RisConFix: Adaptive Risk Control

• RisConFix is a framework integrating risk-aware decision-making, robust uncertainty quantification, and adaptive control in both statistical prediction and cyber-physical systems.
• It employs methods like non-exchangeable conformal risk control, rectified conformal prediction, and stochastic optimization to achieve reliable, locally adaptive risk bounds.
• The system features automated LLM-based configuration repair and decision-theoretic calibration, ensuring practical scalability and high performance across diverse operational environments.

RisConFix encompasses a diverse set of methodologies and systems unified by the goal of risk-aware decision-making, robust uncertainty quantification, and adaptive control in both statistical prediction and cyber-physical systems. The term "RisConFix" appears in conformal inference, stochastic optimization, and control engineering, denoting techniques geared toward dynamically managing risk by adaptation either of prediction sets or system configurations. Below, the primary facets of RisConFix are surveyed, including formal risk control under non-exchangeable data, rectified conformal methods for conditional coverage, risk-aware control under operational uncertainty, and automated repair in complex engineered systems.

1. Non-Exchangeable Conformal Risk Control: Formalism and Guarantees

RisConFix in predictive inference refers to a non-exchangeable extension of conformal risk control (Farinhas et al., 2023). Classical split conformal prediction ensures a risk bound on the expected loss of prediction sets under exchangeability assumptions. RisConFix generalizes this by introducing a flexible weighting scheme:

• Calibration data $(X_i, Y_i)$ receives nonuniform, data-dependent weights $w_i = w(X_i, X_{n+1}) \in [0,1]$ reflecting local relevance (e.g., exponential decay for time series, kernel similarity for covariate shift).
• The weighted empirical risk is

$$\hat R_n(\lambda) = \frac{1}{N_w}\sum_{i=1}^n w_i\,\ell(Y_i, \mathcal{C}_\lambda(X_i))$$

• The risk-control threshold $\hat\lambda$ is the smallest $\lambda$ such that

$$\frac{N_w}{N_w+1}\hat R_n(\lambda) + \frac{B}{N_w+1} \leq \alpha$$

• The main guarantee (Theorem) states

$$\mathbb{E}[\ell(Y_{n+1}, \mathcal{C}_{\hat\lambda}(X_{n+1}))] \leq \alpha + (B-A)\sum_{i=1}^n \tilde w_i\,d_{\mathrm{TV}}(Z, Z^i)$$

where $d_{\mathrm{TV}}$ denotes total-variation distance, and the bound tightens as data "near-exchangeability" improves.

Empirical validation across synthetic drift, real-world time series, and semantic QA confirms that RisConFix corrects miscoverage and enforces robust risk control, outperforming classical methods under distribution drift or local nonstationarity. The predictor construction remains computationally efficient, scaling linearly with calibration set size.

2. Rectified Conformal Methods for Conditional Coverage (RCP)

RisConFix also appears as a score transformation procedure ("Rectified Conformal Prediction," RCP) to enhance conditional coverage under split conformal inference (Plassier et al., 22 Feb 2025). The standard split conformal approach yields marginal but not conditional coverage: $$P(Y \in C_\alpha(X) | X=x) \ll 1-\alpha \quad \text{in adverse regions}$$ RCP ("RisConFix") corrects this by:

• Learning the $(1-\alpha)$ conditional quantile $Q_{1-\alpha}(P_{V|X=x})$ of base conformity scores $V(x,y)$ via quantile regression.
• Transforming scores as $\tilde V(x, y) = V(x, y) / \hat \tau(x)$ (linear) or $V(x, y) - \hat \tau(x)$ (shift), with $\hat \tau(x)$ estimated from data.
• Applying split conformal calibration to $\tilde V(x, y)$ to yield prediction sets adapting locally to data structure.

Key theoretical bound: $$P(Y \in C_\alpha(X) | X=x) \geq 1-\alpha - \epsilon_{\hat\tau}(x) - o_n(1)$$ where $\epsilon_{\hat\tau}(x)$ is controlled by quantile estimation error.

Benchmarks show the method achieves near-nominal coverage in high-risk or heterogeneous regions, especially for multi-output or heteroscedastic tasks. RCP ("RisConFix") ensures marginal coverage and, asymptotically, conditional coverage as quantile estimates improve.

3. Risk-Aware Resource Allocation: Stochastic Optimization Formulation

RisConFix is employed as an acronym for "Risk-Aware Congestion Management with Capacity Limitation Contracts and Redispatch" (Holst et al., 18 Sep 2025). In this domain, RisConFix denotes a two-stage stochastic MILP coordinating capacity limitation contracts (CLC) and redispatch contracts (RC) for congestion management under operational uncertainty.

• The system operator procures flexibility from EV aggregator fleets; risk is quantified via chance-constrained optimization and CVaR restrictions.
• Stage 1 determines CLC volumes; Stage 2 triggers RC activation based on updated forecasts and market conditions.
• Uncertainty in EV charging and redispatch is modeled via Random Forest forecasts, VAR(1) residuals, and copula-based sampling of redispatch market liquidity.

