United States

High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents‚Äîretrieval, description refiner, classifier, validator, and feedback logger‚Äîcoordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an &quot;Item to Evidence to Decision&quot; loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts‚Äîillustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.

ACL 2026

ORCHID: Orchestrated Retrieval-Augmented Classification of High-Risk Property with Intelligent Decision-Making

high risk property identification

agentic system

report generation

classification

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>
<html><button style="display: inline-flex; align-items: center; justify-content: center; white-space: nowrap; border-radius: 9999px; font-weight: bold; background: #7c3aed; color: white; font-family: 'Space Grotesk', sans-serif; height: 40px; font-size: 16px; padding: 0 20px; border: none; cursor: pointer" onclick="window.open('https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing')">Conference Handbook</button></html>
<br><br><br><br>

To access this event page, you need to log in with the **email address you registered with**. <br>Access credentials will be sent to your email from Underline -  subject line "Welcome to ACL 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://2026.aclweb.org/registration/) first.

**Online Registration Form**: https://acl.swoogo.com/acl2026

Registration Required

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

High-Risk Property (HRP) classification is critical at U.S. Department of Energy (DOE) sites, where inventories include sensitive and often dual-use equipment. Compliance must track evolving rules designated by various export control policies to make transparent and auditable decisions. Traditional expert-only workflows are time-consuming, backlog-prone, and struggle to keep pace with shifting regulatory boundaries. We propose ORCHID, a modular agentic framework for HRP classification that pairs retrieval-augmented generation (RAG) with human oversight to produce policy based outputs that can be audited. Small cooperating agents‚Äîretrieval, description refiner, classifier, validator, and feedback logger‚Äîcoordinate via agent-to-agent messaging and invoke tools through the Model Context Protocol (MCP) for model-agnostic on-premise operation. The interface follows an "Item to Evidence to Decision" loop with step-by-step reasoning, on-policy citations, and append-only audit bundles (run-cards, prompts, evidence). In preliminary tests on real HRP cases, ORCHID improves accuracy and traceability over a non-agentic baseline while deferring uncertain items to Subject Matter Experts (SMEs). The demonstration shows single item submission, grounded citations, SME feedback capture, and exportable audit artifacts‚Äîillustrating a practical path to trustworthy LLM assistance in sensitive DOE compliance workflows.

workshop paper

Automating systematic reviews (SRs), i.e., evidence-driven analyses under explicit protocol constraints, is a natural target for retrieval-augmented generation and deep research agents, yet existing benchmarks evaluate isolated subtasks or assume fixed evidence inputs. We introduce RAG4SR-CS-200, a benchmark of 200 computer science systematic reviews designed for protocol-driven systematic review automation. Each instance comprises review objectives, research questions, eligibility criteria, cleaned full-text review structure, references, and extracted tables. These elements support evaluation across key tasks in systematic review creation such as literature retrieval, eligibility screening, citation-grounded review generation, and structured table generation, in both stage-wise and end-to-end settings. RAG4SR-CS-200 provides a foundation for developing more reliable and diagnosable deep research agents for scientific evidence synthesis. Code and data are publicly available (https://github.com/webis-de/rag4sr-cs-200).

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

We enter both tracks of the RAG4Reports challenge with two complementary components built on a shared idea: open-ended question nuggets. PrefNugget derives concise nugget banks from pairwise preferences between system responses. PrefNugget, and its ungrounded ablation RUBRIC, are used to predict the leaderboard of RAG systems. 
Crucible is a RAG system which uses such nugget banks to generate reports with citations. For each nugget, it will extract relevant text spans, which can be taken as-is (extract) or polished with generation.

Crucible@Rag4Reports: Using Nuggets for Evaluation and RAG

This system paper presents AMU's submission to RAG4Reports 2026 Task B: a practical multilingual retrieval-augmented generation pipeline for evidence-supported report generation. The system combines full-query retrieval, optional query rewriting, dense retrieval with Qdrant, cross-encoder reranking, diversity-aware context selection, and structured generation. The best submitted run uses BAAI/bge-m3 embeddings, BAAI/bge-reranker-v2-m3 reranking, and gpt-5.1 generation with medium reasoning effort, using a partial-coverage prompt strategy. On the official leaderboard, it achieved F1=0.4351, sentence_support=0.8280, and nugget_coverage=0.3403, indicating that the generated reports were well grounded but only partially comprehensive.

AMU at RAG4Reports 2026 Task B: A Practical Multilingual RAG Pipeline for Citation-Grounded Reports

Reliable automatic evaluation of retrieval-grounded long-form reports typically requires human annotation or frontier-scale proprietary LLMs, both of which are expensive in constrained settings. Team rgipt participated in RAG4Reports@ACL 2026 Task 1 with a zero-shot nugget-verification system that runs entirely on a single NVIDIA T4 GPU. We compare three ultra-lightweight decoder-only models: Qwen2-0.5B, Qwen2-1.5B, and Qwen2.5-0.5B, under identical inference conditions to examine how small an LLM judge can be while retaining human-aligned ranking signal. Both Qwen2 models produced negative $\tau_{\text{gap}}$, whereas Qwen2.5-0.5B achieved $\tau_{\text{gap}} = 0.0772$ and Pearson $r = 0.2209$, ranking 13th of 21 teams. Within this family and evaluation setting, model generation appears to matter more than parameter count, although this finding is based on three configurations on a single task and warrants further validation.

