The paper introduces The Trinity, a recursive self‑improvement framework for multimodal agents that couples world generation with harness evolution through three roles: Creator, Explorer, and Evolver. By shifting task organization from surface categories to shared capability requirements, it builds a hierarchical capability tree and a reusable harness tree, enabling efficient transfer learning across heterogeneous tasks. Experiments show the evolved harnesses improve mean performance by 34 points on 28 training benchmarks and 26 points on 18 unseen benchmarks, including two real‑world embodied tasks.
arXiv:2609.14857v1 Announce Type: new Abstract: Recent work extends recursive self-improvement (RSI) to agent harnesses for long-horizon coding and terminal tasks, enabling agents to improve executio...
arXiv:2609.33772v2 Announce Type: replace Abstract: Executable environments are critical for post-training agents on tasks that require tool use and multi-step interaction, but constructing executabl...
arXiv:2609.04280v2 Announce Type: replace-cross Abstract: Modern LLM-based agents operate through a harness of tools, reusable skills, and specialist agents that shapes what they observe and what the...
The paper introduces a self‑evolving harness framework where a frozen language‑model agent first solves tasks and then edits its own harness based on run records. Using a 49‑line seed harness, the evolved harness improves average scores on in‑distribution benchmarks by 4.48 points and on out‑of‑distribution benchmarks by 12.64 points, surpassing Codex on the former and matching it on the latter. Continued evolution on a specific out‑of‑distribution benchmark further raises performance, and the study analyzes emergent mechanisms such as output truncation and history compaction.
EVOHARNESSBENCH is a new benchmark that tests how LLM-based agents handle changes in their tool, skill, and agent harnesses over time. It includes 17 deterministic harness streams with 802 tasks, 520 tools, 42 skills, and 62 agents, and evaluates agents in two settings: deployment evaluation and self‑evolving adaptation evaluation. The study finds that harness expansion can cause forgetting, adaptation gains are inconsistent, and preserving old competence does not always aid new capability adaptation, highlighting harness evolution as a distinct challenge for agent development.
arXiv:2608. 06756v1 Announce Type: new Abstract: Vision-language models are increasingly serving as the reasoning core of embodied agents.
arXiv:2602. 07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution.
arXiv:2607. 13854v2 Announce Type: replace Abstract: Multimodal agents that think with images iteratively manipulate visual evidence and invoke tools across many steps.
SelfImprove-Bench is an evaluation framework for recursively self‑improving (RSI) systems that organizes tasks into three core capabilities—Exploring, Planning, and Reasoning—using game‑based scenarios for controllability and reproducibility. It employs scalable task generation and orchestration driven by templates and difficulty coefficients, and introduces a generalization ladder (G0–G4) to assess how well performance gains transfer beyond training conditions. Experiments show that current RSI methods exhibit inconsistent evolutionary gains across these generalization levels.
HiSTEVE 1.0 is a hierarchical VLA agent designed to operate in open‑world games like Minecraft, combining long‑horizon autonomy with native interface control. The system uses a capability‑aware harness with structured memory, a VLM planner, and a policy‑coupled task completion detector to orchestrate subtasks, while a single VLA handles vision‑only perception and native keyboard‑mouse actions. HiSTEVE achieves state‑of‑the‑art performance on atomic tasks and successfully completes long‑horizon tasks such as crafting an iron pickaxe from scratch.
arXiv:2608. 08466v1 Announce Type: new Abstract: Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically treated as a fixed artifact after deployment.
JIT‑Agent is a model that automatically generates task‑adaptive agent harnesses for any off‑the‑shelf LLM, replacing manual, task‑specific harness design. It learns to compose, repair, and evolve harnesses using a fixed four‑module protocol, and its use boosts performance on benchmarks such as DeepSearchQA and OdysseyBench, outperforming several mature agent runtimes. The approach demonstrates that harness intelligence can be trained, transferred, and compounded independently of model scaling.