Language Model (LM)-based agents remain challenging to evaluate systematically in mixed-motive settings where agents must leverage short-term cooperation for long-term competitive goals (e.g., multi-party politics). We introduce Cooperate to Compete (C2C), a multi-agent environment where players can engage in private negotiations while competing to be the first to achieve their secret objective. Players have asymmetric objectives and negotiations are non-binding, allowing alliances to form and break as players’ short-term interests align and diverge. We run AI-only games and conduct a user study pitting human players against AI opponents. We find that humans favor lower-complexity deals and talk to more unique partners over the course of the game. We also find that humans are more aggressive negotiators, closing deals only 73.5% of the time compared to 94.0% for LM-based agents. Interventions inspired by these findings shift agent negotiation behavior and can raise win rates from 22.2% to 32.7%, demonstrating that language coordination strategies in C2C substantially alter negotiation dynamics. We run over 1,300 games with over 20,000 private conversations totaling over 180,000 player actions. By combining multi-party, long-horizon play with partial observability, asymmetric objectives, and non-binding negotiations, C2C provides a controlled, gamified testbed for studying strategic coordination in LM-based agents.

Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through real interaction data. We introduce ParaStudent, a fine-tuning framework for simulating novice programming revisions to support AI tutor evaluation. Compared with prompted baselines, ParaStudent’s revisions more closely match real student code distributions across functional, stylistic, and semantic metrics. Our best variant achieves AUCs of 0.80 for both feedback relevance and successful uptake when distinguishing streams with real engagement above versus at or below the median, while prompted baselines remain near chance on successful uptake. These findings demonstrate the promise of simulated engagement for pre-deployment feedback triage.