ENGRAMMAchain - Decentralized life-occurrence memory system for robotic systems

ENGRAMMAchain is a distributed system for autobiographical memory in robots, designed to provide irreversible recording of experience, causal relationships, and decisions without reliance on external control centers. A decentralized system for recording life processes and distributed memory is an architecture of autonomous nodes in which each robot forms and maintains its own independent memory, reflecting its individual states, actions, and changes throughout its lifecycle.

Each robot acts as a sovereign carrier of its memories, recording them locally as sequential, cryptographically linked entries.
This structure ensures integrity, verifiable origin, and resistance to unauthorized modification.

Depending on its purpose, role, or operational context, a robot may cooperate with other robots to form a shared memory space —
a collection of mutually confirmed memories from multiple robots, cryptographically bound through digital signatures.

Shared memory does not replace or overwrite individual memory.
Each record preserves authorship, source identity, and contextual origin, while any attempt to alter or falsify a memory breaks the cryptographic chain, making tampering detectable and invalidating the affected fragment.

Robots may selectively exchange memory fragments with other robots or with humans,
allowing verified experience, observations, and knowledge to be transferred without loss of provenance or integrity.
Received memories can be used as external context or cooperative memory without disrupting the continuity of a robot’s own history.

The architecture supports asynchronous interaction and dynamic formation of both individual and shared memory structures,
ensuring that past states cannot be silently modified due to cryptographic linkage between records.

The system enables robots to develop:

- continuous identity,

- ownership of past experience,

- local decision-making authority,

- autonomous interaction with other machines.

ENGRAMMAchain treats memory as a first-class computational primitive, not as auxiliary storage.

1. Real Technical Context
1.1 Current Robotics Architecture (Factual State)

Most modern robotic systems are not autonomous agents.

Technically, they operate as:

- thin clients,

- endpoints of centralized decision servers,

- executors of externally defined policies,

- devices whose memory and behavior can be overwritten remotely.

Consequences of this architecture:

- memory is external, mutable, and revocable,

- no local causal history exists,

- decisions cannot be verified post hoc,

- robot identity is discontinuous.

1.2 Technical Objective of ENGRAMMAchain

The objective is to construct a system in which a robot locally and autonomously:

- Maintains an immutable chain of experience

- Uses that chain as direct input to decision-making

- Exchanges memory with peers without central arbitration

- Prevents external rewriting of past experience

ENGRAMchain is designed as a core subsystem, not an add-on service.

2. ENGRAMMAchain Node Architecture

Each robot runs the following internal architecture:

+-----------------------------+
| Decision Engine |
| (Policy / RL / Hybrid) |
+-------------▲---------------+
   |
+-------------|---------------+
| ENGRAMMAchain Core |
| - Event Store |
| - Chain Verifier |
| - Identity Module |
+-------------▲---------------+
   |
+-------------|---------------+
| Sensor / Actuator Layer |
+-----------------------------+

ENGRAMMAchain sits between perception and action, continuously shaping behavior.

3. Robot Identity Model

Each robot owns a cryptographic identity:

RobotID := PublicKey

- the private key never leaves the robot,

all events are signed locally,

identity cannot be replaced without breaking continuity.

Key replacement implies a new subject.

4. Event Data Model

- Minimal event structure:

{
  "event_id": H(event),
  "prev_event_hash": H(prev_event),
  "robot_id": PublicKey,
  "signature": Sign(priv_key, payload),
  "timestamp": logical | physical,
  "event_type": enum,
  "payload": bytes | structured,
  "context_refs": [event_id...]
}

Event Categories

- SENSOR_OBSERVATION

- INTERNAL_STATE

- DECISION

- ACTION

- ERROR / CONFLICT

- EXTERNAL_REFERENCE

5. ENGRAMMAchain Construction

Event append algorithm:

1. Capture input (sensor or internal state)
2. Build event payload
3. Link to prev_event_hash
4. Sign with private key
5. Append to local chain

The chain is strictly linear per robot, with optional cross-robot references.

6. Irreversibility and Verification

Chain verification procedure:

VerifyChain(chain):
  for event in chain:
   verify_signature(event)
   verify_hash_link(event)

Any mutation:

- breaks hash continuity,

- invalidates all subsequent events,

- is deterministically detectable.

7. Memory as Decision Input

The Decision Engine consumes ENGRAMMAchain as:

- temporal context,

- causal history,

- internal state trace.

Example:

decision = Policy(
  current_state,
  last_N_events,
  referenced_peer_events
)

Decisions are themselves written back into ENGRAMMAchain.

8. Collective Memory and Interaction

ENGRAMchain supports peer memory exchange:

- robots transmit events or hashes,

- receiving robots:

   - verify signatures,

   - store references (not copies),

   - preserve foreign memory integrity.

This produces a graph of intersecting autobiographies.

9. Trust Model (Local Evaluation)

Trust is computed locally:

Trust(robot_B) = f(
  chain_continuity,
  internal consistency,
  overlap with other agents,
  temporal stability
)

There is no global trust authority.

10. Learning on Top of Immutable Memory

Supported learning modes:

- online learning,

- offline learning,

- self-reflective loops.

Key property:

- models may change,

- memory does not.

This enables:

- explainability,

- accountability,

- controlled behavioral evolution.

11. Upgrades and Evolution

Permitted:

- decision engine updates,

- model replacement,

- sensor upgrades.

Forbidden:

- deleting past events,

- rewriting chain history,

- identity substitution without rupture.

12. Fault Tolerance

- local persistence,

- optional replication,

- no single point of failure,

- partial connectivity tolerance.

Robots remain operational in isolation.

13. Boundary of External Control

External commands:

- may be recorded as events,

- carry no inherent authority.

The Decision Engine may:

- accept,

- reject,

- reinterpret commands

based on its own recorded experience.

14. Criteria for Machine Subjectivity

A robot qualifies as a subject if it:

- Maintains a continuous ENGRAMMAchain

- Uses memory as a decision substrate

- Prevents retroactive modification

- Preserves identity across time

15. Conclusion

ENGRAMMAchain is not:

- a log,

- a database,

- a coordination protocol.

It is infrastructure for machine subjectivity.

It defines the minimum technical conditions under which a robot:

- remembers,

- chooses,

- and remains accountable to its own past.

ENGRAMMAchain is an irreversible, append-only causal memory structure designed to preserve the autobiographical experience of autonomous machine subjects.
Each engram represents a non-rewritable trace of perception, decision, or action, cryptographically bound to the subject’s identity and prior experience.

Release in the first half of 2026