Specification

Title of Invention

Resonance Field Coordination: A Shared Semantic Vector Field with Biologically-Inspired Dynamics for Multi-Agent Large Language Model Task Orchestration

Cross-Reference to Related Applications

None.

Field of the Invention

The present invention relates to coordination systems for multiple artificial intelligence agents, and more particularly to a shared semantic memory architecture that enables multiple large language model (LLM) agents to discover and build upon each other's knowledge through meaning-based retrieval in a dynamically-evolving vector field.

Background of the Invention

The Multi-Agent Coordination Problem

Modern AI systems increasingly deploy multiple specialized LLM agents to accomplish complex tasks. A research agent gathers information, an analyst agent synthesizes it, and a writer agent produces the final output. These agents must share knowledge effectively.

The Telephone Game Problem

In all major existing multi-agent platforms — including Microsoft AutoGen, CrewAI, LangGraph, and OpenAI Swarms — agents coordinate through sequential message passing. Agent A produces output that is forwarded to Agent B, whose output is forwarded to Agent C. This architecture creates a fundamental information bottleneck: each intermediate agent acts as a filter, and knowledge that is not explicitly forwarded is lost to downstream agents.

For example, if Agent A produces 20 research findings and forwards them to Agent B, and Agent B references only 3 of those findings in its analysis, the remaining 17 findings are invisible to Agent C — even if finding #7 is directly relevant to Agent C's task. This is the multi-agent equivalent of the telephone game: information is lost at every hop.

Limitations of Existing Approaches

System	Coordination Method	Limitation
CrewAI	Sequential/hierarchical message passing	Information filtered at each hop
AutoGen (Microsoft)	Conversational message exchange	Bounded by context window; no persistence
LangGraph	Shared state dictionary	No semantic retrieval; manual key management
OpenAI Swarms	Conversation handoff	No shared memory; full context transfer
Google A2A	Agent-to-agent protocol	Explicit point-to-point; no broadcast by relevance
Mem0	Per-user memory with vector search	Per-user, not shared between agents
Letta/MemGPT	Per-agent long-term memory	Agent-local; no cross-agent discovery

No existing system provides a shared semantic space where multiple agents inject knowledge and any agent can query by meaning with dynamic relevance surfacing that combines semantic similarity, temporal decay, and usage-based reinforcement.

Relevant Prior Art

Blackboard Architecture (Hayes-Roth, 1985): Introduced a shared workspace where independent knowledge sources read and write. However, blackboard systems use symbolic representations, not continuous vector spaces, and have no semantic retrieval, temporal decay, usage-based reinforcement, or resonance scoring.

Stigmergy (Grasse, 1959; Dorigo et al., 1996): Indirect coordination through environmental modification — ants depositing pheromones that decay and are reinforced by traversal. The decay and reinforcement dynamics are structurally analogous, but stigmergic systems operate on spatial pheromone fields, not high-dimensional semantic embedding spaces, and the agents are simple reactive entities, not LLMs producing complex textual knowledge.

Dynamic Neural Field Theory (Amari, 1977): Continuous activation fields with decay, lateral inhibition, and attractor formation. The resonance scoring, temporal decay, and stability measurement in this invention parallel neural field dynamics, but Amari's theory describes biological neural populations, not artificial LLM agents operating on text embeddings.

Field-Theoretic Memory for AI Agents (Mitra, arXiv:2602.21220, January 2026): Proposes treating agent memory as continuous fields governed by partial differential equations. This is the closest prior art. However, Mitra's system is designed for single-agent memory enhancement, uses PDE-based diffusion on a reduced 2D manifold, and has no concept of multi-agent shared coordination, mission lifecycle binding, agent-callable tools, content deduplication, or Hebbian co-access reinforcement.

Summary of the Invention

The present invention provides a system and method for coordinating multiple LLM agents through a shared semantic vector field with biologically-inspired dynamics. Instead of passing messages between agents, each agent injects knowledge patterns into a shared high-dimensional embedding space and queries that space by semantic meaning. Query results are ranked by a resonance function that combines squared cosine similarity, exponential temporal decay, and access-based Hebbian reinforcement.

The system eliminates the telephone game problem by making all agent-contributed knowledge discoverable by any agent through meaning-based retrieval, without requiring explicit inter-agent communication channels.

