12 citations · 13 across the 13 of their papers we have counts for
15 papers
From Hand-Crafted to LLM-Based Variation Operators in Metaheuristics: A Tutorial
Camilo Chacón Sartori, Guillem Rodríguez-Corominas, Christian Blum
Large language models (LLMs) are increasingly being employed as variation operators in metaheuristics, generating or modifying candidate solutions, heuristics, or programs inside i…
EMPATH: A Multilingual Auditor-Judge Benchmark for Safety Evaluation of Emotional-Support Chatbots
Camilo Chacón Sartori
Safety benchmarks often buy scalability by fixing the prompt, the language, and the turn structure. For emotional-support chatbots, that bargain hides precisely where safety failur…
A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test
Camilo Chacón Sartori, José H. García
Retrieval-augmented generation (RAG) systems are often compared by asking a large language model (LLM) judge which answer is better. For multi-hop RAG, this has become a measuremen…
Transferable knowledge graphs with executable learned operators for algorithm design
Camilo Chacón Sartori, José H. García, Andrei Voicu Tomut +1
Procedural knowledge in algorithm design is embedded in source code and rebuilt for each new domain. We introduce Generative Executable Algorithm Knowledge Graphs (GEAKG), a repres…
The Specification Gap: Coordination Failure Under Partial Knowledge in Code Agents
Camilo Chacón Sartori
When multiple LLM-based code agents independently implement parts of the same class, they must agree on shared internal representations, even when the specification leaves those ch…
Coherent Without Grounding, Grounded Without Success: Observability and Epistemic Failure
Camilo Chacón Sartori
When an agent can articulate why something works, we typically take this as evidence of genuine understanding. This presupposes that effective action and correct explanation covary…
Code World Models for Parameter Control in Evolutionary Algorithms
Camilo Chacón Sartori, Guillem Rodríguez Corominas
Can an LLM learn how an optimizer behaves -- and use that knowledge to control it? We extend Code World Models (CWMs), LLM-synthesized Python programs that predict environment dyna…
irace-evo: Automatic Algorithm Configuration Extended With LLM-Based Code Evolution
Camilo Chacón Sartori, Christian Blum
Automatic algorithm configuration tools such as irace efficiently tune parameter values but leave algorithmic code unchanged. This paper introduces a first version of irace-evo, an…
LLM-Based Instance-Driven Heuristic Bias In the Context of a Biased Random Key Genetic Algorithm
Camilo Chacón Sartori, Martín Isla Pino, Pedro Pinacho-Davidson +1
Integrating Large Language Models (LLMs) within metaheuristics opens a novel path for solving complex combinatorial optimization problems. While most existing approaches leverage L…
Architectures of Error: A Philosophical Inquiry into AI and Human Code Generation
Camilo Chacón Sartori
With the rise of generative AI (GenAI), Large Language Models are increasingly employed for code generation, becoming active co-authors alongside human programmers. Focusing specif…
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms
Camilo Chacón Sartori, Christian Blum
Large Language Models (LLMs) have shown notable potential in code generation for optimization algorithms, unlocking exciting new opportunities. This paper examines how LLMs, rather…
Improving Existing Optimization Algorithms with LLMs
Camilo Chacón Sartori, Christian Blum
The integration of Large Language Models (LLMs) into optimization has created a powerful synergy, opening exciting research opportunities. This paper investigates how LLMs can enha…
VisGraphVar: A Benchmark Generator for Assessing Variability in Graph Analysis Using Large Vision-Language Models
Camilo Chacón Sartori, Christian Blum, Filippo Bistaffa
The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structu…
Metaheuristics and Large Language Models Join Forces: Toward an Integrated Optimization Approach
Camilo Chacón Sartori, Christian Blum, Filippo Bistaffa +1
Since the rise of Large Language Models (LLMs) a couple of years ago, researchers in metaheuristics (MHs) have wondered how to use their power in a beneficial way within their algo…
Large Language Models for the Automated Analysis of Optimization Algorithms
Camilo Chacón Sartori, Christian Blum, Gabriela Ochoa
The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs…