From the 2 of 12 linked papers with an AI index.
12 papers
Compile, Then Page: Executable SOP Programs and a Capability-Gated Runtime for Procedural LLM Agents
Chenglin Yu, Li Yin, Ying Yu +4
The paper proposes compiling standard operating procedures into executable pseudo‑code and running them with a program‑guided stack machine that pages the active frame while a larg…
The Hidden Footprint: Making Storage a First-Class Metric for LLM Agent Evaluation
Chenglin Yu, Hongquan Gui, Ying Yu +3
The paper introduces AgentFootprint, a benchmark that measures the persistent storage footprint left by large language model agents after execution, providing metrics on retention,…
Exploring Pass-Rate Reward in Reinforcement Learning for Code Generation
Xin-Ye Li, Ren-Biao Liu, Yun-Ji Zhang +3
Reinforcement learning (RL) from unit-test feedback has become a standard post-training recipe for improving large language models (LLMs) on code generation. However, the pass-all-…
Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization
Zhiyuan Zhai, Bingcong Li, Bingnan Xiao +2
Test-time compute scaling, the practice of spending extra computation during inference via repeated sampling, search, or extended reasoning, has become a powerful lever for improvi…
InfiCoEvalChain: A Blockchain-Based Decentralized Framework for Collaborative LLM Evaluation
Yifan Yang, Jinjia Li, Kunxi Li +7
The rapid advancement of large language models (LLMs) demands increasingly reliable evaluation, yet current centralized evaluation suffers from opacity, overfitting, and hardware-i…
Canonical Intermediate Representation for LLM-based optimization problem formulation and code generation
Zhongyuan Lyu, Shuoyu Hu, Lujie Liu +2
Automatically formulating optimization models from natural language descriptions is a growing focus in operations research, yet current LLM-based approaches struggle with the compo…