United States

Automated Scientific Discovery (ASD) systems can help automatically generate and run code-based experiments, but their capabilities are limited by the code they can reliably generate from parametric knowledge alone. As a result, current systems either mutate a small number of manually-crafted experiment examples, or operate solely from parametric knowledge, limiting quality and reach. We introduce CodeDistiller, a system that automatically distills large collections of scientific Github repositories into a vetted library of working domain-specific code examples, allowing ASD agents to expand their capabilities without manual effort. Using a combination of automatic and domain-expert evaluation on 250 materials science repositories, we find the best model is capable of producing functional examples for 74\% of repositories, while our downstream evaluation shows an ASD agent augmented with a CodeDistiller generated library produces more accurate, complete, and scientifically sound experiments than an agent with only general materials-science code examples.

ACL 2026

CodeDistiller: Automatically Generating Code Libraries for Scientific Coding Agents

The University Big Data Challenge (IUBDC) successfully concluded its latest edition, bringing together national health industry leaders, policymakers, and dignitaries for a conference centered on mental health research and innovation. Participants had the opportunity to showcase their projects in a poster session, engaging with representatives from industry and academia. The event provided a rich network of industry and academic professionals, offering pathways for scholarly publication, access to curated datasets, learning materials materials, and workshops.

Held at the Microsoft Headquarters in Toronto on July 26, 2024, the challenge featured hands-on data science and AI research projects focused on mental health. For over seven years, IUBDC has fostered collaboration between data science and health professionals and motivated university students. This year’s projects, which spanned topics from dementia to addictions, aimed to enhance our understanding of mental health, improve patient lifestyles, and reduce disease risk. Abstracts from teams were published in the NRC Research Press STEM Fellowship Journal, with all team's works featured by Underline publications.

Leveraging Open Data Analytics and Machine Learning to Improve Mental Health Research and Innovation

2024 Inter-University Big Data And AI Challenge -

Thee challenge featured hands-on data science and AI research projects focused on mental health. 

demo

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>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

Registration Required

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

We introduce MixtureKit, a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned checkpoints. MixtureKit supports three complementary strategies: (i) Traditional MoE, using a single router per transformer block to select experts; (ii) BTX (Branch-Train-Mix), adding routers at user-specified sub-layers for fine-grained token routing; and (iii) BTS (Branch-Train-Stitch), preserving experts intact and introducing lightweight stitch layers for controlled hub--expert information exchange. Given a single configuration dictionary, MixtureKit automatically modifies model configuration, patches decoder and causal LM classes, and exports a unified transformers-compatible checkpoint ready for inference or further fine-tuning. We also provide a visualization interface to inspect token routing, expert weight distributions, and layer-wise contributions. Experiments on multilingual code-switched (Arabic--Latin) data show that BTX models built with MixtureKit can outperform dense baselines across multiple benchmarks. The library is accessible at: https://anonymous.4open.science/r/MixtureKit-C47B.

MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models

In this paper we introduce CLINQUERYAGENT, a system for translating natural language population health questions into executable database queries using agents with access to both local and external knowledge bases. Our novel architecture enables the use of powerful cloud-based language models whilst ensuring that no patient data leaves the secure environment. To combat inaccuracies over the course of longer dialogues due to context rot, information retrieval is delegated to a sub-agent. We deploy the system via a chat window embedded within an existing population health management platform where it has been used by 128 staff from 15 healthcare practices covering a total of 148,319 patients in the UK’s National Health Service (NHS). We evaluate the system’s capacity to autonomously handle a range of health informatics tasks on a constructed dataset and via a beta-testing phase. Our results show that both analysts and clinicians are able to easily generate actionable information from patient health records using natural language requests requiring no programming expertise to verify. We make a public demo of the system available:

https://demo-899965260288.europe-west1.run.app/

ClinQueryAgent: A Conversational Agent for Population Health Management

Conversational AI (ConvAI) agents increasingly maintain structured memory to support long-term, task-oriented interactions. In-context memory approaches append the growing history to the model input, which scales poorly under context-window limits. RAG-based methods retrieve request-relevant information, but most assume flat memory collections and ignore structure. We propose **Semantic XPath**, a **tree-structured memory module** to access and update structured conversational memory. **Semantic XPath** improves performance over flat-RAG baselines by **176.7%** while using only **9.1%** of the tokens required by in-context memory. We also introduce **SemanticXPath Chat**, an end-to-end ConvAI demo system that visualizes the structured memory and query execution details. Overall, this paper demonstrates a candidate for the next generation of long-term, task-oriented ConvAI systems built on structured memory.

Semantic XPath: Structured Agentic Memory Access for Conversational AI

Annotated data remains essential for training and evaluating NLP systems. Large language models have increased the scope of what type of data is needed, creating new needs for multimodal and agentic system data. Here, we introduce Potato 2.0, a major update to our open source annotation platform designed for easy deployment, customization, and fully reproducible and sharable annotation designs. Potato offers broad support for many types of annotations in NLP, including 15 different types of annotation tasks, support for text, audio, image, and video modalities---or mixtures thereof,---and the support for labeling agentic system outputs or live interaction with agents. Potato also includes multiple AI-assistance features to help annotators more easily label data. Finally, Potato introduces a new agentic AI-in-the-loop workflow where a single human annotator collaborates with an LLM through iterative prompt refinement, uncertainty-driven instance selection, and progressive autonomy---enabling efficient dataset creation without a large annotation team.

