| # Learning to propose parameter changes |
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| Can a model propose useful changes to another model from structural and conceptual information? |
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| 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. |
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| Status: open problem. Formulation and comparisons: reconstruction. Development: active. |
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| ## Alternative representations |
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| ### Inspection of a token trajectory through layers |
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| Affords: Connects intermediate states with an output. |
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| Limits or losses: An activation trajectory does not identify every relevant parameter or establish a causal explanation. |
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| ### A proposed weight update |
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| Affords: Makes an intervention that can be measured. |
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| Limits or losses: Can improve a local example while degrading unrelated behavior. |
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| ## Tension |
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| Understanding, predicting, and modifying a model are distinct capabilities. |
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| ## Challenge |
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| What evidence shows that a proposed edit generalizes beyond the supplied concept description? |
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| This challenge is an editorial prompt, not a claim that the evidence already resolved it. |
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| ## Proposed next move |
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| Test the proposed new-token edit against ordinary initialization and training, with held-out uses and regression checks. |
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| Status: new model-proposed experiment or extension; not a reported result. |
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| ## Related problems |
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| - [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. |
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| ## Evidence anchors |
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| - 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.] |
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| Root evidence and private recovery mappings are not part of this public release. |
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| [Open in the reader](../index.html#node=models-editing-models) · [All problems](../CATALOG.md) |
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| ## Research editions |
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| - [Memory, Tokens, and Learning Geometry](../documents/memory-tokens-and-learning-geometry.md) (reconstruction). |
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