Prime Agent: A Self-Improving RLM Harness
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-source harness for long-horizon evaluation and coding-agent workflows. A persistent IPython REPL follows the Recursive Language Model abstraction for programmatic context processing and test-time compute, while Continual Harness preserves histories, memories, skills, prompts, and subagent specifications across trajectories. Recursive subagents coordinate through direct agent-to-agent communication, and the Agents View l
Lineage graph
Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 64%Beyond grep: The case for a context-rich AI coding harness →
- PossiblePossibly related (embedding) · 26%pydantic/pydantic-ai →
“Possibly related via embedding similarity 0.57 (not asserted). Timestamp check: artifact slightly before paper (-53d).”
- LinkedLinked via arxiv author · 85%Seth Karten →
“Prime Agent: A Self-Improving RLM Harness”
- LinkedLinked via arxiv author · 85%Alex L. Zhang →
“Prime Agent: A Self-Improving RLM Harness”
- LinkedLinked via arxiv author · 85%Kevin Thomas →
“Prime Agent: A Self-Improving RLM Harness”
- LinkedLinked via arxiv author · 85%Sebastian Müller →
“Prime Agent: A Self-Improving RLM Harness”
- LinkedLinked via arxiv author · 85%Elie Bakouch →
“Prime Agent: A Self-Improving RLM Harness”
- LinkedLinked via arxiv author · 85%Daniel Auras →
“Prime Agent: A Self-Improving RLM Harness”