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---

# Claude Optimize Anything

Researched it — the paper is real and directly relevant. arXiv:2605.19633, "optimize_anything: A Universal API for Optimizing any Text Parameter," dated May 19, 2026, from the Berkeley/MIT group behind DSPy and GEPA (Agrawal, Khattab, Zaharia, Stoica, Gonzalez, Klein, Dimakis). It's shipped as part of the GEPA project itself — `github.com/gepa-ai/gepa`. So this isn't a competitor to your planned GEPA adoption; it's GEPA's own generalized successor interface.

## What it actually is

A declarative API: you provide a seed artifact (or just a natural-language objective in "seedless mode"), an evaluator function that returns a score plus a diagnostic dictionary, and optionally a dataset. The system handles the search. Three modes:

- **Single-task search** — the artifact *is* the solution (optimize one thing for one problem)
- **Multi-task search** — a batch of related problems; insights cross-transfer via a shared Pareto frontier
- **Generalization** — train/val split; the optimized artifact must perform on unseen inputs (this is the mode GEPA's prompt optimization already used)

The genuinely important idea is **Side Information (SI)**: the evaluator returns not just a scalar score but a diagnostic dictionary — error traces, sub-scores, structured feedback, even images. Their ablations show actionable SI yields 4-6× faster convergence and higher final scores than score-only feedback. Their framing: SI is "the text-optimization analogue of the gradient" — it tells the proposer *why* a candidate failed, not just *that* it did.

It works across code, prompts, agent architectures, numeric configs, images — the headline results are real (ARC-AGI agent architecture 32.5%→89.5%, beats AlphaEvolve on circle packing, etc.).

## Would it help with prompt optimization?

Yes — and it actually clarifies something in the self-learning architecture you just paused. The SI contract is *exactly* the right abstraction for the question that whole coherence map kept circling: "what signals feed prompt optimization?" The answer optimize_anything gives is structurally clean — the evaluator returns score + SI dict, and SI is where all your BDSM signals, edit traces, and evaluation findings belong. That's a better contract than the loose "DSPy training data per target" sketch in §4.4 of the coherence map.

It also means your "R5 substrate" gate might be partly answerable by adopting this rather than hand-rolling DSPy integration. optimize_anything *is* a unified optimization API; a good chunk of what R5 substrate needs to be might just be "wire up optimize_anything."

## Task optimization and the Evaluator / Extractor / Revisor modules

Here's where you need the honest caveat, and it's the same wall you hit with the goal-advancement axis and the sycophancy problem.

**optimize_anything needs an evaluator that returns a score.** Every headline result has an objective or near-objective scoring function: circle packing (sum of radii), CUDA kernels (measured speedup), AIME prompts (math answers are right or wrong), agent skills (tests pass or fail). The whole paradigm works because the score is trustworthy.

Your three modules split sharply on this:

**Claim Extractor — best fit.** Extraction has a comparatively objective evaluator. You can score extraction completeness and accuracy against a reference set: did it find all the claims, did it get them right, are the citations correct. This is closer to the CUDA-kernel case. optimize_anything on the Extractor prompt is genuinely promising and the scoring function is tractable.

**Evaluator and Revisor — the universal API works mechanically, but it doesn't solve your actual problem.** optimize_anything *consumes* an evaluator; it doesn't *provide* one. For "did the Evaluator do a good job evaluating this brief" or "did the Revisor's plan actually improve things," your scoring function is either a subjective LLM judge (back to the sycophancy/circularity problem) or human-labeled data (which a single user won't produce at dataset scale). optimize_anything is brilliant when you can trust the score. For subjective legal-quality evaluation, the unsolved problem isn't the optimization algorithm — it's the scoring function, and this paper doesn't touch that. It would happily optimize your Evaluator prompt toward whatever score you give it, including a bad one.

**Whole-task-graph optimization — real but furthest out.** The ARC-AGI result optimized an *entire agent* — code, sub-agent architecture, control flow, prompts — as one text artifact, and tripled accuracy. That maps onto "optimize a whole ELNOR task graph," which is the most ambitious reading of your "overall task optimization" question. It's demonstrated and credible. But it has the same scoring-function dependency, plus it needs the task-graph serialized as text and a dataset of comparable tasks.

