# ReCAP: Recursive Context-Aware Reasoning and Planning for Large Language Model Agents

arXiv: https://arxiv.org/abs/2510.23822 · HTML: https://mlsanity.com/paper/2510.23822/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning.
- Builds on 1 of its 25 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Placed in

- LLM reasoning / [Test-time compute and efficient thinking](https://mlsanity.com/lr/test-time-compute-and-efficient-thinking/)
- LLM agents and tool use / [Agent memory systems](https://mlsanity.com/agents/agent-memory-systems/)

## Builds on (most cited first, 1 of 1)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)

## Built on by (most cited first, 13 of 13)

- [The Evolution of Tool Use in LLM Agents: From Single-Tool Call to Multi-Tool Orchestration](https://mlsanity.com/paper/2603.22862/) (2026)
- [Recursive Models for Long-Horizon Reasoning](https://mlsanity.com/paper/2603.02112/) (2026)
- [TACT: Mitigating Overthinking and Overacting in Coding Agents via Activation Steering](https://mlsanity.com/paper/2605.05980/) (2026)
- [Table-as-Search: Formulate Long-Horizon Agentic Information Seeking as Table Completion](https://mlsanity.com/paper/2602.06724/) (2026)
- [PAACE: A Plan-Aware Automated Agent Context Engineering Framework](https://mlsanity.com/paper/2512.16970/) (2025)
- [SokoBench: Evaluating Long-Horizon Planning and Reasoning in Large Language Models](https://mlsanity.com/paper/2601.20856/) (2026)
- [Multi2: Hierarchical Multi-Agent Decision-Making with LLM-Based Agents in Interactive Environments](https://mlsanity.com/paper/2606.03698/) (2026)
- [Online Monitoring and Corrective Steering of Programming Agents](https://mlsanity.com/paper/2608.06701/) (2026)
- [Security of OpenClaw Agents: Fundamentals, Attacks, and Countermeasures](https://mlsanity.com/paper/2605.25435/) (2026)
- [Post-Training on Office Work Improves Software Engineering: A Behavioral Account of Cross-Domain Transfer](https://mlsanity.com/paper/2608.01604/) (2026)
- [Web Agents Should Use Typed Actions Instead of Click-Based Browsing](https://mlsanity.com/paper/2602.17245/) (2026)
- [HaReCAP: Habitual-action Grounding for Recursive Large Language Model Agents](https://mlsanity.com/paper/2608.16447/) (2026)
- [Plan-and-Patch: Diffusion Language Models for Agentic Planning](https://mlsanity.com/paper/2610.10786/) (2026)

## Abstract (arXiv)

Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-level continuity or incur substantial runtime overhead. We introduce ReCAP (Recursive Context-Aware Reasoning and Planning), a hierarchical framework with shared context for reasoning and planning in LLMs. ReCAP combines three key mechanisms: (i) plan-ahead decomposition, in which the model generates a full subtask list, executes the first item, and refines the remainder; (ii) structured re-injection of parent plans, maintaining consistent multi-level context during recursive return; and (iii) memory-efficient execution, bounding the active prompt so costs scale linearly with task depth. Together these mechanisms align high-level goals with low-level actions, reduce redundant prompting, and preserve coherent context updates across recursion. Experiments demonstrate that ReCAP substantially improves subgoal alignment and success rates on various long-horizon reasoning benchmarks, achieving a 32% gain on synchronous Robotouille and a 29% improvement on asynchronous Robotouille under the strict pass@1 protocol.