Exploring Capability Thresholds in Ultra-Lightweight LLM Judges for Nugget-Based Report Evaluation

We describe EFSG (Evidence-First Structured Generation), our submission to Task B of the RAG4Reports@ACL 2026 shared task. Standard retrieval-augmented generation pipelines allow generation models to write from parametric memory and attach citations retroactively: a behaviour we term post-rationalization. EFSG addresses this structurally through a phase boundary: all evidence is retrieved, extracted, and sealed into a fact pool before any generation begins; each sentence then sees only its single committed source passage. Our best run (t5100k doc corpus) achieved sentence_support of 0.612 and nugget_coverage of 0.126 (F1 = 0.182).

EFSG: Evidence-First Structured Generation for Multilingual RAG Report Generation

In the present article, we have described our system developed for participating in Task B on Multilingual Report Generation under RAG4Reports 2026 at ACL 2026 with submitted run ID ju_nlp_pg. The problem statement is given a report request in English, the system retrieves relevant passages from a four million multilingual document corpus (English, Chinese, Russian, Arabic) and generates a grounded, citation-bearing report. Our core challenge was how to fit a large retrieval corpus along with a capable generative model on a two-GPU node with ‚âà29 GB RAM. We addressed the challenge employing three different techniques: (1) 4-bit NF4 quantization, shrinking the LLM from ‚âà14 GB to ‚âà4 GB; (2) memory-mapped, chunked FAISS index construction over pre-computed multilingual-e5-large embeddings; and (3) strict model-loading order to prevent heap fragmentation. On the other hand, the reports are structured around topic nuggets to directly target the Auto-ARGUE evaluation signal.

JU-NLP-PG at RAG4Reports 2026: Memory-Efficient Multilingual Report Generation with 4-bit Quantized LLMs

Quantization methods are widely used to accelerate inference and streamline the deployment of large language models (LLMs). Although quantization's effects on various LLM capabilities have been extensively studied, one critical area remains underexplored: factual knowledge recall (FKR), the process by which LLMs access stored knowledge. To this end, we conduct comprehensive experiments using three common quantization techniques at distinct bit widths, in conjunction with interpretability-driven analyses on two tasks, knowledge memorization and latent multi-hop reasoning. We show that quantization typically results in information loss within LLMs, consequently diminishing their capacity for FKR. This effect is particularly amplified in smaller models within the same architectural families. However, models quantized at reduced bit precision do not consistently exhibit inferior performance and occasionally quantization may even enhance model FKR. We find that BitSandBytes demonstrates highest preservation of the original full-precision model's FKR. Despite variability across models and methods, quantization causes modest performance degradation and remains an effective compression strategy.

Through a Compressed Lens: Investigating The Impact of Quantization on Factual Knowledge Recall

Recent research has increasingly focused on training large language models (LLMs) using federated learning, known as FedLLM. However, responsible AI (RAI)—which aims to ensure safe and trustworthy responses—remains underexplored in this context. In FedLLM, client-side training data may contain harmful content, resulting in unsafe LLMs that can generate inappropriate responses. Aggregating such models into a global model and redistributing it to clients risks the widespread deployment of unsafe LLMs. To address this, we incorporate two well-established RAI techniques into FedLLM: safety filtering and constitutional AI. Our experiments show that these methods significantly improve LLM safety, achieving over 20% improvement on AdvBench.

Responsible Federated LLMs via Safety Filtering and Constitutional AI

Detecting media bias is increasingly critical in today’s fast-evolving digital landscape. While large language models (LLM) show promise in bias detection, their reasoning is often unreliable, producing opaque errors. To address this, we propose an {\bf uncertainty-aware proxy reasoning method} that combines proxy attribute prediction with uncertainty estimation to assess and stratify reasoning quality. By modeling uncertainty, our method reliably narrows down the intermediate reasoning space and highlights trustworthy bias indicators, enhancing transparency and interpretability for end users and downstream applications. We evaluate our method on the BASIL benchmark and compare it against strong LLM baselines and recent state-of-the-art approaches. Our experiments assess both decision accuracy and quality. Results show a 6.25% relative F1 improvement over competitive baselines, demonstrating that uncertainty-aware reasoning boosts performance and provides insight into the decision-making process, making media bias detection more reliable and interpretable.

Uncertainty-Aware Proxy Attribute Reasoning for Reliable Media Bias Detection

Large language models (LLMs) are increasingly deployed in wide range of applications, yet remain vulnerable to adversarial jailbreak attacks that circumvent their safety guardrails.
Existing evaluation frameworks typically report binary success/failure metrics, failing to capture the temporal dynamics of how attacks succeed under persistent adversarial pressure. This preliminary work proposes a novel evaluation framework that applies survival analysis techniques to characterize LLM jailbreak vulnerability. Our approach models the “time-to-jailbreak” as a survival outcome, enabling estimation of hazard functions, survival curves, and risk factors associated with successful attacks. We evaluate three LLMs against a sub-set of prompts from the HarmBench dataset spanning three attack categories. Our analysis reveals that models exhibit distinct vulnerability profiles: while one model demonstrates rapid degradation under iterative attacks, the wo other models show consistent moderate vulnerability. Our framework provides actionable insights for model and LLM applicaiton developers and establishes survival analysis as a rigorous methodology for LLM safety evaluation.

Downloads

Next from ACL 2026

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

Similar lecture

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

.css-70qvj9{display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}Downloads

Next from ACL 2026

A Pipeline to Bootstrap the Evaluation of Retrieval-Augmented Generation for the Automation of Systematic Reviews in Computer Science

Similar lecture

REFSafE: A RAG-Enabled Framework for Predictive Risk Analysis and Automated Safety Report Generation in Mission-Critical Environments

Downloads