Recursive Agent Hive Architecture

• Recursive agent hive is a multi-agent architecture that recursively decomposes tasks into subtasks and dynamically spawns specialized agents.
• It leverages dynamic delegation graphs, decentralized control, and integrated tool usage to efficiently handle complex challenges in fields like firmware security and autonomous planning.
• The system supports scalability through structured agent spawning, robust failure handling, and an adaptive runtime coordination engine.

A recursive agent hive is a multi-agent system architecture in which autonomous agents possess the intrinsic ability to decompose tasks, dynamically spawn sub-agents, recursively delegate subtasks, and coordinate partial results, thereby forming hierarchical or graph-structured delegation topologies at runtime. Such systems leverage recursion for deep problem decomposition, facilitate distributed tool use, and enable adaptive workflows, with applications ranging from firmware security analysis and autonomous planning to multi-agent reinforcement learning and strategic reasoning. Distinctive features of the recursive agent hive include dynamic task-delegation graphs, decentralized or hybrid control, and support for runtime growth or evolution of the agent structure (Zhu et al., 5 Aug 2024, Zhang et al., 23 Nov 2025, Tang et al., 29 Aug 2025, Ma et al., 2022, Trencsenyi et al., 11 Feb 2025).

1. Formal Models and System Definitions

The recursive agent hive is formalized as a recursive multi-agent system (RMAS):

$$A = (\mathcal{N}, \mathcal{T}, D, \Delta, d_{\mathrm{max}})$$

where:

• $\mathcal{N}$: the (potentially unbounded) set of agents, each with identifier $id_n$ and internal state $$s_n \in \{\mathrm{RUNNING}, \mathrm{WAITING}, \mathrm{DONE}\}$$.
• $\mathcal{T}$: the task or instruction space; each task $t \in \mathcal{T}$ is acted on by an agent.
• $$D \subseteq \mathcal{N} \times \mathcal{T} \times \mathbb{N}$$: the delegation relation mapping $(\mathrm{parent}, \mathrm{task}, \mathrm{depth})$ to child agents.
• $\Delta$: the delegation function; for agent $n$ at depth $d$ solving task $t$,
  • $\Delta_n(t, d) = \mathrm{EXECUTE}(t)$ if $d = d_{\mathrm{max}}$ or task simple enough,
  • else $$\Delta_n(t, d) = \mathrm{DECOMPOSE}(t) \Rightarrow \{t_1, \dots, t_k\}$$ to spawn subtasks.
• $d_{\mathrm{max}} \in \mathbb{N}$: upper limit on recursion depth.

The recursive process unfolds as each agent, subject to $d_{\mathrm{max}}$, decides between direct execution or decomposing a task into subtasks, delegating each subtask to a new child agent at depth $d+1$ (Zhu et al., 5 Aug 2024, Zhang et al., 23 Nov 2025). Generalizations model the system as a dynamic directed acyclic graph $\mathcal{G} = (\mathcal{V},\mathcal{E})$ with vertices $v_i$ as agents, semantic parameters $\Theta_i = (p_i, \tau_i)$ (prompt, tool config), and edges $w_{ij}$ capturing interaction/synergy (Tang et al., 29 Aug 2025).

2. Architectural Components and Toolkit Building Blocks

Designs for recursive agent hives standardize around several core modules:

• Agents: Autonomous entities, often LLM-powered, maintaining identity, depth, parent-child pointers, local context, and invocation methods. Agent instantiation is lightweight to support rapid spawning.
• Tools: Reusable external-resource wrappers (e.g., web search, APIs, code execution), exposed as callable Python classes with method annotation for agent tool-calling (Zhu et al., 5 Aug 2024, Zhang et al., 23 Nov 2025).
• Delegation Schemes: Abstract the mechanics of agent spawning and result retrieval.
  • DelegateOne: Parent blocks until all children finish.
  • DelegateWait: Non-blocking spawn; results can be collected asynchronously.
• Event Logging/System Monitoring: All agent lifecycle, delegation, tool-usage, and communication events are logged (e.g., as JSONL), enabling downstream analysis, error tracing, and run replay (Zhu et al., 5 Aug 2024).
• Runtime Coordination Engine: Manages recursive task delegation and ToA (Tree of Agents) construction at runtime. Each agent can invoke a delegation primitive (e.g., DelegateTool) as a first-class action (Zhang et al., 23 Nov 2025).
• Persistent Knowledge Hub (PKH): Stores structured evidence and intermediate results for dynamic, cross-agent coordination and evidence grounding (Zhang et al., 23 Nov 2025).

