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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.75ACL Anthology · ACL 2026

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

Suparno Roy Chowdhury, Tejas Anvekar Arizona State University, Manan Roy Choudhury Arizona State University, Muhammad Ali Khan Mayo Clinic, Kaneez Zahra Rubab Khakwani Mayo Clinic, M Bassam Sonbol Mayo Clinic, Irbaz Bin Riaz Mayo Clinic, Vivek Gupta Arizona State University

原文摘要

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.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.76ACL Anthology · ACL 2026

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

Anirudh Iyengar Kaniyar Narayana Iyengar Arizona State University, Tampu Ravi Kumar Arizona State University, Manan Suri, Raviteja Bommireddy, Dinesh Manocha University of Maryland, College Park, Puneet Mathur Adobe Systems, Vivek Gupta Arizona State University

原文摘要

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 the number of regions that annotators must create from scratch. Human reviewers accept, edit, or reject each proposed region before export, which structurally limits over-reliance on AI proposals. We release a public demo and installable package to support dataset auditing, grounded supervision creation, and grounded evaluation.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.77ACL Anthology · ACL 2026

SlideGuard: AI-Driven Evaluation of Graduate Student Presentation Materials

Nikolay Alekseevich Butakov ITMO University, Maria Khodorchenko, Mazein Nikita ITMO University, Daniil Gareev, Yuri Falevskiy, Georgii Konev, Denis Nasonov ITMO University

原文摘要

Preparing graduate students for effective professional communication remains a central goal of higher education, yet consistently assessing the quality of presentation slide decks - particularly in fast-growing AI/ML programs - poses significant challenges.We introduce SlideGuard, an evaluation agent that assesses slide decks against a comprehensive framework of expert-defined criteria using a visual language model.The criteria, developed in collaboration with domain experts, span visual design, narrative coherence, and argumentative structure.SlideGuard delivers explicit, interpretable justifications for its scoring decisions, and its content-hash-based caching enables efficient re-evaluation after incremental edits, reducing the time educators spend on slide deck evaluation and accelerating feedback delivery to students.We evaluate the approach on a dataset of 150 annotated slide decks and show that it detects the majority of expert-identified issues, with stronger results on structural and visual criteria and known limitations on subjective dimensions such as research quality.SlideGuard is released under the Apache 2.0 license and is available on GitHub,[https://github.com/Industrial-AI-Research-Lab/SlideGuard] including all criterion prompts, configuration files, and evaluation scripts to facilitate replication.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.78ACL Anthology · ACL 2026

Spectra: A Mechanistic Interpretability Library for Vision-Language Models

Clement Neo Nanyang Technological University and Apart Research, Yongsen Zheng Nanyang Technological University, Kwok-Yan Lam Nanyang Technological University, Luke Ong Nanyang Technological University

原文摘要

Vision-Language Models (VLMs) have become increasingly important in AI applications, yet interpretability tools for these models lag behind those available for text-only language models. While libraries like TransformerLens have enabled significant progress in understanding language models, existing tools for VLMs are limited to basic activation probing and saving. We introduce Spectra, a library specifically designed for mechanistic interpretability of VLMs that provides unified abstractions for activation patching, attention pattern analysis, and meta-functions across diverse VLM architectures. Built on HuggingFace’s Transformers, our library handles architecture-specific complexities through per-checkpoint configurations while maintaining a simple, high-level interface. We demonstrate the library’s capabilities by performing interpretability experiments on a counting task, showing how researchers can easily perform experiments that were previously cumbersome to do. The library currently supports Qwen2.5-VL, Qwen3-VL, LLaVA 1.5 and SmolVLM, with a design that facilitates extension to additional architectures. The library can be found at github.com/clemneo/vlm-spectra.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.79ACL Anthology · ACL 2026

Demonstrating ViviDoc: Generating Interactive Documents through Human-Agent Collaboration

Yinghao Tang State key laboratory of CAD&CG, Zhejiang University, Yupeng Xie, Yingchaojie Feng, Tingfeng Lan, Wei Chen Zhejiang University

