1 citations · 1 across the 6 of their papers we have counts for
8 papers
Waggle: Learning One Anonymous Local Law for Self-Organizing LLM Swarms
Mingxi Zou, Wei Zhu, Zhuo Wang +4
As LLM agents increasingly collaborate on complex tasks, how to organize their interactions becomes a central design question. Existing multi-agent systems typically learn or adapt…
From Attack Success to Attack Severity: Counterfactual Memory Attacks on LLM Agents
Mingxi Zou, Langzhang Liang, Zhuo Wang +3
As LLM agents increasingly rely on persistent memory for long-horizon and personalized behavior, they can retain and reuse information across interactions, but this also creates a…
Remember the Decision, Not the Description: A Rate-Distortion Framework for Agent Memory
Mingxi Zou, Zhihan Guo, Langzhang Liang +6
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, sali…
DARC: Disagreement-Aware Alignment via Risk-Constrained Decoding
Mingxi Zou, Jiaxiang Chen, Junfan Li +4
Preference-based alignment methods (e.g., RLHF, DPO) typically optimize a single scalar objective, implicitly averaging over heterogeneous human preferences. In practice, systemati…
FinEvo: From Isolated Backtests to Ecological Market Games for Multi-Agent Financial Strategy Evolution
Mingxi Zou, Jiaxiang Chen, Aotian Luo +4
Conventional financial strategy evaluation relies on isolated backtests in static environments. Such evaluations assess each policy independently, overlook correlations and interac…
From Implicit Exploration to Structured Reasoning: Leveraging Guideline and Refinement for LLMs
Jiaxiang Chen, Zhuo Wang, Mingxi Zou +4
Large language models (LLMs) have advanced general-purpose reasoning, showing strong performance across diverse tasks. However, existing methods often rely on implicit exploration,…
FinHEAR: Human Expertise and Adaptive Risk-Aware Temporal Reasoning for Financial Decision-Making
Jiaxiang Chen, Mingxi Zou, Zhuo Wang +4
Financial decision-making presents unique challenges for language models, demanding temporal reasoning, adaptive risk assessment, and responsiveness to dynamic events. While large…
Guideline Forest: Retrieval-Augmented Reasoning with Branching Experience-Induced Guidelines
Jiaxiang Chen, Zhuo Wang, Mingxi Zou +2
Retrieval-augmented generation (RAG) has been widely adopted to ground large language models (LLMs) in external knowledge, yet it remains largely underexplored for improving reason…