4 citations · 5 across the 11 of their papers we have counts for
14 papers
Learning Self-Orchestration in Decentralized Agent Swarms
Guowei Xu, Zhenting Qi, Amani Kiruga +2
The capabilities of an agent swarm depend on both its individual members and how they coordinate. Yet existing multi-agent systems often train agents within fixed orchestration str…
Scaling Multi-Agent Intelligence with Substrate-Centric Coordination
Zhenting Qi, Zhuokai Zhao, Rulin Shao +7
Existing large language model (LLM) based multi-agent systems have been shown to scale poorly, and we identify three problems: reading burden that grows with the group, useful info…
Prototype Language Models
Dan Ley, Giang Nguyen, Himabindu Lakkaraju +1
Knowing which training examples drive outputs is fundamental to auditing, correcting, and understanding language models, yet for modern LLMs this remains expensive, approximate, an…
Economy of Minds: Emerging Multi-Agent Intelligence with Economic Interactions
Zhenting Qi, Huangyuan Su, Ao Qu +13
How can a population of agents self-orchestrate and self-adapt into stronger collective intelligence without centralized control? Inspired by Friedrich Hayek's economic theory of d…
Self-Improving Language Models with Bidirectional Evolutionary Search
Guowei Xu, Zhenting Qi, Huangyuan Su +4
Search has been proposed as an effective method for self-improving language models and agentic systems, both for post-training sample generation and for inference. However, widely…
D-PACE: Dynamic Position-Aware Cross-Entropy for Parallel Speculative Drafting
Tianyu Wu, Yu Yao, Zhenting Qi +7
Speculative decoding accelerates LLM inference by having a small drafter propose tokens that a larger target model verifies in parallel. Recent diffusion-based parallel drafters su…
Monitorability as a Free Gift: How RLVR Spontaneously Aligns Reasoning
Zidi Xiong, Shan Chen, Himabindu Lakkaraju
As Large Reasoning Models (LRMs) are increasingly deployed, auditing their chain-of-thought (CoT) traces for safety becomes critical. Recent work has reported that monitorability--…
EvoLM: In Search of Lost Language Model Training Dynamics
Zhenting Qi, Fan Nie, Alexandre Alahi +6
Modern language model (LM) training has been divided into multiple stages, making it difficult for downstream developers to evaluate the impact of design choices made at each stage…
Measuring the Faithfulness of Thinking Drafts in Large Reasoning Models
Zidi Xiong, Shan Chen, Zhenting Qi +1
Large Reasoning Models (LRMs) have significantly enhanced their capabilities in complex problem-solving by introducing a thinking draft that enables multi-path Chain-of-Thought exp…
Detecting LLM-Generated Peer Reviews
Vishisht Rao, Aounon Kumar, Himabindu Lakkaraju +1
The integrity of peer review is fundamental to scientific progress, but the rise of large language models (LLMs) has introduced concerns that some reviewers may rely on these tools…
Generalizing Trust: Weak-to-Strong Trustworthiness in Language Models
Martin Pawelczyk, Lillian Sun, Zhenting Qi +2
The rapid proliferation of generative AI, especially large language models, has led to their integration into a variety of applications. A key phenomenon known as weak-to-strong ge…
Quantifying Generalization Complexity for Large Language Models
Zhenting Qi, Hongyin Luo, Xuliang Huang +5
While large language models (LLMs) have shown exceptional capabilities in understanding complex queries and performing sophisticated tasks, their generalization abilities are often…
Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems
Zhenting Qi, Hanlin Zhang, Eric Xing +2
Retrieval-Augmented Generation (RAG) improves pre-trained models by incorporating external knowledge at test time to enable customized adaptation. We study the risk of datastore le…
A Study on the Calibration of In-context Learning
Hanlin Zhang, Yi-Fan Zhang, Yaodong Yu +5
Accurate uncertainty quantification is crucial for the safe deployment of machine learning models, and prior research has demonstrated improvements in the calibration of modern lan…