how search and model's self-feedback can be leveraged for reasoning tasks. First, we explore differences in ground-truth feedback and self-feedback during search for math reasoning. Second, we observe limitations in applying search techniques to more complex tasks like tool-calling and design domain-specific approaches to address these gaps. Our experiments reveal challenges related to generalization when solely relying on self-feedback during search. For search to work effectively, either access to the ground-truth is needed or feedback mechanisms need to be carefully designed for the specific task.

A Study on Leveraging Search and Self-Feedback for Agent Reasoning

As mobile devices become central to modern life, elderly users often struggle with their complexity, leading to digital divide. This paper explores how the integration of Human-Computer Interaction (HCI) principles and Natural Language Processing (NLP) techniques can enhance the way elderly users learn to use smartphones. To demonstrate this approach, we present AgeMate, a prototype mobile agent designed to support seniors in acquiring smartphone skills more intuitively and effectively. Specifically, we investigate how personalized feedback generated by large language models (LLMs), appropriate granularity in instructional content, and mechanisms for preventing and correcting user errors can contribute to more adaptive and user-friendly learning experiences for elderly users. Rather than focusing solely on system performance, our study emphasizes the instructional value of NLP-enhanced interaction: enabling step-by-step, conversational teaching tailored to users’ real-time context. By analyzing usage patterns and interaction challenges, we propose design strategies that bridge the gap between accessibility and intelligent guidance to better support elderly users in digital environments.

Bridging the Digital Divide: Empowering Elderly Smartphone Users with Intelligent and Human-Centered Design in Agemate

Modern web agents possess computer use abilities that allow them to interact with webpages by sending commands to a virtual keyboard and mouse. While such agents have considerable potential to assist human users with complex tasks, evaluating their capabilities in real-world settings poses a major challenge. To this end, we introduce BEARCUBS, a "small but mighty" benchmark of 111 information-seeking questions designed to evaluate a web agent's ability to search, browse, and identify factual information from the web. Unlike prior web agent benchmarks, solving BEARCUBS requires (1) accessing live web content rather than synthetic or simulated pages, which captures the unpredictability of real-world web interactions; and (2) performing a broad range of multimodal interactions (e.g., video understanding, 3D navigation) that cannot be bypassed via text-based workarounds. Each question in BEARCUBS has a corresponding short, unambiguous answer and a human-validated browsing trajectory, allowing for transparent evaluation of agent performance and strategies. A human study confirms that BEARCUBS questions are solvable but non-trivial (84.7% human accuracy), revealing search inefficiencies and domain knowledge gaps as common failure points. By contrast, state-of-the-art computer-using agents underperform, with the best-scoring system (OpenAI's Operator) reaching only 24.3% accuracy. These results highlight critical areas for improvement, including reliable source selection and more powerful multimodal capabilities. To facilitate future research, BEARCUBS will be updated periodically to replace invalid or contaminated questions, keeping the benchmark fresh for future generations of web agents.

BEARCUBS: A benchmark for computer-using web agents

Large Language Models (LLMs) are increasingly deployed in critical domains, but their vulnerability to jailbreak attacks remains a significant concern. In this paper, we propose a multi-agent, multi-turn jailbreak strategy that systematically bypasses LLM safety mechanisms by decomposing harmful queries into seemingly benign sub-tasks. Built upon a role-based agentic framework consisting of a Question Decomposer, a Sub-Question Answerer, and an Answer Combiner, we demonstrate how LLMs can be manipulated to generate prohibited content without prompt manipulations. Our results show a drastic increase in attack success, often exceeding 90% across various LLMs, including GPT-3.5-Turbo, Gemma-2-9B, and Mistral-7B. We further analyze attack consistency across multiple runs and vulnerability across content categories. Compared to existing widely used jailbreak techniques, our multi-agent method consistently achieves the highest attack success rate across all evaluated models. These findings reveal a critical flaw in the current safety architecture of multi-agent LLM systems: their lack of holistic context awareness. By revealing this weakness, we argue for an urgent need to develop multi-turn, context-aware, and robust defenses to address this emerging threat vector.

Safe in Isolation, Dangerous Together: Agent-Driven Multi-Turn Decomposition Jailbreaks on LLMs

What if artificial intelligence could not only solve problems for which it was trained but also teach itself to tackle novel tasks? In this paper, we finetune Llama 3.1 using reinforcement learning on the grid-world game Frozen Lake and investigate its ability to solve maps it has never encountered—a phenomenon recently termed In-Context Reinforcement Learning (ICRL). Without additional training, the transformer demonstrates the capacity to adapt to both in-distribution and out-of-distribution environment parameterizations. Moreover, it remains effective when trained on data that blends optimal and suboptimal behavior, combines strategies from its context (behavior-stitching), and dynamically adapts to non-stationary environments. These proof-of-concept findings suggest that in-context learning via reinforcement-tuned transformers may form the basis of a promising general-purpose problem-solver.

