4 citations · 9 across the 14 of their papers we have counts for
17 papers
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…
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…
IIRSim Studio: A Dashboard for User Simulation
Saber Zerhoudi, Adam Roegiest, Michael Granitzer
User simulation is a valuable methodology for evaluation in Information Retrieval (IR), enabling low-cost experimentation and counterfactual analysis. However, existing simulation…
Beyond the Click: A Framework for Inferring Cognitive Traces in Search
Saber Zerhoudi, Michael Granitzer
User simulators are essential for evaluating search systems, but they primarily reproduce user actions without modeling the underlying thought process. Large-scale interaction logs…
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…
UXSim: Towards a Hybrid User Search Simulation
Saber Zerhoudi, Michael Granitzer
Simulating nuanced user experiences within complex interactive search systems poses distinct challenge for traditional methodologies, which often rely on static user proxies or, mo…
Generative Agents Navigating Digital Libraries
Saber Zerhoudi, Michael Granitzer
In the rapidly evolving field of digital libraries, the development of large language models (LLMs) has opened up new possibilities for simulating user behavior. This innovation ad…
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…
From SERPs to Agents: A Platform for Comparative Studies of Information Interaction
Saber Zerhoudi, Michael Granitzer
The diversification of information access systems, from RAG to autonomous agents, creates a critical need for comparative user studies. However, the technical overhead to deploy an…
In-Browser Agents for Search Assistance
Saber Zerhoudi, Michael Granitzer
A fundamental tension exists between the demand for sophisticated AI assistance in web search and the need for user data privacy. Current centralized models require users to transm…
PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
Saber Zerhoudi, Michael Granitzer
Large Language Models (LLMs) struggle with generating reliable outputs due to outdated knowledge and hallucinations. Retrieval-Augmented Generation (RAG) models address this by enh…
A Survey of Web Content Control for Generative AI
Michael Dinzinger, Florian Heß, Michael Granitzer
The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI an…