2 citations · 4 across the 12 of their papers we have counts for
12 papers
Learning Dynamics of Strategic Publishers in Generative AI Ecosystems
Sagie Dekel, Omer Madmon, Moshe Tennenholtz +1
Generative AI (GenAI) search systems are transforming how users access information. Unlike ranking-based search systems, where users observe a ranked list of documents, GenAI searc…
Stability in Competitive Search with Results Diversification
Itamar Reinman, Omer Madmon, Moshe Tennenholtz +1
In a competitive search setting, publishers strategically modify their documents in response to induced rankings so as to improve their future ranking. We present a novel game-theo…
Predicting Decisions of AI Agents from Limited Interaction through Text-Tabular Modeling
Eilam Shapira, Moshe Tennenholtz, Roi Reichart
AI agents negotiate and transact in natural language with unfamiliar counterparts: a buyer bot facing an unknown seller, or a procurement assistant negotiating with a supplier. In…
Addressing Corpus Knowledge Poisoning Attacks on RAG Using Sparse Attention
Sagie Dekel, Moshe Tennenholtz, Oren Kurland
Retrieval Augmented Generation (RAG) is a highly effective paradigm for keeping LLM-based responses up-to-date and reducing the likelihood of hallucinations. Yet, RAG was recently…
RLRF: Competitive Search Agent Design via Reinforcement Learning from Ranker Feedback
Tommy Mordo, Sagie Dekel, Omer Madmon +2
Competitive search is a setting where document publishers modify them to improve their ranking in response to a query. Recently, publishers have increasingly leveraged LLMs to gene…
On the Merits of LLM-Based Corpus Enrichment
Gal Zur, Tommy Mordo, Moshe Tennenholtz +1
Generative AI (genAI) technologies -- specifically, large language models (LLMs) -- and search have evolving relations. We argue for a novel perspective: using genAI to enrich a do…
Robust-IR @ SIGIR 2025: The First Workshop on Robust Information Retrieval
Yu-An Liu, Haya Nachimovsky, Ruqing Zhang +3
With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its…
A Multi-Agent Perspective on Modern Information Retrieval
Haya Nachimovsky, Moshe Tennenholtz, Oren Kurland
The rise of large language models (LLMs) has introduced a new era in information retrieval (IR), where queries and documents that were once assumed to be generated exclusively by h…
CSP: A Simulator For Multi-Agent Ranking Competitions
Tommy Mordo, Tomer Kordonsky, Haya Nachimovsky +2
In ranking competitions, document authors compete for the highest rankings by modifying their content in response to past rankings. Previous studies focused on human participants,…
Near-Linear MIR Algorithms for Stochastically-Ordered Priors
Gal Bahar, Omer Ben-Porat, Kevin Leyton-Brown +1
With the rise of online applications, recommender systems (RSs) often encounter constraints in balancing exploration and exploitation. Such constraints arise when exploration is ca…
On the Power of Strategic Corpus Enrichment in Content Creation Games
Haya Nachimovsky, Moshe Tennenholtz
Search and recommendation ecosystems exhibit competition among content creators. This competition has been tackled in a variety of game-theoretic frameworks. Content creators gener…
Prediction-sharing During Training and Inference
Yotam Gafni, Ronen Gradwohl, Moshe Tennenholtz
Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to der…