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Key Takeaways

  • Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses introduces Recuris, an architecture designed to improve how AI agents handle...
  • Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation.
  • This coupling also turns execution into structured evidence that localizes failures to specific memory components.
  • Across tasks, a fixed Meta-Agent turns that evidence into localized, validation-gated updates to Skill Memory that reshape execution and yield new evidence, forming a bounded recursive memory-evolution loop.
  • The advantage widens as the interaction horizon grows, to +32.2 points on the longest tasks, and common long-horizon failures fall by up to 80%.
Paper AbstractExpand

Recursive self-improvement (RSI) remains hard in long-horizon tasks, where growing histories obscure the task state and misalign skill invocation. We introduce Recuris, a recursive Experiential-Working Memory architecture for long-horizon agent harnesses, in which Working Memory tracks task progress and guides skill selection from Experiential Memory, grounding skill use in current needs rather than the full history. This coupling also turns execution into structured evidence that localizes failures to specific memory components. Across tasks, a fixed Meta-Agent turns that evidence into localized, validation-gated updates to Skill Memory that reshape execution and yield new evidence, forming a bounded recursive memory-evolution loop. Across four long-horizon benchmarks and ten models, Recuris improves task success in 35 of the 37 completed model-benchmark pairs, carrying frontier models to SOTA-level task success: on tau-bench it adds +17.8 points to GPT-5.6 Sol and +15.6 to Claude Opus 5, taking Opus 5 to 87.9%, and +16.6/+13.5 points on Qwen3.6-27B/35B on SkillFlow. The advantage widens as the interaction horizon grows, to +32.2 points on the longest tasks, and common long-horizon failures fall by up to 80%. These results position recursively evolving memory as a scalable foundation for RSI, enabling agents to continuously transform accumulated experience into increasingly effective long-horizon behavior. Code: this https URL

Recursive Experiential-Working Memory Evolution for Long-Horizon Agent Harnesses introduces Recuris, an architecture designed to improve how AI agents handle long-horizon tasks. The research addresses the tendency of agents to lose track of goals and misalign skill usage as interaction histories grow. By decoupling memory into a structured Working Memory (WM) and an Experiential Memory (EM), the system grounds skill selection in current task needs rather than the entire conversation history, enabling agents to recursively improve their performance through a validation-gated loop.

The Problem with Long-Horizon Tasks

In long-horizon tasks, agents often struggle because their memory mechanisms are fixed while the task requirements evolve. Existing methods typically retrieve information from the full interaction history or initial instructions, which becomes unreliable as the context fills with completed steps, noise, and outdated information. This leads to agents invoking skills that are no longer relevant to the current state of the task. The authors argue that the core issue is not a lack of experience, but the absence of a reliable, compact representation of the current task state to guide that experience.

How Recuris Works

Recuris introduces a memory-control layer that operates through two primary loops:

  • Within-Task Coupling: The system uses a Working Memory (WM) to track progress and unresolved goals. This WM acts as an interface, ensuring that when the agent needs to invoke a skill from Experiential Memory (EM), the selection is based on the current verified state of the task. After an action is taken, a checker evaluates the environment's response to update the WM, creating a closed loop of state-grounded execution.

  • Cross-Task Evolution: The system records structured execution traces that link actions, observations, and state updates. A fixed Meta-Agent analyzes these traces to localize failures to specific memory components (EM, WM, the invocation policy, or the checker). It then proposes targeted, component-specific patches. These patches are only admitted if they pass a validation gate on held-out tasks, ensuring that the agent does not regress on existing abilities.

Performance and Results

The authors evaluated Recuris across four long-horizon benchmarks and ten models, ranging from 3B open-weight models to frontier models. The system improved task success in 35 of 37 completed model-benchmark pairs. Key results include:

  • Frontier Model Gains: On the τ²-Bench, Recuris added 17.8 points to GPT-5.6 Sol and 15.6 points to Claude Opus 5, bringing the latter to an 87.9% success rate.

  • Horizon Scaling: The performance advantage increased as tasks grew longer, reaching a 32.2-point improvement on the longest tasks.

  • Failure Reduction: Common long-horizon failure modes were reduced by up to 80%.

  • Localization Accuracy: The structured traces allowed for significantly more accurate failure localization (64.8%) compared to relying on final task outcomes alone (13.0%).

Key Considerations

The architecture maintains the underlying LLM and the outer agent harness as fixed components. Recursion is confined to the externalized memory-control layer, meaning the base model itself is not retrained or modified. This approach allows for continuous improvement of the agent's behavior through the evolution of its memory mechanisms, rather than requiring changes to the model's weights. The authors note that because the Meta-Agent and validation procedures are fixed, the system provides a bounded, stable framework for recursive self-improvement.

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