activity
20242026
collaborators

15 papers

cs.GT2026

Do LLMs Take Care of Their Own? Similarity Signals Can Induce Cooperation

Akash Kundu, Emanuel Tewolde, Ratip Emin Berker +2

As LLM-based agents with user-instructed goals are becoming widely deployed, they increasingly encounter each other in strategic interactions, and face challenges of finding mutual…

cs.GT2026

CoopEval: Benchmarking Cooperation-Sustaining Mechanisms and LLM Agents in Social Dilemmas

Emanuel Tewolde, Xiao Zhang, David Guzman Piedrahita +2

It is increasingly important that LLM agents interact effectively and safely with other goal-pursuing agents, yet, recent works report the opposite trend: LLMs with stronger reason…

cs.CL2026

The Memory Curse: How Expanded Recall Erodes Cooperative Intent in LLM Agents

Jiayuan Liu, Tianqin Li, Shiyi Du +7

Context window expansion is often treated as a straightforward capability upgrade for LLMs, but we find it systematically fails in multi-agent social dilemmas. Across 7 LLMs and 4…

cs.GT2026

Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models

Jiayuan Liu, Barry Wang, Jiarui Gan +4

Generative advertising in large language model (LLM) responses requires optimizing sponsorship configurations under two strict constraints: the strategic behavior of advertisers an…

cs.GT2026

Steering No-Regret Learners to a Desired Equilibrium

Brian Hu Zhang, Gabriele Farina, Ioannis Anagnostides +7

A mediator observes no-regret learners playing an extensive-form game repeatedly across rounds. The mediator attempts to steer players toward some desirable predetermined equil…

cs.GT2026

How Many Votes is a Lie Worth? Measuring Strategyproofness through Resource Augmentation

Ratip Emin Berker, Vincent Conitzer, Eden Hartman +2

It is well known, by the Gibbard-Satterthwaite Theorem, that when there are more than two candidates, any non-dictatorial voting rule can be manipulated by untruthful voters. But h…