arXiv AI October 16, 2026 By Kyoko Ogawa, Naoki Mori

T-GADE: Thermodynamical Generative-AI-Driven Evolution of LLM Artifacts

T‑GADE is an evolutionary computation framework that integrates large language models (LLMs) to generate and evolve structured artifacts. It retains classic population and genetic operators while using LLMs for crossover, mutation, and a self‑repair operator called HoxLM. The framework introduces temperature‑controlled free‑energy selection and occupancy rules, and demonstrates improved performance on an online bin‑packing task compared to a baseline Evolution of Heuristics approach.

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llms

arXiv AI

Aug 11

DarwinX: Evolving Agent Harnesses Through Natural Selection

arXiv:2608. 07545v1 Announce Type: cross Abstract: An LLM agent's capability depends not only on model weights but on its harness: prompts, tools, skills, and control flow.

By Yifan Zhang, Yutong Dai, Juntao Tan, Luyu Yang, Rishi Mullur, Thai Hoang, Zhiyuan Hu, James Zhu, Phil Mui, Silvio Savarese, Ran Xu, Zeyuan Chen

llms agents benchmarks

arXiv AI

Oct 15

EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design

arXiv:2610.17379v1 Announce Type: new Abstract: LLM-based automated heuristic design (AHD) discovers heuristics for combinatorial optimization problems (COPs), where an LLM generates candidates in an...

By Oguzhan Gungordu, Ayse Tursucular, Siheng Xiong, Faramarz Fekri

llms reinforcement-learning fine-tuning efficiency

arXiv AI

Jul 13

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

arXiv:2607. 08791v1 Announce Type: cross Abstract: Designing effective multi-objective Bayesian optimization (MOBO) algorithms requires balancing many interdependent design choices whose optimal configuration is problem-dependent and typically demands deep expertise.

By Georgios Laskaris, Reuben Brasher, Niki van Stein, Elena Raponi, Thomas B\"ack, Florian Neukart

llms benchmarks

arXiv AI

Aug 25

XTC: Head-Aware Sampling by Excluding Top Choices

XTC (Exclude Top Choices) is a lightweight, head‑aware decoding operator that improves diversity in autoregressive language models by removing overly probable tokens that dominate the next‑token distribution. It works by identifying tokens above a plausibility threshold, probabilistically excluding the dominant choices, and renormalizing the remaining distribution. Across 60 experiments on models such as Gemma 3 and DeepSeek R1, XTC boosts Distinct‑2 scores by 11–15 % and cuts repeat trigrams by 27–47 %, while a Mechanical Turk study shows a 62.3 % preference for XTC‑generated text without loss of fluency.

By Philipp Emanuel Weidmann, Allen Roush, Judah Goldfeder, Sanjay Basu, Ravid Shwartz-Ziv

llms efficiency

arXiv Machine Learning

Jul 23

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning

arXiv:2607. 19408v1 Announce Type: new Abstract: Using Evolutionary Strategies (ES) for fine-tuning large language models is attractive because it is memory-efficient, parallel, and compatible with black-box or discrete rewards.

By Sung Cho, Gyubin Han

llms fine-tuning

arXiv AI

Oct 15

REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming

arXiv:2610.15513v1 Announce Type: cross Abstract: Safety-aligned large language models (LLMs) remain vulnerable to adversarial prompts that bypass their safety training. Existing automated red-teamin...

By Abrar Alotaibi, Moataz Ahmed

llms multimodal benchmarks safety

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Related stories

DarwinX: Evolving Agent Harnesses Through Natural Selection

EPOCH: Coverage-Aware Evolution with Experiential Preference Optimization for LLM-based Automated Heuristic Design

LLM-Driven Evolutionary Generation of Multi-Objective Bayesian Optimization Algorithms

XTC: Head-Aware Sampling by Excluding Top Choices

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning

REPRO: Reflective Evolutionary Prompt Optimizer for Automated LLM Red-Teaming