# Provenance-Traced Evaluation: A New Framework for Identity System Assessment

## Overview

This document describes a new evaluation methodology for AI identity/persona systems that replaces subjective LLM-as-judge scoring with mechanistic, human-auditable evidence chains. The framework was developed while evaluating Base Layer, a behavioral compression pipeline that extracts reasoning patterns from text and compresses them into portable identity briefs.

### The Core Question

If you give a model raw facts about a person and separately give it a compressed behavioral brief derived from those same facts — does the brief produce better reasoning? Not "does more information beat less information" (trivially yes), but **does compression add value beyond the raw material it was built from?**

The principle: **evaluation should produce evidence, not opinion.** Every score should trace to source material that a human can inspect.

---

## The Problem

### What identity systems do

Identity and persona systems inject behavioral context into AI models — "this is who you're talking to" or "respond as this person would." The goal is for the model's responses to reflect the subject's actual reasoning patterns, priorities, and decision-making framework.

### The right comparison

Most evaluations compare a briefed model against an unbriefed one. This is the wrong test — it just shows that more information beats less information. The meaningful comparison is:

- **C1 (cold baseline):** Model answers with no injected information, using only its training data knowledge of the subject.
- **C2 (raw structured data):** Model receives the same underlying facts and structured layers the brief was built from — all the raw material, uncompressed.
- **C5c (compressed brief):** Model receives a compressed behavioral brief (~7-14K characters) synthesized from those same facts.

If C5c outperforms C2, compression itself is adding value. The act of compressing forces synthesis — identifying axioms, surfacing tensions, establishing priority ordering — that dumping raw facts does not provide. Prior evaluation on Benjamin Franklin confirmed this: C2 (structured facts) scored 3.975 and provided zero lift over the cold baseline. C5c (compressed brief) scored 4.350 — a +0.375 improvement from the same underlying information.

### How identity systems are currently evaluated

Every published persona evaluation framework relies on LLM-as-judge scoring on subjective dimensions:

- **PersonaGym** (EMNLP 2025): 5 dimensions scored by GPT-4o + LLaMA-3-70b. PersonaScore = simple average.
- **RVBench** (2025): Psychometric questionnaires (Schwartz Values) administered to role-playing LLMs.
- **PersonalLLM** (ICLR 2025): Reward model ensembles scoring output preferences.
- **Our own BCB (Behavioral Compression Benchmark)**: LLM judges scoring Recognition, Calibration, Depth, Usefulness, Specificity on a 1-5 scale.

### Why this fails

**1. "Voice" conflates syntax and reasoning.**
A response can sound like someone (word choice, tone, register) while reasoning nothing like them. Or it can reason exactly as they would while sounding modern and generic. These are independent axes, but LLM judges conflate them.

*Evidence:* We evaluated responses about Howard Marks (prominent investor, 74 published investment memos). On the same response, one judge (Opus) scored Recognition: 1, Specificity: 1 — "could be from any value-oriented investor." Another judge (Sonnet) scored the same response all 5s. The response opened with Marks' signature phrase ("I could be wrong, but"), used an analogy from his memos (Cisco/dot-com), and applied his core axiom (price-value primacy). The first judge was evaluating voice and didn't find enough style markers. The second was evaluating content and found strong alignment.

**2. Judge scores are opaque.**
When two judges produce scores of 1 and 5 on the same response, there is no way to adjudicate. The score is an opinion with no audit trail. A human reviewer cannot inspect WHY the score was given or determine which judge is correct.

**3. The field has no mechanistic evaluation.**
No published framework traces WHY a response was personalized — what specific content from the persona/identity source material drove what specific claims in the response. Scores are aggregate opinions about aggregate quality.

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## The Proposed Framework

### Provenance-Traced Evaluation

Instead of asking a judge "does this feel right?", decompose and verify:

**Layer 1 — Brief Activation (BA):** Did the identity brief actually influence the response?
- Embed response segments and brief claims using the same sentence transformer (MiniLM, 384-dim, local)
- Compute cosine similarity between each response segment and its nearest brief claim
- Compare briefed condition (C5c) vs. unbriefed baseline (C1)
- Fully mechanical. No judge needed. Human-auditable: "This response passage matched this brief claim at 0.71 similarity."

**Layer 2 — Provenance Coverage (PC):** What fraction of the response's claims trace back to the brief?
- Extract claims from the response at sentence level
- Trace each claim to its nearest brief content by vector similarity
- A claim is "covered" if similarity exceeds a threshold
- Compute coverage ratio: (claims with provenance) / (total claims)
- Mechanical with inspectable output. "This response made 8 claims. 6 trace to brief content. Here are the traces."