## The strategic point

optimize_anything doesn't let you skip the self-learning work you just paused — it sits on top of it. The SI dict, the evaluator, the dataset all come from the BDSM signals, edit traces, and clustering that the self-learning architecture defines. So this reinforces that the self-learning work is the prerequisite.

But it also gives that paused work a concrete target. When you pick it back up, you're no longer designing signals into a vague "DSPy training data" void — you're designing them to populate an `optimize_anything` evaluator's score and SI dict. That's a sharper design constraint and probably makes the architecture cleaner.

Two things worth doing, neither urgent:

1. When the self-learning work resumes, frame the BDSM signal architecture explicitly around the SI contract — score plus structured diagnostic dict — rather than the looser per-target training-data sketch currently in the coherence map.
2. Add a note to the R5 substrate thinking: evaluate whether adopting optimize_anything directly replaces a hand-rolled DSPy integration. Likely yes for Extractor; partially for the others pending the scoring-function problem.

For right now though — this doesn't change the immediate plan. Red team the DOC23/Addenda B set first. optimize_anything is a R5-era consideration, and R5 is gated behind exactly the self-learning work you've deliberately set aside.



Quick, direct answers to the three:

## Replacement or alongside?

It sits **on top of** DSPy and GEPA — it's a wrapper interface, not a replacement.

The layering, from bottom up:

- **GEPA** is the search *algorithm* — reflective Pareto-based prompt evolution
- **optimize_anything** is a *declarative API* whose default backend "extends and manages information atop GEPA." The paper explicitly says it's "backend agnostic" — GEPA is the current default optimizer, but the interface could drive others.
- **DSPy** is the broader framework for building LLM pipelines; optimize_anything is described as "inspired by DSPy's principle of programming—not prompting" and is part of the same project ecosystem.

So you don't choose between them. optimize_anything is the front door; GEPA is the engine behind it; DSPy is the surrounding framework. If you adopt optimize_anything you're using GEPA — just through a cleaner, more general interface that also handles non-prompt artifacts (agents, code, configs) which raw GEPA didn't. For your ELNOR R5 plans, this means "GEPA for prompt optimization" becomes "optimize_anything for prompt optimization plus potentially agent/config optimization," same underlying machinery.

## Is the score the key thing?

Yes — that's the load-bearing claim, with one important refinement.

The score is what makes the whole paradigm *possible* — without a trustworthy scoring function, you're optimizing toward noise, and for your Evaluator/Revisor modules that's the unsolved problem.

But the paper's actual headline finding is that **the score alone is not enough to make it work well**. Their Side Information ablations are blunt: score-only optimization is 4-6× slower to converge and lands at substantially lower final performance. In one table, multi-task KernelBench with score-only got *0%* of kernels past the speedup threshold; with SI it got 40%. The score tells the optimizer *that* something failed; SI tells it *why and how to fix it*.

So the precise statement: the score is the necessary foundation (no trustworthy score → don't bother), and rich SI is what makes it actually effective rather than just technically functional. For ELNOR that's two separate requirements — a trustworthy score (your hard problem for Evaluator/Revisor) *and* good diagnostic feedback (which your BDSM signals and evaluation findings are well-positioned to provide, once the self-learning work resumes).

## Costs

The paper gives concrete figures. General picture: a single optimization run is a one-time batch cost, not a per-use cost, and it ranges widely by task complexity.

Their reported total optimization costs:

- Numerical blackbox optimization: about $1
- Circle packing: $3.18 (63 evaluations, GPT-5.1 as proposer)
- ARC-AGI agent architecture: $144.70 — the expensive end

The structural points that matter for budgeting:

- **The evaluator dominates total spend, not the optimizer.** "Reflection cost minimal, total spend dominated by the evaluator." Every candidate the system proposes has to be evaluated, and if your evaluator is expensive (running real tasks, calling frontier models), that's where the money goes. For ELNOR, evaluating an Evaluator-module candidate means running actual evaluation work — so your evaluator cost is whatever an evaluation run costs, times the number of candidates explored.
- **Proposer model choice is a real lever.** They compared GPT-5.1 against GPT-5-nano as the proposer and cut costs over 90% on circle packing with the nano model — but final quality consistently dropped. So cheap-proposer/expensive-proposer is a genuine quality-cost dial, which fits your existing tiered-model strategy (local/cheap/frontier).
- **It's amortized.** You pay the optimization cost once, then use the optimized prompt indefinitely. A $5-150 one-time run to improve a module prompt you'll use for months is cheap if it works. The cost concern isn't any single run — it's that you'd re-run periodically as the system and your usage evolve.