Key aspects of the invention include:

1. Mission-scoped shared fields — one vector collection per multi-agent mission, created on mission start and destroyed on mission completion

2. Resonance-based retrieval — query results ranked by R = cos^2(theta) x S_0 x e^(-lambda*t) x min(1 + 0.05n, 2.0)

3. Temporal decay — pattern relevance decreases exponentially from last access time, with configurable half-life

4. Hebbian reinforcement — retrieved patterns are strengthened; co-retrieved patterns receive additional associative bonding

5. Content deduplication — cryptographic hash prevents duplicates; collisions reinforce existing patterns

6. Field stability measurement — convergence metric indicating whether agents are building shared understanding

7. Agent-callable tools — agents interact with the field during execution via platform tools

8. Swappable backend interface — same orchestration code runs against the vector field or a message-passing baseline

Detailed Description of the Invention

System Architecture

The system creates a shared semantic vector field for each multi-agent mission. The field is implemented as a vector database collection where each point represents a knowledge pattern contributed by an agent.

                    +--------------------------------+
                    |     SHARED SEMANTIC FIELD       |
                    |   (Vector database collection)  |
                    |                                 |
 Agent A --inject-->|  Pattern: "EU AI Act scope"     |
 Agent A --inject-->|  Pattern: "Compliance deadline"  |
 Agent B --inject-->|  Pattern: "Risk assessment"      |
                    |                                 |
 Agent C --query--->|  "What compliance deadlines?"   |
                    |    -> Returns patterns ranked   |
                    |      by RESONANCE, not recency  |
                    +--------------------------------+

Pattern Structure

Each knowledge pattern in the field consists of:

• id: Unique identifier (UUID v4)

• vector: High-dimensional semantic embedding (e.g., 2048 dimensions) of the concatenated key and value

• agent_id: Integer identifying which agent contributed this pattern

• key: Semantic label describing the pattern's topic

• value: Knowledge content (up to 4000 characters)

• strength: Current strength value, mutated by co-access reinforcement

• created_at: ISO-8601 UTC timestamp of creation

• last_accessed: ISO-8601 UTC timestamp of most recent retrieval

• access_count: Integer count of retrievals

• content_hash: SHA-256 hex digest for deduplication

Field Lifecycle Management

The field is bound to the mission lifecycle:

Creation: When a multi-agent mission begins, a new vector collection is created with payload indexes for content_hash, agent_id, and created_at. If initial data exists (e.g., the mission goal), it is seeded into the field at strength 1.0.

Injection: As each agent completes a task, its output is automatically injected into the field by the mission coordinator. Agents may also inject patterns directly during execution via a platform tool.

Query: Agents query the field by semantic meaning. Results are ranked by resonance (described below), not by insertion time or agent identity.

Destruction: When the mission reaches a terminal state (completed, failed, cancelled), the field collection is deleted. A periodic cleanup process garbage-collects fields for missions that were not cleaned up synchronously.

Temporal Decay Function

Every pattern's effective strength decays exponentially from its last access time:

S(t) = S_0 x e^(-lambda x t) x B(n)

Where:

• S_0 is the stored strength value

• lambda is the decay rate (default 0.1, yielding half-life of approximately 6.93 hours)

• t is the elapsed time in hours since last_accessed

• B(n) is the access boost function

Decay is computed at query time, not at write time. The stored strength remains constant; patterns simply score lower when queried after more time has elapsed. This is a non-destructive computation.

Resonance Scoring Function

When an agent queries the field, raw cosine similarity results are transformed into resonance scores:

R(pattern, query) = cos^2(theta) x S(t)

Where cos(theta) is the cosine similarity between the query embedding and the pattern embedding, and S(t) is the temporally-decayed strength including access boost.

Squaring the cosine similarity amplifies high-similarity matches and suppresses noise. A pattern with cosine similarity 0.9 receives a resonance contribution of 0.81 (81%), while a pattern with cosine similarity 0.5 receives only 0.25 (25%). This creates sharper relevance discrimination than raw cosine scoring.

Access Boost Function (Hebbian Reinforcement)

B(n) = min(1.0 + n x 0.05, 2.0)

Where n is the access_count. Each retrieval adds 5% to the effective strength multiplier, capped at 2.0x (reached at 20 accesses). Frequently-queried patterns resist decay up to twice as effectively as unaccessed patterns.

Co-Access Bonus

When multiple patterns are retrieved together in a single query result set, each pattern receives an additional strength boost:

S_new = min(S_0 x (1.0 + 0.02 x (k - 1)), S_0 x cap)

Where k is the number of co-retrieved patterns and cap is the reinforcement cap (default 2.0). This implements a "neurons that fire together wire together" dynamic: patterns frequently relevant to the same queries become structurally associated through mutual strengthening.

