arXiv AI
The Last Mile to the Real World: Agent-Native Primitives as Reusable Building Blocks for Solving Real-World Tasks
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arXiv AI
Oct 1
Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer
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.
arXiv AI
Oct 28
Spine: Compiling Subtask Trees into Structured Context for Long-Horizon Coding Agents
Spine is a new interface and runtime that aligns a language model’s context with the nesting and completion of subtasks in long‑horizon coding problems. By letting agents declare explicit subtask boundaries through work scopes, Spine records interactions in a persistent SpineTree and compiles only the necessary unfinished subtask history into the model’s prompt, replacing completed subtasks with their results while reusing prefix‑cache. Integrated into Codex and Pi, SpineCodex and the reusable Pi package achieve significant performance gains—improving SWE‑Milestone and ProgramBench scores and reducing costs—without the overhead of recursive subagent delegation.
arXiv AI
Aug 11
Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses
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.
arXiv AI
Aug 11
Argus: A General-Purpose Agentic Reasoning Runtime for Long-Horizon Tasks
arXiv:2608. 05144v2 Announce Type: replace Abstract: Long-horizon reasoning requires an agentic runtime that can persist when evidence supports its current approach and pivot when measurements reveal failure, hidden constraints, or a misspecified objective.
arXiv AI
Aug 18
StateM: Reaching 95.3% Raw Accuracy, or a \$15 Frontier Run, on Terminal-Bench 2.1 via Harness Scaling
arXiv:2608. 15089v1 Announce Type: new Abstract: Long-horizon agents can fail even when their underlying models can solve the constituent steps.
arXiv Machine Learning
Aug 27
JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution
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.