Component	Function	Example Implementation
Agent	Autonomous task decomposition	Python/LLM class (ReDel)
Tool	Extends agent's capabilities	HTTPTool, DBTool
Delegation Scheme	Spawns/manages children	DelegateOne, DelegateWait
Event Logger	Tracks agent/tool events	JSONL log, Web UI

3. Algorithms and Coordination Protocols

Recursive agent hives implement systematic workflows for runtime task decomposition and coordination:

• Recursive Delegation Workflow (Zhu et al., 5 Aug 2024, Zhang et al., 23 Nov 2025):
  1. Agent $n$ receives $(t, d)$.
  2. If $d \ge d_{\mathrm{max}}$ or task is simple, execute $t$ directly.
  3. Otherwise, decompose $t$ to subtasks $\{t_1,\dots,t_k\}$.
  4. For each $t_i$, spawn a child agent at $d+1$; hand off $t_i$.
  5. Parent tracks state (WAITING/RUNNING), aggregates child results upon completion, and returns result up the delegation chain.

• Graph-Based Routing and Evolution (Tang et al., 29 Aug 2025):

  • Forward pass samples sparse subgraphs via Multi-Armed Bandit mechanisms.
  • Bandit parameters, knowledge-based mismatch, and synergy scores guide which succession of agents are activated per input.
• Message Passing and Backpropagation (HiVA):
  • Each node $v_i$ receives input aggregated from its predecessors, computes output $y_i$, and passes results to successors.
  • On the backward pass, textual gradients (computed by LLMs) propagate recursively upstream over the active subgraph for coordinated semantic and topological updates.
• Cross-Agent Communication (FIRMHIVE):
  • Structured parent-child tuples for task delegation.
  • Child–Parent communication in JSON, including finding, evidence, and status.
  • Integration with global knowledge base (PKH) for evidence correlation and memory.
• Depth and Breadth Metrics:

$$d(a) = \begin{cases} 1 & \text{if } \mathrm{children}(a)=\emptyset\ \max_{c\in \mathrm{children}(a)}d(c)+1 & \text{otherwise}\end{cases}$$

$b(a) = |\mathrm{children}(a)|$

• Failure Handling:
  • Up to three retries per failed LLM call.
  • Partial results with "deferred" annotations on persistent errors (Zhang et al., 23 Nov 2025).

4. System Instantiations and Empirical Benchmarks

4.1 ReDel Toolkit

ReDel exemplifies LLM-powered recursive hives, supporting flexible delegation, fine-grained tool use, and event-driven debugging. TravelPlanner, a published case paper, demonstrates multi-depth agent hierarchies with the root agent decomposing trip planning into flights, hotels (with its own nested agent decomposition), and activities, aggregating results into a final itinerary (Zhu et al., 5 Aug 2024).

4.2 FIRMHIVE

FIRMHIVE operationalizes a runtime Tree of Agents (ToA) using a Recursive Delegation Engine and Persistent Knowledge Hub for autonomous firmware security analysis. Evaluation on Karonte firmware images in multiple security scenarios demonstrated that FIRMHIVE achieved substantial improvements:

System	Alerts	Reasoning Steps	Files Accessed	Precision
MAS pipeline	1.4	290.2	121.2	37%
MAS+Orchestrator	2.1	430.3	19.4	42%
Mango	1,109	–	–	47.9%
FIRMHIVE	36.8	5,007.1	79.9	71%

FIRMHIVE achieved ≈16× deeper reasoning and ≈2.3× broader exploration than MAS+Orchestrator on end-to-end vulnerability detection tasks (Zhang et al., 23 Nov 2025).

4.3 HiVA/Graph-Structured Agents

The HiVA framework models the hive as a semantic-topological evolving DAG of LLM agents, with bandit-driven forward routing and LLM-derived textual feedback for optimizing both prompt/tool semantics and the pipeline's topology. Empirical evaluations on hierarchical benchmarks (e.g., HotpotQA, 2WikiHop) show accuracy improvements of 13–18 points relative to vanilla LLM orchestration, along with cost efficiency gains and increased pass@1 on code synthesis tasks (Tang et al., 29 Aug 2025).

