# Relevant Is Not Warranted: Evidence-Force Calibration for Cited RAG arXiv: https://arxiv.org/abs/2605.28044 ยท HTML: https://mlsanity.com/paper/2605.28044/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 24 of its 35 references that are in the map. - First posted on arXiv 27 May 2026. ## Builds on (most cited first, 24 of 24) - [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020) - [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021) - [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015) - [WebGPT: Browser-assisted question-answering with human feedback](https://mlsanity.com/paper/2112.09332/) (2021) - [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018) - [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023) - [Enabling Large Language Models to Generate Text with Citations](https://mlsanity.com/paper/2305.14627/) (2023) - [Evaluating Verifiability in Generative Search Engines](https://mlsanity.com/paper/2304.09848/) (2023) - [Teaching language models to support answers with verified quotes](https://mlsanity.com/paper/2203.11147/) (2022) - [Measuring Attribution in Natural Language Generation Models](https://mlsanity.com/paper/2112.12870/) (2021) - [FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information](https://mlsanity.com/paper/2106.05707/) (2021) - [Structural Scaffolds for Citation Intent Classification in Scientific Publications](https://mlsanity.com/paper/1904.01608/) (2019) - [Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://mlsanity.com/paper/2405.16908/) (2024) - [Understanding Retrieval Augmentation for Long-Form Question Answering](https://mlsanity.com/paper/2310.12150/) (2023) - [AttributionBench: How Hard is Automatic Attribution Evaluation?](https://mlsanity.com/paper/2402.15089/) (2024) - [Does Tone Change the Answer? Evaluating Prompt Politeness Effects on Modern LLMs: GPT, Gemini, and LLaMA](https://mlsanity.com/paper/2512.12812/) (2025) - [Retrieval Augmented Generation for Domain-specific Question Answering](https://mlsanity.com/paper/2404.14760/) (2024) - [Enhancing Financial Report Question-Answering: A Retrieval-Augmented Generation System with Reranking Analysis](https://mlsanity.com/paper/2603.16877/) (2026) - [CiteEval: Principle-Driven Citation Evaluation for Source Attribution](https://mlsanity.com/paper/2506.01829/) (2025) - [GaRAGe: A Benchmark with Grounding Annotations for RAG Evaluation](https://mlsanity.com/paper/2506.07671/) (2025) - [Sustainable Hybrid Document-Routed Retrieval for Financial RAG: Resolving the Robustness-Precision Trade-off](https://mlsanity.com/paper/2603.26815/) (2026) - [Reflect-Guard: Enhancing LLM Safeguards against Adversarial Prompts via Logical Self-Reflection](https://mlsanity.com/paper/2605.24834/) (2026) - [CATCH: A Modular Cross-domain Adaptive Template with Hook](https://mlsanity.com/paper/2510.26582/) (2025) - [Alleviating Attention Hacking in Discriminative Reward Modeling through Interaction Distillation](https://mlsanity.com/paper/2508.02618/) (2025) ## Built on by (most cited first, 14 of 14) - [Bridging Reasoning Trajectories in On-Policy Distillation via Near-Future Guidance](https://mlsanity.com/paper/2606.00305/) (2026) - [Beyond Accuracy: Measuring Bias Acknowledgment in Chain-of-Thought Reasoning for Responsible AI Evaluation](https://mlsanity.com/paper/2606.15127/) (2026) - [When AUC 0.998 Is Not Enough: A Candidate Evaluation Protocol for Hidden-State Probes of Indirect Prompt Injection in Multimodal Computer-Use Agents](https://mlsanity.com/paper/2606.22864/) (2026) - [DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation](https://mlsanity.com/paper/2606.28725/) (2026) - [Same Question, Different Answer? Measuring and Mitigating Prompt Privilege for Equitable AI Access](https://mlsanity.com/paper/2608.08942/) (2026) - [Energy-Efficient On-Device RAG on a Mobile NPU: System Design and Benchmark on Snapdragon X Elite](https://mlsanity.com/paper/2606.11257/) (2026) - [Chains That See, Answers That Don't: A Multi-Aspect Evaluation Recipe for Forced Chain-of-Thought on Video-MME](https://mlsanity.com/paper/2606.22862/) (2026) - [Auditing the Audit: Five Failure Modes in Benchmark-Validity Audits](https://mlsanity.com/paper/2607.02586/) (2026) - [CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answering](https://mlsanity.com/paper/2607.24236/) (2026) - [Agentic-SQL Revisited: Autonomy-Based Taxonomy and Empirical Benchmark Analysis for LLM Text-to-SQL](https://mlsanity.com/paper/2608.15389/) (2026) - [Modality Disentangled Learning for Incomplete Multimodal Emotion Recognition: A Primitive Memory Distillation Perspective](https://mlsanity.com/paper/2608.30563/) (2026) - [MOSAIC: Orchestrating Collaborative Knowledge Tracing with Hierarchical Semantic Alignment](https://mlsanity.com/paper/2606.29049/) (2026) - [Probe Choice Changes Canary-Memorization Verdicts: Three Post-Hoc Disagreement Case Studies in a Text-Dominant LoRA-Tuned Autoregressive Testbed](https://mlsanity.com/paper/2606.31168/) (2026) - [Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit](https://mlsanity.com/paper/2608.20351/) (2026) ## Abstract (arXiv) Cited RAG evaluation often treats visible sources as a grounding signal, but a real, topically relevant citation can still under-warrant the attached wording. We study this diagnostic failure as citation laundering: a related source is presented as warrant for an over-strong claim. We introduce FORCEBENCH, a contrastive stress test for evidence-force calibration. Each item holds a cited passage fixed and pairs an evidence-calibrated claim with a localized force-raised variant across five operational axes: relation, modality, scope, temporal validity, and numeric specificity. A calibrated evaluator should score the evidence-calibrated claim higher. Headline experiments use a fixed, locality-filtered 198-pair evaluation set. A citation-presence sanity check is uninformative by design; token and entity overlap still violate monotonicity on 32.8--36.4% of pairs. Across four reported model judges, standard generic support prompting is insufficient for this force-calibration stress test (aggregate MVR 47.2%), while explicit warrant-strength prompting lowers MVR to 24.5% but remains imperfect. We release the benchmark, prompts, outputs, and plug-in pipeline so citation evaluators can report monotonicity violation rate and force sensitivity alongside conventional support metrics.