For ELNOR specifically, the cost you can't read off the paper is your evaluator cost, because that depends entirely on what "evaluate one candidate Evaluator prompt" means in your system — and that's defined by the self-learning architecture you've paused. Another reason that work is the real gate: it determines both whether you *can* score these modules and what each scoring call *costs*.

### What `optimize_anything` actually is

It's a declarative universal optimization API from the GEPA team (Berkeley/MIT — Khattab, Zaharia, Dimakis, et al.). The core insight: a wide range of optimization problems can be reframed as "improve a text artifact evaluated by a scoring function." You provide a seed artifact (or just a natural-language objective in seedless mode), an evaluator that returns a score plus optional diagnostic feedback, and optionally a dataset. The system handles everything else.

It's not a different algorithm from GEPA — it runs GEPA's Pareto-based reflective search as its default backend. What's new is (a) the universal interface that works across artifact types, (b) two new optimization modes beyond GEPA's generalization mode, and (c) Side Information elevated to a first-class API contract.

Three things matter for ELNOR specifically.

**Side Information (SI) as a first-class contract.** The evaluator returns a score *and* a structured `side_info` dict — error traces, sub-scores, per-test results, critiques, even images. The paper calls SI "the text-optimization analogue of the gradient": a scalar score tells the optimizer *that* a candidate failed; SI tells it *why* and *how to fix it*. The ablation is striking — actionable SI yields 4-6× faster convergence and substantially higher final scores than score-only feedback. On one prompt-optimization task, with-SI reached a validation score in 100 rollouts that score-only needed ~600 to reach.

**Three modes, one interface.** Single-task search (the artifact *is* the solution — no dataset). Multi-task search (a batch of related problems; insights cross-transfer through a shared Pareto frontier; produces N specialized artifacts). Generalization (train/val split; produces one artifact that generalizes to unseen inputs — this is classic GEPA prompt optimization). Mode is selected purely by whether you pass `dataset` and `valset`.

**Seedless mode.** You can provide just a natural-language objective instead of a seed artifact, and the system bootstraps the first candidate. Lowers the barrier when writing even a bad seed needs expertise.

Results span agent architectures (ARC-AGI 32.5%→89.5%), cloud scheduling (40% cost savings), CUDA kernels (87% match/beat PyTorch), prompts (AIME 46.7%→60%, beating MIPROv2), and circle packing (beats AlphaEvolve). Costs ran $1–$144 per optimization, evaluator-dominated.

### Would it help with prompt optimization?

Yes — and it's essentially a strict superset of what your DSPy joint pass already plans to use. GEPA is the optimization backend in both cases. `optimize_anything` is the same algorithm behind a cleaner declarative interface. Practically, three things are worth pulling into the DSPy joint-pass thinking:

**SI is the most important takeaway.** Your planned DSPy targets (Revisor, Evaluator, Compiler, Judge) shouldn't be optimized against scalar scores. They should be optimized against scores *plus* structured diagnostic feedback. You already have exactly the right substrate for this — the per-criterion findings, the RepairCycleSignal qualitative deltas, the Judge's per-dimension scores. That's textbook SI. The paper's ablation says wiring it as a first-class evaluator return rather than an afterthought is worth 4-6× convergence. When the joint pass specifies the DSPy metric functions, it should specify SI-returning evaluators, not score-only metrics. This directly answers a finding from the red team review (the DSPy composite-metric design) — the composite shouldn't collapse to a scalar; it should expose sub-scores as SI.

**Multi-task mode fits ELNOR's structure.** You don't optimize one Evaluator prompt — you have many related evaluation tasks across task types. Multi-task search optimizes across a batch of related tasks with cross-transfer through the shared Pareto frontier, and produces N specialized artifacts rather than one compromise. That maps cleanly onto "optimize the Evaluator across legal-research-memo tasks, brief tasks, contract-review tasks" and getting a specialized variant per type that still benefited from shared discovery. The paper shows multi-task beats independent single-task optimization at equal per-problem budget.

**Seedless mode is useful for cold-start.** When you have a module with no good prompt yet, you can hand it a natural-language objective and bootstrap.

### Overall task optimization, and the Evaluator / Extractor / Revisor modules

This is where it gets more interesting, and where it connects to the deferred TIE concept.