原文摘要

Interactive articles help readers engage with complex ideas through exploration, yet creating them remains costly, requiring both domain expertise and web development skills. Recent LLM-based agents can automate content creation, but naively applying them yields uncontrollable and unverifiable outputs. We present Vividoc, a human-agent collaborative system that generates interactive educational documents from a single topic input. Vividoc introduces a multi-agent pipeline (Planner, Executor, Evaluator) and the Document Specification (DocSpec), a human-readable intermediate representation that decomposes each interactive visualization into State, Render, Transition, and Constraint components. The DocSpec enables educators to review and refine generation plans before code is produced, bridging the gap between pedagogical intent and executable output. We collect a dataset of 101 real-world interactive documents across 11 domains and conduct a user study showing that ViviDoc produces documents comparable in quality to human-authored ones. Our demo is available at https://vividoc.vercel.app/ and a video demonstration at https://www.youtube.com/watch?v=rJrnPJLyHUI.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.8ACL Anthology · ACL 2026

dLLM: Simple Diffusion Language Modeling

Zhanhui Zhou, Lingjie Chen, Hanghang Tong University of Illinois at Urbana-Champaign, Dawn Song University of California Berkeley

原文摘要

Although diffusion language models (DLMs) are evolving quickly, many recent models converge on a set of shared components. These components, however, are distributed across ad-hoc research codebases or lack transparent implementations, making them difficult to reproduce or extend. As the field accelerates, there is a clear need for a unified framework that standardizes these common components while remaining flexible enough to support new methods and architectures.To address this gap, we introduce dLLM, an open-source framework that unifies the core components of diffusion language modeling—training, inference, and evaluation—and makes them easy to customize for new designs. With dLLM, users can reproduce, finetune, deploy, and evaluate open-source large DLMs such as LLaDA and Dream through a standardized pipeline.The framework also provides minimal, reproducible recipes for building small DLMs from scratch with accessible compute—including converting any BERT-style encoder or autoregressive LM into a DLM. We also release the checkpoints of these small DLMs to make DLMs more accessible and accelerate future research.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.80ACL Anthology · ACL 2026

Praat++: Multimedia Annotation System for Speech and Vocalization

Weiran Zhang University of Texas at Arlington, Kenny Q. Zhu University of Texas at Arlington

原文摘要

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), website (https://redgiant.uta.edu/praat) and source code (https://github.com/UTA-ACL2/PraatPlusPlus) are now publicly available.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.81ACL Anthology · ACL 2026

AgentFactory: A Self-Evolving Framework Through Executable Subagent Accumulation and Reuse

Zhang Zhang Peking University, Shuqi Lu BAAI, Hongjin Qian, Di He Peking University and Microsoft, Zheng Liu

原文摘要

Building LLM-based agents has become increasingly important. Recent works on LLM-based agent self-evolution primarily record successful experiences as textual prompts or reflections, which cannot reliably guarantee efficient task re-execution in complex scenarios. We propose AgentFactory, a new self-evolution paradigm that preserves successful task solutions as executable subagent code rather than textual experience. Crucially, these subagents are continuously refined based on execution feedback, becoming increasingly robust and efficient as more tasks are encountered. Saved subagents are pure Python code with standardized documentation, enabling portability across any Python-capable system. We demonstrate that AgentFactory enables continuous capability accumulation: its library of executable subagents grows and improves over time, progressively reducing the effort required for similar tasks without manual intervention. Our implementation is open-sourced at https://github.com/zzatpku/AgentFactory, and our demonstration video is available at https://youtu.be/iKSsuAXJHW0.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.82ACL Anthology · ACL 2026

OpenGlass: A Sensing-Computing Split Architecture for Local MLLM-Driven Real-Time Visual Assistance

Mengzhang Li, Yuan Yao National University of Singapore

原文摘要

We present OpenGlass, an open-source, privacy-oriented, local-first system for low-latency multimodal visual assistance, with a primary focus on blind and low-vision users. Cloud MLLM assistants offer strong visual understanding, but often require uploading first-person visual data and can suffer multi-second network delays; wearable glasses are ideal for sensing, but cannot host large models under tight compute and power budgets. OpenGlass addresses this gap with a sensing-computing split: an ESP32-based glasses-side unit captures visual context, while a nearby consumer-grade device performs local MLLM inference and local speech output, reducing cloud reliance and keeping raw egocentric visual data on user-controlled devices by default. We evaluate response quality, query-ready-to-audio latency, safety-aware abstention, and auditable logs. Under real ESP32 Wi-Fi capture, OpenGlass reaches 993 ms median user-to-audio latency with resized payloads and 1625 ms with raw 1280\times720 payloads; 97.5% and 93.3% of trials fall below 2 s, respectively. OpenGlass is a user-initiated visual-assistance reference platform for obstacle/hazard awareness, sign/object queries, and image-quality self-checking, rather than a certified navigation aid. We release source code, hardware instructions, prompts, evaluation data, and logs.

