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From the 1 of 7 linked papers with an AI index.

collaborators

7 papers

cs.GT2026

Strategy, Not Payoffs: A Behavioural Embedding of Normal-Form Games

Joshua Caiata, Sreepriya Pulyassary, Xiang Li +1

The paper introduces a lightweight behavioural embedding for normal-form games, using Nash equilibrium entropy and response sensitivity, to predict how fine‑tuning large language m…

cs.AI2026

Your Recourse, My Loss? Algorithmic Recourse under Shared Constraints

Zahra Khotanlou, Kate Larson, Amir-Hossein Karimi

Decision makers are increasingly relying on machine learning in sensitive situations. Algorithmic recourse aims to provide individuals with actionable and minimally costly steps to…

cs.AI2026

Active Evaluation of General Agents: Problem Definition and Comparison of Baseline Algorithms

Marc Lanctot, Kate Larson, Ian Gemp +1

As intelligent agents become more generally-capable, i.e. able to master a wide variety of tasks, the complexity and cost of properly evaluating them rises significantly. Tasks tha…

cs.LG2025

The Alignment Game: A Theory of Long-Horizon Alignment Through Recursive Curation

Ali Falahati, Mohammad Mohammadi Amiri, Kate Larson +1

In self-consuming generative models that train on their own outputs, alignment with user preferences becomes a recursive rather than one-time process. We provide the first formal f…

cs.AI2025

Evaluating Agents using Social Choice Theory

Marc Lanctot, Kate Larson, Yoram Bachrach +6

We argue that many general evaluation problems can be viewed through the lens of voting theory. Each task is interpreted as a separate voter, which requires only ordinal rankings o…

cs.MA2025

Soft Condorcet Optimization for Ranking of General Agents

Marc Lanctot, Kate Larson, Michael Kaisers +7

Driving progress of AI models and agents requires comparing their performance on standardized benchmarks; for general agents, individual performances must be aggregated across a po…