📄Paper: RORA-VLM: Robust Retrieval Augmentation for Vision Language Models
The paper titled 'RORA-VLM: Robust Retrieval Augmentation for Vision Language Models' was presented at ICLR 2025 but was unfortunately rejected. It proposes a framework that enhances Vision Language Models (VLM) by integrating external knowledge retrieval to improve question answering. The approach includes a two-stage retrieval process and noise-resilient training to ensure stable reasoning despite potential inaccuracies in retrieved information.
- ▪The paper introduces a robust retrieval framework for Vision Language Models.
- ▪It employs a two-stage retrieval process to enhance the model's ability to answer questions using external knowledge.
- ▪The training method includes intentionally introducing noise to help the model learn to ignore irrelevant information.
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/qq5yu/paper-rora-vlm-robust-retrieval-augmentation-for-vision-language-models-5b4l |
| Publication time | Fri, 29 May 2026 04:13:04 +0000 |
| Retrieval time | 2026-05-29T04:29:41.942Z |
| Last seen | 2026-05-29T04:29:41.942Z |
| 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 | SuYjxNHJV0LJ |
| 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
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3189362) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Mercy Posted on May 29 📄Paper: RORA-VLM: Robust Retrieval Augmentation for Vision Language Models #ai #vlm #rag #paper Public At International Conference on Learning Representations (ICLR) 2025 💡 Why I read this 最近在找論文的 idea 剛好找到這篇，發表在 ICLR 2025，不過被 Reject 了有點可惜 這篇主要是把 RAG 應用到 VLM ，讓模型在回答問題時可以利用外部知識 在很多 VQA 的任務中，答案其實不在圖片裡面，而是需要額外的背景知識 例如一張圖顯示一種鳥，問題是：「這種鳥主要分布在哪裡？」 圖片只能讓你看出鳥長什麼樣，但像棲地這種資訊一定要查資料才知道 這篇主要在解決：「當 retrieved knowledge 有 noise 時，VLM 怎麼還能穩定推理？ 🧠 Core idea 作者提出一個 robust…
Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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