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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&#39;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.

ACL 2026

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

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. <br>
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) <br><br><br>
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The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

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.

poster

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.

Efficient Agent Evaluation via Diversity-Guided User Simulation

LLM-based machine translation (MT) evaluation can closely match human judgments, but in practice it remains large diagnostic, with the signals rarely translating into direct quality improvements under real production constraints. We propose a two-stage, evaluator-guided automatic post-editing framework that turns MQM-style evaluation into targeted repairs: a retrieval-augmented LLM evaluator outputs structured, span-level MQM diagnoses under an explicit edit contract, and a separate LLM post-editor applies minimal edits restricted to those diagnoses. This separation improves controllability and reduces paraphrastic drift compared to one-stage "judge-and-refine'' baselines. In a systematic study involving seven LLMs spanning three model providers and English-to-German and English-to-Chinese test sets representing the e-commerce domain, our best configuration consistently improves both COMET-22 and CometKiwi scores over one-stage post-edit methods, while the evaluator's error spans and severities show strong agreement with human MQM annotations and human editor preferences.

Diagnose, Then Repair: A Two-Stage MQM-Guided Post-Editing Framework for Domain-Specific Machine Translation

Enterprise deep research often fails to produce decision-ready reports due to uneven information coverage, context explosion, and premature stopping. We propose a \textit{scalable} Enterprise Deep Research (EDR) architecture to address these failures. Our system (i) decomposes requests into coverage-driven objectives via outline generation with reflection, (ii) localizes context with dependency-guided execution and explicit information sharing, and (iii) enforces evidence-based completion criteria so agents iteratively collect information until sufficiency conditions are met. We evaluate on an internal sales-enablement task and the public DeepResearch Bench benchmark, where our proposed system design achieves the strongest overall performance against competitive deep-research baselines. The results show that dependency-controlled context and explicit evidence sufficiency criteria reduce premature stopping and improve the consistency and depth of enterprise research outputs. We will make our code and permitted data publicly available.

Don’t Stop Early: Scalable Enterprise Deep Research with Controlled Information Flow and Evidence-Aware Termination

Financial Large Language Models (LLMs) exhibit strong domain expertise but remain vulnerable to financially harmful prompts. To systematically assess this vulnerability, we introduce \textbf{FinHarmBench}, a benchmark designed to evaluate financially harmful and confusable benign prompts. Our analysis reveals a concerning result that financial LLMs can be less robust than general-purpose models, suggesting that domain adaptation alone does not guarantee financial safety alignment. To address this issue, we propose \textbf{Financial Refusal Steering Distillation (FiRSD)}, an unsupervised training framework that strengthens financial-domain safety by learning and distilling a financial refusal direction at the representation level. FiRSD enhances refusal behavior without requiring annotated refusal responses. Experiments show that FiRSD substantially improves safety while largely preserving task capability. These results highlight the importance of domain-aware safety alignment for high-stakes financial applications.

FinHarmBench: Financial Jailbreak Benchmark and Unsupervised Safety Fine-Tuning via Refusal Steering Distillation

Multi-hop question answering (MHQA) is a practical bottleneck in industry applications such as enterprise assistants, customer-support copilots, and compliance analysis, where systems must combine evidence across multiple documents before answering. Large language models (LLMs) remain brittle in this setting: iterative retrieval can commit too early to low-recall trajectories, while planning-only approaches can produce static query sets that fail to adapt when intermediate evidence changes. We propose \textbf{Planned Active Retrieval and Reasoning RAG (PAR$^2$-RAG)}, a training-free two-stage framework that separates \emph{coverage} from \emph{commitment}. PAR$^2$-RAG first performs breadth-first anchoring to build a high-recall evidence frontier, then applies depth-first refinement with evidence sufficiency control in an iterative loop. This design targets deployment constraints by avoiding retraining cycles, reducing maintenance overhead under changing corpora, and improving scalability across domains. Across four MHQA benchmarks, PAR$^2$-RAG consistently outperforms strong baselines: compared with IRCoT, it achieves up to \textbf{23.5\%} higher answer accuracy and up to \textbf{10.5\%} NDCG gains in retrieval quality.

PAR2-RAG: Planned Active Retrieval and Reasoning for Multi-Hop Question Answering

Despite the success of Large Language Models (LLMs) in structured query generation, OData—a critical RESTful protocol for enterprise APIs—remains under-researched due to a lack of high-fidelity, execution-validated datasets. 
To bridge this gap, we introduce \textsc{OmniOData}, a framework that generates \textsc{SynOData}, the first large-scale OData corpus featuring execution-grounded queries and reasoning traces. 
Using this corpus, we develop \textsc{OmniOData-R1} (1.5B–3B parameters), a family of models that match or surpass frontier proprietary systems, such as GPT-4o and Gemini 3, on realistic industrial benchmarks. 
Our results demonstrate that the synergy of execution-verified synthetic data and Reinforcement Learning (RL) effectively unlocks the latent reasoning of Small Language Models (SLMs), providing a high-performance, low-latency solution for specialized enterprise query generation.
The code and data will be released under an open-source license.

