# Learning to propose parameter changes

Can a model propose useful changes to another model from structural and conceptual information?

The proposal ranges from interpreting hidden layers to predicting operations and choosing updates. A concrete suggested test is adding a new token and proposing its parameters from a conceptual description.

Status: open problem. Formulation and comparisons: reconstruction. Development: active.

## Alternative representations

### Inspection of a token trajectory through layers

Affords: Connects intermediate states with an output.

Limits or losses: An activation trajectory does not identify every relevant parameter or establish a causal explanation.

### A proposed weight update

Affords: Makes an intervention that can be measured.

Limits or losses: Can improve a local example while degrading unrelated behavior.

## Tension

Understanding, predicting, and modifying a model are distinct capabilities.

## Challenge

What evidence shows that a proposed edit generalizes beyond the supplied concept description?

This challenge is an editorial prompt, not a claim that the evidence already resolved it.

## Proposed next move

Test the proposed new-token edit against ordinary initialization and training, with held-out uses and regression checks.

Status: new model-proposed experiment or extension; not a reported result.

## Related problems

- [A system that can revise its own representation language](evolving-representation-language.md) — raises implementation questions (retrospective_connection). A changing representational language invites questions about corresponding parameter changes, but the mechanisms are not established as identical.
- [Memory represented through adaptable parameters](memory-as-adaptation.md) — shares a proposed update surface (retrospective_connection). Memory adaptation and predicted model edits may use adaptable parameters; they need different tests.
- [When transferred learning affects governing policies](learning-and-policy-permeability.md) — adds a policy-level test (model_proposed). A task-directed edit should be investigated for changes beyond the task. This test is an editorial extension.

## Evidence anchors

- basis-models-editing-models: The material proposes models that interpret hidden layers, predict operations, and alter parameters, including during their own operation. One suggested test adds a new token and asks a model to set its parameters from a conceptual description of that token. [Editorial paraphrase; presence of a proposal does not verify its truth.]

Root evidence and private recovery mappings are not part of this public release.

[Open in the reader](../index.html#node=models-editing-models) · [All problems](../CATALOG.md)

## Research editions

- [Memory, Tokens, and Learning Geometry](../documents/memory-tokens-and-learning-geometry.md) (reconstruction).