Closed-Loop Self-Correcting Execution

• Closed-loop, self-correcting execution is a paradigm that uses continuous feedback and detection of discrepancies to adapt actions under uncertainty.
• It integrates planning, execution, and evaluation modules to trigger real-time re-planning in domains such as robotics, AI planning, and code generation.
• The approach enhances reliability and efficiency through techniques like model predictive control, error learning, and equilibrium refinement.

Closed-loop, self-correcting execution is a systems engineering paradigm in which an agent or control process not only acts on its environment, but continuously monitors the outcomes of its actions, detects discrepancies or errors, and applies feedback mechanisms that adapt future actions—or replan altogether—to achieve a desired performance or correct for uncertainty. This approach is fundamentally opposed to open-loop execution, in which plans are generated and carried out without regard for ongoing feedback. Closed-loop, self-correcting strategies are characteristic of robust control, adaptive AI, and emerging intelligent systems that must function reliably under nonstationary conditions, uncertainty, or incomplete information. Such frameworks have been instantiated across domains including robotics, AI planning, generative modeling, programming agents, and quantum error correction.

1. Fundamental Principles and System Architectures

The closed-loop, self-correcting execution principle mandates an ongoing cycle of planning, action, online outcome assessment, and feedback-driven correction. The system’s architecture typically includes:

• A planner or policy that generates candidate actions or plans, possibly conditioned on history, internal belief, task objectives, and perceived environment state.
• An actuator or execution subsystem that issues these actions to the physical world, a simulator, or a computational environment.
• A perception or evaluation module that observes the post-action environment, either through sensors, simulation feedback, or programmatic tests, and yields an assessment or error metric.
• A feedback channel that encodes discrepancies between predicted and actual outcomes, and routes these back into the decision-making module, often either as explicit correction signals (e.g., control errors, test failures) or implicit policy updates.

For example:

• In embodied robotics, CLEA’s architecture explicitly compartmentalizes perception (Observer), belief summarization (Summarizer), planning (Planner), and action evaluation (Critic), wiring these modules in a loop where failed actions trigger immediate hierarchical re-planning (Lei et al., 2 Mar 2025).
• In code generation, CoCoS treats generation and test-failure-driven revision as a two-turn MDP with trajectory-level rewards, leveraging actual code execution results to guide iterative self-improvement (Cho et al., 29 May 2025).
• In point cloud completion, ACL-SPC enforces invariance by explicitly closing the loop through synthetic input perturbations and consistency loss, requiring all perturbed completions to converge (Hong et al., 2023).

The closed-loop paradigm may be realized at multiple abstraction levels—from low-level adaptation (e.g., correcting continuous-valued motor commands), to discrete symbolic plan repair, to high-level “self-reflective” rerouting or tool synthesis in multi-agent AI.

2. Formalization and Control-Theoretic Foundations

Closed-loop approaches often admit formal control-theoretic or reinforcement learning interpretations:

• Model Predictive Control (MPC): Explicit receding-horizon or finite-horizon planning with feedback after each action is a canonical closed-loop method. FICO’s finite-horizon closed-loop factorization models MAPF as a receding-horizon control problem, replanning from the true observed state at every timestep, with uncertainty realized as stochastic delays and agent additions (Li et al., 17 Nov 2025).
• Energy Minimization and Equilibrium Models: Closed-loop Transformers (EqT) instantiate a principle that, before committing to an output, the model iteratively refines its hidden state until it minimizes a consistency-enforcing energy function, undoes prior inconsistencies, and coordinates bidirectional, memory, and confidence constraints—formally posing the latent representation update as approximate MAP inference in an EBM (Jafari et al., 26 Nov 2025).
• Error Learning and Adaptive Control: In feedback linearizable nonlinear systems, the addition of a closed-loop error learning law (via sliding mode or neuro-fuzzy adaptation) augments classic feedback controllers. Such error-learning controllers can guarantee finite-time convergence of tracking error (via Lyapunov proofs, e.g. CLELC (Kayacan, 2021)), even under dynamic uncertainty, by adapting internal parameters online from observed error signals.
• Multi-turn RL for Correction: Iterative, feedback-driven refinement is also modeled as a multi-turn MDP or RL process. In CoCoS, rewards at each correction step are shaped to not only improve bug-fixing (Δ^{i→c} rate) but also penalize unnecessary corrections, with policy gradients optimized against a trajectory-level, progressively discounted reward function. KL regularization is used to prevent catastrophic divergence from a pretrained initialization (Cho et al., 29 May 2025).