cs.CL · cs.CV

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.83ACL Anthology · ACL 2026

TartanMaroon: Multi-Agent Academic Advising with Iterative Negotiation and Transparent Collaboration

Peidi Dong Carnegie Mellon University, Houda Bouamor Carnegie Mellon University, Yunze Xiao, Devi G Kurup Carnegie Mellon University

原文摘要

We present TartanMaroon, a deployable multi-agent academic advising system that handles the full complexity spectrum of student queries, from factual lookups to constrained multi-semester planning. We make three contributions: (1) a proposal–critique negotiation protocol in which a Planning Agent generates degree plans evaluated in parallel by domain-specialized agents, enabling detection of cross-domain constraint violations that single-pass outputs miss; (2) a real-time transparency interface streaming agent reasoning and negotiation rounds to users, supported by pilot feedback showing increased trust over standard LLM chatbots; and (3) TartanBench, a difficulty-stratified benchmark of 220 advising queries across five complexity tiers, released open-source without exposing individual student records. A five-configuration ablation study establishes a complexity–necessity curve: single-agent systems perform competitively on simple queries, while multi-agent coordination yields gains of up to +31 points on planning tasks.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.84ACL Anthology · ACL 2026

GAMED.AI: A Hierarchical Multi-Agent Framework for Automated Educational Game Generation

Shiven Agarwal Arizona State University, Yash Shah, Ashish Raj Shekhar Arizona State University, Priyanuj Bordoloi, Vivek Gupta Arizona State University

原文摘要

We introduce GameDAI, a hierarchical multi-agent framework that transforms instructor-provided questions into fully playable, pedagogically grounded educational games validated through formal mechanic contracts. Built on phase-based LangGraph sub-graphs, deterministic Quality Gates, and structured Pydantic schemas, GameDAI supports two template families encompassing 15 interaction mechanics across spatial reasoning, procedural execution, and higher-order Bloom’s Taxonomy objectives.Evaluated on 200 questions spanning five subject domains, the system achieves a 90% validation pass rate, 98.3% schema compliance, and 73% token reduction over ReAct agents 73,500 → 19,900 tokens/game) at 0.46 per game. Within this model configuration, these results suggest that phase-bounded architectural structure correlates more strongly with alignment quality than prompting strategy alone.Our demonstration lets attendees generate Bloom's-aligned games from natural language in under 60 seconds, inspect Quality Gate outputs at each pipeline phase, and browse a curated library of 50 games spanning all 15 mechanic types.

cs.CL · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.85ACL Anthology · ACL 2026

MOOSE-Copilot: A Web-Based Interactive Assistant for Unified Exploratory and Fine-Grained Scientific Hypothesis Discovery

Hongran An Central Conservatory of Music, Zonglin Yang MiroMind

原文摘要

Large language models (LLMs) show remarkable potential in scientific hypothesis discovery. However, existing approaches face two critical limitations: they treat divergent exploratory search and convergent fine-grained refinement as isolated tasks, and they operate autonomously with little to no human guidance. We present MOOSE-Copilot, the first unified framework to bridge this abstraction gap through a formalized human–AI interaction (HAII) protocol. Our system empowers scientists to steer the generative process via three explicit signals: initial blueprints, inter-stage routing, and intra-stage feedback. Using an oracle-simulated evaluation in which an LLM provides idealized expert signals, we show that injecting these structured signals significantly outperforms purely autonomous baselines, characterizing the gains achievable under high-quality guidance. Furthermore, we build a web-based interface that turns the framework into a no-code workflow: researchers pose a question, watch the hypothesis search unfold as an interactive tree, and steer it by selecting hypotheses, routing between stages, and injecting feedback—no command-line agents required. This makes end-to-end hypothesis discovery directly accessible to interdisciplinary researchers.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-demo.9ACL Anthology · ACL 2026

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

Hiromu Takahashi Independent Researcher, Shotaro Ishihara Nikkei Inc.

原文摘要

We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) against large language models (LLMs). MIA has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. However, computational demands have grown substantially: recent methods rely on repeated inference, while practical auditing requires large-scale evaluation. Progress is further hindered by existing implementations that execute methods independently, redundantly computing shared intermediate results such as log-probabilities. To address these challenges, Fast-MIA combines two strategies: (1) high-throughput batch inference via vLLM, achieving approximately 5\times speedup, and (2) a cross-method caching architecture that computes intermediate results once and shares them across methods.The library includes representative MIA methods under a unified framework, integrates with established benchmarks, and supports flexible YAML configuration.We release Fast-MIA under the Apache License 2.0 to support scalable and reproducible MIA research.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.10ACL Anthology · ACL 2026

R³A: Reinforced Reasoning for Relevance Assessment for RAG in User-Generated Content Platforms

Xiaowei Yuan, Lei Jin, Haoxin Zhang, Ziyang Huang, Yan Gao, Yiwu, Yao Hu, Jun Zhao 等 9 位作者

原文摘要

Retrieval-augmented generation (RAG) plays a critical role in user-generated content (UGC) platforms, but its effectiveness critically depends on accurate query–document relevance assessment. Despite recent advances in applying large language models (LLMs) to relevance modeling, UGC platforms present unique challenges: 1) ambiguous user intent due to sparse user feedback in RAG scenarios, and 2) asymmetric relevance, where relevance is driven by localized answer-bearing content rather than global query–document similarity. To address these issues, we propose the Reinforced Reasoning model for Relevance Assessment (R³A), which decomposes relevance assessment into intent inference and evidence grounding. R³A leverages auxiliary high-clicked documents to infer latent query intent, and extracts verbatim evidence fragments to ground relevance decisions, reducing noise sensitivity and improving asymmetric relevance modeling. Experimental results demonstrate that R³A substantially outperforms strong baselines on offline benchmarks, while the distilled R³A-1.5B model achieves significant gains in large-scale online A/B testing, effectively balancing performance and practical deployability.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.100ACL Anthology · ACL 2026

Effective Performance Measurement: Challenges and Opportunities in KPI Extraction from Earnings Calls

Rasmus T. Aavang, Rasmus Tjalk-Bøggild, Alexandre Iolov, Giovanni Rizzi, Mike Zhang, Johannes Bjerva

原文摘要

Earnings calls are a key source of financial information about public companies. However, extracting information from these calls is difficult.Unlike the templatic filings required by the U.S. Securities and Exchange Commission (SEC) to report a company’s financial situation, earnings conference calls have no built-in labels, are unstructured, and feature conversational language.We explore this challenging domain by assessing the information captured by models trained on SEC filings and in-context learning methods. To establish a baseline, we first evaluate the generalization capabilities of SEC-trained models across established SEC datasets.To support our investigation, we introduce three novel benchmarks: (1) SEC Filings Benchmark (SECB), (2) Earnings Calls Benchmark (ECB), and ECB-A, a subset with 5,346 expert annotations to support our qualitative analysis.We find that encoder-based models struggle with the domain shift. Finally, we propose a system utilizing LLMs to perform open-ended extraction from unstructured call transcripts, verified by human evaluation (79.7% precision), providing a baseline for this valuable domain through the consistent tracking of emergent KPIs.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.101ACL Anthology · ACL 2026

Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts

Shivam Chourasia, Hitesh Kapoor, Nilesh Patil

原文摘要

Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex environments. Variations in naming conventions, inconsistent transliteration across scripts, and frequent data entry errors make it difficult to unify user identities, an essential requirement for Know Your Customer (KYC) compliance. While Large Language Models have shown promise in understanding natural language, they often struggle with the structured ambiguity present in such domain-specific settings. This paper introduces Structure-Guided Entity Resolution (SGER), a novel framework that fine-tunes an LLM through a two-phase curriculum. The model is first trained to parse the grammatical and semantic structure of personal names, then optimized for the downstream task of binary entity matching. We evaluate SGER in the challenging context of Indian identity data, one of the most linguistically diverse and noisy environments globally. SGER achieves 99.02% accuracy and an F1 of 0.994 on a held-out set of 50,000 real-world pairs, outperforming GPT-4o few-shot prompting and single-stage fine-tuning baselines. The system is fully deployed in production at Dream11, the world’s largest fantasy sports platform, serving 250M+ users. Our results demonstrate that curriculum-guided training enables robust, high-precision entity resolution in real-world multilingual systems at scale.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.102ACL Anthology · ACL 2026