The paper's headline claim is that the *same* API optimizes agent architectures — not just prompts. The ARC-AGI experiment optimized "the entire agent system: code, sub-agent architecture, control flow, helper functions, and prompts ... as a single text artifact," going from a 10-line seed to a 300+ line 4-component system. That's not prompt tuning; that's architecture search.

For the Evaluator/Extractor/Revisor modules specifically:

**As prompt optimization** — directly applicable now, via the joint DSPy pass. The modules have fixed generic prompts; `optimize_anything` (GEPA backend) optimizes those prompt artifacts. Nothing new here beyond "use SI and consider multi-task mode."

**As task/architecture optimization** — this is the conceptual bridge to TIE. `optimize_anything` is, in effect, a concrete engine for part of what TIE would do. Recall the TIE tiers: Tier 1-2 are artifact and pattern changes; Tier 4 is architecture/code changes. `optimize_anything`'s generalization mode over agent artifacts is a working implementation of automated Tier 4 — give it the task graph or module configuration as the artifact, an evaluator that runs it and returns SI, and it evolves the structure. The ARC-AGI result is exactly "evolve the Evaluator pipeline's topology," not just its prompt.

But — and this matters — it is not a substitute for TIE, for three reasons:

1. **It needs a clean automated evaluator.** Every result in the paper has a crisp programmatic score: test pass rates, wall-clock speedup, cost, geometric constraint satisfaction. ELNOR's evaluation quality is itself partly subjective and partly the thing being learned. You'd need the Loop Effectiveness Test (independent Judge delta) to be solid first — that's the evaluator `optimize_anything` would consume. The measurement substrate is the precondition.
2. **It optimizes; it doesn't diagnose and propose for human review.** `optimize_anything` runs a search loop and hands back an improved artifact. TIE's value is the reasoned diagnostic report, the multi-LLM red team on the recommendation, the gated human review. For a litigator's system where changes touch privileged workflows, you want TIE's review pipeline wrapped around any automated optimization — not raw artifact replacement.
3. **It's per-artifact, not system-wide.** It improves the artifact you point it at. TIE's Tier 3 cross-task system audit ("why is Revisor convergence low across all task types") is a different, broader reasoning activity.

The right mental model: `optimize_anything` is a powerful *engine* that TIE could call as a tool. When TIE (Tier 1/2/4) decides "the Evaluator prompt for legal-research-memo tasks should be optimized" or "this task graph topology should be evolved," it can invoke `optimize_anything` with the appropriate artifact, evaluator, and SI — rather than TIE generating the improved artifact freehand. TIE provides the diagnosis, the scoping, the review gating, and the system-wide view; `optimize_anything` provides the optimization search. They compose.

### Concrete recommendations

**For the DSPy joint pass (when it resumes):** Adopt the `optimize_anything` framing rather than raw DSPy. Same GEPA backend, better interface, and three concrete wins — SI-returning evaluators (4-6× convergence; also fixes the composite-metric finding from the red team review), multi-task mode for per-task-type specialized module variants, seedless mode for cold-start. It's open-source as part of the GEPA project (github.com/gepa-ai/gepa), so it's available to build against.

**For the held self-learning / TIE work:** Note `optimize_anything` as the optimization engine TIE invokes for Tiers 1, 2, and 4. This actually *strengthens* the TIE architecture — TIE's harder-to-specify part was "how does it generate the actual improved artifact." Answer: for anything with a usable evaluator, it calls `optimize_anything`. TIE keeps the diagnosis, the SI design, the red team review, the human gating, and the system-wide audit. The SI concept also reframes your per-step diagnostic data nicely: the structured diagnostic data TIE reasons over *is* SI, and the same data feeds the optimizer.

**Sequencing:** This doesn't change your current plan. Self-learning and DSPy stay held for the memory-system reorganization. But when DSPy resumes, it should resume as "`optimize_anything` integration" rather than "DSPy integration" — and the joint-pass plan document should be updated to reflect that. The Loop Effectiveness Test remains the precondition: it's the evaluator the whole thing runs on.

