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.
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.
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...
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.
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.
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.
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...
Standard decoding rules for autoregressive language models promote diversity by rescaling the full next-token distribution or truncating its low-probability tail. These strategies overlook a common re...
arXiv:2606. 29082v1 Announce Type: cross Abstract: Would experience designing faster GPU kernels also help close in on a long-standing open mathematical conjecture?
arXiv:2606. 13799v1 Announce Type: cross Abstract: Finding the shortest program that generates a sequence is uncomputable, and for six decades that fact has been mistaken for a wall around finding any generating program.
Meta-Reflective Evolution Effectively Improves Production AI Systems describes CoCoEvolve, an evolutionary optimizer that uses meta-reflection to retrieve evidence from the entire evolutionary history and probe live systems before proposing mutations. The approach is applied to data agents, LLM-powered data pipelines, and data engineering workflows, achieving state‑of‑the‑art results on DABStep, multiple LLM pipeline workloads, and data-eng-bench. Ablation studies confirm that each component of CoCoEvolve contributes to its performance gains.
arXiv:2608. 10694v1 Announce Type: cross Abstract: Evolutionary optimization of LLM prompts and agentic programs (e.
arXiv:2610.28568v1 Announce Type: new Abstract: Generative retrieval (GR) casts search and recommendation as autoregressive generation of discrete semantic identifiers (SIDs). SID design largely dete...