Autonomous Nested Team Formation

• Autonomous nested team formation is the self-directed organization of agents into hierarchical teams and subteams to solve complex collaborative challenges.
• Mechanism design, reinforcement learning, and auction-based methods provide fairness, efficiency, and adaptive coordination in both human and robotic systems.
• Hierarchical and decentralized frameworks enable dynamic, scalable cooperation with proven improvements in mission success, fairness, and computational efficiency.

Autonomous nested team formation refers to the self-directed organization of multiple agents—typically human, robotic, or artificial intelligence entities—into hierarchical, multi-layer collaborative structures. In these settings, agents form teams which themselves may subdivide into subteams, with decision mechanisms adapted for autonomous operation at every level. This topic spans mechanism design, reinforcement learning, combinatorial optimization, and distributed robotics, and is central to addressing complex collaborative problems in domains ranging from multi-agent AI and online crowd work to distributed robotics and LLM orchestration.

1. Mechanism Design Foundations for Team Formation

Mechanism design for team formation focuses on eliciting truthful agent preferences, maximizing social welfare, and achieving fairness in partitioning agents into teams. Each agent $i$ is assumed to have additively separable preferences $u_i(j)$ for partnering with any other agent $j$, and the mechanism designer aims to optimize allocations over partitions $\mathcal{Q}$, with social welfare $$SW(\mathcal{Q}) = \frac{1}{|N|} \sum_{S \in \mathcal{Q}} \sum_{i,j \in S} u_i(j)$$. Four principal mechanisms delineated in the literature are:

• Random Serial Dictatorship (RSD): Agents select teams in random order; strategyproof and Pareto efficient but may yield inequitable outcomes.
• Harvard Business School (HBS) Draft: Alternates team captain selection; not strategyproof nor Pareto efficient.
• One-Player-One-Pick (OPOP) Draft: Captains and agents iteratively assign themselves to teams based on their utility, defined as $\sum_{j \in S} u_i(j) + (v_S - 1) \mu_i$ where $\mu_i$ is the mean utility toward unassigned agents. Exhibits empirically strong fairness and welfare, with low observed "regret" from truth-telling, but lacks formal strategyproofness.
• A-CEEI-TF: An adaptation of Competitive Equilibrium from Equal Incomes, finding price equilibria with guarantees of fairness (envy bounded by a single teammate) in large markets via a price update:

$$f_{t_{TF}}(\tilde{p})_j = t(\tilde{p})_j + \frac{1}{|N'|}\left[ (1+\epsilon - \frac{\epsilon}{\bar{b}} t(\tilde{p})_j) D_j - U_j \right]$$

where $D_j$ is excess demand and $U_j$ an indicator for exclusive demand.

In the context of nested team formation, these mechanisms can be recursively applied, partitioning a set of agents into teams and subteams, aiming to preserve incentive compatibility, fairness, and efficiency at all levels (Wright et al., 2015).

2. Learning-based and Protocol-agnostic Nested Formation

Deep reinforcement learning frameworks enable teams to form adaptively, negotiating cooperation and reward allocation without assumptions on the specific negotiation protocol. Each instance overlays an underlying cooperative game (e.g., weighted voting games with quota constraints) with a negotiation protocol learned from experience. The Shapley value formalizes fairness:

$$\phi_i(v) = \frac{1}{n!} \sum_{\pi \in \Pi} [ v(S_\pi(i) \cup \{i\}) - v(S_\pi(i)) ]$$

Empirical benchmarks reveal RL agents approaching fair Shapley allocations, especially when team members possess moderate heterogeneity. Protocol-agnostic frameworks function in both non-spatial environments (sequential proposal/acceptance) and spatial contexts (agents move and declare demands in a grid world). Location and proximity materially affect negotiation outcomes and team formation. In nested team formation, underlying cooperative games and negotiation protocols can be layered, supporting recursive or hierarchical coalition building (Bachrach et al., 2020).

3. Hierarchical and Adaptive Approaches in Robotics

Recent decentralized robotics approaches solve the autonomous nested team formation problem by dynamically partitioning large agent sets into context-dependent groups, coordinating within those groups via joint optimization and maintaining global properties through adaptive group formation.

• Group Planning in Aerial Robot Teams: Agents partition into groups based on spatial closeness (inter-agent distance $d_{ij} \leq d_{\text{safe}}$ and $n_{\text{min}} \leq n \leq n_{\text{max}}$). Coordination employs efficient Multi-Agent Pathfinding (MAPF) for initial collision-free path generation, followed by joint MINCO trajectory optimization. The optimization objective penalizes control effort, time, dynamic infeasibility, and reciprocal collision risks:

$$\min \sum_k \lambda \cdot [J_e, J_t, J_d, J_o, J_w, J_u]$$

Robustness and scalability are demonstrated with up to 50 drones dynamically switching between single-agent and group planning modes (Hou et al., 2022).

• Adaptive Formation Control Using Bi-level Learning: The AFOR approach integrates spring-damper formation maintenance into hierarchical learning. At the coordination level, a graph neural network propagates spatial relationships; at the policy level, PPO trains individual robot actions. The reward,

$$R_{\text{spring}} = -k |d_{ij} - D_{ij}| - c |v_{ij}|$$

encourages maintenance of desired inter-robot distances and velocities. Both in simulation and real robots, this enables smooth navigation with formation adaptation (Deng et al., 2 Apr 2024).

