Long-horizon interactions accumulate noisy histories that obscure task state and misalign skill use; Recuris flips the problem by keeping a compact Working Memory to guide skill selection from an evidence-backed Experiential Memory and using localized, validation-gated updates to evolve skills over time.
Key Findings
- Structured coupling of Working Memory and Experiential Memory localizes execution failures to specific memory components, making updates more targeted and reliable.
- A fixed Meta-Agent converts execution evidence into gated updates to Skill Memory, producing a bounded recursive loop that reshapes future execution.
- Broad empirical gains: improved success in 35 of 37 model–benchmark pairs; +17.8 points on GPT-5.6 Sol and +15.6 on Claude Opus 5 (taking Opus 5 to 87.9%); +16.6/+13.5 on Qwen3.6-27B/35B on SkillFlow; up to +32.2 points on the longest tasks and up to 80% reduction in common long-horizon failures.
- Code and artifacts released for reproducibility and follow-up experimentation.
Who it's for and trade-offs
Great fit if you build or evaluate LLM-driven agents on long-horizon tasks and need a systematic way to convert episodic experience into reusable, validated skills. The approach scales with interaction horizon and is model-agnostic across modern LLM backbones. Look elsewhere if your tasks are strictly single-step or if your primary constraint is removing all external orchestration (Recuris assumes an external harness and a Meta-Agent for policy updates).