One caveat worth keeping: every headline result in the paper has a clean, cheap, automatable score. ELNOR's legal-writing quality does not. The technique is real and the framing is genuinely useful, but its leverage in ELNOR is bounded by how good your evaluator is — which is exactly why the measurement substrate (Loop Effectiveness Test, independent Judge) is the thing that has to be solid first. The optimizer is only as good as the score you can hand it

# ChatGPT Optimize Anything



Yes — **GEPA / `optimize_anything` looks genuinely relevant to Elnor**, and I would incorporate it. But I would **not** treat it as a new free-floating prompt lab or a runtime self-modification mechanism. The right move is to make it one supported backend inside the already-reserved **R5 optimization substrate**, governed by PropA/DOC23/DOC8/BDSM safety gates.

My bottom line:

```text
GEPA classic = very relevant for prompt optimization.
optimize_anything = broader and more important: optimize prompts, agent instructions, configs, skills, context-product policies, evaluators, and maybe task/module designs.

Elnor already contemplates DSPy/GEPA in PropA/DOC23, but mostly as prompt optimization.
The new paper means the R5 substrate should be generalized from “DSPy/GEPA prompt optimizer”
to “text-artifact optimization substrate,” with GEPA / optimize_anything as one backend.
```

## What `optimize_anything` is

The paper’s basic move is simple but powerful: represent the thing to be improved as text, run it through an evaluator, collect a score plus diagnostic feedback, and let an LLM propose better candidates. The authors frame this as a universal API for text artifacts: prompts, code, agent architectures, configurations, scheduling policies, SVG/CAD artifacts, etc. ([arXiv](https://arxiv.org/html/2605.19633v1))

The API requires a seed artifact or objective, an evaluator, optional dataset/validation set, and optional background constraints. The system handles reflection, candidate generation, selection, and search. ([arXiv](https://arxiv.org/html/2605.19633v1)) ([gepa-ai.github.io](https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-optimize-anything/))

The key feature is **Actionable Side Information** — not just scalar scores, but traces, errors, sub-scores, structured diagnostics, profiler output, rendered images, or critiques that the proposer can read. The paper reports that this feedback gives much faster convergence and better final results than score-only optimization. ([arXiv](https://arxiv.org/html/2605.19633v1))

The other key feature is **Pareto-efficient search**: it keeps candidates that are best on different examples or metrics instead of collapsing everything to one average score. That matters a lot for Elnor, because your system constantly balances competing objectives: accuracy, privacy, scope safety, prompt length, citation quality, user burden, and cost. ([gepa-ai.github.io](https://gepa-ai.github.io/gepa/blog/2026/02/18/introducing-optimize-anything/))

## The results are impressive, but use the right caution

The reported results are strong: agent architecture discovery, cloud scheduling cost savings, CUDA kernel generation, AIME prompt improvement, and circle packing. The paper claims, among other things, ARC-AGI gains from 32.5% to 89.5%, cloud cost reductions around 40%, and AIME prompt optimization from 46.67% to 60.00%. ([arXiv](https://arxiv.org/html/2605.19633v1))

But there are real caveats. The authors acknowledge that results depend on the proposer LLM, evaluator cost can be high, artifacts must be representable as text, multi-task transfer helps only when tasks share structure, and designing good side information still requires domain expertise. ([arXiv](https://arxiv.org/html/2605.19633v1))

That maps directly to Elnor: this is only as good as your eval harness, trace quality, policy gating, and promotion discipline.

## Is this already contemplated in your system?

**Partly, yes. But not fully.**

Your current Elnor stack already contemplates **DSPy/GEPA prompt optimization**. DOC23 Addenda A R4.1 explicitly reserves runtime DSPy/GEPA optimization for R5 and says R4.1 must not expose runtime DSPy optimization, mutate instructions, emit `optimization_out`, or show a Promote button. It lists the missing prerequisites: proper evaluation datasets, JudgeMetricAdapter, static Python runner, pinned versions, policy-gated trainsets, promotion ledger, rollback, human approval, post-promotion shadow runs, Pareto option, and conflict detection.

Your OP-A also tracks that DSPy/GEPA remains gated until the R5 substrate is implemented, with Addenda A / PropA obligations for target registration and safe activation.

So: **GEPA itself is already on your roadmap.** The new part is that `optimize_anything` should broaden the R5 concept from “prompt optimizer” to **general text-artifact optimizer**.