• Subteaming and Hierarchical Graph Learning: The STAF method unifies hierarchical learning, with high-level deep graph cut for dividing robots into subteams (embedding attention coefficients and classifier $\tau(H)$), intermediate-level graph learning for subteam coordination, and low-level RL policies for individual control. Adaptive formation control employs spring-damper rewards over inter-robot distances and velocities. Experimental results demonstrate 100% success rates and robust formation integrity in complex environments, with high contextual adaptivity (Deng et al., 19 Sep 2025).

4. Decentralized Auction and Multi-level Adaptation for Self-organization

The multi-level adaptation framework consists of autonomous team formation via second-price auctions and individual learning through local solution search. Utility functions combine individual contribution $\phi(\cdot)$ and collective effects:

$$U(d_{mt}, D_{mt-1}) = \alpha \phi(d_{mt}) + \beta \frac{1}{M-1} \sum_{r \neq m} \phi(d_{rt})$$

Agents submit bids for team slots, with truthful revelation incentivized by the Vickrey auction mechanism. Individual learning—random exploration and forgetting—updates agent capabilities. The dynamics of adaptation are governed by the complexity of the environment: high adaptation rates work for simple tasks, whereas stability is required for highly complex projects. These insights guide optimal nested team formation strategies, suggesting that periodic team reorganization should be tuned to problem complexity, and layers of hierarchies may benefit from differentiated adaptation rates (Blanco-Fernández et al., 2021).

5. Human-centered and Capability-aware Nested Formation

• Self-Organizing Teams (SOT): In online human work, allowing agents (workers) explicit choice over teammates leads to emergent nested collaborative clusters. Aggregation of pairwise preferences $p(i, j)$ in an affinity matrix and greedy matching maximize team compatibility and collective performance. SOTs outperform random or strictly algorithmic pairing in both output quality and satisfaction. The model for team aggregation,

$$\text{Maximize}~\sum_{(i, j) \in T_k} [p(i, j) + p(j, i)]$$

can be extended to nested teams via hierarchical clustering on affinity graphs (Lykourentzou et al., 2021).

• Capability-aware Task Allocation: Team formation for cooperative exploration is modeled as an MDP framework where state space combines environmental, robot, and mission progress components. Robot capabilities $\xi$ and health states $x^h$ (irreversible sensor/mobility failures) are incorporated:

$\mathcal{T}(s'|s,a) = Pr(s^m'|s,a) \left[\prod_i Pr(s_i^x'|s_i,a_i)\right] Pr(s^r'|s,a)$

Tasks are allocated by suitability, e.g., wheeled robots cover flat sectors, legged robots climb challenging structures, with deployment adaptively recomputed on robot failures. Comparisons to DARPA SubT human strategies show autonomous nested team formation yields faster missions and lower risks, especially in heterogeneous teams (Ginting et al., 1 Nov 2024).

6. Adaptive Nested Formation in Multi-agent LLM Systems

The adaptive in-conversation team-building paradigm employs a master agent ("Captain Agent") that decomposes complex tasks into subtasks and dynamically forms nested teams of specialized LLM agents. Roles are filled using embedding similarity:

$$\text{top-}k_1~\text{CosineSimilarity}(f(r_k), f(a_{lib}))$$

Team conversations are nested with a reflection mechanism—outputs are reviewed post-dialogue for correctness, explanation, and contradiction detection. If inconsistency occurs, the process initiates another verification round. Empirical improvements of 21.94% average accuracy over static baseline teams in problem solving, programming, and data analysis confirm the efficacy of adaptive nested formation. Furthermore, team composition is cost-efficient, minimizing redundant participation and scaling well with open LLM models (Song et al., 29 May 2024).

7. Recursive Mechanisms and Theoretical Guarantees

Recursive and sequential proposal mechanisms, such as the Rotating Proposer Mechanism (RPM), implement Pareto efficient and subgame perfect Nash equilibrium allocations. At each stage, a proposer solves:

$$\pi_i = \arg\max_{T \in \mathcal{T}_i,~j \in T ~ s.t. ~ R_j(T) \leq \beta}~u_i(T)$$

with accept/reject governed by opportunity cost thresholds. RPM can be iteratively applied at multiple hierarchy levels—teams of teams—yielding individually rational and Pareto efficient outcomes. The major challenge is computation, as recursive backward induction over exponential team options is intractable at scale. Heuristic variants (HRPM) and decentralized implementations are therefore of practical interest (Low et al., 2022).

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Autonomous nested team formation, as detailed above, spans rigorous mechanism design, experience-driven learning, hierarchical graph models, auction-based self-organization, human-centered clustering, and multi-agent orchestration. Key methodological advances center on recursive mechanism application, decentralized adaptation, capability-aware assignment, and robust protocol-agnostic negotiation. Outstanding challenges include incentive compatibility complexity in nested settings, scalable fairness and welfare guarantees across levels, and efficient distributed computation. These frameworks lay a robust foundation for collaborative autonomy in sophisticated multi-agent systems, whether robotic, human, or artificial intelligence-driven.