arXiv Computer Vision By Pengce Wang, Lucia Ronchi Darre, Matt Briney, Michaell Bakalars, Dan Rutkowski, Ursula Hardy, David Wolf, Laura Hoffman, Allen Kim, Shawn Wright, Juan Lavista Ferres

The Living Library: Transforming Archival Collections into Conversational Knowledge Systems -- Lessons from the Theodore Roosevelt Presidential Library

Read the original on arXiv Computer Vision →

The Living Library is an end‑to‑end framework that converts fragmented digital archives into governed, conversational exhibit experiences. Developed at the Theodore Roosevelt Presidential Library, it digitizes a 300,000‑record collection, enriches it with OCR and metadata, and publishes it to a hybrid dense/semantic index. The system supports curator review via the Archivist App, powers a researcher interface, and runs Talk to TR—a museum exhibit where a digital human embodiment of Theodore Roosevelt answers visitors’ questions using Cross‑Era Analogical Grounding and dual‑path retrieval to keep responses grounded and responsive.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

arXiv AI
Sep 18

TRACE: Accountable Agentic Retrieval for Source Discovery in Digital Archives

TRACE is a training‑free, agentic retrieval framework that enables accountable source discovery in historical archives, addressing challenges such as OCR degradation and genre heterogeneity. Developed for the DECIDON project on French Third Republic political discourse, it is deployed internally for 24 researchers across six institutions. On the HistoriQA‑ThirdRepublic benchmark, TRACE achieves R@10 of 0.856 and MRR of 0.653, outperforming sparse, dense, graph‑based, and other agentic RAG baselines, especially on multi‑hop and cross‑corpus questions, while costing only about $0.02 per question.

By Donghan Bian (ENC, LRE), Marie Puren (LRE, ENC), Florian Cafiero (LRE, ENC)
Hugging Face Trending Papers
Aug 2

TrajWiki: Source-Grounded Memory Trajectories for Long-Horizon Dialogue Agents

Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.

arXiv AI
Jun 3

From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Members

arXiv:2606. 03876v1 Announce Type: cross Abstract: With the growing prevalence of modern ubiquitous computing technologies, multi-modal tracking systems hold promise for providing timely awareness and reassurance to stakeholders such as remote family members (RFMs) of older adults, who play a central role in care coordination.

By Jiachen Li, Reina Szeyi Chan, Akshat Choube, Xiang Zhi Tan, Elizabeth Mynatt, Varun Mishra