Semantic Fusion: Verifiable Alignment in Decentralized Multi-Agent Systems

TL;DR

Semantic Fusion tackles decentralized coordination by enforcing ontology-scoped memory slices with local validation and scoped refresh, enabling agents to reason and update without centralized control. A central result is a bisimulation between each agent's local memory trace $M_a(t)$ and the projection $\pi_{O_a}(\mathcal{M}(t'))$ of the global semantics, holding in both deterministic and probabilistic settings. Deterministic guarantees include semantic coherence, slice convergence, and causal isolation, while probabilistic guarantees extend to nondeterministic update generators and almost-sure convergence; communication per update scales as $O(d)$ with a matching $\Omega(d)$ lower bound. Empirically, a lightweight reference architecture and a 250-agent SAR-style simulation validate the theoretical results, demonstrating convergence, safety, bounded communication, and resilience to agent failure under asynchronous conditions. Collectively, SF provides a formal, scalable basis for verifiable autonomy in decentralized systems, accommodating heterogeneous, learning-based components through ontology-driven validation and scoped memory semantics.

Abstract

We present Semantic Fusion (SF), a formal framework for decentralized semantic coordination in multi-agent systems. SF allows agents to operate over scoped views of shared memory, propose structured updates, and maintain global coherence through local ontology-based validation and refresh without centralized control or explicit message passing. The central theoretical result is a bisimulation theorem showing that each agent's local execution is behaviorally equivalent to its projection of the global semantics, in both deterministic and probabilistic settings. This enables safety, liveness, and temporal properties to be verified locally and soundly lifted to the full system. SF supports agents whose update proposals vary across invocations, including those generated by learned or heuristic components, provided updates pass semantic validation before integration. We establish deterministic and probabilistic guarantees ensuring semantic alignment under asynchronous or degraded communication. To validate the model operationally, we implement a lightweight reference architecture that instantiates its core mechanisms. A 250-agent simulation evaluates these properties across over 11,000 validated updates, demonstrating convergence under probabilistic refresh, bounded communication, and resilience to agent failure. Together, these results show that Semantic Fusion can provide a formal and scalable basis for verifiable autonomy in decentralized systems.

Figures (5)

• Figure 1: Reference architecture instantiating Semantic Fusion (SF). Agents operate over scoped semantic slices, proposing and validating structured updates to shared semantic memory. Coherence and coordination arise from ontology-scoped validation and refresh propagation rather than explicit messaging or global control.
• Figure 2: Probabilistic bisimulation between an agent’s local memory execution and its projected global memory slice under probabilistic update generation and refresh synchronization. The sync transition is depicted as aligning the agent’s memory with the global projection at time $t{+}1$ for illustrative clarity. In general, however, an agent’s local memory may lag, and the corresponding global state reflected in its slice may have been committed at an earlier time $t' \le t{+}1$, consistent with Theorem \ref{['thm:prob_slice_bisim']}
• Figure 3: A survivor is detected by search1, triggering an ontology-valid update to shared memory. This change activates relay1, which moves to the zone and signals presence. Upon detecting relay support, rescue20 initiates recovery. Each agent acts independently based on scoped semantic memory, without direct coordination or explicit messages.
• Figure 4: Message complexity scaling under semantic overlap. Each point shows the average number of agents receiving an update on a semantic key, as a function of the slice fraction $f$, averaged over 100 randomized slice assignments. Agents subscribe to a random $f$-fraction of ontology keys, and each update is received by all agents whose slice overlaps with the updated key. The expected message cost is $k+1$ with $k = \mathrm{round}(f \cdot 200)$. Observed values closely match the ideal bound $f \cdot 200 + 1$ (dashed line), with highest variance at low $f$ due to small slice sizes. The dotted line shows the broadcast baseline (201 messages).
• Figure 5: Empirical survival functions $S(k)$ vs. fitted exponential decays $e^{-\lambda k}$ for per-key alignment delays at different communication probabilities ($\rho$). All curves exhibit exponential tails, confirming the bound predicted by Theorem \ref{['thm:dynamic-convergence']}. Minor mid-range deviation above the bound reflects conservative tail modeling and non-i.i.d. system structure, but asymptotic decay remains faster than the theoretical threshold.

Theorems & Definitions (38)

• definition 1: Memory Projection
• definition 2: Semantic Slice
• definition 3: Update Proposal
• definition 4: Validation
• definition 5: Refresh Notification
• Remark 1
• lemma 1: Eventual Delivery of Scoped Updates
• theorem 1: Monotonic Semantic Coherence
• corollary 1: Slice Validity Preservation
• proposition 1: Localized Failure Containment