Memory Network
Model Families AIMech:MemoryNetwork DRAFT
Aliases: Memory Networks
A family of learning models that combines an addressable memory with input conversion, memory updating, output inference and response components, allowing predictions to use stored information through learned or otherwise specified component implementations.
Identity Notes & Concept Scope
Compared all 442 pinned AIO and 602 native IDs, names, aliases and scoped synonyms; no normalized identity match. Ignored- and hidden-inclusive fixed-string searches of the recovery, older discovery and original Documents checkouts returned 108, 12, 12 lines. All distinct match contents were inspected. Semantic matching covered 65 AIO and 86 native definitions, with every matched definition read. 8 full annotated AIO node/edge/generated contexts and 3 full native records were inspected. The Section2 I/G/O/R framework explicitly permits non-neural components, while Section3 MemNN is a particular strongly supervised hard-retrieval neural implementation. The record denotes the original general family and carefully scopes that implementation. MemNN is searched as context, not asserted as an exact family-wide alias. MemN2N, external stack/list controllers and LLM memory systems have distinct identities.
Claims & Grounded Evidence
6 Claims
Mechanism
#memory-and-four-components
The original framework combines an addressable memory array with four components: I converts an input into an internal representation, G updates stored memories, O computes an output representation using the new input and memory, and R produces a response. The displayed processing order is input conversion, memory update, output inference and response. These components may be learned and are not required to be neural networks; the source explicitly permits other machine-learning methods.
Scope
#state-update-versus-parameter-learning
The memory state can change both during training and at test time while learned model parameters remain fixed at test time. Equation 1 illustrates a simple G that writes an input representation to one selected memory slot and leaves other slots unchanged. More complex updates and forgetting are framework possibilities, but the source says it does not explore forgetting experimentally. Writable memory does not establish unlimited physical storage or automatic online parameter learning.
Scope
#neural-instantiation-is-specific
Section 3 instantiates the framework as a neural MemNN for text question answering. Its basic memory update appends the raw input to the next empty slot; hard maximization retrieves a first support and then a second support conditioned on the question and first support. A response scorer selects an answer word, with an RNN response alternative also described. Learned embedding scores use distinct query/support roles and separate output and response parameters. A shared positive-semidefinite embedding metric does not make the full role-conditioned question-to-memory score symmetric. These choices describe this instantiation, not every permissible Memory Network.
Limitation
#support-supervision-boundary
The reported basic MemNN training uses questions, answers and annotated supporting sentences. Margin losses compare correct choices with sampled wrong choices, and training knows the support-selection decisions. This does not establish gradient propagation through an unknown hard argmax or answer-only end-to-end training. The stream-input extension learns a segmentation decision using supervision derived from supporting sentences. Other supervision regimes remain distinct variants or later work.
Limitation
#retrieval-extensions-and-order
Optional word or learned-cluster hashing restricts the candidate memories scored during retrieval and can exclude the globally highest-scoring item. The temporal extension adds relative memory-write-order features to pairwise support comparisons and selects a winner through the Appendix C loop. Its equivalence to scalar-score maximization is stated for the case without those time features; unrestricted pairwise preferences need not be transitive or independent of candidate order. Memory write order is not a general semantic interpretation of tense or event time.
Limitation
#evaluation-is-instantiation-specific
The experiments evaluate particular neural variants on a large extracted-triple question-answering task and a restricted simulated world with supplied support supervision. The large task reranks candidate answers and uses a different feature/retrieval configuration from the simulation. The simulated language omits phenomena such as coreference and complex noun phrases, and answer-only recurrent baselines receive less support supervision. Multiword response results use the Appendix F semantic correctness rule rather than exact output-string identity. These results do not validate every allowed framework instantiation, general language understanding or arbitrary reasoning depth.
⚠️ Curated Limitations & Negative Results
- The record denotes the original general framework. MemNN names its specific neural instantiation here and is not asserted as an exact alias for all permissible implementations.
- Memory-state updates differ from learning model parameters; test-time writable storage is not an automatic online-learning guarantee.
- Full supporting-fact supervision and restricted experiments apply to the reported neural instantiations. The later soft-read MemN2N architecture has a distinct identity and supervision protocol.
- All 15 pages of arXiv1410.3916v11 were visually read, including Appendices A–F. Other versions and author code were not inspected; chosen calculations illustrate boundaries, not trained-model behavior or benchmark reproduction. DRAFT remains.
Artificial Intelligence Ontology (AIO) Grounding
Mapped AIO Classes
| AIO Target Class | Mapping Relation | Mapping Rationale |
|---|---|---|
AIO:Network
Network
|
broad_match | The original framework is a connected computational system of memory and interacting input, update, inference and response components. The pinned Network target broadly includes interconnected entities for computation and data exchange; it does not require neural implementation. |
Evidence Sources & Primary Literature
1 Source| Source ID | Publication / Document | Type / Access | Reference & Checksum |
|---|---|---|---|
weston2015
|
Jason Weston, Sumit Chopra, Antoine Bordes
|
primary paper
full_text
|
ID: arXiv:1410.3916v11
sha256:978ecaac3081…
|
Curation Provenance
Curator: Codex
Method: ai_assisted
Model: GPT-6
Created: 2026-10-13T10:10:28Z
Updated: 2026-10-13T10:15:04Z
Source File:
data/curated/MemoryNetwork.yaml