United States

Financial reporting systems increasingly use large language models (LLMs) to extract and summarize corporate disclosures. However, most assume a single-market setting and do not address structural differences across jurisdictions. Variations in accounting taxonomies, tagging infrastructures (e.g., XBRL vs. PDF), and aggregation conventions make cross-jurisdiction reporting a semantic alignment and verification challenge.
We present FinReporting, an agentic workflow for localized cross-jurisdiction financial reporting. The system builds a unified canonical ontology over Income Statement, Balance Sheet, and Cash Flow, and decomposes reporting into auditable stages including filing acquisition, extraction, canonical mapping, and anomaly logging. Rather than using LLMs as free-form generators, FinReporting deploys them as constrained verifiers under explicit decision rules and evidence grounding.
Evaluated on annual filings from the US, Japan, and China, the system improves consistency and reliability under heterogeneous reporting regimes. We release an interactive demo supporting cross-market inspection and structured export of localized financial statements.

ACL 2026

FinReporting: An Agentic Workflow for Localized Reporting of Cross-Jurisdiction Financial Disclosure

Our aim is to encourage research into the quality and credibility of peer review and scientific publication, to establish the evidence base on which scientists can improve the conduct, reporting, and dissemination of scientific research.
 
 **Welcome!**
 
 The JAMA Network, The BMJ, and METRICS welcome you to the Ninth International Congress on Peer Review and Scientific Publication. We have continued our efforts to broaden the scope of the Congress to all aspects of peer review and publication—from funding to postpublication—and to all sciences.
 
 We will have 3 days for presentations of new research into peer review and all aspects of scientific publication, bias, quality of reporting, and information access and dissemination. There are 50 plenary session research presentations and 5 plenary invited talks. Each plenary session research presentation will be followed by equal time for discussion and questions from the audience. In addition, there are 125 posters. All posters, including virtual posters, are available to view throughout the meeting. In-person posters will also be presented at designated times on September 9 and 10. If you are attending virtually, please visit the individual poster pages and ask your questions in the chat box. See the link to Posters on the left.
 
 We hope you will take an active part in the program, as we depend on your participation in the discussion sessions to make the Congress a success.

 [Peer Review Congress Organizers, Directors, and Advisory Board](https://peerreviewcongress.org/organizers-and-advisory-board/)
 
 [Health and Safety Policies](https://web.cvent.com/event/1b505794-6a0b-428c-a44c-6c46c4c50e99/websitePage:152962bb-4567-4561-9d81-21ebfa97f487)

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A PDF of the schedule is available [here](https://drive.google.com/file/d/1-Jd191W-nxbWcN1prdX5o9nlSfeo9_-H/view?usp=sharing) and a flipbook version of the complete program and abstracts is included below. See also the link to the interactive schedule to the left.

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Registration is required to attend this event, please use the URL linked below to secure your ticket. Registration will close on Friday, September 2 at 5:00 PM CT. 

[**Register Here**]( https://web.cvent.com/event/1b505794-6a0b-428c-a44c-6c46c4c50e99/summary)

Peer Review Congress 2022

Our aim is to encourage research into the quality and credibility of peer review and scientific publication, to establish the evidence base on which scientists can improve the conduct, reporting, and dissemination of scientific research.



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. 
[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.

Agentic systems are becoming more capable: agents define strategies, take actions, and interact with different environments. This freedom poses serious challenges to overseeing and assessing the agent's behavior. Most current tools are limited, focusing on observability with basic evaluation or creating a static taxonomy of agent errors. To address this gap, we present Agentic CLEAR, an automatic, dynamic, and easy-to-use evaluation framework. It produces textual insights into the agent behavior on three levels of granularity: system, trace, and node. Agentic CLEAR operates above the observability layer, enabling seamless integration and featuring an intuitive UI that makes agent evaluation highly accessible. In our experiments on four benchmarks, seven agentic settings, and tens of thousands of LLM calls, we show that Agentic CLEAR produces high-quality, data-dependent, insightful feedback. Our analysis shows strong alignment with human-annotated errors and the ability to predict task success rate.

Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents

Clinicians exploring oncology trial repositories often need ad-hoc, multi-constraint queries over biomarkers, endpoints, interventions, and time, yet writing SQL requires schema expertise. We demo FD-NL2SQL, a feedback-driven clinical NL2SQL assistant for SQLite-based oncology databases. Given a natural-language question, a schema-aware LLM decomposes it into predicate-level sub-questions, retrieves semantically similar expert-verified NL2SQL exemplars via sentence embeddings, and synthesizes executable SQL conditioned on the decomposition, retrieved exemplars, and schema, with post-processing validity checks. To improve with use, FD-NL2SQL incorporates two update signals: (i) clinician edits of generated SQL are approved and added to the exemplar bank; and (ii) lightweight logic-based SQL augmentation applies a single atomic mutation (e.g., operator or column change), retaining variants only if they return non-empty results. A second LLM generates the corresponding natural-language question and predicate decomposition for accepted variants, automatically expanding the exemplar bank without additional annotation. The demo interface exposes decomposition, retrieval, synthesis, and execution results to support interactive refinement and continuous improvement.

FD-NL2SQL: Feedback-Driven Clinical NL2SQL that Improves with Use

Diagram question answering (Diagram QA) requires reasoning-level attribution that links each question–answer pair to all visual regions needed to derive the answer, rather than only the region containing the final response. Creating such structured evidence across diagrams, charts, maps, circuits, and infographics is time-consuming, and existing annotation tools tightly couple their interfaces to dataset-specific formats. We present **DIAGRAMS**, a lightweight, schema-driven review framework that decouples interface logic from dataset-specific JSON structures through an internal meta-schema and dataset adapters. Given an image and QA pair with optional candidate regions, the system performs QA-conditioned evidence selection and proposes the regions required for reasoning. When QA pairs or candidate regions are missing, it generates them and supports human verification and refinement. Across six Diagram QA datasets, model-suggested evidence achieves 85.39\% precision and 75.30\% recall against reviewer-final selections (micro-averaged). These results indicate that the review-first framework reduces manual region creation while maintaining high agreement with final reasoning-level attributions. We release a public demo and installable package to support dataset auditing, grounded supervision creation, and grounded evaluation.

DIAGRAMS : A Review Framework for Reasoning-Level Attribution in Diagram QA

High-quality time-aligned annotation is fundamental to speech processing and animal vocalization research, yet precise boundary localization and consistent labeling remain challenging in collaborative settings. We present Praat++, a web-based multimedia annotation system designed for collaborative, video-informed, and AI-assisted timeline labeling of audio and video data. The system tightly synchronizes waveform, spectrogram, pitch, intensity, and time-aligned video playback with fine-grained region-based editing, enabling precise boundary refinement and improved label accuracy within a unified interface. Praat++ further incorporates role-aware workflow management and human-in-the-loop AI-assisted pre-annotation to enhance inter-annotator consistency and reduce labeling time. Through real-world multimodal speech and animal vocalization annotation scenarios, we demonstrate that Praat++ provides an integrated infrastructure for improving annotation quality and efficiency in dataset construction workflows. The demo video (https://www.youtube.com/watch?v=YboCoBRF5lg) and website (https://redgiant.uta.edu/praat) are now publicly available.

Praat++: Multimedia Annotation System for Speech and Vocalization

As large language models (LLMs) continue to advance, significant effort is spent on manually crafting prompts to unlock their full potential. While existing prompt optimization methods automate this process, they often underperform due to their reliance on learning exclusively from incorrect samples. We propose the Learning from Contrastive Prompts (LCP) framework, which leverages contrastive prompts to distinguish between high- and low-performing cases. By identifying and amplifying the differences that make prompts effective, LCP systematically extracts principles underlying successful prompt design. On the Big-Bench Hard benchmark, LCP achieves an 87.5\% win rate on Claude-3-Sonnet and 75.7\% on Claude-4-Sonnet. Experiments on DeepSeek-R1 (88.2\% win rate) and SuperGLUE further confirm that LCP generalizes across both proprietary and open-source models and diverse NLU benchmarks.
The framework offers a principled and scalable foundation for automated prompt engineering, reducing manual intervention in adapting LLMs to diverse applications.