## Where it would help Elnor most

### 1. Prompt optimization for extraction and memory classification

This is the cleanest first use. You have many prompt-like targets:

```text
- DOC72 graph extraction prompts
- PropA sensitivity / visibility classification prompts
- DOC73 corpus extraction prompts
- AssertionCandidate extraction prompts
- CU generation prompts
- Claim extraction prompts
- Judge/Evaluator prompts
- Revisor / Outcome Compiler prompts
```

Your current OP-A already contemplates PropA owning DSPy target IDs for claim extractor, outcome evaluator, revision compiler, and outcome compiler prompts, with PropA as the shared DSPy lane.

`optimize_anything` would fit well here because these prompts can be evaluated against fixture packs, expected JSON schemas, false-positive/false-negative cases, policy violations, and trace diagnostics.

### 2. Memory-context delivery optimization

This is probably the most Elnor-specific value.

You are not just optimizing prompts. You are optimizing **context products**:

```text
- Topic Notice vs Topic Slice
- Library Notice vs Library Source Slice
- Assertion Packet format
- warrant headers
- source-support labeling
- Recent Work Orientation
- IssueFrame Orientation
- search/pull affordance wording
- reference-only / blocked notice wording
- final prompt section order
```

The research-to-requirements work in the DAMS V5 review already emphasized: inject fewer items, label them better, place them more intelligently, and expose search/pull affordances for the long tail. GEPA/`optimize_anything` is exactly the kind of tool that can tune those tradeoffs, provided your evaluator checks factuality, usefulness, over-injection, policy safety, and omission cost.

This is where `optimize_anything` may be better than narrow DSPy prompt optimization: the artifact could be a **context-product template**, **selection policy**, or **prompt shell**, not just a single prompt.

### 3. Task Agent / prompt improvement workflows

Your current Addenda B already pushes toward one user-facing **Improve Prompt** capability, with quick and deep modes, Task Agent orchestration, deep prompt testing as a tracked DOC23 prompt-evaluation task, and DSPy/GEPA reserved until the optimization substrate lands.

`optimize_anything` is a strong candidate for the “deep mode” backend:

```text
Quick mode:
  Prompt Advisor rewrite / critique.

Deep mode:
  DOC23 PromptEvaluationTask
  → Experiment variants
  → Judge scoring
  → Claim Extractor / outcome validation
  → optimize_anything candidate generation
  → promotion ledger / shadow run / rollback
```

### 4. Agent skills, task templates, and decision cards

The paper and GEPA docs explicitly include optimizing agent skills and agent architectures as text artifacts. The GEPA GitHub docs describe GEPA as optimizing textual parameters using traces, errors, reasoning logs, and Pareto-aware selection. ([GitHub](https://github.com/gepa-ai/gepa))

For Elnor, this could optimize:

```text
- Task Agent design instructions
- MemoryAgent retrieval instructions
- DocumentIntelligenceAgent extraction instructions
- CANDOR/red-team reviewer prompts
- task template instructions
- decision-card rubrics
- prompt profiles
- critic routing profiles
```

But I would keep agent-architecture optimization **offline and proposal-only**, not automatic self-modification.

### 5. Policy/config optimization

This is useful but dangerous.

`optimize_anything` can optimize textual policies/configs, but Elnor policy is safety-critical. For PropA, memory visibility, sealed/firewalled sources, privilege boundaries, export/carryover/delegation, and learning visibility scopes, the optimizer should only propose changes. It should never directly mutate operative policy.

Your existing PropA posture is right: PropA owns policy semantics, and EC is the sole durable writer. PropA’s self-improvement/DSPy scope must not become a second runtime brain or hidden writer.

## What I would change in your specs

I would update the R5 optimization substrate from:

```text
DSPy/GEPA prompt optimization
```

to:

```text
TextArtifactOptimization substrate
```

Then define GEPA/DSPy/`optimize_anything` as backend adapters.

