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首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-industry.30ACL Anthology · ACL 2026

Reason-Code: Reliable Code Generation via Test-Driven Monte Carlo Tree Search

Zixu Li, Zhiqi Peng

原文摘要

Large Language Models (LLMs) are widely used for code generation, but their performance degrades on tasks requiring multi-step logical reasoning. In practice, reliability is often improved through multi-sample inference, but its cost grows linearly with the sample size, making it impractical under strict latency constraints. To address this, we propose Reason-Code, an inference-time framework that formulates code generation as a search process guided by execution feedback. It integrates Monte Carlo Tree Search (MCTS) with a lightweight execution sandbox, where candidate programs are evaluated via unit tests. To control inference cost, Reason-Code adopts a conditional budgeting strategy that activates search only when greedy generation fails. Compared with large-sample Best-of-N sampling, Reason-Code is designed to improve reliability without paying the full linear cost of additional sampling under strict latency budgets. Experiments on HumanEval and MBPP show that Reason-Code matches strong sampling baselines (e.g., Best-of-10) with lower token cost and no regression. Additional matched-budget analyses show that execution-guided adaptive inference improves over independent sampling/filtering baselines, while differences between UCB-guided search and simpler iterative repair are limited at low budget.

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.31ACL Anthology · ACL 2026

Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance

Gilad Fuchs, Leonid Ekimov, Fei Dong, Jiahong Xie, Wei Liu, Maxim Manco, Alexander Nus

原文摘要

Conversational AI is increasingly used at eBay to deliver personalized customer support. We present a production RAG-based How-To Assistant that answers support and how-to queries by grounding responses in a proprietary knowledge base. We study three factors that drive quality: (1) document chunking and contextualization for indexing, (2) query refinement methods, and (3) automatic LLM-based evaluation for rapid iteration and reliable measurement. We also describe the end-to-end system workflow - from offline indexing to real-time serving and report deployment metrics, offering practical guidance for building scalable, high-precision RAG assistants in commercial support settings.

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.32ACL Anthology · ACL 2026

UNIVID: Unified Vision-Language Model for Video Moderation

Kejuan Yang, Yizhuo Zhang, Mingyuan Du, Yue Zhang, Dixin Zheng, Kaili Zhao, Yang Xiao, Hanzhong Liang 等 9 位作者

原文摘要

Global-scale video moderation faces a dual challenge: the need for fine-grained multimodal reasoning and the demand for interpretable outputs to support downstream enforcement. Traditional moderation systems often rely on fragmented black-box classifiers that are difficult to maintain and lack transparency.In this paper, we present UNIVID, a Unified Vision-Language model for Video Moderation. Unlike standard classification models, UNIVID generates policy-aware captions that serve as an interpretable intermediate representation, enabling human-verifiable decisions and multi-task reusability. While existing open-source and commercial VLMs often suffer from safety-guardrail refusals and lack fine-grained policy alignment, we develop a specialized training data recipe that combines expert human-refined labels with synthetic data to align the model with our safety guidelines.By integrating UNIVID as the core captioner, we design a novel end-to-end video moderation system that reduces violation leakage by 42.7% and overkill rate by 37.0% relatively. Meanwhile, by replacing over 1,000 policy-specific models with a single UNIVID backbone, we recycle extensive computational resources while significantly reducing engineering maintenance overhead. To our knowledge, this is one of the first reports of a high-efficiency captioning VLM successfully supporting industrial-scale moderation and cross-functional business.

cs.CL · cs.CV

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.33ACL Anthology · ACL 2026

Learning from Textual Radiology Reports: A Benchmark Dataset for Coronary CT Angiography

Sudharshan Balaji, Zhiyu Liu, Zhengyuan Jiang, Shuo Lei, Yimin Chen, Yang Xiao, Shone O. Almeida, Mathew Joseph Karivelil 等 10 位作者

原文摘要

While coronary imaging is widely used for anatomical assessment, CCTA reports play a distinct last-mile role in clinical care. Ratherthan serving as an intermediate signal, CCTA provides an assessment of coronary disease severity (known as the CAD-RADS score) toguide patient management. However, real-world clinical text exhibits substantial heterogeneity in terminology and structure, leadingto inconsistent interpretation by automated systems, even for clinically similar cases. Recent work leverages a direct application ofLLMs for automated CAD-RADS scoring, but is limited by small, non-public, and homogeneous clinical data. We introduce CCTA-RADS, the largest publicly available dataset of 940 real-world CCTA reports from a major cardiovascular center, each annotated with CAD-RADS scores. Our analysis reveals that direct approaches, including state-of-the-art LLMs (GPT-4o, GPT-o3) and fine-tuned BERT models underperform on diverse real-world clinical data. To address these limitations, we propose a two-stage pipeline that decouples structuring from classification: an LLM-based parser normalizes heterogeneous reports into structured format, followed by fine-tuned BERT classification. This approach substantially improves the F1-score by 6%-13% compared with direct methods. We deploy our system as an interactive web interface that allows clinicians to upload CCTA reports for automated CAD-RADS assessment with SHAP and LIME explainability visualizations.

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.34ACL Anthology · ACL 2026

Teaching LLM to be Persuasive: Reward-Enhanced Policy Optimization for Alignment from Heterogeneous Rewards

Xia Zeng, Yihan Chen, Luhui Liu, Chao Luo, Ye Chen, Zhuangzhuoran

原文摘要

We deploy large language models (LLMs) as business development (BD) agents for persuasive price negotiation in online travel agencies (OTAs). The agent must follow a multi-stage Standard Operating Procedure (SOP) and strict guardrails (no over-promising and no hallucinations), while remaining human-like and effective over long, multi-turn dialogues.We propose Reward-Enhanced Policy Optimization (REPO), a reinforcement learning post-training method that combines heterogeneous rewards: a preference-trained reward model (RM), an LLM-as-a-judge (RJ) for nuanced behaviors (e.g., emotional value and SOP compliance), and rule-based reward functions (RF) (mainly regex-based) for deterministic checks on numerics, formatting, and guardrails. In expert consensus evaluation (three human experts; 30 online conversations and 45 curated bad cases), REPO improves average dialogue rating to 4.63 (+0.33 over GRPO) and raises the share of conversations with at least one excellent response to 66.67% (+23.34 pp over GRPO), while achieving a 93.33% bad-case fix rate with 75.56% clean fixes.In a production A/B test on 9,653 real customer conversations (vs. an intent-driven dialogue system), REPO improves response rate by +12.14 pp and task success rate by +5.94 pp (p<0.001).

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.35ACL Anthology · ACL 2026

NEST: Nested Evidence Survival for Retrieval

Akshay Verma, Siddharth Pillai, Prateek Sircar, Deepak Gupta

原文摘要

Retrieval-Augmented Generation (RAG) systems degrade sharply under extreme noise, where relevant evidence is sparse and easily pruned by static retrieval decisions. Existing approaches fixed top-k retrieval, hierarchical chunking, cross-encoder reranking, or policy-based iterative control- either rely on rigid heuristics or incur substantial computational overhead, and often fail to recover context-dependent evidence without introducing redundancy or latency. We introduce NEST (Nested Evidence Survival for Retrieval), a lightweight, training-free RAG framework that improves factual grounding by explicitly separating recall amplification from precision selection. NEST first maximizes recall through Nested Evidence Survival, evaluating candidates under nested retrieval contexts to rescue evidence that would otherwise be pruned by static chunking. It then applies a survival-consistent Mean Reciprocal Rank (MRR) selection mechanism to retain evidence that remains salient across retrieval scopes, removing redundancy without harming recall. Evaluated on WebQuestions, HotpotQA (distractor setting), and a proprietary InternalQA benchmark with 50M Common Crawl distractors, NEST consistently outperforms strong adaptive RAG baselines, including DeepRAG, improving EM by up to +2.4 pp and F1 by +2.1 pp, while increasing retrieval recall by +6.8 pp. These gains are achieved with only 12–18 ms additional latency. Ablation studies confirm that Nested Evidence Survival drives recall improvements, while MRR-based selection converts these gains into precision, demonstrating that recall-first retrieval with principled pruning can outperform iterative control and model scaling in retrieval-augmented generation.

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.36ACL Anthology · ACL 2026

Search-P1: Path-Centric Reward Shaping for Stable and Efficient Agentic RAG Training

Tianle Xia, Ming Xu, Lingxiang Hu, Yiding Sun, Wenwei Li, Linfang Shang, Liqun Liu, Peng Shu 等 10 位作者

原文摘要

Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by incorporating external knowledge, yet traditional single-round retrieval struggles with complex multi-step reasoning.Agentic RAG addresses this by enabling LLMs to dynamically decide when and what to retrieve, but current RL-based training methods suffer from sparse outcome rewards that discard intermediate signals and low sample efficiency where failed samples contribute nothing.We propose Search-P1, a framework that introduces path-centric reward shaping for agentic RAG training, comprising two key components: (1) Path-Centric Reward, which evaluates the structural quality of reasoning trajectories through order-agnostic step coverage and soft scoring that extracts learning signals even from failed samples, and (2) Dual-Track Path Scoring with offline-generated reference planners that assesses paths from both self-consistency and reference-alignment perspectives.Experiments on multiple QA benchmarks demonstrate that Search-P1 achieves significant improvements over Search-R1 and other strong baselines, with an average accuracy gain of 7.7 points.

cs.CL · cs.AI

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.37ACL Anthology · ACL 2026

AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use

Yuanjie Lyu, Chengyu Wang, Haonan Zheng, Yuanhao Yue, Junbing Yan, Ming Wang, Jun Huang

