A collaborator
that remembers why.

A decision means little without its constraints. Capture brings together the contribution, its source, the task it belongs to and the reason it was useful. An observation, a person’s preference and a model’s suggestion need different treatment.

Comes in

A conversation, work event or reviewed contribution.

Stays with it

Who or what it concerns, where it came from, when it applied and who may use it.

Design choice

Retaining everything creates noise and unnecessary exposure. Capture should be selective, with explicit retention and correction choices.

In the team example: preserve “we chose batch processing because a daily update was enough,” alongside the choice itself.

Different forms of memory serve different timescales. These are explanatory groups of responsibilities; they are not a ladder that automatically promotes every saved conversation.

A reusable lesson might be “choose freshness requirements before choosing a processing pattern,” rather than “always use batch processing.”

A new task supplies the question and its permitted scope. Retrieval finds candidate records; selection considers their relevance, freshness, origins and applicability before placing useful evidence in working context.

1. Understand the task and scope
2. Find candidate evidence
3. Check and rank it
4. Return context with its origins

Associative relationships can help reveal a connected decision that a phrase match misses. They can also bring distracting material. The useful result is a focused set of evidence the collaborator can inspect—not the whole archive.

Return the earlier decision and its “daily updates” assumption. That assumption is what the team needs to reconsider.

An outcome becomes useful feedback when it can be connected to the memory actually returned and the task in which it was used. Otherwise a successful task could reinforce unrelated information.

Reviewed mechanisms include outcome-linked reliability and promotion or demotion of candidate knowledge. Their value depends on trustworthy feedback, enough observations and careful attribution. A successful outcome alone does not prove that memory caused it.

A correction

Revises an inaccurate statement or obsolete assumption, preserving the reason for the change.

An outcome

Provides evidence about whether recalled knowledge helped in a particular situation.

A learning claim

Needs comparison: does the revised memory improve later work against an appropriate baseline?

A new freshness requirement changes where the old decision applies. It does not make the original reasoning wrong for its original situation.

Keeping experience and knowledge outside a particular language model creates a route to continuity. Different models can receive selected evidence through a common memory interface, while contributing their own strengths to the task.

That separation does not guarantee that every model interprets a record equally well. Meaning, scope and source references must survive translation into the new model’s context, and the resulting behaviour still needs evaluation.

This is learning in the surrounding system: better records, recall and reusable methods. Updating a neural model’s parameters is a separate process.