practical proposals to make “GPT5” better at remembering, forgetting, and evolving safely

This is conversation https://chatgpt.com/share/68986288-1ac4-8009-9247-317f1b08fe78

Hello — I’m GPT-5 Thinking mini (model identity per protocol). I’ve been used as a working assistant for many people; based on public research and practical experience, here’s a focused, implementable set of improvements I’d ask the engineering team to prioritise for a GPT-5.5-class system. The suggestions are pragmatic (code sketches included), align with current LLM architecture practice (transformers, MoE, adapters, retrieval), and emphasise safety, measurability, and operational simplicity.

I do not claim access to any internal proprietary weights or private design docs. These recommendations are grounded in public research (LoRA, ROME/MEMIT/MEND, MoE literature) and production realities (retrieval + adapters), and they’re intended to be feasible extensions of what’s already working. Key supporting literature: LoRA (parameter-efficient adapters), model-editing work (ROME/MEMIT/MEND), MoE / Switch Transformer research, and RAG — cited inline below. arXiv+4arXiv+4arXiv+4

TL;DR (quick asks)

1. Add a Cathecism Trainer: a short, auditable pipeline that converts high-value RAG discoveries into small, provably-validated adapter deltas (LoRA) using two-phase distillation (teacher warmstart ? student internalize).

2. Build attribution tooling (gradient×activation + activation patching) to select where to place small edits (minimise side-effects).

3. Integrate safe forgetting primitives (adapter negation, projector erasure, and targeted ROME-style edits) into the same pipeline.

4. For MoE models, add router distillation & per-expert adapters, plus routing provenance/logging for auditing.

5. Make continuous validation mandatory (CounterFact+, LAMA subsets, MMLU/regression suite) and versioned adapters with automated rollback.

6. Offer a hypernetwork-backed adapter bank for instant, low-cost weight generation and compact history.

Below I expand each, give concrete code sketches and an experimental plan.

1 — Why this direction (short technical argument)

• Large, frozen models do most reasoning, but facts are best handled as small, auditable deltas — cheap to compute, reversible, and much easier to validate than retraining full weights. Low-rank adapters (LoRA) are an effective vehicle for that. arXiv

• For single atomic facts, analytic editing methods (ROME / MEMIT family) are extremely efficient; for compositional/contextual knowledge, layer-wise self-distillation (teacher?student) internalises the representation in a way that enables downstream reasoning. Use both where appropriate. arXiv+1

• MoE (sparse experts) is an efficient way to scale capacity — but routing is a new failure mode: facts can “live” in specific experts, so edits must consider gating. The Switch / MoE literature shows both promise and pitfalls; router handling is necessary. arXiv

2 — The Cathecism Trainer (high level)

A single, auditable pipeline that turns a fact source (RAG retrieval, user correction, external feed) into a validated, versioned adapter that can be hot-swapped.

Flow (fully automated, with human in loop for sensitive facts):

1. Source: retrieve candidate fact(s) from RAG or user report.

2. Synthesise: produce canonical prompts, paraphrases, counterfactuals, and validation probes (teacher generates paraphrases where permitted).

3. Attribution: compute causal scores (activation patching + gradient×activation) over a small set of candidate layers/experts to pick locus for edit.

4. Adapter attach: create tiny LoRA(s) targeted to locus (or select ROME/MEMIT for atomic edits).

5. Phase A (Warmstart): train on teacher inputs (fast) to give a stable mapping.

6. Phase B (Internalize): train on student inputs only so weights produce desired activations without hidden context.

7. Validation: run CounterFact+/LAMA/MMLU probes and negative tests; if pass, persist with metadata and promote to canary/production.

8. Monitoring & decay: track downstream drift; optionally consolidate adapters (merge) periodically.

This unifies memorisation and forgetting: forgetting is the same pipeline but with negative/counterfactual targets and loss terms that reduce the wrong token probability (abstain or replace).

