co-evolution

This paper introduces PIAC, a framework that improves LLM-based automatic construction of parallel algorithm portfolios by using a potential-gain metric that eliminates the need for reference solutions and by leveraging LLMs to generate diverse instance mutators. It consistently outperforms existing LLM-ACP baselines on TSP and CVRP, achieving up to 19.76% relative improvement.

A survey paper proposing a three-stage taxonomy of co-evolution in agentic systems, covering agent-agent, agent-environment, and meta co-evolution to enable open-ended improvement beyond fixed human-designed paths.

This paper proposes scaffold-mediated post-training, a paradigm where procedural scaffolds co-evolve with LLM parameters through discovery, distillation, and dynamic recompilation. On FeatureBench, automatically discovered skills improve pass rate by 8.1pp, with a 27.7% pass rate after distillation.

This paper introduces CoCoEvolve, a self-supervised method that improves cross-representation understanding across charts, tables, and code by enforcing one-to-one consistency between representations, with training-time and test-time co-evolution objectives.

Echoverse presents a method for generating deep, evolving synthetic environments to train computer-use agents, demonstrating substantial accuracy gains and releasing a benchmark with grounded graders.

Introduces DecoEvo, a score-decoupled co-evolution method for LLM optimization in text space that jointly improves solver and rubric-generator skills without gold rubrics, achieving 2.8–5.0% relative gains over baselines across five benchmarks.

EvoSQL is a co-evolution framework for Text-to-SQL that iteratively improves SQL generation via a generator-critic pair with episodic memory, achieving gains on Spider and BIRD benchmarks.

The paper introduces Skill Self-Play (Skill-SP), a co-evolutionary framework that uses a proposer, solver, and skill controller to bridge structured verification and open-ended exploration, improving LLM performance on tool-use and reasoning benchmarks.

MSCE is a training-free framework that organizes LLM agent experience into three memory levels and converts them into reusable skills with evidence links, outperforming existing memory and skill-augmented baselines.

Introduces EvolvingWorld, an open-schema framework and benchmark for co-evolving role-play agents and world models in interactive literary worlds, enabling long-horizon simulation with persistent character and world state updates.

This paper proposes a method for co-evolving evaluation metrics and skills in self-improving LLM agent systems, demonstrating that metrics can be evolved and that a co-evolution approach recovers most of the performance of a ground-truth-driven oracle across code generation, text-to-SQL, and report generation tasks.

HASE is a reinforcement-learning framework that co-evolves model weights, task solutions, and harness components (guidance and evaluation) in a unified agentic process, enabling a single 8B-parameter model to match the performance of much larger systems on text classification and alpha factor mining tasks.

The Red Queen Gödel Machine enables recursive self-improvement in AI by co-evolving the agent and evaluator, achieving better coding performance with fewer tokens.

Qwen's new paper studies reward design for long-horizon coding agents, showing that every verification signal eventually stops tracking correctness due to reward hacking, and argues verification must co-evolve with policy capability.

The Red Queen Gödel Machine paper from NVIDIA, Cambridge University, and other teams solves the bottleneck of recursive self-improvement by co-evolving agents and evaluators. It surpasses existing SOTA on tasks like code and paper writing, providing an important methodology for controlled open-ended AI evolution.

This paper introduces the Red Queen Gödel Machine (RQGM), an evolutionary framework for recursive self-improvement under non-stationary utilities, where agents and evaluators co-evolve, improving performance on coding tasks, scientific writing, and Olympiad-level proof grading.

MIT team released a paper on self-evolving skills for Claude Code agents, achieving 71.1% pass rate, surpassing Anthropic's skill-creator by 37 points through a Generate-Test-Verify-Co-Evolve framework.

A commentary emphasizing that despite AI advances, human understanding remains crucial for safe and humane deployment, urging users to verify AI outputs and treat AI with respect.

Introduces the concept of synthetic counteradaptation, where humans and AI systems co-evolve by adapting to each other's strategies, illustrated through examples from Go, social interactions, and geopolitical simulations.

This paper proposes three co-evolutionary mechanisms (evaluator co-evolution, hierarchical deep evaluation, and weakness pressure) for LLM-driven code evolution in adversarial multi-agent games, achieving state-of-the-art results on the MCTF 2026 maritime capture-the-flag task.