Cascading Misinterpretation and Suboptimal Joint Actions

TITLE OF THE INVENTION

Joint Interpretability‑Trust Framework for Multi‑Agent Coordination with Adaptive Trust and Bounded Sub‑Optimality

FIELD OF THE INVENTION

The present invention relates to artificial intelligence, specifically to multi‑agent systems that coordinate under uncertainty. It further concerns the integration of interpretability, adaptive trust propagation, and provably bounded sub‑optimality into a modular coordination framework.

BACKGROUND AND PRIOR ART

Multi‑agent pipelines frequently suffer from cascading misinterpretation, whereby a single misreading is amplified across the network, producing a “sink” effect that can increase error rates by more than 17× relative to a single‑agent baseline ^[v8414]. The root cause is the absence of formal communication contracts; agents exchange raw text or loosely defined JSON, leading to semantic drift downstream ^[v16509]. Structured orchestration that enforces typed schemas, validation, and recovery logic can mitigate this risk ^[v1259]. However, even with such safeguards, distributed responsibility and hidden feedback loops can still foster emergent misinterpretation, underscoring the need for continuous observability and human‑in‑the‑loop oversight ^[v2277]. Existing joint interpretability‑trust frameworks embed transparent reasoning but lack adaptive trust propagation and provable sub‑optimality guarantees ^[v14084], ^[v8492], ^[v10752]. The present invention addresses these gaps.

SUMMARY OF THE INVENTION

The invention discloses a Joint Interpretability‑Trust (JIT) framework that couples a Contextual Graph‑Conditioned Explanation (CGCE) layer, a Dynamic Trust‑Score Propagation (DTSP) layer, and a Joint Policy Re‑Optimization with Sub‑Optimality Bounds (JPRO‑SOB) layer. CGCE constructs a contextual graph of local observations and received messages, enabling semantic inconsistency detection via a transformer‑based encoder or graph neural network ^[9], ^[11]. DTSP attaches Bayesian trust scores to messages, updating them with a lightweight filter that incorporates historical consistency and explanation confidence, thereby mitigating the sink effect ^[7], ^[2], ^[8]. JPRO‑SOB performs cooperative re‑optimization of policy parameters using a bounded‑approximation algorithm that guarantees a sub‑optimality gap ≤ ε, triggered when trust scores fall below a threshold ^[5], ^[6]. The modular design permits independent tuning or replacement of each layer, enabling deployment across heterogeneous devices and adversarial environments.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Embodiment 1 – Contextual Graph‑Conditioned Explanation (CGCE)

1. Each agent constructs a directed graph G = (V,E) where vertices V represent local observations, received messages, and internal state variables, and edges E encode temporal or causal relationships. 2. A transformer‑based encoder with L = 12 layers and hidden dimension H = 768 processes the adjacency matrix and node features to produce a contextual embedding h ∈ ℝ^H. 3. The embedding is fed to a semantic consistency module that compares the predicted action â with the agent’s local transition model; a mismatch exceeding a threshold τ triggers an explanation generation sub‑module. 4. The explanation is produced by a large language model conditioned on h, yielding a natural‑language rationale that is transmitted to downstream agents. 5. The CGCE layer may alternatively employ a graph neural network (GNN) with message‑passing depth D = 4 ^[11] for resource‑constrained deployments.

Embodiment 2 – Dynamic Trust‑Score Propagation (DTSP)

1. Each message m carries an initial trust score τ₀ ∈ [0,1]. 2. A Bayesian filter updates τ via τ ← α·τ_prev + (1 – α)·c, where α ∈ [0,1] is a decay factor and c is the confidence derived from the explanation module. 3. The filter incorporates historical consistency statistics (e.g., number of consistent actions over the past T steps) and current explanation confidence, yielding a composite trust value τ_comp. 4. A trust threshold θ_trust (e.g., 0.6) is maintained; messages with τ_comp < θ_trust are flagged and may be discarded or re‑queried. 5. DTSP mitigates the sink effect by attenuating the influence of low‑trust messages, as demonstrated in prior block‑propagation studies ^[7] and sink‑effect analyses ^[2].