Accurate Legal Reasoning at Scale: Neuro-Symbolic Offloading and Structural Auditability for Robust Legal Adjudication

Stanisław Sójka, Witold Kowalczyk

原文摘要

Legal texts often contain computational legal clauses—provisions whose understanding requires complex logic. While frontier Large Reasoning Models (LRMs) can describe such clauses, building production-ready systems is limited by reasoning errors and the high cost of inference. We propose Amortized Intelligence, a neuro-symbolic approach where we use an LLM once to translate a legal text into Deterministic Autonomous Contract Language (DACL): a typed graph intermediate representation. Adjudication then relies on deterministic graph executions with a visually auditable trace. In comparison against runtime LRM baselines (including GPT-5.2 and Gemini 3 Pro), our DACL-based Agent achieves near-perfect consistency and mitigates the “reasoning cliff” observed in probabilistic models. The system reduces compute costs by over 90% in high-volume workflows while satisfying the strict auditability requirements of legal adjudication.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.103ACL Anthology · ACL 2026

CI-Work: Benchmarking Contextual Integrity in Enterprise LLM Agents

Wenjie Fu, Xiaoting Qin, Jue Zhang, Qingwei Lin, Lukas Wutschitz, Robert Sim, Saravan Rajmohan, Dongmei Zhang

原文摘要

Enterprise LLM agents can dramatically improve workplace productivity, but their core capability, retrieving and using internal context to act on a user’s behalf, also creates new risks for sensitive information leakage. We introduce CI-Work, a Contextual Integrity (CI)-grounded benchmark that simulates enterprise workflows across five information-flow directions and evaluates whether agents can convey essential content while withholding sensitive context in dense retrieval settings.Our evaluation of frontier models reveals that privacy failures are prevalent (violation rates range from 15.8%-50.9%, with leakage reaching up to 26.7%) and uncovers a counterintuitive trade-off critical for industrial deployment: higher task utility often correlates with increased privacy violations.Moreover, the massive scale of enterprise data and potential user behavior further amplify this vulnerability. Simply increasing model size or reasoning depth fails to address the problem. We conclude that safeguarding enterprise workflows requires a paradigm shift, moving beyond model-centric scaling toward context-centric architectures.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.104ACL Anthology · ACL 2026

From Pixels to Policies: Reinforcing Spatial Reasoning in Language Models for Content-Aware Layout Design

Sha Li, Stefano Petrangeli, Yu Shen, Xiang Chen

原文摘要

We introduce LaySPA, a reinforcement learning framework that equips large language models (LLMs) with explicit and interpretable spatial reasoning for content-aware graphic layout design. LaySPA addresses two key challenges: LLMs’ limited spatial reasoning and the lack of transparency in design decision making. Instead of operating at the pixel level, we reformulate layout design as a policy learning problem over a structured textual spatial environment that explicitly encodes canvas geometry, element attributes, and inter-element relationships. LaySPA produces dual-level outputs comprising interpretable reasoning traces and structured layout specifications, enabling transparent and controllable design decision making. Layout design policy is optimized via a multi-objective spatial critique that decomposes layout quality into geometric validity, relational coherence, and aesthetic consistency, and is trained using relative group optimization to stabilize learning in open-ended design spaces. Experiments demonstrate that LaySPA improves structural validity and visual quality, outperforming larger proprietary LLMs and achieving performance comparable to specialized state-of-the-art layout generators while requiring fewer annotated samples.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.105ACL Anthology · ACL 2026

Practical Guidelines for Model Merging in LLMs Pre-Training

Giuseppe Curci, Stefano Simonazzi, Andrea molinari, Andrea Zugarini

原文摘要

Model merging is widely used to combine fine-tuned models trained with different data distributions, tasks, or hyperparameters, yet its role during LLM pre-training remains underexplored. We systematically study checkpoint merging across training phases, focusing on the transition from stable to decaying learning rates. Across multiple scales, we find that simple averaging methods consistently improve performance during stable learning rate regimes, but gains sharply diminish during decay. We link this effect to reduced checkpoint diversity and show that merging effectiveness correlates with parameter-space variation. Strategies such as synthetic variability, task-vector merging, and cross-run merging yield only modest improvements. Our results provide practical insights on when merging is most effective in large-scale pre-training.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.106ACL Anthology · ACL 2026

Are Large Language Models Economically Viable for Industry Deployment?

Abdullah Mohammad, Sushant Kumar Ray, Pushkar Arora, Rafiq Ali, Ebad Shabbir, Gautam Siddharth Kashyap, Jiechao Gao, Usman Naseem

原文摘要

Generative AI—powered by Large Language Models (LLMs)—is increasingly deployed in industry across healthcare decision support, financial analytics, enterprise retrieval, and conversational automation, where reliability, efficiency, and cost control are critical. In such settings, models must satisfy strict constraints on energy, latency, and hardware utilization—not accuracy alone. Yet prevailing evaluation pipelines remain accuracy-centric, creating a Deployment–Evaluation Gap—the absence of operational and economic criteria in model assessment. To address this gap, we present EDGE-EVAL—a industry-oriented benchmarking framework that evaluates LLMs across their full lifecycle on legacy NVIDIA Tesla T4 GPUs. Benchmarking LLaMA and Qwen variants across three industrial tasks, we introduce five deployment metrics—Economic Break-Even (Nbreak), Intelligence-Per-Watt (IP W ), System Density (ρsys), Cold-Start Tax (Ctax), and Quantization Fidelity (Qret)—capturing profitability, energy efficiency, hardware scaling, serverless feasibility, and compression safety. Our results reveal a clear efficiency frontier—models in the < 2B parameter class dominate larger baselines across economic and ecological dimensions. LLaMA-3.2-1B (INT4) achieves ROI break-even in 14 requests (median), delivers 3× higher energy-normalized intelligence than 7B models, and exceeds 6,900 tokens/s/GB under 4-bit quantization. We further uncover an efficiency anomaly—while QLoRA reduces memory footprint, it increases adaptation energy by up to 7× for small models—challenging prevailing assumptions about quantization-aware training in edge deployment.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.107ACL Anthology · ACL 2026

Let LLM Tutors Ask First: Proactive LLM-Based Tutoring at Scale in a 1,500-Student Online Classroom

Jonghoon Lee, Geonjae Youn, Seongmin Lee, Chaemoon Im, Joongheon Kim, Chuck Yoo

原文摘要

Large-scale introductory CS courses, often enrolling thousands of students, struggle to provide personalized support and encourage active participation. While recent large language models (LLMs) have enabled AI teaching assistants at scale, most existing systems remain reactive, responding only after students explicitly initiate queries. We present SCALA, a student-centered AI learning assistant designed to provide proactive support for students. SCALA introduces predictive query management, a mechanism that generates likely student questions and answers ahead of lectures. Students may choose to view these pre-generated question–answer pairs or engage in interactive conversations with our tutoring model via the same interface. We evaluate SCALA through a semester-long deployment in an undergraduate Python course with over 1,500 students, and find that predictive queries are frequently selected in practice and substantially overlap with real student questions. Based on student feedback, learners preferred SCALA’s responses to their real queries over alternatives such as GPT-4o. These results suggest proactive support as a promising direction for future development of AI-powered teaching assistants. We will release our codebase and interactive demo upon acceptance.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.108ACL Anthology · ACL 2026

Hierarchical Reason-of-Contact Detection in Retail Banking Customer Interactions via LLM-Driven Taxonomy Induction

Yoages Kumar Mantri, Yagneswaran Sriraja, John Hartley, Greig A Cowan, Raad Khraishi, Abhinav Gupta

原文摘要

Retail banks handle high volumes of customer interactions across different channels that span various topics. Early and accurate detection of the intent of the customer is critical towards streamlining contact-center operations through efficient routing and handling of conversations. Mining of customer interactions leads to identification of friction points in customer journeys and offers valuable insights about customer needs. Existing approaches to define customer intents or contact reasons remain fragmented, manually maintained across organizations and relying on knowledge of specific business processes. We propose a framework that develops a dynamic hierarchical Reason-of-Contact (RoC) taxonomy to cover customer topics across hundreds of business processes. We further demonstrate the implementation of this taxonomy to a robust solution that identifies intents for all customer conversations across different channels. Our deployed system supports real time use with a 150 to 300 ms turnaround per conversation. It achieves up to 10% improvement in F1 score over baseline approaches on a reference dataset. We also detail deployment considerations, including dynamic taxonomy updates, out-of-domain detection, and auditability. Finally, we present ablations and error analyses to characterize effectiveness.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.109ACL Anthology · ACL 2026

