MAIF: Multimodal Artifact File Format

• MAIF is an AI-native, self-describing file container that integrates diverse modalities like text, images, audio, and sensor streams with verifiable metadata.
• It utilizes a hierarchical, block-based architecture—comprising header, modality, semantic, security, and lifecycle blocks—for efficient, auditable data management.
• MAIF employs advanced algorithms (ACAM, HSC, CSB) to ensure rapid cross-modal retrieval, strict regulatory compliance, and robust cryptographic integrity.

The Multimodal Artifact File Format (MAIF) is an AI-native, self-describing file container engineered to address pressing challenges in AI trust, auditability, and regulatory compliance. Designed as a persistent, verifiable data artifact, MAIF unifies diverse data modalities—including text, image, audio, sensor streams, and serialized AI models—with semantic embeddings, cryptographic provenance, granular access control, and complete lifecycle metadata. By consolidating these elements into a single "portable AI context unit," MAIF transforms passive data storage into an active substrate for trust enforcement and compliance, particularly in response to requirements such as those outlined in the EU AI Act (Narajala et al., 19 Nov 2025).

1. Architectural Organization

MAIF adopts a hierarchical, block-based architecture inspired by ISO BMFF/MP4, optimized for persistent, structured, and auditable AI-centric data encapsulation. The primary functional units within a MAIF container are as follows:

• Header block: Encodes the file identifier, version, global root hash (for integrity), and a manifest pointer.
• Modality blocks: Contain text, image/video, audio, sensor streams, or serialized AI models (e.g., ONNX).
• Semantic Layer block: Stores dense multimodal embeddings, knowledge-graph fragments, and an embedded index such as HNSW for efficient retrieval.
• Security Metadata block: Encapsulates cryptographic proofs (block hashes, digital signatures), access control lists (ACLs), encryption key metadata, and the provenance chain.
• Lifecycle Metadata block: Maintains version history, dynamic schema adaptation rules, and an auditable event log.

Formally, the container’s schema is defined by the following EBNF-style representation:

MAIF ::= Header BlockList Header ::= ⟨‘MAIF’, Version, RootHash, ManifestPointer⟩ BlockList ::= Block* Block ::= ModalityBlock | SemanticBlock | SecurityBlock | LifecycleBlock ModalityBlock ::= ⟨TypeID, Length, Payload⟩ SemanticBlock ::= ⟨‘SEM’, Length, Embeddings, KG, Index⟩ SecurityBlock ::= ⟨‘SEC’, Length, ProvenanceChain, ACLs, Signatures⟩ LifecycleBlock ::= ⟨‘LIFE’, Length, Versions, Rules, Log⟩

A layered diagram (see original) conveys the sequential organization: Header → Modality Blocks → Semantic Layer → Security Metadata → Lifecycle Metadata.

2. Semantic Representation and Indexing

The semantic layer is central to MAIF’s support for explainability and semantic search. Embeddings are stored as 64-byte aligned float32 vectors for cache-optimal batch access. The container embeds a Hierarchical Navigable Small World (HNSW) index [Malkov & Yashunin, TPAMI’20], supporting $O(\log n + k)$ query time over millions of entries.

Similarity metrics rely on canonical forms:

• Cosine similarity:

$$\mathrm{CS}(e_i,e_j) = \frac{e_i \cdot e_j}{\|e_i\| \, \|e_j\|}$$

• Scaled dot-product attention:

$$\mathrm{Attention}(Q,K,V) = \mathrm{softmax}\left(\frac{QK^\top}{\sqrt{d_k}}\right)V$$

Embeddings from multiple modalities are projected into a unified representation via affine projection per modality:

$$e_{\mathrm{joint}} = W_p^{(m)}e^{(m)} + b_p^{(m)} \quad \forall m \in \{\text{text}, \text{vision}, \ldots\}$$

This enables high-throughput, cross-modal retrieval and supports explainable chain-of-reasoning via embedded knowledge graphs.

3. Provenance, Security, and Cryptographic Binding

MAIF enforces cryptographic provenance at both block and container levels using a combination of hashing, Merkle chaining, and digital signatures:

• Block-level hash: $h_i = \mathrm{SHA256}(\text{Payload}_i)$ for block $i$.
• Merkle-chain linkage:

$$H_0 = \mathrm{root}; \quad H_i = \mathrm{SHA256}(H_{i-1} \| h_i \| \mathrm{meta}_i)$$

• Digital signatures: Each $H_i$ is signed with ECDSA (Secp256r1), bound to the agent’s decentralized identifier (DID):

$\sigma_i = \mathrm{Sign}_{\mathit{SK}}(H_i)$

• Cryptographic binding for data and semantics:

$C = \mathrm{SHA256}(E(x) \| x \| n)$

where $E(x)$ is the semantic embedding, $x$ the raw data, and $n$ a nonce, supplying non-repudiable commitments.

