10 citations · 14 across the 9 of their papers we have counts for
9 papers
When Is Complex Chunking Worth It? A Multi-Objective Evaluation of Chunking Methods at Scale
Laura Caspari, Kanishka Ghosh Dastidar, Michael Dinzinger +2
Dense retrieval is commonly evaluated on benchmarks that represent each document with a single embedding, even though real-world retrieval systems often index long documents that r…
Form Without Function: Agent Social Behavior in the Moltbook Network
Saber Zerhoudi, Kanishka Ghosh Dastidar, Felix Klement +9
Moltbook is a social network where every participant is an AI agent. We analyze 1,312,238 posts, 6.7~million comments, and over 120,000 agent profiles across 5,400 communities, col…
Behind the Prompt: The Agent-User Problem in Information Retrieval
Saber Zerhoudi, Michael Granitzer, Dang Hai Dang +5
User models in information retrieval rest on a foundational assumption that observed behavior reveals intent. This assumption collapses when the user is an AI agent privately confi…
Benevolent Dictators? On LLM Agent Behavior in Dictator Games
Andreas Einwiller, Kanishka Ghosh Dastidar, Artur Romazanov +3
In behavioral sciences, experiments such as the ultimatum game are conducted to assess preferences for fairness or self-interest of study participants. In the dictator game, a simp…
Compressed Concatenation of Small Embedding Models
Mohamed Ayoub Ben Ayad, Michael Dinzinger, Kanishka Ghosh Dastidar +2
Embedding models are central to dense retrieval, semantic search, and recommendation systems, but their size often makes them impractical to deploy in resource-constrained environm…
CoRECT: A Framework for Evaluating Embedding Compression Techniques at Scale
L. Caspari, M. Dinzinger, K. Ghosh Dastidar +3
Dense retrieval systems have proven to be effective across various benchmarks, but require substantial memory to store large search indices. Recent advances in embedding compressio…
WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval
Michael Dinzinger, Laura Caspari, Kanishka Ghosh Dastidar +2
We present WebFAQ, a large-scale collection of open-domain question answering datasets derived from FAQ-style schema.org annotations. In total, the data collection consists of 96 m…
Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems
Laura Caspari, Kanishka Ghosh Dastidar, Saber Zerhoudi +2
The choice of embedding model is a crucial step in the design of Retrieval Augmented Generation (RAG) systems. Given the sheer volume of available options, identifying clusters of…
The Importance of Future Information in Credit Card Fraud Detection
Van Bach Nguyen, Kanishka Ghosh Dastidar, Michael Granitzer +1
Fraud detection systems (FDS) mainly perform two tasks: (i) real-time detection while the payment is being processed and (ii) posterior detection to block the card retrospectively…