原文摘要

Modern industrial applications increasingly demand language models that act as agents, capable of multi-step reasoning and tool use in real-world settings. These tasks are typically performed under strict cost and latency constraints, making small agentic models highly desirable. In this paper, we introduce the AgenticQwen family of models, trained via multi-round reinforcement learning (RL) on synthetic data and a limited amount of open-source data. Our training framework combines reasoning RL and agentic RL with dual data flywheels that automatically generate increasingly challenging tasks. The reasoning flywheel increases task difficulty by learning from errors, while the agentic flywheel expands linear workflows into multi-branch behavior trees that better reflect the decision complexity of real-world applications. We validate AgenticQwen on public benchmarks and in an industrial agent system. The models achieve strong performance on multiple agentic benchmarks, and in our industrial agent system, close the gap with much larger models on search and data analysis tasks.

cs.CL · cs.AI

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.38ACL Anthology · ACL 2026

From Short Video to Clickable Search: RLVR-Enabled Listwise Query Suggestion with Retrieval-Augmented Context

Mingkai Tian, Xuye, Long Meng, Liwei Chen, Zhiheng Qin, Yi Wang

原文摘要

Short-video platforms now present tappable search entries beneath the video player, making it effortless for users to shift from passively watching to actively searching for information. Prior work on bottom-bar query generation conditions on titles and OCR to generate a single query per forward pass, constrains decoding with a trie, and evaluates against a single reference using edit-distance–style supervision—making it difficult to cover the diverse intents a video can trigger and to credit semantically equivalent query variants. Motivated by these limitations, we propose four complementary improvements. First, we reformulate the task as one-shot list generation, producing multiple distinct queries per video, and build multi-query ground truth from exposure and CTR logs. Second, we redesign offline evaluation with \operatorname{CTR\text{-}HungF1}, a CTR-weighted set-matching metric via optimal assignment over token-level F1 score. Third, we enrich context with a video-to-video-to-query (V2V2Q) RAG pipeline to provide behavior-grounded background knowledge. Finally, we apply thinking-free RLVR with deterministic format checks and \operatorname{CTR\text{-}HungF1} rewards to train a compact LLM without reward models or CoT distillation. The resulting system yields strong offline and online improvements, and has been deployed on Kuaishou to serve hundreds of millions of users daily.

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.39ACL Anthology · ACL 2026

Small Agents, Big Gains: Journey-Aware and Critic-Guided Simulation for Long-Horizon Shopping Dialogues

Qing Ping, Changyou Chen, Binxuan Huang

原文摘要

Modern e-commerce assistants must go beyond simple product search to support inspiration, comparison, and tool-grounded fact-checking across non-linear shopping journeys. However, distilling these complex behaviors into efficient, deployable models is bottle-necked by a lack of post-training data: trajectories must cover diverse agentic workflows with high fidelity, yet the desired outputs are open-ended without a single ground truth. We propose a closed-loop Multi-Agent Simulation Framework to synthesize diverse, faithful, and policy-aligned shopping trajectories. The system orchestrates a journey-aware, stateful user simulator to drive exploration, a shopping agent that manages both tools and UI elements, and a critic agent that provides rubric-driven feedback to iteratively refine the data. On a domain-specific benchmark, this synthetic data enables a small model to significantly outperform same-size baselines and surpass a large-model baseline, achieving near-zero tool-calling errors with 8\times higher inference throughput.

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.4ACL Anthology · ACL 2026

Leveraging Generative AI for Extracting Business Requirements from Legacy COBOL and PL/I Code

Ankur Kalohia

原文摘要

Recovering business requirements fromCOBOL and PL/I portfolios is difficult becauselogic is scattered across interdependentprograms and data definitions, and existinganalyses seldom yield stakeholder-facingartifacts. We introduce an LLM-augmentedreverse-engineering pipeline that providesdeterministic parsing, schema-constrainedLLM generation with bidirectional traceabilityto code. It couples grammar-based parsingand control-flow and data-flow analysis with alarge language model to translate an enrichedintermediate representation into structuredspecifications. This is not raw-code promptingor generic summarization, the novelty is theLLM-centered generation over an enriched IR,with structured JSON outputs and traceabilityfor compliance-sensitive settings. The pipelineproduces business requirements documents,explicit rule catalogs, end-to-end data lineage,create–read–update–delete matrices, and field-level source-to-target mappings, each linkedto the supporting code. In a financial industrysetting, containing 3.4M+ LoC includingcomments / 3.2M excluding comments ofCOBOL, the system achieves 93% agreementwith expert-authored business rules andreduces documentation effort by approximately70%, as measured against manually producedrequirement documents and rule sets. On theinternal corpus spanning 3.4M lines acrossonline, batch, and job control workloads, theapproach yields approximately 3.2–3.3× fasteranalysis while improving artifact consistencyand traceability.

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.40ACL Anthology · ACL 2026

Progressive Fine-Tuning for Cost-Effective Structured Attribute Generation in E-commerce

Lakshman Kolasani, Fatemeh Taheri Dezaki

原文摘要

Large language models (LLMs) excel at structured information generation but face cost and latency challenges when deployed at scale in user-facing products. We present a parameter efficient supervised fine-tuning pipeline for adapting a small language model (SLM) to structured attribute generation in e-commerce product listing, enabling continuous model improvement with implicit user feedback without expensive manual annotation. Our approach involves completeness-deficit guided curation, which ranks samples by divergence between model predictions and catalog listing attributes, selecting the highest completeness gap examples for progressive fine-tuning. Our system is deployed on a large-scale product listing service, reducing inference costs by 98% and p90 latency by 70% using a fine-tuned SLM relative to the baseline LLM while preserving an 86.4% user acceptance rate, translating to significant monthly infrastructure savings.

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.41ACL Anthology · ACL 2026

Grounded Multimodal In-Context Learning for Product Weight Estimation at Scale in E-commerce

Bhavuk Singhal, Arsh Keshari, Ravindra Kumar Yadav

原文摘要

Accurately inferring implicit physical attributes of products, such as weight, is critical for large-scale e-commerce logistics but challenging due to sparse or unreliable textual metadata and high visual variability. We formulate weight estimation as a grounded multimodal reasoning problem and investigate whether large vision-language models (LVLMs) can infer discretized weight buckets through in-context learning (ICL) over product images and descriptions. We introduce a scalable inference framework that conditions predictions on automatically retrieved, category-specific exemplars and propose a distribution-calibrated retrieval strategy that aligns few-shot contexts with the empirical weight distribution of each product sub-category. This calibration substantially improves few-shot multimodal reasoning compared to random or embedding-based retrieval baselines. Across 14 high-variance categories, our approach significantly outperforms strong multimodal KNN baselines in both exact-match accuracy and near-bucket reliability. Deployed in production on a large e-commerce platform, our system processes millions of listings daily and reduces shipping-related revenue leakage by \sim22%, demonstrating that multimodal ICL can serve as a practical and cost-effective alternative to manual or hardware-based verification.

cs.CL · cs.CV

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.42ACL Anthology · ACL 2026

Towards Faithful Industrial RAG: A Reinforced Co-adaptation Framework for Advertising QA

Wenwei Li, Ming Xu, Tianle Xia, Lingxiang Hu, Yiding Sun, Linfang Shang, Liqun Liu, Peng Shu 等 10 位作者

原文摘要

Industrial advertising question answering (QA) is a high-stakes task in which hallucinated content, particularly fabricated URLs, can lead to financial loss, compliance violations, and legal risk. Although Retrieval-Augmented Generation (RAG) is widely adopted, deploying it in production remains challenging because industrial knowledge is inherently relational, frequently updated, and insufficiently aligned with generation objectives. We propose a reinforced co-adaptation framework that jointly optimizes retrieval and generation through two components: (1) Graph-aware Retrieval (GraphRAG), which models entity-relation structure over a high-citation knowledge subgraph for multi-hop, domain-specific evidence selection; and (2) evidence-constrained reinforcement learning via Group Relative Policy Optimization (GRPO) with multi-dimensional rewards covering faithfulness, style compliance, safety, and URL validity. Experiments on an internal advertising QA dataset show consistent gains across expert-judged dimensions including accuracy, completeness, and safety, while reducing the hallucination rate by 72%. A two-week online A/B test demonstrates a 28.6% increase in like rate, a 46.2% decrease in dislike rate, and a 92.7% reduction in URL hallucination. The system has been running in production for over half a year and has served millions of QA interactions.

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.43ACL Anthology · ACL 2026

A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting

Yu Lei, Zixuan Wang, Yiqing Feng, Junru Zhang, Yahui Li, Liu Chu, Wang Tongyao, Dongyang Li

原文摘要

Recent industrial credit scoring models remain heavily reliant on manually tuned statistical learning methods. Despite their potential, deep learning architectures have struggled to consistently outperform traditional statistical models in industrial credit scoring, largely due to the complexity of heterogeneous financial data and the challenge of modeling evolving creditworthiness. To bridge this gap, we introduce FinLangNet, a novel framework that reformulates credit scoring as a multi-scale sequential learning problem. FinLangNet processes heterogeneous financial data through a dual-module architecture that combines tabular feature extraction with temporal sequence modeling, generating probability distributions of users’ future financial behaviors across multiple time horizons. A key innovation is our dual-prompt mechanism within the sequential module, which introduces learnable prompts operating at both feature-level granularity for capturing fine-grained temporal patterns and user-level granularity for aggregating holistic risk profiles. Notably, real world deployment yielded a 6.3 pp improvement in KS, along with a 9.9% reduction in bad debt rate.

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.44ACL Anthology · ACL 2026

DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition

Le Zhang, Shengming Zhang, Rui Zha, Wu Yunpeng, Jingbo Zhou, Jizhou Huang

原文摘要

Accurate Point of Interest (POI) attribute acquisition is essential for location-based services, yet traditional modular Interactive Voice Response (IVR) systems suffer from error accumulation and high maintenance overhead. We present DuIVRS-2, a large language model (LLM)-based end-to-end framework designed for large-scale POI attribute acquisition at Baidu Maps. To address the long-tail distribution of real-world interactions, our methodology first employs a finite state machine (FSM)-guided data augmentation strategy to synthesize a balanced and diverse training dataset. We then streamline dialogue management via a selective generation scheme combined with a Chain-of-Thought (CoT) mechanism, which ensures output stability and effectively eliminates hallucinations in industrial settings. To facilitate continuous policy refinement with minimal manual effort, we design a cooperative iterative learning framework that leverages a dual-evaluator voting system. Deployed in production for two months, DuIVRS-2 processed 0.4 million calls daily and achieved a 83.9% Task Success Rate (TSR), outperforming its predecessor by 4 percentage points while maintaining a low reaction time of 130ms. This work provides a production-proven reference for developing robust, cost-effective LLM agents for large-scale industrial dialogue applications.

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.45ACL Anthology · ACL 2026

SAJA: A Simple Approach to Judge Alignment for LLM-as-a-Judge

Sneha Kola, Pankaj Kumar Sharma, Soumyadeep Dey, Bamdev Mishra, Mayur Datar

原文摘要

LLM-as-a-Judge systems are increasingly used to evaluate text at scale, yet production deployment demands low latency, minimal cost, and compatibility with closed-source APIs. Current approaches fall short in different ways: some require many LLM calls and per-dataset prompt tuning, others depend on logit access unavailable in commercial APIs, and yet others demand multiple rounds of LLM interaction for iterative feature discovery. We present SAJA (Simple Approach to Judge Alignment), built on the principle that task-specific alignment should reside in a lightweight calibration head, not in elaborate prompts or model internals. SAJA makes exactly one LLM call per item using a fixed structured rubric prompt, extracts a multi-dimensional feature vector, and maps it to a human-aligned score via a calibration head trained on a small number of human labels. No iterative prompt search, no logit access, and no multi-round LLM interaction are needed. Yet SAJA matches far more complex systems across four evaluation paradigms: 86% F1 on MT-Bench pairwise preference (vs. 78% uncalibrated), competitive performance on five classification benchmarks with a single call, and +5.71% F1 over prompt-optimized baselines on proprietary data. Ablations confirm that multi-dimensional rubric features outperform one-dimensional calibration (SummEval \rho improves from 0.60 to 0.74) and that coarse rubric outputs recover the same human alignment as full logit distributions (\rho = 0.36 vs. 0.37), establishing that logit access is unnecessary for calibrated judge alignment. Moreover, SAJA is model-agnostic: a 9B open-source model with SAJA (\rho{=}0.70) surpasses raw GPT-4.1 (\rho{=}0.60). Its single-call design yields up to 4.8\times cost savings over per-question approaches.

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.46ACL Anthology · ACL 2026

ColorBrowserAgent: Complex Long-Horizon Browser Agent with Adaptive Knowledge Evolution

Jihong Wang, Jiamu Zhou, Weiming Zhang, Teng Wang, Weiwen Liu, Zhuosheng Zhang, Xingyu Lou, Weinan Zhang 等 10 位作者

原文摘要

With the advancement of vision-language models, web automation has made significant progress. However, deploying autonomous agents in real-world settings remains challenging, primarily due to site heterogeneity, where generalist models lack domain-specific priors for diverse interfaces, and long-horizon instability, characterized by the accumulation of decision drift over extended interactions. To address these challenges, we introduce ColorBrowserAgent (Complex Long-Horizon Browser Agent), a knowledge-evolving agent for robust web automation. Our approach addresses these challenges through two synergistic mechanisms: human-in-the-loop knowledge adaptation that transforms sparse human feedback into reusable domain knowledge, and knowledge-aligned progressive summarization that stabilizes long interactions through memory compression. Extensive experiments on WebArena, WebChoreArena and industrial deployment show that ColorBrowserAgent consistently outperforms strong baselines. It achieves a state-of-the-art success rate of 71.2% on WebArena and maintains 47.4% performance under zero-shot transfer setting on WebChoreArena. In commercial deployment, it improves user satisfaction by 19.3% relatively, verifying its robustness in real-world scenarios.

cs.CL · cs.CV

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.47ACL Anthology · ACL 2026

ClinDet-Bench: Beyond Abstention, Evaluating Judgment Determinability of LLMs in Clinical Decision-Making

Yusuke Watanabe, Yohei Kobashi, Takeshi Kojima, Yusuke Iwasawa, Yasushi Okuno, Yutaka Matsuo

原文摘要

Clinical decisions are often required under incomplete information. Clinical experts must identify whether available information is sufficient for judgment, as both premature conclusions and unnecessary abstention can compromise patient safety. To evaluate this capability of large language models (LLMs), we developed ClinDet-Bench, a benchmark based on clinical scoring systems that decomposes incomplete-information scenarios into determinable and undeterminable conditions. Identifying determinability requires considering all hypotheses about missing information, including unlikely ones, and verifying whether the conclusion holds across them. We find that recent LLMs fail to identify determinability under incomplete information, producing both premature conclusions and excessive abstention, despite correctly explaining the underlying scoring knowledge and performing well under complete information. These findings suggest that existing benchmarks are insufficient to evaluate the safety of LLMs in clinical settings. ClinDet-Bench provides a framework for evaluating determinability recognition, leading to appropriate abstention, with potential applicability to medicine and other high-stakes domains, and is publicly available.

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.48ACL Anthology · ACL 2026

AIDA-SEAT: Towards Reliable AI Doctor Assistant via State-Evaluation-Action Tree Enhanced LLMs in Online Hospital

Lianxin Sun, Xiaoying Ying, Guangya Yu, Weiyan Zhang, Chenhao Guan, Hao He, Mingxi Shang, Jianhua Li 等 10 位作者

原文摘要

Artificial intelligence doctor assistants (AIDAs) help streamline clinical decision-making and reduce physician workload. While existing systems primarily utilize Large Language Models (LLMs) or retrieval-augmented generation (RAG), these methods typically retrieve static facts—whether as text passages or structured graphs—lacking the explicit logical pathways essential for multi-step reasoning. In this paper, we propose the AIDA-SEAT framework to provide reliable clinical decision-making support. First, we design the state-evaluation-action tree (SEAT), which covers diagnosis, treatment, and examination. To develop this tree, we refine and transform SEAT collected from medical documents and doctors. Then, we propose an adaptive method to select optimal trees tailored to the current patients’ state. Finally, we leverage LLMs to perform state assessment, evaluation, and action execution based on the tree, thereby generating reliable responses. To evaluate the effectiveness of our method, we conducted extensive experiments on a self-built dataset. Our method achieves 1.01% higher than current state-of-the-art (SOTA) baselines across five departments, including common RAG-based methods. Furthermore, analysis of 200 consultation records during deployment on an online hospital revealed that system-assisted responses are 24.16 seconds faster on average than manual ones, improving efficiency by 26.85%.

cs.CL · cs.AI

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.49ACL Anthology · ACL 2026

IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance

Chathurangi Shyalika, Dhaval C Patel, Amit Sheth

原文摘要

Industrial maintenance environments increasingly rely on AI systems to assist operators in understanding asset behavior, diagnosing failures, and evaluating interventions. Although large language models (LLMs) enable fluent natural-language interaction, deployed maintenance assistants routinely produce generic explanations that are weakly grounded in telemetry, omit verifiable provenance, and offer no testable support for counterfactual or action-oriented reasoning that undermine trust in safety-critical settings. We present IndustryAssetEQA, a neurosymbolic operational intelligence system that combines episode-centric telemetry representations with a Failure Mode and Effects Analysis Knowledge Graph (FMEA-KG) to enable Embodied Question Answering (EQA) over industrial assets. We evaluate on four datasets covering four industrial asset types, including rotating machinery, turbofan engines, hydraulic systems, and cyber–physical production systems. Compared to LLM-only baselines, IndustryAssetEQA improves structural validity by up to +0.51, counterfactual accuracy by up to +0.47, and explanation entailment by +0.64, while reducing severe expert-rated overclaims from 28% to 2% ( 93%). Code, datasets, and the FMEA-KG are available at: https://github.com/IBM/AssetOpsBench/tree/IndustryAssetEQA/IndustryAssetEQA

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.5ACL Anthology · ACL 2026

Is Agentic RAG worth it? An experimental comparison of RAG approaches

Pietro Ferrazzi, Milica Cvjetićanin, Alessio Piraccini, Davide Giannuzzi

原文摘要

Retrieval-Augmented Generation (RAG) systems are usually defined by the combination of a generator and a retrieval component that extracts textual context from a knowledge base to answer user queries. However, such basic implementations exhibit several limitations, including noisy or suboptimal retrieval, misuse of retrieval for out-of-scope queries, weak query–document matching, and variability or cost associated with the generator. These shortcomings have motivated the development of “Enhanced” RAG, where dedicated modules are introduced to address specific weaknesses in the workflow.More recently, the growing self-reflective capabilities of Large Language Models (LLMs) have enabled a new paradigm, often referred to as “Agentic” RAG. In this approach, an LLM orchestrates the entire process, deciding which actions to perform, when to perform them, and whether to iterate. Despite the rapid adoption of both paradigms, it remains unclear which approach is preferable under which conditions.In this work, we conduct an empirically driven evaluation of “Enhanced” and “Agentic” RAG across multiple scenarios and dimensions. Our results provide practical insights into the trade-offs between the two paradigms, offering guidance on selecting the most effective RAG design for real-world applications, considering both performance and costs.

cs.CL · cs.AI

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.50ACL Anthology · ACL 2026

Evidence-Driven Reasoning for Industrial Maintenance Using Heterogeneous Data

Fearghal O’Donncha, Nianjun Zhou, Natalia Martinez, James T Rayfield, Fenno F. Heath III, Abigail Langbridge, Roman Vaculin

原文摘要

Industrial maintenance platforms contain rich but fragmented evidence, including free-text work orders, heterogeneous operational sensors or indicators, and structured failure knowledge. These sources are often analyzed in isolation, producing alerts or forecasts that do not support conditional decision-making: given this asset history and behavior, what is happening and what action is warranted?We present Condition Insight Agent, a deployed decision-support framework that integrates maintenance language, behavioral abstractions of operational data, and engineering failure semantics to produce evidence-grounded explanations and advisory actions. The system constrains reasoning through deterministic evidence construction and structured failure knowledge, and applies a rule-based verification loop to suppress unsupported conclusions.Case studies from production CMMS deployments show that this verification-first design operates reliably under heterogeneous and incomplete data while preserving human oversight. Our results demonstrate how constrained LLM-based reasoning can function as a governed decision-support layer for industrial maintenance.

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.51ACL Anthology · ACL 2026

MobileLLM-Flash: Latency-Guided On-Device LLM Design for Industry Scale Deployment

Hanxian Huang, Igor Fedorov, Andrey Gromov, Bernard Beckerman, Naveen Suda, David Eriksson, Maximilian Balandat, Rylan Conway 等 17 位作者

原文摘要

Real-time AI experiences call for on-device large language models (OD-LLMs) optimized for efficient deployment on resource-constrained hardware. The most useful OD-LLMs produce near-real-time responses and exhibit broad hardware compatibility, maximizing user reach. We present a methodology for designing such models using hardware-in-the-loop architecture search under mobile latency constraints. This system is amenable to industry-scale deployment: it generates models deployable without custom kernels and compatible with standard mobile runtimes like Executorch. Our methodology avoids specialized attention mechanisms and instead uses attention skipping for long-context acceleration. Our approach jointly optimizes model architecture (layers, dimensions) and attention pattern. To efficiently evaluate candidates, we treat each as a pruned version of a pretrained backbone with inherited weights, thereby achieving high accuracy with minimal continued pretraining. We leverage the low cost of latency evaluation in a staged process: learning an accurate latency model first, then searching for the Pareto-frontier across latency and quality.This yields MobileLLM-Flash, a family of foundation models (350M, 650M, 1.4B) for efficient on-device use with strong capabilities, supporting up to 8k context length. MobileLLM-Flash delivers up to 1.8x and 1.6x faster prefill and decode on mobile CPUs with comparable or superior quality. Our analysis of Pareto-frontier design choices offers actionable principles for OD-LLM design.

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.52ACL Anthology · ACL 2026

LEAP: Layer-wise Exit-Aware Pretraining for Efficient Transformer Inference

Shashank Kapadia, Deep Narayan Mishra, Sujal Reddy Alugubelli, Haoan Wang, Saipraveen Vabbilisetty, Rishi Bhatia, Anupriya Sharma

原文摘要

Layer-aligned distillation and convergence-based early exit represent two predominant computational efficiency paradigms for transformer inference; yet we establish that they exhibit fundamental incompatibility under standard deployment conditions for convergence-based early exit. Distillation objectives that align intermediate student layers to teacher representations suppress the representational convergence that early-exit mechanisms exploit, rendering such mechanisms ineffective on distilled models.We introduce LEAP (Layer-wise Exit-Aware Pretraining), an auxiliary training objective that reconciles this incompatibility. LEAP requires no architectural modifications; it augments standard distillation with a single constraint ensuring intermediate layers approximate final-layer representations. LEAP-MiniLM achieves 1.61\times measured wall-clock speedup (batch = 1, NVIDIA L4) at \theta = 0.95, with 91.9% of samples exiting by layer 7 and 1.80\times theoretical layer reduction, where standard distilled models achieve zero effective speedup. We validate across sentence similarity (STS-B: 0.760 \pm 0.006) and retrieval benchmarks (BEIR), providing operational guidance including latency measurements, decision thresholds, and deployment 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.53ACL Anthology · ACL 2026

Think Like You Execute: Verifiable Chain of Thought from Program Traces

Shailja Thakur, Vaibhav Saxena, Rohan Kulkarni, Shivdeep Singh, Parameswaran Selvam, Hiroshi Kanayama, Hima Patel

原文摘要

Teaching language models to reason about code execution is still an open problem. Current synthetic Chain-of-Thought (CoT) training data often consists of plausible-sounding explanations generated by teacher models, not verifiable accounts of actual program behavior. This causes models to learn logically flawed reasoning patterns despite syntactic correctness.We address this by grounding CoT generation directly in program execution traces. Our pipeline instruments code to capture dynamic behavior, narrates execution traces into natural language, and actively verifies each rationale against the trace. We systematically create 54,000 execution-verified, bi-directional rationales that teach models to reason both forward (input\rightarrowoutput) and backward (output\rightarrowinput). Models fine-tuned on our verified data achieve substantial improvements, with a performance boost of +24.2 on LiveCodeBench-Exec, +22.3 on CruxEval-Output, and +21.1 on CruxEval-Input, demonstrating that verification quality directly determines both reasoning and code generation capabilities.

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.54ACL Anthology · ACL 2026

From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines

Sunkyung Lee, Jihye Back, Donghyeon Jeon, Soonhwan Kwon, Moonkwon Kim, Inho Kang, Jongwuk Lee

原文摘要

Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existing works primarily optimize for relevance while often overlooking document trustworthiness. This is critical in high-stakes domains like healthcare and finance, where relying solely on semantic relevance risks retrieving unreliable information. To address this, we propose an Authority-aware Generative Retriever (AuthGR), the first framework that incorporates authority into GenIR. AuthGR consists of three key components: (i) Multimodal Authority Scoring, which employs a vision-language model to quantify authority from textual and visual cues; (ii) a Three-stage Training Pipeline to progressively instill authority awareness into the retriever; and (iii) a Hybrid Ensemble Pipeline for robust deployment. Offline evaluations demonstrate that AuthGR successfully enhances both authority and accuracy, with our 3B model matching a 14B baseline. Crucially, large-scale online A/B tests and human evaluations conducted on the commercial web search platform confirm significant improvements in real-world user engagement and reliability.

cs.CL · cs.CV

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.55ACL Anthology · ACL 2026

Enhancing Online Recruitment with Category-Aware MoE and LLM-based Data Augmentation

Minping Chen, Bing Xu, Zulong Chen, Chuanfei Xu, Ying Zhou, Zui Tao, Zeyi Wen

原文摘要

Person-Job Fit (PJF) is a critical component for online recruitment. Existing approaches face several challenges, particularly in handling low-quality job descriptions and similar candidate-job pairs, which impair model performance. To address these challenges, this paper proposes a large language model (LLM) based method with two novel techniques: (1) LLM-based data augmentation, which polishes and rewrites low-quality job descriptions by leveraging chain-of-thought (COT) prompts, and (2) category-aware Mixture of Experts (MoE) that assists in identifying similar candidate-job pairs. This MoE module incorporates category embeddings to dynamically assign weights to the experts and learns more distinguishable patterns for similar candidate-job pairs. We perform offline evaluations and online A/B tests on our recruitment platform. Our method relatively surpasses existing methods by 2.40% in AUC and 7.46% in GAUC, and boosts click-through conversion rate (CTCVR) by 19.4% in online tests, saving millions of CNY in external headhunting expenses.

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.56ACL Anthology · ACL 2026

EfficientTool: A Cost-Effective Aligning Framework for Tool-Conditioned Agents in SME Scenarios

Yuanqi Mu, Bingfeng Pi, Defei Xia, Lei Zuo, Yongqi Zhang

原文摘要

Large language models (LLMs) are increasingly adopted in downstream industries, yet aligning proprietary agents remains challenging due to limited high-quality data and hardware constraints in small and medium-sized enterprises (SMEs).We propose EfficientTool, a cost-effective, tool-conditioned alignment framework forming a closed loop over data collection, iterative training, and deployment-oriented evaluation.EfficientTool adopts a self-evolving bootstrapping-based Trajectory Collection Pipeline for high-quality trajectory generation, followed by iterative Model Training Pipeline using tool-conditioned parameter-efficient fine-tuning (PEFT).We evaluate the model with Interaction and Evaluation Pipeline in public and private benchmarks, and deploy for an internal enterprise agent.Results show that EfficientTool effectively aligns model in SME scenarios while preserving general tool-calling capability.

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.57ACL Anthology · ACL 2026

Unleashing Low-Bit Inference on Ascend NPUs: A Comprehensive Evaluation of HiFloat Formats

Pengxiang Zhao, Hui-Ling Zhen, Xing Li, Han Bao, Weizhe Lin, Zhiyuan Yang, Yu Zi Wei, Xin Wang 等 11 位作者

原文摘要

As LLMs scale, low-bit floating-point formats like MXFP and NVFP4 offer new opportunities for precision and efficiency. In this work, we evaluate HiFloat (HiF8 and HiF4), a family of formats tailored for Ascend NPUs. Through rigorous comparison across weight-activation and KV-cache tasks, we provide three key insights: (1) INT8 suits narrow-range data, while floating-point formats excel with high-variance data; (2) in 4-bit regimes, HiF4’s hierarchical scaling prevents the accuracy collapse seen in integer formats; and (3) HiFloat is fully compatible with state-of-the-art post-training quantization frameworks. Overall, HiFloat provides a solution for high-efficiency LLM inference on NPUs.

cs.CL

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