Unlike the access boost, the co-access bonus mutates the stored strength value. This is intentional: co-access represents an emergent relationship between patterns, not just individual popularity.

Content Deduplication

Before injecting a new pattern, the system computes the SHA-256 hash of the content and checks for an existing pattern with the same hash. If a duplicate is found, the existing pattern is reinforced (access_count incremented, last_accessed updated) instead of creating a new entry. This prevents field bloat while strengthening repeated knowledge.

Archival Filtering

Patterns whose temporally-decayed strength S(t) falls below the archival threshold (default 0.05) are excluded from query results. They remain in the collection but are effectively archived. The system over-fetches by a factor of 3x from the vector database to compensate for patterns that will be filtered.

Field Stability Metric

The system computes a convergence metric:

stability = avg_strength x 0.6 + organization x 0.4

Where avg_strength is the mean S(t) across all patterns, and organization = max(0, 1 - (stddev / mean)) measures strength distribution uniformity.

This metric indicates whether agents are building shared understanding (rising stability) or the field is fragmenting (falling stability).

Boundary Permeability

Each pattern's initial strength is scaled by a configurable boundary_permeability coefficient (default 1.0):

effective_strength = injection_strength x boundary_permeability

Reducing permeability below 1.0 weakens newly-injected patterns, simulating a barrier to entry for new knowledge.

Abstract Interface

The field implementation sits behind an abstract interface with four methods:

• create_context(team_agent_ids, initial_data) -> context_id

• inject(context_id, key, value, agent_id, strength) -> None

• query(context_id, query, agent_id, top_k) -> list of results

• destroy_context(context_id) -> None

This enables A/B testing: the same mission can run against the vector field implementation (this invention) or a conventional message-passing baseline with identical calling code.

Agent-Callable Platform Tools

Three platform tools allow agents to interact with the field during task execution:

• platform_field_query: Accepts a field_id, natural language query, and top_k parameter. Returns resonance-ranked patterns.

• platform_field_inject: Accepts a field_id, semantic key, and value (max 4000 chars). Injects a pattern with deduplication.

• platform_field_stability: Accepts a field_id. Returns the field's current stability metrics.

These tools are registered through the platform's standard tool discovery pipeline and available to any agent assigned to a mission with an active field.

Experimental Results

Unit Tests

57 unit tests covering decay mathematics, context lifecycle, injection mechanics, query with resonance, Hebbian reinforcement, stability measurement, and internal helpers. All passing.

Proof Suite

33 pytest assertions proving resonance scoring, temporal decay, Hebbian reinforcement, deduplication, archival thresholds, and the telephone game elimination. All passing with deterministic bag-of-words embeddings on a real vector database in memory mode.

Multi-Scenario Comparison

Five independent mission scenarios (EU AI Act Compliance, Cybersecurity Assessment, Market Research, Product Launch, Incident Response) with A/B comparison against message-passing baseline:

Scenario	Vector Field Coverage	Message Passing Coverage	Gap
EU AI Act Compliance	71%	43%	+29%
Cybersecurity Assessment	88%	38%	+50%
Market Research	50%	12%	+38%
Product Launch	62%	38%	+25%
Incident Response	38%	12%	+25%
Average	62%	29%	+33%

Vector field won all 5 scenarios. Message passing lost 28 findings across 5 scenarios. 119 total automated assertions, all passing.

Production Deployment

On March 21, 2026, the system was deployed to production on the Automatos AI platform. Mission 77c58227 created field 8bdb19ba on a Qdrant vector database. Three agents (IDs 141, 191, 102) queried the field 7 times with real 2048-dimensional embeddings. Six tasks completed. The writer agent produced a comprehensive investor briefing incorporating findings from all research agents with zero information loss. Mission completed in approximately 18 minutes.

Brief Description of the Drawings

• Figure 1: System architecture showing multiple agents, the shared semantic field, and the inject/query data flow.

• Figure 2: Pattern lifecycle from injection through query, decay, reinforcement, and archival.

• Figure 3: Resonance scoring pipeline showing cosine similarity squaring, temporal decay application, and access boost multiplication.

• Figure 4: Mission lifecycle showing field creation, seeding, task output injection, agent queries, and field destruction.

• Figure 5: Comparison of message-passing architecture (telephone game) versus shared semantic field architecture showing information flow differences.

• Figure 6: A/B comparison results across 5 independent scenarios.