4.4 Multi-Agent Recursive Reasoning (R2G, PR2)

Recursive Reasoning Graph (R2G) and Probabilistic Recursive Reasoning (PR2) approaches formalize the agent hive as graph-structured mutual modeling architectures for MARL, where each agent holds explicit models for best-responses or opponent conditional policies. Training proceeds using centralized training/decentralized execution or fully decentralized variational updates, with robust convergence properties and empirical success in complex interactive environments (Ma et al., 2022, Wen et al., 2019).

4.5 Hypergame-Based Architectures

Recursive agent hives in formal strategic reasoning settings (e.g., beauty contest games) leverage LLM-powered revision and decision modules orchestrated by a central umpire. Nested belief models (hypergames) allow explicit tracking and analysis of reasoning depth ($k$-level, $\kappa$-level), facilitating measurement and profiling of emergent hive reasoning (Trencsenyi et al., 11 Feb 2025).

5. Design, Monitoring, and Scalability Practices

Operational best practices for recursive agent hives, distilled from experimental work, include:

• Delegation Scheme Tuning: Select scheme (parallel vs. deferred) tailored to LLM capabilities and workload characteristics.
• Recursion Depth Limiting: Enforce $d_{\mathrm{max}}$ to avoid runaway computation; 2–4 is typical depending upon domain.
• Decomposition Prompt Engineering: Explicitly instruct LLM agents to break down large tasks and provide decomposition exemplars.
• Event Log Analysis: Monitor tokens_used events, chain lengths, and graph structure to detect inefficiencies such as under- or over-commitment (Zhu et al., 5 Aug 2024).
• Context Management: Limit retrieval and agent memory size to avoid pushing essential instructions out of context.
• Interactive Debugging and Replay: Employ web tools to inspect agent state, message history, and event traces to rapidly diagnose and correct workflow failures.
• Load Balancing and Recovery: Distribute reasoning in breadth or depth as needed; employ controlled retry logic for LLM or tool failures (Zhang et al., 23 Nov 2025).

6. Applicability, Strengths, and Limitations

Recursive agent hives demonstrate adaptive self-organization, deep task decomposition, and cross-component evidence synthesis across domains such as:

• Firmware and software auditing (FIRMHIVE) (Zhang et al., 23 Nov 2025)
• Autonomous planning and information gathering (ReDel, HiVA) (Zhu et al., 5 Aug 2024, Tang et al., 29 Aug 2025)
• Strategic and social reasoning with nested belief modeling (Hypergame frameworks) (Trencsenyi et al., 11 Feb 2025)
• MARL applications demanding mutual model building and stable convergence (R2G, PR2) (Ma et al., 2022, Wen et al., 2019)

Principal strengths include flexibility, long-horizon reasoning, and ability to adaptively construct workflows without hardcoded pipelines. Principal limitations:

• Higher LLM cost and latency due to proliferation of agent instances.
• Residual LLM hallucination risk, partially mitigated by evidence grounding or PKH.
• Potential for quadratic blow-up in belief or communication structures in highly nested or dense agent architectures.
• Need for explicit measures of semantic coherence and correctness in complex reasoning hierarchies (Zhang et al., 23 Nov 2025, Trencsenyi et al., 11 Feb 2025).

Recursive agent hives are extendable to multi-level code audits, legal document review, biomedical data mining, and adaptive workflow optimization in unknown environments (Zhang et al., 23 Nov 2025, Tang et al., 29 Aug 2025).

7. Research Outlook

Research in recursive agent hives is trending toward richer, more automated self-organization—via adaptive graph evolution, meta-learning of delegation/recursion strategies, and integration of belief modeling with learned attention over peer agents. Empirical evidence supports that textual-feedback-driven, graph-evolving hives (e.g., STEV in HiVA) outperform both static pipelines and naive loops, especially for deep, multi-step tasks requiring specialization and coordination (Tang et al., 29 Aug 2025). Centralized training/decentralized execution and modular role separation (revision/decision, as in hypergame frameworks) have emerged as unifying paradigms for large-scale, scalable deployment. Coverage and correctness verification, semantic validity measures, and efficient scalability to arbitrarily large agent populations remain ongoing research challenges.