The Temporal Tug-of-War: Visualizing and Detecting RAG Conflicts in Diffusion Models via Trajectory Variance

Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in practice. We present ConflictRAG, a conflict-aware RAG framework that detects, classifies, and resolves knowledge conflicts prior to answer generation. The framework introduces three contributions: (1) a two-stage conflict detection module combining a lightweight embedding-based MLP classifier with selective LLM refinement, reducing API costs by 62% while maintaining 90.8% detection accuracy; (2) an Entropy-TOPSIS framework for data-driven source credibility assessment, improving selection accuracy by 7.1% over manual heuristics; and (3) a Conflict-Aware RAG Score (CARS) for diagnostic evaluation of conflict-handling capabilities. Experiments on three benchmarks against six baselines demonstrate 88.7% conflict-detection F1 and consistent 5.3--6.1% correctness gains over the strongest conflict-aware baseline, with the pipeline transferring effectively across backbone LLMs.

Retrieval-Augmented Generation (RAG) improves the factuality of large language models with external knowledge, yet conflicting evidence remains a fundamental challenge in dynamic and adversarial environments. Existing approaches often treat conflicts as static inconsistencies and select more reliable knowledge, overlooking that the same conflict may arise from legitimate knowledge evolution, malicious manipulation, or unresolved uncertainty. We formulate conflict origin attribution as a new problem in RAG: identifying which explanation of conflicting evidence is supported by observable context rather than simply which fact should be trusted. We propose EvoTrustRAG, a training-free framework for evolution-aware conflict attribution and evidence handling before answer generation. EvoTrustRAG represents span-grounded retrieved facts as a conflict evidence graph, evaluates grounded evolution and directional intervention hypotheses using temporal relations, support structure, and auxiliary consistency, and projects local decisions onto a globally consistent explanation of each conflict group. The attribution determines whether earlier and later states are preserved as temporal knowledge, an intervention candidate is separated from the primary context, or an unresolved conflict remains visible to the generator. Unlike provenance-based approaches focused on post-hoc analysis, EvoTrustRAG determines during inference whether conflicting evidence follows plausible knowledge evolution, exhibits intervention-like support, or cannot be reliably attributed. Experiments show that EvoTrustRAG achieves 81.4% average accuracy on benchmark-native conflict settings, improves attribution macro-F1 from 72.2% to 79.1% over the strongest baseline, and reduces the error rate under the strongest coordinated attack from 31.2% to 16.0%.

The Context-Compliance Regime in Retrieval-Augmented Generation (RAG) occurs when retrieved context dominates the final answer even when it conflicts with the model's parametric knowledge. Accuracy alone does not reveal how retrieved context causally shapes answers under such conflict. We introduce Context-Driven Decomposition (CDD), a belief-decomposition probe that operates at inference time and serves as an intervention mechanism for controlled retrieval conflict. Across Epi-Scale stress tests, TruthfulQA misconception injection, and cross-model reruns, CDD exposes three patterns. P1: context compliance is measurable in an upper-bound adversarial setting, where Standard RAG reaches 15.0% accuracy on TruthfulQA misconception injection (N=500). P2: adversarial accuracy gains transfer across model families -- CDD improves accuracy on Gemini-2.5-Flash and on Claude Haiku/Sonnet/Opus -- but rationale-answer causal coupling does not transfer. CDD reaches 64.1% mistake-injection causal sensitivity on Gemini-2.5-Flash, while sensitivities for all three Claude variants fall in the [-3%, +7%] range, suggesting that the Claude-side accuracy gains operate through a mechanism distinct from the explicit conflict-resolution trace. P3: explicit conflict decomposition improves robustness under temporal drift and noisy distractors, with CDD reaching 71.3% on temporal shifts and 69.9% on distractor evidence on the full Epi-Scale adversarial benchmark. These three patterns identify context-compliance as a structural axis along which standard RAG can be probed and intervened on, distinct from retrieval-quality or single-method robustness questions, and motivate releasing Epi-Scale for systematic study across model families and retrieval pipelines.