7 citations · 12 across the 8 of their papers we have counts for
8 papers
Probing the Lack of Stable Internal Beliefs in LLMs
Yifan Luo, Kangping Xu, Yanzhen Lu +2
Persona-driven large language models (LLMs) require consistent behavioral tendencies across interactions to simulate human-like personality traits, such as persistence or reliabili…
Monadic Context Engineering
Yifan Zhang, Yang Yuan, Mengdi Wang +1
The proliferation of Large Language Models (LLMs) has catalyzed a shift towards autonomous agents capable of complex reasoning and tool use. However, current agent architectures ar…
Group Representational Position Encoding
Yifan Zhang, Zixiang Chen, Yifeng Liu +6
We present GRAPE (Group Representational Position Encoding), a unified framework for positional encoding based on group actions. GRAPE unifies two families of mechanisms: (i) multi…
Towards Automated Formal Verification of Backend Systems with LLMs
Kangping Xu, Yifan Luo, Yang Yuan +1
Software testing plays a critical role in ensuring that systems behave as intended. However, existing automated testing approaches struggle to match the capabilities of human engin…
On the Design of KL-Regularized Policy Gradient Algorithms for LLM Reasoning
Yifan Zhang, Yifeng Liu, Huizhuo Yuan +3
Policy gradient algorithms have been successfully applied to enhance the reasoning capabilities of large language models (LLMs). KL regularization is ubiquitous, yet the design sur…
Tensor Product Attention Is All You Need
Yifan Zhang, Yifeng Liu, Huizhuo Yuan +4
Scaling language models to handle longer input sequences typically necessitates large key-value (KV) caches, resulting in substantial memory overhead during inference. In this pape…
On the Diagram of Thought
Yifan Zhang, Yang Yuan, Andrew Chi-Chih Yao
Large Language Models (LLMs) excel at many tasks but often falter on complex problems that require structured, multi-step reasoning. We introduce the Diagram of Thought (DoT), a fr…
Meta Prompting for AI Systems
Yifan Zhang, Yang Yuan, Andrew Chi-Chih Yao
We introduce Meta Prompting (MP), a framework that emphasizes the formal structure of a task rather than content-specific worked examples. We give a categorical formalization in wh…