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ARXIV:2605.14473 · RAG DIAGNOSTICS · SUBMITTED 15 MAY · 20:12 UTC · FRESHNESS FRESH
ARXIV:2605.14473RAG DIAGNOSTICSSUBMITTED 15 MAY · 20:12 UTCFRESHNESS FRESHYihang Chen · Pin Qian · Su Wang · Sipeng Zhang · Huan Xu · Shuhuai Lin · +1 at arXiv
A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information.
Opportunity summary
Pain A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information. Accuracy alone does not reveal how retrieved context causally shapes answers under such conflict.
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…
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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.…
RAG Diagnostics moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
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mobile layout uses overflow-hidden min-w-0 break-wordsOpportunity summary
Score6.0Public score shown from the verified overall while the stale axis breakdown refreshesAnalysis summary
A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information.
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10.48550/arXiv.2605.14473A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information.
Abstract
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.
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PROBLEM
A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information. Accuracy alone does not reveal how retrieved context causally shapes answers under such conflict.
METHOD
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 suc...
RESULT
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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. Code a...
WHY NOW
RAG Diagnostics moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information. Accuracy alone does not reveal how retrieved context causally shapes answers under such conflict.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 6.0/10 on the public viability pass. 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. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
RAG Diagnostics moved forward this cycle; last verified May 2026. Public score 6.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A diagnostic tool to measure and intervene on context compliance in Retrieval-Augmented Generation systems, improving robustness against conflicting information.
Segment
RAG Diagnostics
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