A dataset had gaps — how much got patched from Wikipedia versus pulled from a live web search?
How much of the incomplete candidate data came from Wikipedia versus live retrieval?
This explores how much of a dataset's missing or incomplete candidate information was filled from a static source like Wikipedia, compared with information pulled from live search. That reads like a question about one specific paper's data pipeline, and none of the retrieved notes report that kind of breakdown.
This explores how much of a dataset's missing or incomplete candidate information was filled from a static source like Wikipedia, compared with information pulled from live search. The short answer is that the corpus can't tell you. None of the twelve retrieved notes reports a Wikipedia-versus-live-retrieval split for any dataset, so any number given here would be made up. If you have a specific paper in mind, its methods or data-construction section is where that breakdown would be. A search using that paper's title should surface it if it's in the library.
The corpus does cover the trade-off behind the question: what you gain or lose by relying on a fixed reference snapshot instead of searching in real time. Why do search agents beat memorized retrieval on hard questions? makes the case for live search. Agents trained on the real web beat models that rely on what they memorized during training. The reason isn't better reasoning. Live search avoids two problems: knowledge that stops at a fixed date, and the lossy way facts get compressed into a model's weights. A Wikipedia dump is a middle ground. It's explicit and searchable, but it's still frozen at one point in time.
Time is the angle a source-mix question can miss. Can retrieval systems ground answers in the right time? shows that when the same document exists in several dated versions, scoring for how recent it is as well as how relevant it is improves time-sensitive answers by up to 74%. So it matters less what share came from a static source than whether the data was current for the question being asked.
If the real concern is how much to trust data that was patched together from mixed sources, two notes offer guardrails. Can RAG systems refuse to answer without reliable evidence? describes a system that gives up some coverage for reliability by declining to answer when its sources are too noisy to back a claim. Can RAG systems safely learn from their own generated answers? shows how to keep generated or retrieved additions from polluting a knowledge base: each one must pass checks that it follows from its sources, can be traced to them, and adds something new. Both suggest that how incomplete data gets filled matters more than which source filled it.
Sources 4 notes
DeepResearcher agents trained on live web search beat static knowledge models on knowledge-intensive tasks. The mechanism is not better reasoning but retrieval: real-time search avoids temporal bounds and probabilistic compression that plague training-data memorization.
TempRALM adds a temporal term to retrieval scoring alongside semantic similarity, achieving up to 74% improvement over baseline systems when documents have multiple time-stamped versions. The approach requires no model retraining or index changes.
A multilingual RAG system for noisy historical newspapers succeeds by aggressively expanding retrieval while constraining generation to only grounded answers. The grounded-refusal prompt prevents hallucination when OCR errors and language drift degrade source quality, trading coverage for integrity.
Systems can add generated answers to their retrieval corpus when outputs pass entailment verification, source attribution checks, and novelty detection. This prevents hallucinations from polluting future retrievals while allowing genuine knowledge accumulation.
Papers this line draws on 8
The research behind the notes this line reads — ranked by how closely each paper relates.
- UR2: Unify RAG and Reasoning through Reinforcement Learning
- CLaRa: Bridging Retrieval and Generation with Continuous Latent Reasoning
- A Hybrid RAG System with Comprehensive Enhancement on Complex Reasoning
- DRAGIN: Dynamic Retrieval Augmented Generation based on the Information Needs of Large Language Models
- Retrieval-augmented reasoning with lean language models
- Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
- It's About Time: Incorporating Temporality in Retrieval Augmented Language Models
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection