Retrieval-Augmented Detection of Abusive Clauses in Chilean Terms of Service
Researchers present a RAG framework for automated detection and classification of potentially abusive clauses in Chilean Terms of Service agreements, designed for local execution with open-weight language models. They introduce the Chilean Abusive Terms of Service Extended corpus with 100 contracts and 10,029 annotated clauses across 24 legally grounded categories. Experiments show RAG prompting substantially improves performance, enabling local models to approach larger cloud-based systems at reduced computational and token cost. The work also contributes a refined legal annotation scheme for AI-assisted consumer contract review.
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LegalHalluLens: Typed hallucination auditing and calibrated multi-agent debate for legal AI
Researchers introduce LegalHalluLens, an auditing framework for hallucination in legal AI systems, evaluated across 510 contracts and 249,252 clause-level instances from the CUAD dataset. The framework introduces typed hallucination profiles across four claim categories (numeric, temporal, obligation/entitlement, factual) and a Risk Direction Index (RDI) that distinguishes omission from invention errors. A calibrated multi-agent debate pipeline reduces fabricated detections by 45% using a 4B-parameter model competitive with commercial APIs. The work reveals that aggregate hallucination rates (~52%) mask a 38-40 percentage-point gap between claim types and that two systems with identical aggregate rates can have opposite risk profiles.
Maat: ReAct-Based Agentic Legal Research Assistant for Competition Law
Maat is a ReAct agent designed specifically for competition law research, orchestrating tools for RAG-based retrieval, web search fallback, and citation generation. Built iteratively with domain experts, it addresses hallucination and citation gaps found in general assistants (Claude, ChatGPT) and legal-specific models (SaulLM-7B, LegalGPT). Maat significantly outperforms baselines on case-specific tasks and matches top baselines on theoretical questions. The evaluation dataset is publicly released on GitHub.
AgentCL: A Rigorous Evaluation Framework for Continual Learning in Language Agents
AgentCL is a new benchmark and evaluation framework designed to rigorously assess continual learning in language agents, addressing gaps in existing benchmarks that focus on retrieval over long-context documents or use naive task streams with limited cross-task analysis. The framework constructs compositional task streams where earlier sub-solutions, evidence, or workflows are intentionally reusable in later tasks, contrasting them with naive streams to measure transfer gains. The authors also introduce MemProbe, a probing method that stores interactions, insights, and skills while filtering unreliable experiences during consolidation. Empirical results across coding, deep research, and language understanding tasks show that controlled streams better distinguish memory design quality, and that naive streams can mask memory-induced degradation.
Expert Support Case Study: Bolstering a RAG App with LLM-as-a-Judge
Hugging Face published a case study describing how Digital Green used an LLM-as-a-Judge approach to evaluate and improve a retrieval-augmented generation (RAG) application. The post covers the methodology for using LLMs to score and validate RAG outputs, providing a practical deployment pattern for quality assurance in production AI systems. It serves as a concrete example of enterprise-grade evaluation pipelines built on top of RAG architectures.
Mistral AI: Using LLM-as-a-Judge with Structured Outputs for RAG Evaluation
Mistral AI published a technical guide on evaluating Retrieval-Augmented Generation (RAG) systems using the 'LLM as a Judge' paradigm combined with their structured outputs API feature. The approach implements the RAG Triad framework—context relevance, groundedness, and answer relevance—using Pydantic schemas to enforce machine-readable evaluation outputs. Mistral models serve as both the generator and judge components, enabling scalable automated evaluation without human annotators.
Automated Benchmark Auditing for AI Agents and Large Language Models (ABA)
The paper introduces Auto Benchmark Audit (ABA), an agentic framework that systematically audits AI benchmark tasks for issues such as ambiguous specifications, environment conflicts, and incorrect ground truths. Applied to 168 benchmarks across nine domains including NeurIPS publications, ABA identifies critical issues in over 25.7% of evaluated tasks. The authors demonstrate that filtering out flawed tasks materially shifts model rankings and improves average performance on SWE-bench Verified and Terminal-Bench 2 by 9.9% and 9.6% respectively, indicating that current benchmark scores are significantly distorted by task quality problems. The agentic tool and annotations are released publicly.
ACL-Verbatim: Hallucination-Free Extractive QA System for Research Papers
The paper introduces ACL-Verbatim, an extractive question answering system built on VerbatimRAG that maps user queries directly to verbatim text spans in ACL Anthology papers, eliminating hallucination by design. The authors contribute a new ground-truth benchmark dataset created via human NLP-researcher annotation over synthetic queries generated using a ScIRGen-based pipeline. A 150M-parameter ModernBERT token classifier trained on silver supervision achieves the best word-level F1 of 53.6, outperforming the strongest LLM-based extractor at 48.7. The work demonstrates that smaller extractive models can outperform large generative LLMs on precision-critical retrieval tasks.
AprielGuard: A Guardrail for Safety and Adversarial Robustness in Modern LLM Systems
ServiceNow AI has released AprielGuard, a guardrail system designed to improve safety and adversarial robustness in LLM deployments. The system targets prompt injection, jailbreaks, and other adversarial inputs that bypass standard safety measures. It is presented as a component for enterprise LLM pipelines seeking more robust content moderation and safety filtering.