$\texttt{OmniOData}$: Unleashing Small Language Models for OData Query Generation with Synthetic Data and Reinforcement Learning

Small language models (SLMs) are promising for real-world deployment due to their efficiency and low operational cost. However, their limited capacity struggles with high-stakes legal reasoning tasks that require coherent statute interpretation and logically consistent deduction. Furthermore, training SLMs for such tasks demands high-quality, concise reasoning trajectories, which are prohibitively expensive to manually collect and difficult to curate via standard rejection sampling, which lacks granularity beyond final verdicts. 
To address these challenges, we propose LegalDrill, a diagnosis-driven synthesis framework that extracts and iteratively refines reasoning trajectories from a capable teacher via fine-grained prompting, then a self-reflective verification is employed to adaptively select the most effective data for the SLM student. The resulting data empower SLM training through supervised fine-tuning and direct preference optimization. Extensive experiments on several legal benchmarks demonstrate that LegalDrill significantly bolsters the legal reasoning capabilities of representative SLMs while bypassing the need for scarce expert annotations, paving a scalable path toward practical legal reasoning systems.

LegalDrill: Diagnosis-Driven Synthesis for Legal Reasoning in Small Language Models

The integration of Large Language Models (LLMs) into recruitment workflows has introduced a critical security vulnerability: indirect prompt injection attacks embedded within resumes can manipulate screening tools to override instructions, effectively jailbreaking the hiring process. Frontier LLMs can detect such anomalies, but deploying them at the scale required for high-volume recruitment is prohibitively slow and costly. At the same time, existing generic prompt injection detectors lack the domain specificity needed for nuanced resume attacks. To address this gap, we introduce RAPIDS, a scalable detection framework with three contributions. First, we release a synthetically generated dataset of injection snippets derived from curated attack seeds spanning multiple adversarial strategies to address data scarcity in this domain. Second, we fine-tune a lightweight Small Language Model (SLM) on this data that outperforms the best off-the-shelf detector by over 50% in relative F1 and approaches frontier LLM accuracy. Third, we propose a cascade architecture in which the fine-tuned SLM serves as a high-recall first stage followed by an LLM verifier. This design achieves 97.5% end-to-end recall on both evaluated datasets while delivering a 25-28$\times$ latency reduction over standalone frontier LLMs (GPT-5-mini)—bringing expected per-request latency to 97-141ms at less than 3% of the API cost.

RAPIDS: Resume Attack Prompt Injection Detection at Scale

Generative AI writing assistants are now integrated into consumer platforms such as Apple Intelligence and Microsoft Copilot, enabling millions of users to automatically rewrite and stylize their text. While positioned as productivity tools, their deployment at scale introduces important and underexplored implications for privacy and platform safety. This paper examines the dual-use nature of platform-level text stylization. Stylization can enhance privacy by suppressing stylistic signals used for profiling and personal data inference. However, the same transformations can be leveraged to evade automated safeguards, including misinformation detection systems. We conduct empirical case studies on emotion inference and misinformation detection across benchmark datasets using deployed stylization modes. We evaluate downstream impact with fine-tuned open-source models and GPT-4o in a zero-shot setting. Our results show that stylization reduces emotion inference accuracy, lowering profiling risk, while increasing error rates in misinformation detection. This discovery reveal a measurable trade-off among privacy protection, moderation robustness, and stylization, highlighting new design and governance challenges for industry deployment.

No Innocence in Styling: Discovery of Privacy Protection Capabilities and Security Risks in Consumer Generative AI Writing Assistants

Qualitative research emphasizes constructing meaning through iterative engagement with textual data. Traditionally, this human-driven process requires navigating coder fatigue and interpretative drift, thus posing challenges when scaling analysis to larger, more complex datasets. Computational approaches to augment qualitative research have been met with skepticism, partly due to their inability to replicate the nuance, context-awareness, and sophistication of human analysis. LLMs, however, present new opportunities to automate aspects of qualitative analysis while upholding rigor and research quality. In this work, we present and benchmark Muse, an interactive qualitative research system that allows researchers to identify themes and annotate datasets, achieving an inter-rater reliability between Muse and humans of Cohen’s $\kappa = 0.71$ for well-specified codes.

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