The MILP objective is to minimize the total cost of CLCs and RCs subject to risk constraints on congestion violations. Numerical experiments identify optimal tradeoffs depending on fleet size and activation timing. Key insights:

• Small fleets: granular RC activation infeasible, CLC preferred.
• Medium fleets: RC increasingly used for intraday forecast correction.
• Large fleets: market liquidity risk limits optimal RC use.
• Optimal RC activation occurs after major forecast uncertainty clears, but before trading windows close.

Parameter sensitivity confirms that relaxing risk tolerance ($\varepsilon$) slightly can significantly reduce cost without sacrificing empirical reliability.

4. Automated LLM-Based Configuration Repair in CPS

A distinct instantiation of RisConFix involves real-time LLM-based repair of risk-prone drone configurations (Han et al., 8 Dec 2025). Here, RisConFix is a cyber-physical feedback system operating as follows:

• Continuous monitoring acquires high-frequency sensor data from the drone.
• Rule-based anomaly detection identifies critical failures (e.g., deviation, thrust loss).
• Upon anomaly detection, an LLM is prompted with the current configuration vector and official parameter bounds to propose corrective changes.
• An iterative repair loop applies LLM suggestions, monitoring subsequent flight stability, terminating on success or after preset iterations.

High-throughput empirical benchmarking on ArduPilot (1,421 misconfiguration groups) with DeepSeek and Qwen LLMs yielded a best-in-class repair success rate of 97% and mean repair iterations of 1.17 for DeepSeek. Key implementation lessons:

• Explicit domain constraints (parameter bounds) sharply improve repair efficacy.
• Reasoning capacity of LLM (paid vs. free tier) impacts reliability.
• Certain hardware/sensor faults remain unsolvable by parameter update.

RisConFix demonstrates the feasibility of closed-loop, automated diagnosis and repair in CPS, with direct applicability beyond UAVs.

5. Decision-Theoretic Foundations: Risk-Averse Calibration

RisConFix methodology finds strong decision-theoretic support in the context of risk-averse agent optimization (Kiyani et al., 4 Feb 2025). Here, risk is formalized via Value-at-Risk (VaR), with the goal to guarantee, at level $1-\alpha$, a minimal utility certificate post prediction: $$v_\alpha(a;x) := \inf\{v: \Pr[u(a,Y) \leq v | X=x] \geq \alpha \}$$ Optimal risk-averse policies are provably max–min over prediction sets $C(x)$ with conformal-style coverage: $$a_\text{RA}(C(x)) = \arg\max_{a\in\mathcal{A}} \min_{y\in C(x)} u(a,y)$$

$$\nu_\text{RA}(C(x)) = \max_{a\in\mathcal{A}} \min_{y\in C(x)} u(a,y)$$

The Risk-Averse Calibration (RAC) algorithm computes optimal prediction sets via finite-sample split-conformal calibration, enforcing exact coverage and yielding robust certificates for downstream action. Empirical demonstrations in medical diagnosis and recommendation settings verify marked improvements in safety-utility tradeoff—critical errors are reduced by up to 75% at minor average utility cost.

6. Practical Implementation, Guidelines, and Empirical Insights

Across domains, RisConFix implementations are characterized by:

• Modular calibration/data acquisition pipelines, amenable to parallelization.
• Flexible adaptation to local or global risk via user-defined or algorithmic weightings and quantile transformations.
• Strong finite-sample and asymptotic guarantees, leveraging (generalized) conformal inference and stochastic programming principles.
• Scalability to thousands of calibration samples or real-time anomaly feedback loops.

Empirical results across benchmarks (classification, regression, time series, open-domain QA, engineered systems) confirm that RisConFix architectures outperform classical or static risk control in the presence of drift, nonstationarity, or adversarial events, providing robust risk certificates and actionable, adaptive system control.

7. Limitations and Forward Directions

While RisConFix methodologies substantially extend the boundaries of risk-aware control, several caveats and ongoing challenges remain:

• Accuracy and tightness of local quantile or weight estimation is paramount; misspecification can degrade conditional guarantees.
• Cyber-physical implementations depend on sensor fidelity, actuator reliability, and LLM reasoning quality; prompt engineering and constraint injection are essential.
• Current RAC-type calibrations (decision-theoretic risk control) assume exchangeability or can be loosened for drift, but further research is needed for adversarial or highly nonstationary regimes.

Future work includes generalizing RisConFix approaches to broader CPS domains (smart factories, robotics), integrating closed-loop learning for improved repair policies, and developing adaptive quantile estimation for sharper local guarantees in statistical inference.

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Key References:

• “Non-Exchangeable Conformal Risk Control” (Farinhas et al., 2023)
• “Rectifying Conformity Scores for Better Conditional Coverage” (Plassier et al., 22 Feb 2025)
• “Risk-Aware Congestion Management with Capacity Limitation Contracts and Redispatch” (Holst et al., 18 Sep 2025)
• “RisConFix: LLM-based Automated Repair of Risk-Prone Drone Configurations” (Han et al., 8 Dec 2025)
• “Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents” (Kiyani et al., 4 Feb 2025)