70 citations · 301 across the 67 of their papers we have counts for
77 papers
EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design
Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong +1
LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an evolutionary search. Existin…
Evaluator-in-the-Loop Monte Carlo Tree Search via LLM Agents for Motif Scaffolding in Protein Design
Haotian Hu, Oguzhan Gungordo, Siheng Xiong +1
Motif-scaffolding systems commonly follow a generate-then-filter paradigm, in which candidate proteins are generated independently and structural evaluation is used primarily for t…
DIVE: Unlocking Self-Improvement in Frozen Language Models Through Diversity-Driven Skill Evolution
Siheng Xiong, Ali Payani, Oguzhan Gungordu +1
Large language models (LLMs) cannot retain post-deployment experience without parameter updates. We introduce DIVE, a diversity-driven framework that enables frozen LLMs to improve…
NeSyFS: A Neuro-symbolic Fast-Slow Thinking Framework for LLM Agent under Partial Observability
Duo Xu, Faramarz Fekri
Recently Large Language Models (LLMs) have been increasingly deployed as autonomous agents in applications such as self-reflection, retrieval-augmented generation, and scientific d…
MOSAIC: Adversarial Co-evolution of Specialist Heuristics and Problem Instances for LLM-based Automated Heuristic Design
Oguzhan Gungordu, Siheng Xiong, Faramarz Fekri
Automated heuristic design (AHD) with large language models (LLMs) has produced strong heuristics for combinatorial optimization problems (COPs). Yet existing frameworks optimize f…
SCOUT: Cyclic Causal Discovery Under Soft Interventions with Unknown Targets
Alpar Turkoglu, Muralikrishnna G. Sethuraman, Faramarz Fekri
Learning causal relationships between variables from data is a fundamental research area with many applications across disciplines. Most existing causal discovery algorithms rely o…