3. Representative Instantiations Across Research Domains

Closed-loop, self-correcting execution appears in diverse technical contexts:

Domain	System/Framework	Feedback Modality
Robotics	CLEA, HiCRISP, ExploreVLM	Perception/action/plan critique, failures trigger hierarchical replanning (Lei et al., 2 Mar 2025, Ming et al., 2023, Lou et al., 16 Aug 2025)
Planning/MAPF	FICO	Observed agent delay/addition, receding-horizon replanning (Li et al., 17 Nov 2025)
Code Generation	CoCoS, LASSI	Program test execution/corrective prompts (Cho et al., 29 May 2025, Dearing et al., 30 Jun 2024)
Learning	Self-healing NNs	Activation deviation from “manifold”, staged correction via Pontryagin principle (Chen et al., 2022)
Generative Models	SLD, Self-consuming loops	Prompt/attribute-detection-based self-editing, or correction via expert-informed function (Wu et al., 2023, Gillman et al., 11 Feb 2024)
Browser Agents	Recon-Act	Step-divergence analysis, tool synthesis as correction (He et al., 25 Sep 2025)
Quantum QEC	In situ calibration	Bayesian updates of error rates from syndrome output (Kunjummen et al., 2 Nov 2025)

These systems differ in the semantics of “feedback”—ranging from programmatic test results, real or simulated sensor measurements, structured “critic” outputs, to explicit symbolic error reports—but converge on the architectural pattern of acting, evaluating, comparing, and revising in a recurring, cyclic manner.

4. Self-Correction Mechanisms and Feedback Signals

The efficacy of closed-loop execution depends critically on the mechanisms by which error is detected, quantified, and corrected:

• Consistency and Invariance Losses: In ACL-SPC for point cloud completion, perturbing the input and penalizing output differences (closed-loop consistency loss $L^{cons}$) encourages the solution of the fixed-point equation $f(P_v) \approx f(P_0)$, driving the network toward invariance under viewpoint and occlusion (Hong et al., 2023).
• Automatic Critique and Planning Review: Platforms such as CLEA and ExploreVLM employ a “critic” module (often a VLM or LLM) that predicts the feasibility of an action given current belief and sensory input, and flags inadequate plans for immediate re-planning (Lei et al., 2 Mar 2025, Lou et al., 16 Aug 2025). Self-reflection modules enforce structured plan preconditions and postconditions.
• Execution-Based Rewards or Correction: In self-correcting code generation (CoCoS), each turn’s code is actually run, and pass/fail outcomes serve as dense reward signals for the sequence policy (Cho et al., 29 May 2025). In LASSI, compilation/runtime errors from generated scientific code are injected verbatim into LLM prompts to drive code revision (Dearing et al., 30 Jun 2024).
• Physical or Simulation Priors: Self-correcting self-consuming loops in generative modeling rely on domain-specific correction functions—such as physics-based controllers in human motion synthesis—to project generated samples toward known-valid regimes, stabilizing iterative synthetic-data finetuning (Gillman et al., 11 Feb 2024).
• Bayesian Inference and Priors Update: In quantum error correction, noisy syndrome measurements feed into a decoder whose soft output is recursively averaged (in a Kalman-like manner) to update the prior error rates on each qubit, driving the system toward improved logical error scaling (Kunjummen et al., 2 Nov 2025).

Self-correction can be symbolic (plan-level or code-level), continuous (control values), or probabilistic (belief/prior update). In vision-language task planning, action validation (step-wise) and re-invocation of LLM plan-generation or correction routines enable robust, error-tolerant execution even against dynamic environment changes (Lou et al., 16 Aug 2025, Ming et al., 2023).

