24 citations · 28 across the 14 of their papers we have counts for
19 papers
Faithful Query Resolution for Conversational Search
Saber Zerhoudi, Ivica Kostric, Jelena Mitrovic
Conversational query rewriting makes context-dependent requests explicit for retrieval, but additional context can introduce an information need the user did not express. This rais…
The Fellowship of the Query: Learning Retrieval Actions
Mohammed Al-Maamari, Saber Zerhoudi, Michael Granitzer +1
Retrieval-augmented question answering requires control decisions about when to decompose a question, search, reformulate, extract evidence, synthesize facts, verify progress, and…
The Compaction Cliff in Long-Running AI Agent Memory
Saber Zerhoudi, Jelena Mitrovic, Michael Granitzer
A safety rule and an episodic log compete for the same tokens in an AI agent's context. When the budget overflows, both are summarized at the same rate; only the rule needs exact w…
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…
As We May Search
Saber Zerhoudi, Adam Roegiest, Jelena Mitrovic +1
The sensitive information in personal documents, legal files, and medical records is among the most valuable things to search, yet current retrieval-augmented generation systems st…
Metadata, Structure, or Strategy? A Decomposition of RAG Context Enrichment
Saber Zerhoudi, Michael Granitzer, Jelena Mitrovic
Retrieval-augmented generation (RAG) systems increasingly enrich retrieved passages by attaching quality metadata, structuring them into explicit records, and adopting multi-hop re…
NuggetIndex: Governed Atomic Retrieval for Maintainable RAG
Saber Zerhoudi, Michael Granitzer, Jelena Mitrovic
Retrieval-augmented generation (RAG) systems are frequently evaluated via fact-based metrics, yet standard implementations retrieve passages or static propositions. This unit misma…
AgentSim: A Platform for Verifiable Agent-Trace Simulation
Saber Zerhoudi, Michael Granitzer, Jelena Mitrovic
Training trustworthy agentic LLMs requires data that shows the grounded reasoning process, not just the final answer. Existing datasets fall short: question-answering data is outco…
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…
OwlerLite: Scope- and Freshness-Aware Web Retrieval for LLM Assistants
Saber Zerhoudi, Michael Dinzinger, Michael Granitzer +1
Browser-based language models often use retrieval-augmented generation (RAG) but typically rely on fixed, outdated indices that give users no control over which sources are consult…
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…
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…
Krony-PT: GPT2 compressed with Kronecker Products
Mohamed Ayoub Ben Ayad, Jelena Mitrovic, Michael Granitzer
We introduce Krony-PT, a compression technique for GPT-2 based on Kronecker products. We specifically target the feed-forward weights of each transformer block, and systematically…
SUDS: A Strategy for Unsupervised Drift Sampling
Christofer Fellicious, Lorenz Wendlinger, Mario Gancarski +2
Supervised machine learning often encounters concept drift, where the data distribution changes over time, degrading model performance. Existing drift detection methods focus on id…
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…
Computational Approaches to the Detection of Lesser-Known Rhetorical Figures: A Systematic Survey and Research Challenges
Ramona Kühn, Jelena Mitrović, Michael Granitzer
Rhetorical figures play a major role in our everyday communication as they make text more interesting, more memorable, or more persuasive. Therefore, it is important to computation…
A Benchmark of PDF Information Extraction Tools using a Multi-Task and Multi-Domain Evaluation Framework for Academic Documents
Norman Meuschke, Apurva Jagdale, Timo Spinde +2
Extracting information from academic PDF documents is crucial for numerous indexing, retrieval, and analysis use cases. Choosing the best tool to extract specific content elements…
HateBERT: Retraining BERT for Abusive Language Detection in English
Tommaso Caselli, Valerio Basile, Jelena Mitrović +1
In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in…