US 2026/0300533 A1Published application

Adaptive neuro-symbolic context fabric for explainable integrity in ai-agentic enterprise data operations

OwnerAnkit Anand Lnu, Kinshuk Dutta +1inventors

InventorsAnkit Anand Lnu, Kinshuk Dutta, Upendra Singh Jadon

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Abstract

A computer-implemented system and method providing a four-term Graph-Enhanced Semantic Density Scoring (G-SDS) formula for reducing LLM context window consumption by 60-80% in enterprise master data management (MDM) operations, combined with a SPDZ-protocol federated secure multi-party computation (SMPC) mechanism for binary compliance-vote aggregation across distributed MDM silos, wherein individual silo veto votes remain cryptographically hidden under zero-knowledge proof guarantees while the global veto/approve decision is revealed. The system further comprises a Logical Neural Network (LNN) with AND/OR/NOT/IMPLIES/EQUIV gate neurons maintaining real-valued truth bounds [L, U], integrated with a PPO reinforcement learning policy network that dynamically adapts soft-constraint thresholds to minimize false-positive vetoes. A hard symbolic veto gate permanently blocks any transaction violating governance constraints.

Claims20

Dependent claims

A computer-implemented system for context-adaptive master data validation, comprising:

a) a G-SDS scoring engine comprising a semantic similarity module computing cosine similarity between an agent query embedding vector q and entity attribute embedding vectors ve, using a pre-trained Sentence-BERT model, a graph centrality module computing PageRank scores over an entity knowledge graph, a temporal recency module computing exponential decay scores per attribute, a GraphRAG path module computing inverse normalized shortest-path distance via k-step random walk, and a cost normalization module dividing the weighted sum by token length Cost(token), wherein the G-SDS scoring engine prunes attributes below a threshold θ to produce a Narrative State Object stored in volatile memory with TTL expiration;
b) a Reference Data Firewall comprising an interception interface receiving probabilistic write-back transactions from autonomous AI agents, a subgraph extraction engine performing O(1) indexed lock-free retrieval via MVCC, and an ephemeral in-memory graph sandbox in isolated volatile memory applying the transaction to produce a simulated transaction state;
and c) a Neuro-Symbolic Stewardship Engine comprising a Logical Neural Network (LNN) with logical gate neurons of types AND, OR, NOT, IMPLIES, and EQUIV, each maintaining real-valued truth bounds [L, U] in [0,1]{circumflex over ( )}2, configured to enforce deterministic constraints from a Reference Data Dictionary and generate natural-language explanations by backtracking through a proof graph, a PPO policy network operatively coupled to the LNN via a false-positive reward signal configured to adjust soft-constraint thresholds based on a reward rt, and a symbolic veto gate configured to permanently block any transaction violating a constraint regardless of neural confidence score, wherein the system commits a transaction to a persistent System of Record only in the absence of a veto signal from the Neuro-Symbolic Stewardship Engine.

A computer-implemented method for privacy-preserving federated validation of master data merge decisions across distributed enterprise silos, comprising:

receiving, at each of n≥2 distributed enterprise MDM silos, a proposed master data transaction from an autonomous AI agent;
instantiating, at each silo, an ephemeral in-memory graph sandbox and applying the proposed transaction to a locally extracted subgraph to produce a local simulated transaction state;
executing, at each silo, deterministic SHACL/OWL constraints against the local simulated state to produce a local binary veto vote vi;
encrypting each vi into additive secret shares [vi] using Paillier homomorphic encryption;
transmitting encrypted shares to a coordinator node, wherein the coordinator never possesses a complete raw veto vote from any single silo;
computing, at the coordinator, a global veto decision [v1 OR v2 OR . . . OR vn] via SPDZ protocol with Beaver triple preprocessing, without revealing any individual vi;
generating a ZK consistency proof π={C, e, z} and producing a SHA-256 audit hash as an immutable compliance record;
committing the proposed transaction to the persistent System of Record across all silos only if the global veto decision is APPROVED;
and blocking the proposed transaction at all silos if the global veto decision is VETO, with the blocking reason injected to the originating AI agent via a natural-language LNN explanation.

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