5. Algorithmic, Experimental, and Theoretical Certifiability

Closed-loop, self-correcting execution enables both practical and formal guarantees:

• Empirical Robustness: CLEA demonstrates a 67.3% improvement in task success rates and 52.8% increase in completion rates over open-loop baselines in dynamic, real-world robotics (Lei et al., 2 Mar 2025). ACL-SPC achieves point cloud completion accuracy superior to supervised/unsupervised baselines on real-world datasets (Hong et al., 2023). LASSI achieves 80–85% translation success for scientific code—most often with just one or two correction iterations—even on LLMs with no explicit HPC training (Dearing et al., 30 Jun 2024).
• Sample Efficiency and Reduced Iteration: In LASSI, ~66% of successful translations required zero correction iterations (no feedback rounds), with almost all others succeeding within 1–2 loops (Dearing et al., 30 Jun 2024).
• Theoretical Convergence/Stability: The self-consuming GAN/MLE loop is shown to admit exponential contraction (i.e., convergence of parameters toward optimum $\theta^*$) whenever the correction weight $\gamma$ and synthetic data ratio $\lambda$ lie within explicit bounds, and even permits $\lambda\to1$ (mostly synthetic data) if a sufficiently powerful correction is used (Gillman et al., 11 Feb 2024). EqT’s latent refinement is guaranteed to converge linearly under mild local smoothness/convexity of the energy and properly chosen step-size (Jafari et al., 26 Nov 2025).
• Component Ablations: Experiments confirm the indispensable role of each feedback component; e.g., in ACL-SPC, removing weighted Chamfer loss leads to trivial solution collapse, and eliminating closed-loop consistency prevents completion beyond the observed portion (Hong et al., 2023). Similarly, closed-loop scenario generation in Bench2ADVLM probes true system faults revealed only under online feedback, not static input (Zhang et al., 4 Aug 2025).

Theoretical frameworks also explain why equilibrium refinement (EqT) outperforms open-loop inference exactly on hard instances—demonstrated on parity tasks with significant improvement in per-token accuracy for long-range sequences (Jafari et al., 26 Nov 2025).

6. Limitations, Open Problems, and Impact

Despite broad applicability, current closed-loop, self-correcting frameworks encounter several known challenges:

• Feedback Delay and System Latency: In real-time systems (robotics, code compilation), the cycle time for error detection, propagation, and correction determines realizable control frequency—sometimes requiring design tradeoffs between loop depth and reactivity (Dearing et al., 30 Jun 2024, Lei et al., 2 Mar 2025).
• Quality and Granularity of Feedback: The discriminative power of the critic (VLM/LLM), or precision of runtime errors, directly limits correction fidelity. Insufficient feedback granularity can yield slow or incomplete adaptation.
• Stability under Highly Nonstationary or Adversarial Conditions: While formal contraction or Lyapunov arguments guarantee stability under fixed-class perturbations, adversarial or rapidly shifting domains may still induce system collapse (e.g., in self-consuming generative models if expert correction is unavailable or weak) (Gillman et al., 11 Feb 2024).
• Computational Overhead: EqT and similar equilibrium models may incur nontrivial FLOP increases per sample compared to open-loop baselines (~3× for K=8 iterations) (Jafari et al., 26 Nov 2025).
• Dependency on Correction Oracles or Human-in-the-Loop: Some systems, such as Recon-Act at Level 3, still require human tool review and error analysis, limiting full autonomy (He et al., 25 Sep 2025).

Nevertheless, systematic use of closed-loop, self-correcting execution is increasingly seen as foundational for robust, adaptive, and scalable intelligent systems. By encoding feedback-driven correction into system architectures—enabling explicit error detection, local or global re-planning, self-reflection, and fine-grained learning—such frameworks underpin the next generation of fault-tolerant AI and robotics, resilient scientific and software automation, and accurate physical control across cyber-physical domains.