Granular access control is realized via stream-level ACLs with per-block AES-256 encryption keys. The ACL data model is:

$$\mathrm{ACL} = \{ (\mathit{entityID}, \mathit{role}, \mathit{permSet}, K_{\text{enc}}) \}^*$$

with $$\mathit{permSet} \subseteq \{\text{read}, \text{write}, \text{append}\}$$.

Real-time access verification is achieved using constant-time ACL lookup and key release via the following logic:

function authorize(entity, blockID, action): entry = ACL.lookup(blockID, entity) if entry == null or action not in entry.permSet: return DENY return ALLOW, decryptKey=entry.K_enc

4. Core Algorithms for Multimodal Trust

MAIF deploys a series of algorithms that underlie its semantic comprehension, storage efficiency, and verifiable execution:

• Adaptive Cross-Modal Attention (ACAM): Modifies scaled dot-product attention denominators with trust-aware cosine similarity, i.e.,

$$\alpha_{ij} = \mathrm{softmax}\left( \frac{Q_iK_j^\top}{\sqrt{d_k} \cdot \mathrm{CS}(E_i, E_j)} \right)$$

• Hierarchical Semantic Compression (HSC): Implements semantic clustering (e.g., DBSCAN), vector quantization per semantic cluster, and entropy coding (Brotli), achieving average compression ratios up to $64.2\times$ (claim range $2.5\times - 5\times$; max observed $480\times$), while controlling semantic distortion by

$D_{\mathrm{SEM}} = \| E(x) - E(\hat{x}) \|_2$

• Cryptographic Semantic Binding (CSB): Pseudocode procedure for fusing data and embedding in a tamper-evident commitment:

function bindSemantic(data x, embedding E): nonce = random_bytes(16) payload = concat(E(x), x, nonce) commitment = SHA256(payload) return { commitment, nonce }

These algorithms enable rapid cross-modal retrieval, ensure semantic fidelity post-compression, and provide cryptographic evidence for auditing.

5. Performance Characteristics and Benchmarks

MAIF demonstrates high throughput, efficient search, and low-latency access control in real-world benchmarks:

Domain	Claim	Actual	Peak	Status
Streaming	500 MB/s	2,720.7 MB/s	–	Exceeded
Video Proc.	400 MB/s	1,342 MB/s	–	Exceeded
Compression	2.5–5×	64.2× (avg)	480×	Exceeded
Search Latency	<50 ms	30.5 ms (1M vecs)	29.6 ms	Exceeded

The high-speed streaming implementation supports continuous hash verification at 2,420 MB/s, with cryptographic tamper detection probability $1 - 2^{-256}$. Search latency remains below 31 ms for indexes with 1 million vectors. These results support MAIF's applicability in rate-constrained, high-assurance settings.

6. Advanced Auditability and Security Features

MAIF integrates real-time tamper detection and behavioral anomaly analysis. Tamper detection employs a continuous hash-verification pipeline with negligible computational overhead and strong theoretical guarantees.

Behavioral anomaly detection leverages LSTM-based action models, computing the temporal anomaly score

$$s_t = \frac{|\Delta t_i - \mu_{\Delta t}|}{\sigma_{\Delta t}}$$

Marking anomalies when $|t_\text{pred} - t_\text{obs}| > \tau$, which facilitates detection of rapid-fire operations, clock manipulations, and privilege escalations during AI agent execution.

Lifecycle metadata and non-repudiable action logs ensure that every agent decision is archivally recorded. Embedded knowledge graphs and forensic analyzers support post-facto examination for bias, data lineage, and consent verification.

7. Regulatory Compliance and Trust Implications

MAIF directly implements audit trails, decentralized identifiers (DIDs), and verifiable credentials required for regulatory regimes such as the EU AI Act, specifically aligning with traceability (Art. 10), human oversight (Art. 14), and risk management (Art. 9) mandates.

A canonical compliance workflow is as follows:

1. Ingest training data, encapsulate in MAIF, and sign with data owner DID.
2. Log each model update as a MAIF version block with adaptation rules.
3. For audit, use the built-in ForensicAnalyzer to validate hash chains, verify provenance via DIDs and credentials, and reconstruct the event timeline from lifecycle metadata.

MAIF supports automated internal querying for bias audits and consent checks, and maintains accountability through intrinsic, non-repudiable action logs and explainable reasoning chains embedded as knowledge graphs.

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MAIF provides a unified, artifact-centric data format for AI systems, merging multimodal content, semantic representations, cryptographic assurance, and regulatory-aligned governance. Its architecture and algorithms enforce verifiability, security, and compliance at the data artifact level, offering a technological solution for trust and accountability in AI deployment at scale (Narajala et al., 19 Nov 2025).