### Proposed spec shape

```ts
type OptimizationBackend =
  | "manual_ab_test"
  | "dspy_gepa"
  | "gepa_optimize_anything"
  | "custom_local_optimizer"
  | "external_optimizer_manual_review_only";

type OptimizableArtifactKind =
  | "prompt_template"
  | "prompt_shell"
  | "context_product_template"
  | "judge_rubric"
  | "claim_extractor_prompt"
  | "revision_compiler_prompt"
  | "task_agent_instruction"
  | "memory_agent_instruction"
  | "document_intelligence_instruction"
  | "policy_candidate_text"
  | "routing_config"
  | "skill_instruction"
  | "agent_architecture_candidate";
```

### Optimization run contract

```ts
type TextArtifactOptimizationRun = {
  run_id: string;
  backend: OptimizationBackend;
  artifact_kind: OptimizableArtifactKind;

  seed_artifact_ref?: string;
  objective: string;
  background_constraints: string[];

  trainset_ref?: string;
  valset_ref?: string;
  evaluator_ref: string;

  metric_contract: OptimizationMetricContract;
  side_information_contract: SideInformationContract;

  policy_gate: OptimizationPolicyGate;
  data_class: "public" | "internal" | "privileged" | "sealed" | "firewalled";

  max_metric_calls: number;
  cost_cap_usd?: number;

  candidate_output_refs: string[];
  best_candidate_ref?: string;

  promotion_state:
    | "draft"
    | "evaluated"
    | "shadow_run_required"
    | "human_review_required"
    | "approved"
    | "rejected"
    | "rolled_back";

  promotion_ledger_ref?: string;
};
```

### Side information contract

This is where Elnor can be unusually strong:

```ts
type SideInformationContract = {
  include: Array<
    | "per_example_score"
    | "judge_rationale"
    | "claim_extraction_failures"
    | "schema_validation_errors"
    | "policy_decision_reasons"
    | "retrieval_trace"
    | "context_product_manifest"
    | "final_prompt_proof"
    | "user_feedback"
    | "cost_latency_trace"
    | "omission_false_suppression_signal"
    | "conflict_detection_result"
  >;
  redact_privileged_spans: boolean;
  sealed_source_behavior: "exclude" | "same_firewall_only";
};
```

That gives GEPA the “why it failed” feedback it needs, without letting sensitive source material leak into unsafe optimization runs.

## Critical guardrails

Do **not** let this become:

```text
“Elnor automatically rewrites its own prompts/policies whenever a score improves.”
```

The safe path is:

```text
generate candidate
→ evaluate on frozen dataset
→ compare to baseline
→ inspect side effects
→ check policy/scope/privacy
→ shadow run
→ human approve
→ promote with hash and rollback
→ post-promotion monitoring
```

This is already consistent with your R4.1 reservation language: no optimizer until the R5 substrate has dataset handling, policy-gated trainsets, static runner, pinned deps, promotion ledger, rollback, human approval, and shadow runs.

## What I would not use it for

I would **not** use GEPA/`optimize_anything` to optimize canonical memory contents, legal conclusions, user facts, or policy truth.

Bad uses:

```text
- “Optimize the user's stored memory.”
- “Optimize legal rules in the graph.”
- “Optimize policy to maximize successful delivery.”
- “Optimize around privacy blocks.”
- “Rewrite canonical Assertions for usefulness.”
```

Good uses:

```text
- improve extraction prompts;
- improve context product templates;
- improve prompt shells;
- improve judge rubrics;
- improve task-agent instructions;
- improve evaluator/revisor/compiler prompts;
- propose better routing configs for review;
- tune non-authoritative delivery strategy.
```

## My recommendation

Adopt it, but as a **R5/R6 substrate update**, not an immediate runtime feature.

I would add one new planning item:

```text
DOC23 Addenda A R5 / PropA R7 / DOC8-BDSM optimization substrate
must support GEPA optimize_anything as a backend for text-artifact optimization,
not merely dspy.GEPA prompt compilation.
```

And I would add one hard invariant:

```text
Optimization may propose candidates for prompts, templates, rubrics, skills,
agent instructions, and non-authoritative configs.
Optimization may not directly mutate canonical memory, policy decisions,
user directives, source evidence, or operative system invariants.
```

## Final answer

This is **very likely useful** for Elnor. In fact, it fits your system better than many systems because Elnor is already built around receipts, traces, judges, evaluation runs, final-prompt proofs, policy gates, and promotion ledgers — exactly the diagnostic substrate GEPA needs.

But it should be framed correctly:

```text
Not: “Add a cool prompt optimizer.”
Yes: “Define a governed text-artifact optimization substrate, with GEPA / optimize_anything as one backend, operating through DOC23 Experiments/Judges, PropA policy gates, DOC8/BDSM learning signals, promotion ledger, rollback, and human approval.”
```

So my answer is: **yes, it is already partially contemplated, but the new `optimize_anything` paper justifies broadening and sharpening the R5 optimization substrate.**