InstAgent: A Recursively Self-Improving Agent Operating System for Heterogeneous Robot Collaboration

Recursive self-improvement (RSI) lets a system improve from its own outcomes; in LLM-based multi-agent systems, Agents refine one another within a task, and outcomes improve how they collaborate across tasks. However, existing multi-agent collaboration leaves this loop open: collaboration is pre-defined at the operator level, topology-only learning keeps verbatim exchange that propagates errors, and reward maximization on a system's own outcomes concentrates on a few teams. To address these challenges, we propose CollabFlow, an RSI system of Learned Agent Collaboration: a trainable Collab-Director constructs teams of complete Agents, a frozen executor runs them, and each round's outcomes retrain the director. Within each round, the edges of a collaboration graph carry protocols of Evidence-Conditioned Communication: a receiver adopts a differing answer only when the sender's evidence is stronger by a margin, so the director learns who communicates and how. Across rounds, we further propose Collaborative Trajectory Balance (CTB), a flow-based objective that credits each team once across its construction orders and targets a reward-proportional distribution over teams, so several good teams stay in play. We also bound how far this self-generated target moves between rounds, which shrinks as records accumulate. On twelve datasets, CollabFlow outperforms all baselines and keeps improving across rounds. Code is available at https://anonymous.4open.science/r/CollabFlow-631E.

Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizon tasks. Despite this potential, several challenges remain underexplored: (1) Centralized LLM schedulers scale poorly as team size and environmental complexity increase. A single model must process excessive contextual information, and long-context approximation may degrade reasoning quality; (2) Existing task formulations insufficiently consider dynamic settings, while robust adaptation to evolving task conditions is essential for real-world deployment; (3) Domain-specific data scarcity limits specialized robotic reasoning, making proprietary general-purpose models inefficient for expert tasks. To address these limitations, we propose DynaHMRC, a decentralized framework in which each robot acts as a role-aware LLM agent. This design mitigates the single-model context bottleneck and supports flexible collaboration across heterogeneous team configurations. DynaHMRC organizes collaboration as a four-stage closed-loop process: self-description, task allocation with leadership bidding, leader election, and reflective execution, supported by executable robot interfaces. We further develop a benchmark covering three task families, four dynamic variations, and six team configurations to systematically study dynamic task modeling. In addition, we conduct an empirical analysis to guide the construction of domain-specific expert datasets and fine-tune pretrained LLMs to improve specialized competence. Experiments show that DynaHMRC achieves higher success rates than strong baselines with fewer action and communication steps, while demonstrating promising scalability trends as team size grows within the evaluated settings.

A generalist robot should not only perform diverse tasks but also improve through experience, turning what it learns during execution into capabilities that later tasks can reuse. Robot agents that act through code can already repair programs from execution feedback, yet it remains a central challenge to organize this experience around the task structure that gives it meaning, so that each repair is attributed to the responsible capability, supported by execution evidence, and validated before it is reused. We introduce RoboRSI, a robot self-improvement system built on Top-Down Skill Refinement (TSR). TSR decomposes tasks into compound, atomic, and base skills with scoped responsibilities and explicit input--output contracts, attributes each execution outcome to the responsible branch, and confines revision to that branch. Building upon this structure, a Manager, Planner, Engineer, and Reviewer coordinate planning, execution, diagnosis, and the validated release of new skills, while people steer the process through objectives and corrections; stable skill sequences are further consolidated into reusable compound skills. On a mobile manipulator, RoboRSI develops multi-object household cleanup over 104 rounds. In simulation, it achieves the highest success rate on LIBERO, LIBERO-PRO, LIBERO-Plus, and RoboTwin, exceeding the strongest baseline by 2.7 to 11.0 percentage points.