RL + Transformer = A General-Purpose Problem Solver

Benchmarks for Software Engineering (SE) AI agents, most notably SWE-bench, have catalyzed progress in programming capabilities of AI agents. However, they overlook critical developer workflows such as Version Control System (VCS) operations. To address this issue, we present GitGoodBench, a novel benchmark for evaluating AI agent performance on Version Control System (VCS) tasks. GitGoodBench covers three core Git scenarios extracted from permissive open-source Python, Java, and Kotlin repositories. Our benchmark provides three datasets: a comprehensive evaluation suite (900 samples), a rapid prototyping version (120 samples), and a training corpus (17,469 samples). We establish baseline performance on the prototyping version of our benchmark using GPT-4o equipped with custom tools, achieving a 21.11% solve rate overall. We expect GitGoodBench to serve as a crucial stepping stone toward truly comprehensive SE agents that go beyond mere programming.

GitGoodBench: A Novel Benchmark For Evaluating Agentic Performance On Git

Extracting structured information from complex unstructured documents is an essential but challenging task in today's industrial applications. Complex document content, e.g., irregular table layout, and cross-referencing, can lead to unexpected failures in classical extractors based on Optical Character Recognition (OCR) or Large Language Models (LLMs). In this paper, we propose the AID-agent framework that synergistically integrates OCR with LLMs to enhance text processing capabilities. Specifically, the AID-agent maintains a customizable toolset, which not only provides external processing tools for complex documents but also enables customization for domain and task-specific tool requirements. In the empirical validation on a real-world use case, the proposed AID-agent demonstrates superior performance compared to conventional OCR and LLM-based approaches.

AID-Agent: An LLM-Agent for Advanced Extraction and Integration of Documents

While server-side Large Language Models (LLMs) demonstrate proficiency in function calling and complex reasoning, deploying Small Language Models (SLMs) directly on devices brings opportunities to improve latency and privacy but also introduces unique challenges for accuracy and memory. We introduce CAMPHOR, an innovative on-device SLM multi-agent framework designed to handle multiple user inputs and reason over personal context locally, ensuring privacy is maintained. CAMPHOR employs a hierarchical architecture where a high-order reasoning agent decomposes complex tasks and coordinates expert agents responsible for personal context retrieval, tool interaction, and dynamic plan generation. By implementing parameter sharing across agents and leveraging prompt compression, we significantly reduce model size, latency, and memory usage. To validate our approach, we present a novel dataset capturing multi-agent task trajectories centered on personalized mobile assistant use-cases. Our experiments reveal that fine-tuned SLM agents not only surpass closed-source LLMs in task completion F1 by ~35% but also eliminate the need for server-device communication, all while enhancing privacy.

CAMPHOR: Collaborative Agents for Multi-input Planning and High-Order Reasoning On Device

Reinforcement learning (RL) offers adaptive solutions to portfolio optimization, yet standard methods such as proximal policy optimization (PPO) rely exclusively on historical price data and overlook the impact of investor sentiment. We introduce sentiment-augmented PPO (SAPPO), a reinforcement learning framework that incorporates real-time sentiment signals extracted from Refinitiv financial news. Daily sentiment scores are generated using LLaMA 3.3. SAPPO integrates these signals into the PPO advantage function via a sentiment-weighted term, enabling allocation strategies that respond to both price movements and market sentiment. Experiments on a three-asset portfolio demonstrate that SAPPO increases the Sharpe ratio from 1.55 to 1.90 and reduces drawdowns relative to PPO. The optimal configuration uses a sentiment influence parameter $\lambda = 0.1$, as validated through ablation studies and statistically significant $t$-tests ($p < 0.001$). These findings show that sentiment-aware reinforcement learning improves trading performance and offers a robust alternative to purely price-based strategies.

Leveraging LLM-based sentiment analysis for portfolio optimization with proximal policy optimization

Embodied agents performing complex tasks are susceptible to execution failures, motivating the need for effective failure recovery mechanisms. In this work, we introduce a conditional multi-stage failure recovery framework that employs zero-shot chain prompting. The framework is structured into four error-handling stages, with three operating during task execution and one functioning as a post-execution reflection phase. Our approach utilises the reasoning capabilities of LLMs to analyse execution challenges within their environmental context and devise strategic solutions. We evaluate our method on the TfD benchmark of the TEACH dataset and achieve state-of-the-art performance, outperforming a baseline without error recovery by 11.5% and surpassing the strongest existing model by 19%.

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