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Haokun Liu

University of Chicago

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#artificial intelligence Preprint Sep 2026

Robust Failure, Conservative Repair: Textual Knowledge Distillation from Cross-Model Failures

Early experiment shows rule distillation from a single model's failures underperforms the baseline cheat sheet on non-source model families, which motivates Robust Failure, Conservative Repair (RFCR), a textual distillation procedure that derives rules from failures shared across models, sharpens their application boun...

Andrew Ren, Haokun Liu, Chen-Hao Tan · 0 citations
Preprint Aug 2026

The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams

The results suggest that multi-agent performance depends less on the number of agents than on the information they exchange, and interaction helps only when agents share the right information at the right time.

Summer Eunhyung Ann, Haokun Liu, Chen-Hao Tan · 3 citations
Review Jul 2026

VERITAS: Towards a General-Purpose Replication Tool for Scientific Research

This work presents VERITAS, a domain-agnostic replication framework built around CLI coding agents, which achieves state-of-the-art performance and leads on every metric on both benchmarks.

Haokun Liu, Filbert Aurelian Tjiaranata, Chenhao Tan · 1 citation

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