# TRIZa: Guiding Agent Evolution from Pattern Recognition to Solution Invention

arXiv: https://arxiv.org/abs/2610.04555 · HTML: https://mlsanity.com/paper/2610.04555/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use.
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 3 Oct 2026.

## Placed in

- LLM agents and tool use / [Agent threats, guardrails and governance](https://mlsanity.com/agents/agent-threats-guardrails-and-governance/)

## Builds on (most cited first, 7 of 7)

- [AlphaEvolve: A coding agent for scientific and algorithmic discovery](https://mlsanity.com/paper/2506.13131/) (2025)
- [Time-uniform, nonparametric, nonasymptotic confidence sequences](https://mlsanity.com/paper/1810.08240/) (2018)
- [AutoSOTA: An End-to-End Automated Research System for State-of-the-Art AI Model Discovery](https://mlsanity.com/paper/2604.05550/) (2026)
- [OmniScientist: Toward a Co-evolving Ecosystem of Human and AI Scientists](https://mlsanity.com/paper/2511.16931/) (2025)
- [AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models](https://mlsanity.com/paper/2403.13002/) (2024)
- [PowerDuck: A GOOSE Data Set of Cyberattacks in Substations](https://mlsanity.com/paper/2207.04716/) (2022)
- [TRIZ Agents: A Multi-Agent LLM Approach for TRIZ-Based Innovation](https://mlsanity.com/paper/2506.18783/) (2025)

## Abstract (arXiv)

We propose TRIZa (TRIZ exponentiated by an agent), a general R&D automation paradigm that combines TRIZ inventive theory with LLM-driven agent evolutionary search. TRIZ's 40 inventive principles and contradiction matrix provide structured, explainable directions for solution generation, replacing random or untyped mutation with theory-guided ideation. Functional information (FI), operationalized under a frozen reference contract, is combined with TRIZ Ideality to measure useful and harmful function on a commensurable information scale, while hard gates keep promotion distinct from metric improvement. We validate TRIZa in cybersecurity--an adversarial and rapidly evolving domain--on PowerDuck GOOSE, CICIoT2023, and CIC-DDoS2019. Under paired-rerun protocols with protocol fingerprinting and hard-gate validation, the legacy experiments yield absolute F1 improvements of +2.88, +4.23, and +0.15 percentage points, respectively. A completed 45-activity CICIoT2023 campaign further increases macro-F1 from 0.8325 to 0.8483, but does not pass its frozen promotion gate. Every result remains traceable from contradiction identification and TRIZ principle selection to code transformation, evaluation metrics, and promotion decision.