11 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…
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…