arXiv AI
Aug 11

Time Present and Time Past: Benchmarking Large Language Models on Temporally Evolving Document Understanding

arXiv:2608. 08512v1 Announce Type: new Abstract: Evolving documents, such as laws, tax codes, and software documentation, are amended, replaced, and sometimes reverted over time, so a question has different correct answers at different dates.

By Mahbub E Sobhani, Md. Faiyaz Abdullah Sayeedi, Fahmid Hasan Chowdhury, Md Adnan Arefeen, Farig Sadeque, Md. Faizul Bari, Swakkhar Shatabda
arXiv Computation and Language
Sep 28

Stale-Document Poisoning: When Outdated Retrieval Overrides Correct Model Answers

The paper introduces "stale‑document poisoning," a temporal alignment failure where outdated retrieval evidence causes retrieval‑augmented generation models to produce incorrect answers even when the model could answer correctly without retrieval. A benchmark of 317 verified knowledge reversals across medicine, law, software, and platform policy shows that outdated evidence flips 30–37% of Llama and Qwen answers, rising to 66–75% when models are explicitly instructed to trust the document. The study demonstrates that providing explicit validity information and a recency‑aware re‑ranker can substantially reduce poisoning, highlighting the need for models to assess whether retrieved evidence remains applicable.

By Md Shamim Ahmed, Lukas Galke Poech, Richard R\"{o}ttger
arXiv AI
Sep 25

Ingest-Time Fact Compilation for Cost-Efficient and Reliable Question Answering over Revised Corpora

The paper introduces ingest‑time fact compilation, an architecture that preprocesses and compiles corpus data into self‑contained facts with resolved revisions, deletions, and source trust. By storing this compiled state, query‑time models can retrieve answers directly, avoiding costly reconstruction from raw passages. Experiments show that this approach reduces read cost per question by 12.89× and token usage by 21.6× while maintaining accuracy.

By Kyle Wild, Yusuke Takahashi, Asako Uraki
arXiv AI
Oct 28

When Retrieval Is Not Enough: Obsolete Facts in Agent Memory

The study shows that in memory‑agent benchmarks, simply retrieving the correct fact does not guarantee accurate answers. On the FactConsolidation task, increasing the evidence budget from 64 to 512 tokens caused accuracy to drop, even though the newest statement was present in every retrieved set. Experiments with Qwen models revealed that replacing an unrelated statement with an obsolete competing statement drastically reduced accuracy, indicating that outdated information can be more harmful than irrelevant evidence.

By Oleeviya Babu Poikarayil, Tony Basil Babu, Abdulrahman Nahhas, Het Darshan Mehta, Jawid Ahmad Baktash, Ernesto William De Luca, Klaus Turowski
arXiv Computation and Language
Sep 23

Knowledge Pull Requests for Continual Document Authoring

The paper introduces Knowledge Pull Requests (KPRs), a framework that enables continual document authoring by making each change interpretable. KPRs extract claims from new knowledge sources, filter and route them to appropriate sections, and flag conflicts with existing content, producing a ChangeLog that separates knowledge changes from textual edits. Experiments on revising Wikipedia and updating query-driven reports show that KPRs integrate more information, better preserve existing content, and improve question answering performance compared to rewriting from scratch or using frontier models with search.

By Alexander Martin, Benjamin Van Durme