Potato 2.0: A Comprehensive Annotation Platform with AI-in-the-Loop Support

We present mllm-shap, an open-source
Python platform for researchers and ML practitioners that extends Shapley value (SV)
explainability from text-only large language
models to multimodal LLMs (MLLMs) that
jointly process text and audio. Building
on the token-level SV framework introduced
by TokenSHAP, mllm-shap addresses three
challenges absent in the text-only setting:
(1) modality-aware coalition masking that
handles the coexistence of text tokens and
dense audio encoder frames within a single input, (2) multi-turn conversation tracking with
per-token role and modality metadata, and
(3) audio token grouping via phonetic alignment that reduces the coalition space by 10–50 times. The platform ships as a pip-installable
package implementing five SV estimation strategies – including a Complementary Contributions estimator with Neyman-optimal allocation that outperforms Monte Carlo baselines – together with an interactive web GUI
for real-time attribution visualization. To
our knowledge, mllm-shap is the first publicly available framework for complete, reproducible SV-based explainability of text-audio
MLLMs. The package is MIT-licensed with
full source code on GitHub and a demonstration video included as supplementary material.

mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models

Understanding the emotions that individuals in crisis express is a clinically relevant goal. Here, we introduce an automated method for extracting present and past personal emotion expressions from text-based crisis conversations, enabling nuanced analyses of how these emotional profiles vary by age. We develop a three-tier emotion taxonomy and leverage both real conversation data and synthetic sentences to train a transformer-based model that captures contextual distinctions between true personal emotion expressions and other mentions. Our RoBERTa-based classifier outperforms both a regex baseline and a model trained only on real conversation data, achieving an F1 score of 0.856. Subsequent analysis of 338,924 crisis conversations shows that age is correlated with distinct patterns in emotional expressions. These findings underscore the clinical value of age-sensitive emotion analysis and constitute an initial step toward characterizing lexical variations across demographic groups.

Extraction of Texters' Explicit Emotion Expressions in Crisis Conversations

Event annotation is important for identifying, monitoring, and understanding sociological trends. Although expert annotators set the gold standard, they are expensive and inefficient. While state-of-the-art NLP models are an attractive alternative, they are often evaluated on standalone subtasks rather than entire workflows. Thus, we evaluate a holistic workflow that summarizes news with event coreference resolution and argument extraction in three modes: AI-only, AI assistance, and human only. Although AI's recall is seven times higher than the tf-idf baseline at coreference resolution, it is far from replacing experts. However, experts adopt AI-extracted arguments 60% of the time, reducing extraction time by 25%. Our code and data are in https://github.com/Obertura777/gtd-data.

Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators

Large Language Models (LLMs) have fundamentally transformed natural language processing (NLP), demonstrating remarkable capabilities across a wide spectrum of tasks. However, when applied to instruction-based text editing, LLMs continue to exhibit some limitations. Different from free-form generation, instruction-based editing requires precise, targeted modifications that respect two essential properties: faithfully implementing the specific instruction and local fidelity. Existing approaches often overlook these properties, treating editing as a generic text generation problem. As a result, they either over-edit or fail to apply modifications consistently. To address this gap, we propose HyperEdit, a framework that adaptively processes each editing request to best align with it. To achieve this, HyperEdit generates request-specific dynamic weights that guide the editing process. The computational overhead of producing these weights is minimized through a carefully designed hypernetwork. With this design, HyperEdit achieves a relatively 9% improvement over the state-of-the-art editing model.

HyperEdit: Unlocking Instruction-based Text Editing in LLMs via Hypernetworks

Evaluating how large language models (LLMs) capture the grammatical structure of low-resource languages remains underexplored. This paper presents the Urdu Benchmark of Linguistic Minimal Pairs (UrBLiMP)—a diagnostic suite of 5,696 minimal pairs that contrast grammatical acceptability across ten core syntactic and morpho-syntactic phenomena in Urdu. The dataset is constructed from the Urdu Treebank and diverse text corpora, and human validation achieves a 96.1\% inter-annotator agreement, confirming its reliability. We evaluate twenty one multilingual LLMs, including LLaMA-3-70B and Gemma-3-27B-PT, and additionally assess the proprietary GPT-4o model using grammar-prompting techniques. GPT-4o (grammar-prompted) attains the highest average accuracy (97.4\%), reaching near-human performance on regular phenomena such as aspect agreement and ergativity. However, all models continue to struggle with flexible syntactic patterns like word-order variation and long-distance subject–verb agreement. UrBLiMP provides the first controlled evaluation framework for probing morpho-syntactic competence in Urdu and highlights both the progress and remaining challenges of multilingual and proprietary LLMs in low-resource settings.

 UrBLiMP: A Benchmark for Evaluating the Linguistic Competence of Large Language Models in Urdu

Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management. Existing approaches fall into two paradigms: single-vector retrieval, which is efficient but coarse, and multi-vector retrieval, which is accurate but computationally expensive. To address this trade-off, we propose HEAVEN, a plug-and-play two-stage hybrid-vector framework. In the first stage, HEAVEN efficiently retrieves candidate pages using a single-vector method over Visually-Summarized Pages (VS-Pages), which assemble representative visual layouts from multiple pages. In the second stage, it reranks candidates with a multi-vector method while filtering query tokens by linguistic importance to reduce redundant computations. To evaluate retrieval systems under realistic conditions, we also introduce ViMDoc, a benchmark for visually rich, multi-document, and long-document retrieval. Across four benchmarks, HEAVEN attains 99.87% of the Recall@1 performance of multi-vector models on average while reducing per-query computation by 99.82%, achieving efficiency and accuracy. Our code and datasets are available at: https://github.com/juyeonnn/HEAVEN

Downloads

Next from ACL 2026

MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models

Similar lecture

Exploring the Intersection of Autism Spectrum Disorder and Mental Health Through a Mixed-Method Analysis

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES