United States

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.

ACL 2026

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

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.

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AAAI 2026

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

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

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.

Development and Benchmarking of a Blended Human-AI Qualitative Research Assistant

Recent advances in multimodal retrieval-augmented generation (MM-RAG) have shifted toward minimal parsing, relying on page-level images for producing retriever embeddings and for answer generation. While efficient, this trend often neglects the rich, structured information embedded in complex enterprise documents, instead depending on pre-trained embeddings or vision-language models to implicitly capture such structure. In this work, we take a more direct approach: MM-BizRAG proactively extracts and represents document structure, applying explicit layout-aware parsing for report-type documents and leveraging page-level representations for slide-type documents, guided by a document structure-aware split.
We present MM-BizRAG, a system which distinguishes between vertically structured (e.g., reports) and horizontally structured (e.g., slide decks) documents, unifying targeted document parsing with LLM-driven artifact transformation and flexible multimodal context assembly.
Through experiments on a large, heterogeneous enterprise dataset and two public benchmarks (SlideVQA and FinRAGBench-V), we show that MM-BizRAG’s proactive parsing and artifact transformation pipeline consistently outperforms state-of-the-art vision-centric baselines, especially on report style layouts. 
Furthermore, we introduce FastRAGEval, a single-call LLM Judge metric for fine-grained generative recall.

MM-BizRAG: Rethinking Multimodal Retrieval-Augmented Generation for General Purpose Enterprise Q&A

In the fields of advertising design, artistic creation, and cultural dissemination, there is an increasingly urgent demand for high-quality images that cater to fine-grained aesthetic preferences. Although existing large-scale models can generally meet basic requirements for clarity and alignment with textual elements, they still face significant bottlenecks in achieving precise control and aesthetic optimization. To address this limitation, we propose a set of comprehensive preference indicators across two major dimensions, text-image consistency and aesthetic quality, encompassing multiple criteria ranging from exposure and clarity to visual guidance and innovativeness. Building on these indicators, we have developed a generative framework named AesX to steer the model consistently toward a generation path that more closely aligns with human aesthetic sensibilities. 
Our experimental findings demonstrate that this approach yields significant improvements in both target recognition accuracy and overall visual aesthetic presentation.

AesX: Enhance Your Images with Stunning Aesthetic Beauty

Advanced reasoning typically requires Chain-of-Thought prompting, which is accurate but incurs prohibitive latency and substantial test-time inference costs. The standard alternative, fine-tuning smaller models, often sacrifices interpretability while introducing significant resource and operational overhead. To address these limitations, we introduce Prompt-Level Distillation (PLD). We extract explicit reasoning patterns from a Teacher model and organize them into a structured list of expressive instructions for the Student model's System Prompt. Evaluated on the StereoSet and Contract-NLI datasets using Gemma-3 4B, PLD improved Macro F1 scores from 57\% to 90.0\% and 67\% to 83\% respectively, enabling this compact model to match frontier performance with negligible latency overhead. These expressive instructions render the decision-making process transparent, allowing for full human verification of logic, making this approach ideal for regulated industries such as law, finance, and content moderation, as well as high-volume use cases and edge devices.

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

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