4 citations · 4 across the 2 of their papers we have counts for
17 papers
ROLoad-PMP: Securing Sensitive Operations for Kernels and Bare-Metal Firmware
Wende Tan, Chenyang Li, Yangyu Chen +3
A common way for attackers to compromise victim systems is hijacking sensitive operations (e.g., control-flow transfers) with attacker-controlled inputs. Existing solutions in gene…
ELMER: Evolutionary Language Model that Explores and Refines
Matthew Siper, Ahmed Khalifa, Julian Togelius
Program evolution can measure whether a mutation helped, but it rarely controls how far the mutation moves in behavior space. Syntactic edit size is an unreliable proxy: a small co…
Evolutionary Wave Function Collapse
Dipika Rajesh, Ahmed Khalifa, Julian Togelius
Wave Function Collapse (WFC) is a widely used procedural content generation method that learns local adjacency constraints from example inputs to generate larger outputs. In this p…
PCGRLLM: Large Language Model-Driven Reward Design for Procedural Content Generation Reinforcement Learning
In-Chang Baek, Sung-Hyun Kim, Sam Earle +4
Reward design plays a pivotal role in the training of game AIs, requiring substantial domain-specific knowledge and human effort. In recent years, several studies have explored rew…
GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Games
Yuchen Li, Cong Lin, Muhammad Umair Nasir +3
We introduce GVGAI-LLM, a video game benchmark for evaluating the reasoning and problem-solving capabilities of large language models (LLMs). Built on the General Video Game AI fra…
Continuous Program Search
Matthew Siper, Muhammad Umair Nasir, Ahmed Khalifa +3
Genetic Programming yields interpretable programs, but small syntactic mutations can induce large, unpredictable behavioral shifts, degrading locality and sample efficiency. We fra…