 Learning from Contrastive Prompts: An Automated Prompt Optimization Framework

Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time visual-state adaptation of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.

VChain: Chain-of-Visual-Thought for Reasoning in Video Generation

The adoption of large language models (LLMs) is transforming the peer review process, from assisting reviewers in writing detailed evaluations to generating entire reviews automatically. While these capabilities offer new opportunities, they also raise concerns about fairness and reliability. In this paper, we investigate bias in LLM-generated peer reviews through controlled interventions on author metadata, including affiliation, gender, seniority, and publication history. Our analysis consistently shows a strong affiliation bias favoring authors from highly ranked institutions. We also identify directional preferences associated with seniority and prior publication record, which can influence acceptance decisions for borderline papers. Gender effects are smaller but present in several models. Notably, implicit biases become more pronounced when examining token-level soft ratings, suggesting that alignment may mask but not fully eliminate underlying preferences.

Justice in Judgment: Unveiling (Hidden) Bias in LLM-assisted Peer Reviews

Wikipedia is a critical resource for modern NLP, serving as a rich repository of up-to-date and citation-backed information on a wide variety of subjects. The reliability of Wikipedia---its groundedness in its cited sources---is vital to this purpose. This work analyzes both how grounded Wikipedia is and how readily fine-grained grounding evidence can be retrieved. To this end, we introduce PeopleProfiles---a large-scale, multi-level dataset of claim support annotations on biographical Wikipedia articles. We show that: ~22% of claims in Wikipedia *lead* sections are unsupported by the article body; ~30% of annotated claims in the article *body* are unsupported by their (publicly accessible) sources; and real-world Wikipedia citation practices often differ from documented standards. Finally, we show that complex evidence retrieval remains a challenge---even for recent reasoning rerankers.

How Grounded is Wikipedia? A Study on Structured Evidential Support and Retrieval

Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simply feeding more documents into the context window fails to capture how evidence should be connected. We address this gap with thought templates, reusable reasoning patterns derived from prior problem solving that structure how evidence is combined and guide multi-hop inference alongside factual documents. To keep these templates effective, we propose an update strategy that iteratively refines templates derived from training data through natural-language feedback. Across diverse benchmarks and LCLM families, our approach delivers consistent gains over strong baselines in both retrieval-based and retrieval-free settings. Furthermore, we show that optimized templates can be distilled into relatively smaller open-source models, demonstrating its broad applicability. We refer to our framework as Thought Template Augmented LCLMs (ToTAL).

When Thoughts Meet Facts: Reusable Reasoning for Long-Context LMs

Recently, Large Language Models (LLM) have emerged as a promising paradigm for sequential recommendation. In sequential recommendation, effectively integrating diverse user preferences is essential for improving LLM performance, as users often exhibit multiple interests across different contexts. However, most existing LLM-based methods rely primarily on item descriptions or utilize user preferences independently. As a result, they overlook the relationships among preferences and fail to filter out less-relevant items that introduce noise. This makes it difficult to accurately capture the user's interests, leading to suboptimal recommendations. To overcome these limitations, we propose UCGRec (User-Centric Graph Learning for LLM-based Sequential Recommendation), a novel method that effectively integrates diverse user-relevant preference signals into a unified user-centric graph. Then, we inject the graph-based knowledge into the LLM through end-to-end training with graph neural networks. We conduct extensive experiments on four widely used sequential real-world recommendation datasets. Our experimental results demonstrate that UCGRec significantly outperforms conventional and state-of-the-art LLM-based methods.

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Agentic CLEAR: Automating Multi-Level Evaluation of LLM Agents

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