Papers › Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture

Memoria: Resolving Fateful Forgetting Problem through Human-Inspired Memory Architecture

4 Oct 2023arXiv:2310.03052archive 2025-07-28

Sangjun Park, JinYeong Bak

Making neural networks remember over the long term has been a longstanding issue. Although several external memory techniques have been introduced, most focus on retaining recent information in the short term. Regardless of its importance, information tends to be fatefully forgotten over time. We present Memoria, a memory system for artificial neural networks, drawing inspiration from humans and applying various neuroscientific and psychological theories. The experimental results prove the effectiveness of Memoria in the diverse tasks of sorting, language modeling, and classification, surpassing conventional techniques. Engram analysis reveals that Memoria exhibits the primacy, recency, and temporal contiguity effects which are characteristics of human memory.

PaperPDFCodeCode Syntology ran

In Syntology View this paper on Syntology: its repositories, every harvested function with whether it ran, its licence and the call to fetch it.

Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2310.03052")

Code

Syntology Ran 1 of 10 code samples harvested from 1 repository linked to this paper; 9 have no recorded run. Of those that ran: 1 ran · our draft was wrong.

By repository: official repository: 10 samples from 1 repository, 1 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

cosmoquester/memoria officialmentioned in papermentioned on GitHubpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

10 samples harvested; 1 ran; 0 honoured the contract we drafted; 9 have no recorded run. Read from Syntology's graph 2026-09-25; that is when this build read the record, not when the samples ran.

1ran · our draft was wrong
9unverified

Licence: 0 of the 10 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from cosmoquester/memoria. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

super_unique cosmoquester/memoria/memoria/memoria.py official repository ran · our draft was wrong MIT (permissive) · 59b9c689c49ce98e · report
EngramConnection cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · 016bc554b88242c4 · report
EngramHistory cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · 7fdd64a498902104 · report
EngramInfo cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · b734315081919701 · report
EngramType cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · 9703ecf04d370320 · report
Engrams cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · cc2f2af69c5477a8 · report
EngramsInfo cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · ea9f9eace0456273 · report
Firing cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · 0e095a46ce900394 · report
HistoryManager cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · e9aac109fcec9e93 · report
Memoria cosmoquester/memoria/memoria/memoria.py official repository unverified MIT (permissive) · 9fa5ceabb0d59581 · report

Tasks

Language ModelingLanguage ModellingText Classificationtext-classification

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

AdamAttentionAttention DropoutBERTBPECosine AnnealingDense ConnectionsDiscriminative Fine-TuningDropoutFocusGPTLayer NormalizationLinear LayerLinear Warmup With Cosine AnnealingLinear Warmup With Linear DecayMemory NetworkMulti-Head AttentionResidual ConnectionSoftmaxWeight DecayWordPiece

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections