The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context
The paper discusses the challenges of verifying whether language models rely on retrieved context or their internal memory. It introduces a new method called Computational Reality Monitoring (CRM) to address the issue of attribution blind spots in language models. The authors demonstrate that internal representations can reveal insights about evidence provenance that are not visible at the output level.
- ▪The attribution blind spot occurs when language models produce outputs that appear context-consistent but are actually generated from memory.
- ▪Computational Reality Monitoring (CRM) is proposed as a solution to detect internal representation divergence.
- ▪The study shows that this divergence is measurable and can inform the development of systems that better understand evidence provenance.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26778 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | TfI25rJARVUK |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.26778 (cs) [Submitted on 26 May 2026] Title:The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context Authors:Zhe Yu, Wenpeng Xing, Yunzhao Wei, Bo Yang, Chen Ye, Gaolei Li, Meng Han View a PDF of the paper titled The Attribution Blind Spot: Detecting When Language Models Rely on Memory Rather Than Retrieved Context, by Zhe Yu and 6 other authors View PDF HTML (experimental) Abstract:Retrieval-augmented generation promises to ground language model outputs in external evidence, yet the field has no reliable way to verify whether retrieved context actually governs generation -- a prerequisite for any high-stakes deployment.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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