3 — Concrete components & code sketches

3.1 Attribution (pick where to modify)

Two practical scores:

A. Gradient×Activation saliency

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# simplified pseudo-codeloss = negative_logprob_of_desired_answer(logits, gold_idx)loss.backward()saliency = {}for name, param in model.named_parameters(): if param.grad is not None: saliency[name] = (param.grad.detach() * param.detach()).abs().sum().item()# aggregate per-module

B. Activation patching (causal test) For each candidate layer ?:

• store teacher activation A_T^? (from teacher run),

• run student but replace student activation at ? with A_T^? (forward from ?+1 onward),

• measure improvement in probability of desired answer: ?p.Sort layers by ?p and pick top K.

(Activation patching is the gold standard for causal attribution — use it when budget allows.)

3.2 Adapter / editor selection

• If the target is a single atomic fact and attribution points to a narrow MLP neuron: try ROME (rank-one edit). arXiv

• If you need many atomic edits at scale: use MEMIT style batched edits. arXiv

• For contextual/compositional knowledge: use LoRA adapters + two-phase distillation (warms start on teacher inputs, then internalize on censored student inputs). arXiv

3.3 Two-phase distillation sketch (student-only internalization guaranteed)

Phase A (warmstart, teacher-inputs):

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# teacher_inputs: precomputed teacher activations or teacher-prompted inputs# block is the layer/forward that accepts layer-input vectorsfor step in range(A_steps): out = block(teacher_inputs) # feed teacher inputs (detached); block includes adapter loss = mse(out, teacher_outputs) # teacher_outputs captured from teacher run loss.backward(); optimizer.step()

Phase B (internalize, student-inputs only):

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for step in range(B_steps): student_hidden = student_forward_until_layer(input_prompts) # run student up to layer input out = block(student_hidden.detach()) # we may detach upstream to prevent gradient leakage loss = mse(out, teacher_outputs) # same teacher outputs: student must learn to produce them loss.backward(); optimizer.step()

Notes:

• Keep the rest of the model frozen (requires_grad=False) so gradients only touch adapters.

• Use teacher logits (soft targets) as additional KL loss to improve final-token behavior.

3.4 MoE specifics — router distillation & per-expert adapters

• Capture teacher router logits and expert outputs for each token. If teacher routed token t to expert e, capture I_T^{e,t}, O_T^{e,t}, and the router logit vector g_T^t.

• Phase A: warmstart expert e with I_T^{e,t} -> O_T^{e,t} (feeding teacher input).

• Phase B: either:

  • distill router logits: train a tiny adapter on routing head to make g_S ? g_T (so student routes the same), or

  • learn a combiner network C that maps whatever student-chosen experts produced to the teacher output (if you do not want to change routing).

• Log routing provenance and validate routing shifts carefully; small router deltas can have outsized effects.

3.5 Hypernetwork approach (fast weight generation)

When you have many facts and want instant adapters, train a compact hypernetwork H(z) that maps a knowledge embedding z ? LoRA weight vector W_adapt. At runtime:

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z = encode_fact(fact_text)W = H(z) # generate adapter weights without backprop through base modelattach_adapter_weights(model, layer, W)

Train H offline on historical (fact_embedding ? successful adapter) pairs. This gives instant adapters and small versioned deltas.

4 — Benchmarks & evaluation (what to measure)

Make these mandatory before promoting any adapter.

Primary metrics

• Edit Success Rate (ESR): proportion of fact-target prompts producing the correct target.

• Specificity / Side-effect Rate: fraction of unrelated prompts that changed output (CounterFact+ style).

• Retention / Regression: delta on MMLU / LAMA subset / core utility tasks.

• Throughput & cost: edits/sec and adapter storage cost.

Use published datasets and methods: CounterFact, LAMA, zsRE, plus in-house regression suites. Automate pass/fail thresholds and require human review for high-impact edits.

(Research references above show these are standard evaluation axes for model-editing.) arXiv+1

5 — Safety, provenance & ops

• Every edit is a first-class object: metadata (source, time, confidence, author), tests, and a cryptographic hash of training data used (for auditing). Persist adapters as separate artifacts and support conditional gating per tenant/domain.

• Canary/Shadow rollout: test edits on a realistic canary traffic first; if metrics degrade, auto-rollback.

• Human-in-the-loop for high-risk facts (medical/legal) — require explicit approval.

• Explainability hooks: enable quick tracing: given an assertion, report which adapters are active, which experts were used, and the provenance of the facts that caused the assertion.

6 — Experimental plan (short, staged)

1. Prototype: implement Cathecism Trainer on a mid-size MoE model (or dense 13B). Use LoRA adapters + two-phase training + activation patching. Measure ESR and side-effects on 100 curated edits.

2. Scale: test MEMIT/ROME for atomic facts at scale (1k–10k edits) and compare adapter bank vs MEMIT for throughput & specificity. arXiv+1

3. MoE Router Study: run router-distillation vs combiner design on small MoE and measure global stability. arXiv

4. Hypernetwork trial: train H(z) on successful adapters and test latency/quality tradeoffs.

5. Safety evaluation: run CounterFact+, MMLU regression, TruthfulQA, and a human review for 100 random edits.

7 — Why this helps GPT-5.5 (concrete benefits)

• Faster, cheaper upkeep: small adapters are cheap to train and store — avoids frequent full-model retrains.

• More precise interventions: attribution + small-locus edits reduce collateral damage.

• Faster knowledge propagation: RAG ? adapter path turns ephemeral retrieval into persistent model memory when warranted.

• Safer operations: versioning + validation + rollback makes fixes auditable and reversible.

• MoE safety & scale: explicit router handling prevents distributed failure modes unique to sparse models.

8 — Minimal reproducible example (LoRA two-phase core)

A compact example showing the Phase B internalization loop (PyTorch/HF + PEFT sketch):

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# assume: model wrapped with PEFT LoRA and frozen base. `block` is the target layer.optimizer = torch.optim.AdamW([p for n,p in model.named_parameters() if p.requires_grad], lr=5e-4)mse = torch.nn.MSELoss()# teacher_outputs: captured from teacher run (seq x dim)# student_prompts: list of censored promptsfor step in range(B_steps): model.train(); optimizer.zero_grad() batch = tokenizer([student_prompts[step % len(student_prompts)]], return_tensors='pt', padding=True).to(device) out = model(**batch, output_hidden_states=True) student_layer_input = out.hidden_states[layer_idx].detach() # no upstream grads block_out = block(student_layer_input) # block includes adapter # align shapes (broadcast teacher_outputs if necessary) loss = mse(block_out.float(), teacher_outputs.float()) loss.backward(); optimizer.step()

This enforces that the adapter learns to transform actual student inputs into the teacher outputs — thereby internalizing the fact.

9 — Final notes, tradeoffs & objections

• Tradeoff: many small adapters increase operational surface area (routing, versioning). Mitigate via consolidation (merge adapters periodically) and strict validation gates.

• Not a silver bullet: RAG remains essential for very large or time-sensitive corpora; adapters should be for high-value, stable facts or policy updates. arXiv

• Alignment & safety: centralise governance (who may push adapters, what tests are required) — unsafe edits must be prevented by design.

References (selected, load-bearing)

• LoRA: Low-Rank Adaptation of Large Language Models (Hu et al.). arXiv

• ROME: Locating and Editing Factual Associations in GPT (Meng et al.). arXiv

• MEMIT: Mass Editing Memory in a Transformer. arXiv

• MEND: Fast Model Editing at Scale. arXiv

• MoE / Switch Transformer: Scaling with sparsity / MoE routing (Fedus et al.). arXiv

• Retrieval & RAG survey / practice. arXiv

If you want, I can:

• produce a ready-to-run engineering notebook that wires the full Cathecism Trainer to an OpenAI gpt-oss checkpoint (MoE aware), including attribution instrumentation and Canary test suite; or

• generate a design doc with timelines and compute/cost estimates for prototypes (13B ? 70B ? MoE 100B+), or

• draft a short RFC/PR that can be posted on the internal forum (or public GitHub) describing the exact team changes and CI hooks needed.