Fanar-Sadiq: A Multi-Agent Architecture for Grounded Islamic QA

Ummar Abbas, Mourad Ouzzani, Mohamed Y. Eltabakh, Omar Sinan, Gagan Bhatia, Hamdy Mubarak, Majd Hawasly, Mohammed Qusay Hashim 等 10 位作者

原文摘要

Large language models (LLMs) can answer religious knowledge queries fluently, yet they often hallucinate and misattribute sources, which is especially consequential in Islamic settings where users expect grounding in canonical texts (Qur’an and Hadith) and jurisprudential (fiqh) nuance. Retrieval-augmented generation (RAG) improves grounding, however, a single retrieve-then-generate pipeline is insufficient for diverse Islamic queries, including verbatim scripture, citation-grounded guidance, and rule-constrained computations such as zakat and inheritance. To address these challenges, we present Fanar-Sadiq, a bilingual Arabic-English Islamic QA system built on a multi-agent, tool-augmented architecture. It is a core component of the Fanar AI platform. Fanar-Sadiq routes Islamic queries to specialized modules within an agentic tool architecture. It supports intent-aware routing, retrieval-grounded fiqh answers with normalized citations and verification traces, exact verse lookup with quotation validation, and deterministic Sunni zakat and inheritance calculators with madhhab-sensitive branching. We evaluate the end-to-end system on public Islamic QA benchmarks and show strong effectiveness and efficiency. It is publicly accessible through an API and Web application and has received over 1.9M accesses in less than a year (https://api.fanar.qa/docs).

cs.CL · cs.AI · cs.MA

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.11ACL Anthology · ACL 2026

HAD: HAllucination Detection Language Models Based on a Comprehensive Hallucination Taxonomy

Fan Xu, Xinyu Hu, Zhenghan Yu, Li Lin, Xu Zhang, Yang Zhang, Wei Zhou, Jinjie Gu 等 9 位作者

原文摘要

The increasing reliance on natural language generation (NLG) models, particularly large language models, has raised concerns about the reliability and accuracy of their outputs. A key challenge is hallucination, where models produce plausible but incorrect information. As a result, hallucination detection has become a critical task. In this work, we introduce a comprehensive hallucination taxonomy with 11 categories across various NLG tasks and propose the HAllucination Detection (HAD) models, which integrate hallucination detection, span-level identification, and correction into a single inference process. Trained on an elaborate synthetic dataset of about 90K samples, our HAD models are versatile and can be applied to various NLG tasks. We also carefully annotate a test set for hallucination detection, called HADTest, which contains 2,248 samples. Evaluations on in-domain and out-of-domain test sets show that our HAD models generally outperform the existing baselines, achieving state-of-the-art results on HaluEval, FactCHD, and FaithBench, confirming their robustness and versatility.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.110ACL Anthology · ACL 2026

GuideTree: Guideline-Induced Review Trees for Long Medical Records

Chengze Ge, Ruiqing Zhang, Yining Wang, Shengping Liu, Liang Jiaen, Weihuang, Weihua Chen

原文摘要

Reviewing medical records for clinical and insurance decisions must handle long, heterogeneous documents while producing consistent, traceable, guideline-compliant outcomes under strict latency and cost constraints. We propose GuideTree, which compiles textual guidelines into a fixed review tree of evidence-grounded verification primitives. GuideTree uses short per-document summaries only for routing each check to a minimal set of document types and candidates; final verification always reads full document text and returns structured evidence. The tree is induced offline via a cost-aware split-and-prune search and updated safely through regression-tested, versioned patches. Across 1,000 cases from four industrial review scenarios and four LLM backbones, GuideTree achieves 84.5–92.8 Macro-F1, outperforming the strongest non-expert baselines by 3.3–7.6 points and matching ExpertTree within 0.2–0.6 points (avg. 0.38). On chronic disease with Qwen3-235B-A22B-Instruct, GuideTree reduces average I/O volume to 74K input+output characters (-82% vs. long-context prompting) and average latency to 22s (-83% vs. long-context prompting), while reaching 99% decision consistency over K=5 reruns.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.111ACL Anthology · ACL 2026

Automatic Prompt Engineering for Scalable Prompt Inversion in Text-to-Image Ad Generation

Zixin Ding, Qi Zeng, Boying Gong, Wenlong Deng, Bo Pan, Yuxin Chen

原文摘要

While prompt engineering offers effective control over Text-to-Image (T2I) generation, it remains labor-intensive for large-scale production. We present PRISM-DUEL, a black-box framework that formalizes prompt optimization as Automatic Prompt Engineering (APE), motivated by advertising workflows requiring low-latency, diverse variants faithful to a human-designed ads. Since zero-shot LLMs are unreliable judges of image quality, PRISM-DUEL obtains label-free pairwise preferences and rationales from an LLM judge over pairs of generated images, then uses a dueling-bandit optimizer to optimize a prompt for generating controlled variations while matching the reference ad’s visual content. By iteratively steering the prompt distribution towards higher-quality generations and improving posterior calibration, PRISM-DUEL preserves visual similarity and semantic faithfulness while increasing diversity. Experiments on PartiPrompts and DreamBooth across Gemini 2.5 Flash Image, FLUX.1, and Qwen-Image show consistent gains over strong baselines in visual faithfulness and prompt interpretability.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.112ACL Anthology · ACL 2026

Efficient Agent Evaluation via Diversity-Guided User Simulation

Itay Nakash, George Kour, Ateret Anaby Tavor

原文摘要

Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of full agent-user conversations to estimate success. This approach is computationally inefficient - reprocessing identical conversation prefixes across runs, and often fails to uncover deep failure modes triggered by rare user behaviors.We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), a snapshot-based, coverage-guided user simulation framework for efficient and systematic exploration of multi-turn agent behavior. DIVERT captures the full agent–environment state at critical junctions and resumes execution from these points, reusing shared prefixes to avoid redundant regeneration and reduce token cost. From each junction, it branches with targeted, diverse user responses, enabling directed exploration of alternative interaction paths while preserving task intent.By reallocating computation from redundant restarts to behaviorally salient mid-trajectory states, DIVERT steers evaluation toward under-explored semantic regions and rare interaction failures. Experiments on realistic multi-domain benchmarks show that our method consistently improves failure discovery efficiency and task-level coverage compared to standard linear rollout evaluation, without increasing overall cost.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.113ACL Anthology · ACL 2026

What Question Did You Answer? Refining Contact Center Evaluation Plans via Backward Questions

Prajwal Sood, Rushikesh Pawar, Digvijay Anil Ingle, Anup Pattnaik

原文摘要

Capturing organization-specific domain knowledge remains a critical challenge for deploying cost-efficient language models in specialized tasks like contact center Quality Assurance (QA). While large LMs implicitly capture expert judgment, smaller LMs require explicit evaluation criteria that domain experts struggle to articulate. We introduce Backward Question-based Refinement (BQR), a diagnostic framework that generates backward questions, revealing what a model understood rather than what was asked, to systematically distill implicit reasoning from large LMs into explicit evaluation plans. Through experiments on 12 QA questions, BQR achieves performance improvements on 8 questions with absolute gains of up to 27.8% in Macro F1. Our analysis establishes empirical parallels to gradient-descent optimization and reveals a cross-family advantage where small LMs benefit more from large LMs of different families. These findings confirm BQR as an effective approach for bridging the gap between implicit expert knowledge and explicit evaluation criteria.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.114ACL Anthology · ACL 2026

Natural-Language Policies to Executable Decisions: An Interpretable Large Language Model Framework

Ziqiang Zhang, Jing Ma, Zilong Wang, Jiayuan Chen, Yi Qiao, Yu He, Wei Zhang, Dai Cheng 等 9 位作者

原文摘要

Pricing automation in large-scale tourism is challenging because travel orders are highly unstructured, while pricing policies are complex, rapidly evolving, and inherently open-ended. Traditional rule engines are brittle and costly to maintain, whereas unconstrained LLM agents lack the reliability and auditability required for financial decisions. We present a production-grade LLM-powered pricing system with a strict decision boundary: LLMs perform structured extraction and bounded policy/path selection, while all numeric pricing, including total-price computation, is executed deterministically. Policies are compiled into interpretable condition trees, enabling open-ended support for new clauses and evolving rules without code changes, while exposing auditable artifacts for human-in-the-loop control. Periodic fine-tuning on logged traces further improves tree induction and path matching. Deployed at a municipal state-owned tourism enterprise across 7 scenic sites and 12 business categories with 1,500+ operators and 1,000+ active policies, the system processed 3,960 orders in six months, reduced the order management team from 15-20 to 3, and cut per-order handling time from 10 minutes to <2 minutes.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track) · 2026 · 正式发表