MMAC: Multi-Dimensional Multi-Agent Critiques

• MMAC is a framework that ensures safe multi-agent consensus and robust distributed optimization through dimension-aware filtering and resilient control strategies.
• It employs auxiliary point-based resilient control and memory-based hold-and-use mechanisms to mitigate the effects of adversarial attacks and communication losses.
• The paradigm guarantees benign agents remain within a safe convex hull, enabling reliable convergence even under DoS, Byzantine disruptions, and heterogeneous objectives.

Multi-Dimensional Multi-Agent Critiques (MMAC) comprise methodologies and frameworks for analyzing, critiquing, and ensuring robust behavior in multi-agent systems operating over high-dimensional state or action spaces. The MMAC paradigm intersects control theory, distributed optimization, and reinforcement learning, often under non-ideal conditions such as adversarial attacks, intermittent communication, and heterogeneous agent objectives. Recent advances emphasize geometric consensus protocols, resilient control logic, and safety guarantees for high-dimensional consensus and distributed optimization.

1. Multi-Dimensional Consensus in Adversarial Settings

The central problem addressed is achieving consensus among a network of agents, where each agent possesses a state in $\mathbb{R}^d$ and the system may be subject to agent-based (malicious, Byzantine, stubborn) and communication-based (Denial-of-Service, DoS) attacks. The goal is to design protocols such that the benign subset of agents reaches agreement on a common vector despite adversarial disturbances in both agent behavior and communication topology (Chen et al., 8 Oct 2025).

Traditional consensus protocols, typically relying on linear averaging or weighted sums of neighbor states, are vulnerable when agents are compromised or network links are blocked. In the multi-dimensional case, the additional geometric structure of $\mathbb{R}^d$ exposes vulnerabilities where adversarial agents can drive consensus outside the safe subspace defined by benign initializations or inject outlier influence in particular directions.

2. Resilient Control via Auxiliary Point and Safe Kernel Methods

To counter these threats, the auxiliary point-based resilient control scheme innovatively updates each benign agent's state using a construct termed the “safe kernel.” At every iteration and for each agent $i$, the set of received (and possibly held-over, see Section 4) neighbor states $\mathcal{X}_i(t_k)$ in $\mathbb{R}^d$ is filtered to discard potential outlier effects from up to $F$ adversarial neighbors.

Formally, the safe kernel is computed as: $$\Psi(\mathcal{X}_i(t_k), F) \triangleq \bigcap_{S \in \mathcal{S}(\mathcal{X}_i(t_k), F)} \mathrm{Conv}(S)$$ where $\mathcal{S}(\mathcal{X}_i(t_k), F)$ is the collection of all subsets of $\mathcal{X}_i(t_k)$ of cardinality $|\mathcal{N}_i^{\mathrm{in}}| - F$, and $\mathrm{Conv}(S)$ denotes the convex hull of $S$.

The actual control update is: $$u_i(t_k) = (1 - \alpha_i)[\tilde{x}_i(t_k) - x_i(t_k)]$$ where $\alpha_i$ is a design weight and $\tilde{x}_i(t_k)$ is the centroid of a subset $\Lambda_i(t_k)$ combining coordinate-wise extremes among the filtered neighbor states. Unlike mere componentwise medians or means, this approach maintains the consensus vector strictly within the original convex hull of benign agents and is robust to adversarial state manipulations.

3. Robustness to Denial-of-Service and Communication Loss

Realistic distributed settings introduce time-varying, potentially disconnected network topologies induced by DoS attacks on communication links. The protocol preserves its safety and convergence properties by employing a “hold-and-use” (or memory-based) mechanism: if communication from neighbor $j$ to agent $i$ is unavailable at $t_k$, agent $i$ uses the last available and valid state $x_j(t_k^-)$, effectively carrying forward trusted historical data across DoS intervals. This mechanism prevents artificial injection of extreme default values and sustains sufficient information redundancy for the safe kernel computation (Chen et al., 8 Oct 2025).

4. Multi-Dimensional Distributed Optimization Under Attack

Expanding beyond consensus, the protocol generalizes to resilient multi-dimensional distributed optimization (RMDO). Each benign agent $i$ possesses a convex (possibly non-differentiable) local function $f_i(x)$; the joint network seeks to minimize the aggregate $\sum_{i \in \mathcal{V}_B} f_i(x)$ in a distributed manner, while still ensuring resilience to corrupted cost functions and communication attacks.

The control input adopts an additional subgradient descent term: $$u_i(t_k) = (1 - \alpha_i)[\tilde{x}_i(t_k) - x_i(t_k)] - \beta_{i, k} \cdot d_i(t_k)$$ where $d_i(t_k)$ is a subgradient of $f_i(\cdot)$ at a convex combination of $\tilde{x}_i(t_k)$ and $x_i(t_k)$, and $\beta_{i, k}$ is a diminishing, square-summable, non-summable step-size. This modification enables asymptotic convergence to the minimizer of the true (benign) cost sum, even in the presence of corrupted data and adversarial optimization behaviors.

5. Theoretical Guarantees and Geometric Invariance

The protocol’s main theoretical results are as follows:

• Convex Hull Confinement: Under appropriate network robustness assumptions (specifically, $(d+1)F+1$-robust or $(dF+1, F+1)$-robust digraphs), all benign agent states remain within the evolving convex hull of benign initializations at every step.
• Delta-Validity: For any $\delta > 0$, states of benign agents remain within a $\delta$-expansion of the original convex hull, i.e., $\delta$-validity holds throughout evolution.
• Global Consensus and Optimization: Differences between benign agent states vanish asymptotically, yielding vector consensus, and the distributed optimization protocol converges to the global minimizer under standard diminishing step conditions.
• Resilience to DoS: Both the consensus and RMDO protocols maintain their performance guarantees despite intermittent, potentially lengthy DoS periods, as long as the “hold-and-use” mechanism is in place.

Numerical simulations illustrate that, for both consensus and distributed optimization, benign agent states indeed remain inside the desired safe region and converge as predicted.

6. Implications and Relevance for Multi-Dimensional Multi-Agent Critiques

The proposed methodology exemplifies MMAC principles in several ways:

• Dimension-Aware Robust Consensus: By inherently respecting the geometry of multi-dimensional agent states, the system critiques and filters potentially unsafe collective updates before state changes are made.
• Safety-Driven Filtering: The safe kernel serves as a geometric critique filter on incoming information, systematically excluding outlier or adversarial contributions from process evolution.
• Resilient Integration of Optimization and Consensus: The approach is modular, extending from consensus to distributed optimization tasks without loss of resilience—a property essential for multi-agent systems deployed in adversarial or uncertain environments.
• Provable Guarantees Under Adversarial and Network Dynamics: The explicit analysis and proofs ensure that the multi-dimensional critique—implemented via filtering and geometric invariance—is mathematically sound, even under worst-case attack and communication loss scenarios.

7. Summary Table: Core Mechanisms and Guarantees

Mechanism	Description	Robustness Guarantee
Safe kernel	Intersection of convex hulls after adversarial filtering	Maintains benign consensus within original safe region
Hold-and-use memory	Recall of last valid state under DoS attack	Preserves redundancy, prevents misleading updates
Auxiliary point	Centroid of filtered neighbor values for updates	Ensures vector updates cannot be manipulated by attackers
RMDO update	Adds subgradient descent to consensus control	Global convergence to benign cost optimum under attack

In totality, these constructs define and realize the key elements of Multi-Dimensional Multi-Agent Critiques, offering principled design and provable safety for vector consensus and distributed optimization in adversarial, heterogeneous, and dynamic communication environments (Chen et al., 8 Oct 2025).