Embodiment 3 – Joint Policy Re‑Optimization with Sub‑Optimality Bounds (JPRO‑SOB)

1. Agents maintain a shared policy parameter vector θ ∈ ℝ^d. 2. When τ_comp falls below θ_trust, agents initiate a cooperative re‑optimization routine. 3. The routine solves a constrained optimization problem: minimize L(θ) subject to ||θ – θ_prev|| ≤ δ, where δ is a step‑size bound. 4. A bounded‑approximation algorithm (e.g., projected gradient descent with Lipschitz constant L_L) guarantees that the resulting policy π_θ satisfies J* – J_π_θ ≤ ε, where ε is a pre‑specified sub‑optimality tolerance ^[5], ^[6]. 5. The re‑optimization is performed asynchronously across agents, leveraging a decentralized consensus protocol that respects local communication constraints.

Embodiment 4 – Modular Integration

1. The CGCE, DTSP, and JPRO‑SOB layers are encapsulated as independent modules with well‑defined interfaces. 2. Each module can be swapped for an alternative implementation (e.g., transformer ↔ GNN, Bayesian filter ↔ deterministic decay, gradient‑based re‑optimization ↔ primal‑dual method) without affecting the overall system. 3. The framework supports heterogeneous devices by allowing lightweight LLMs or SLMs for explanation generation on edge nodes ^[v4285], and by scaling the graph size and model depth according to available compute.

Embodiment 5 – Trust Calibration and Human‑in‑the‑Loop Oversight

1. A human operator can adjust θ_trust or the decay factor α in real time. 2. The system logs all trust updates and explanation outputs, enabling audit trails that satisfy regulatory requirements. 3. The modular architecture permits integration of external risk‑control agents (e.g., a Risk Control Agent that detects adversarial prompts) ^[v10752].

CLAIMS

1. A method for coordinating a plurality of autonomous agents in a distributed system, comprising: constructing a contextual graph of local observations and received messages; generating a contextual embedding via a transformer‑based encoder; detecting semantic inconsistencies between predicted actions and a local transition model; producing a natural‑language explanation conditioned on the contextual embedding; attaching a trust score to each message; updating the trust score using a Bayesian filter that incorporates historical consistency and explanation confidence; and performing a cooperative policy re‑optimization when the trust score falls below a predetermined threshold, wherein the re‑optimization guarantees a sub‑optimality gap no greater than a specified ε.

2. The method of claim 1, wherein the contextual graph is constructed using a graph neural network with message‑passing depth of at least 4.

3. The method of claim 1, wherein the Bayesian filter employs a decay factor α set between 0.5 and 0.9.

4. The method of claim 1, wherein the sub‑optimality bound ε is set to 0.05 of the optimal joint reward.

5. The method of claim 1, wherein the trust score threshold is set to 0.6.

6. A system for coordinating a plurality of autonomous agents, comprising: a contextual graph‑conditioned explanation module configured to generate natural‑language explanations from a transformer‑based encoder; a dynamic trust‑score propagation module configured to update trust scores via a Bayesian filter; and a joint policy re‑optimization module configured to perform cooperative re‑optimization with a bounded‑approximation algorithm that guarantees a sub‑optimality gap no greater than ε, wherein the modules are interfaced such that the re‑optimization is triggered when the trust score falls below a predetermined threshold.

7. The system of claim 6, wherein the explanation module further includes a multimodal graph transformer that processes image patches, textual queries, and inter‑agent role priors.

8. The system of claim 6, wherein the trust‑score propagation module incorporates a hierarchical trust verification step that discards messages with a composite trust score below 0.6.

9. The system of claim 6, wherein the joint policy re‑optimization module employs a projected gradient descent algorithm with Lipschitz constant L_L to guarantee the sub‑optimality bound.

10. The system of claim 6, wherein the modules are encapsulated as independent software components that can be swapped without affecting overall system functionality.

ABSTRACT

A joint interpretability‑trust framework for multi‑agent coordination is disclosed. The framework integrates a contextual graph‑conditioned explanation layer that detects semantic inconsistencies via transformer or graph neural network encoders, a dynamic trust‑score propagation layer that updates message trust using a Bayesian filter to mitigate cascading misinterpretation, and a joint policy re‑optimization layer that performs cooperative policy updates with provable sub‑optimality bounds. The modular architecture permits independent tuning or replacement of each layer, enabling deployment across heterogeneous devices and adversarial environments. The system achieves robust coordination by coupling transparent reasoning with adaptive trust and bounded sub‑optimality, thereby addressing limitations of prior art in cascading misinterpretation, static trust, and unbounded policy performance.