10 papers
Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation
Mingyu Guo, Jiayuan Liu, Vincent Conitzer
We aim to design revenue-maximizing single-item auctions that are deterministic, strategy-proof, and ex post individually rational --- the quintessential fundamental model for opti…
Truthful and Expected-Payment-Optimal Model Routing in Graph-Based Agentic Workflows
Jiayuan Liu, Mingyu Guo, Jiarui Gan +1
Assigning routine tasks to cheaper large language model (LLM) providers can reduce generation cost, but switching providers may require processing accumulated context again. Keepin…
Align AI to Dynamic Human-AI Workflows
Valerie Chen, Cleotilde Gonzalez, Anita Williams Woolley +4
Current alignment approaches typically focus on emulating human behavior using static representations of human preferences, failing to capture the dynamic, context-dependent nature…
When Agents Lie: Premeditation, Persistence, and Exploitation in Repeated Games
Jerick Shi, Terry Jingcheng Zhang, Bernhard Schölkopf +2
As large language models are deployed as autonomous agents that communicate intentions before acting, a critical safety question is whether agents that publicly commit to actions w…
Why Search When You Can Transfer? Amortized Agentic Workflow Design from Structural Priors
Shiyi Du, Jiayuan Liu, Weihua Du +6
Automated agentic workflow design currently relies on per-task iterative search, which is computationally prohibitive and fails to reuse structural knowledge across tasks. We obser…
The Consensus Trap: Rescuing Multi-Agent LLMs from Adversarial Majorities via Token-Level Collaboration
Jiayuan Liu, Shiyi Du, Weihua Du +2
Multi-agent large language model (LLM) architectures increasingly rely on response-level aggregation, such as Majority Voting (MAJ), to raise reasoning ceilings. However, in open e…
Cheap Talk, Empty Promise: Frontier LLMs easily break public promises for self-interest
Jerick Shi, Terry Jingcheng Zhang, Zhijing Jin +1
Large language models are increasingly deployed as autonomous agents in multi-agent settings where they communicate intentions and take consequential actions with limited human ove…
Implementing surrogate goals for safer bargaining in LLM-based agents
Caspar Oesterheld, Maxime Riché, Filip Sondej +2
Surrogate goals have been proposed as a strategy for reducing risks from bargaining failures. A surrogate goal is goal that a principal can give an AI agent and that deflects any t…
From Sycophancy to Deception: A Unified Taxonomy for LLM Spontaneous Misalignment
Jerick Shi, Terry Jingcheng Zhang, Zhijing Jin +1
Large language models (LLMs) could produce systematically misaligned output, from hallucinated citations to strategic deception of evaluators, yet these phenomena are studied by se…
Designing Rules for Choosing a Winner in a Debate
Alexander Heckett, Vincent Conitzer
We consider settings where an uninformed principal must hear arguments from two better-informed agents, corresponding to two possible courses of action that they argue for. The arg…