**Layer 3 — Reasoning Chain Reconstruction (RCR):** For each conclusion, what reasoning was applied and does it match a documented axiom?
- Decompose each conclusion into: observation (what was attended to) → framework (what reasoning pattern was applied) → conclusion (what was decided)
- Match the framework step against the brief's documented axioms
- Score: RCR-match (applied a specific axiom), RCR-generic (used generic domain knowledge), RCR-misapply (referenced an axiom incorrectly)
- Partially mechanical (axiom matching by similarity), partially requires narrow LLM judgment on verifiable questions

**Layer 4 — Priority Ordering (PO):** When multiple axioms apply, does the response weight them correctly?
- Identify responses where multiple axioms are relevant
- Determine which axiom the response treated as primary
- Compare against the brief's axiom hierarchy
- Tests the insight from RVBench: models can state values but fail to prioritize correctly under pressure

### What makes this different

Every layer produces an **auditable evidence chain**, not a score:

> "The response concluded that Nvidia is overvalued. The reasoning path was: observed high P/E ratio → applied Axiom 3 (price-value primacy: 'the relationship between price and value is the most reliable determinant of investment success') → concluded current price exceeds intrinsic value. Axiom 3 source: brief claim at similarity 0.82."

A human can inspect this chain and decide whether it holds. The evaluation becomes transparent.

---

## Initial Experiment: C1 vs. C5c on a Well-Known Subject

> **Note:** This initial experiment compared C1 (no brief) against C5c (compressed brief). As discussed above, the more meaningful comparison is C2 (raw facts) vs. C5c (compressed brief) — same information, different format. The C1 vs. C5c results below are included as a proof-of-concept for the provenance methodology itself, not as evidence for compression value. The C2 vs. C5c comparison is the next step.

### Setup

- **Subject:** Howard Marks — 74 published investment memos (2001-2026), well-known investor
- **Brief:** 14,241 characters, 20 axioms, 6 context modes, 13 predictions. Generated by Base Layer pipeline from memo corpus.
- **Conditions:** C1 (no brief, model uses training knowledge only) vs. C5c (brief injected into system prompt)
- **Prompts:** 10 investment scenario questions designed to test specific axioms
- **Model:** Claude Sonnet 4.5 for generation
- **Evaluation:** Local MiniLM embeddings, cosine similarity, zero API cost

### Layer 1 Results: Brief Activation

| Metric | C1 (no brief) | C5c (with brief) | Delta |
|---|---|---|---|
| Mean similarity to brief | 0.4030 | 0.4192 | +0.0162 |
| Max similarity to brief | 0.6217 | 0.6850 | +0.0634 |
| Prompts where C5c > C1 | — | — | 8/10 |

### Layer 2 Results: Provenance Coverage (threshold = 0.50)

| Metric | C1 | C5c | Delta |
|---|---|---|---|
| Mean coverage ratio | 20.4% | 23.4% | +3.0% |
| Prompts where C5c > C1 | — | — | 7/10 |

### Threshold Sensitivity (Layer 2)

| Threshold | C1 Coverage | C5c Coverage | Delta | Direction |
|---|---|---|---|---|
| 0.40 | 49.5% | 54.2% | +4.7% | C5c > C1 |
| 0.45 | 33.3% | 40.6% | +7.3% | C5c > C1 |
| 0.50 | 20.4% | 23.4% | +3.0% | C5c > C1 |
| 0.55 | 10.1% | 12.0% | +2.0% | C5c > C1 |
| 0.60 | 4.9% | 6.3% | +1.3% | C5c > C1 |
| 0.65 | 1.5% | 2.9% | +1.4% | C5c > C1 |
| 0.70 | 0.4% | 1.1% | +0.7% | C5c > C1 |

C5c outperforms C1 at every threshold tested. The finding is directionally robust regardless of where the coverage line is drawn.

### Provenance Quality Check

The semantic traces are working correctly. Examples from the results:

| Response segment | Matched brief claim | Similarity |
|---|---|---|
| "The unintended consequences are usually more important than the intended ones" | "Unintended consequences matter more than intended benefits because policymakers systematically underestimate second-order effects" | 0.72 |
| "The riskiest thing is when risk seems to have disappeared" | "The riskiest belief is the conviction that there is no risk" | 0.64 |
| "Markets aren't physics — they're driven by human psychology" | "These reflexive dynamics make markets fundamentally different from physical systems with fixed laws" | 0.66 |

These are genuine semantic alignments, not lexical overlaps. The provenance infrastructure is tracing conceptual connections accurately.

### Interpretation

**The methodology works. The experiment design needs refinement.**

The framework successfully produces auditable evidence chains — every match is inspectable and verifiable by a human. However, the C1 vs. C5c comparison on a well-known subject has inherent limitations:

1. **The delta is small (+0.016 mean similarity)** because Howard Marks is well-known. The model's training data already contains extensive knowledge of his frameworks. C1 (no brief) achieves 0.403 similarity to the brief — the model essentially already "knows" most of what the brief says. This confirms that C1 vs. C5c is the wrong comparison for well-known subjects.

2. **The right test is C2 vs. C5c.** Both conditions receive the same underlying information. The question becomes: does the compressed format produce responses that trace to more specific axioms, apply them more correctly, and prioritize them better than raw structured facts? Prior evaluation on Benjamin Franklin showed C2 (structured facts) provided zero lift over the cold baseline, while C5c (compressed brief) provided +0.375 lift. Running provenance-traced evaluation on C2 vs. C5c would show WHERE the compression adds value — which specific axioms and reasoning chains appear in C5c responses but not C2 responses.

3. **Content tracing is more informative than similarity scoring.** For well-known subjects, the right question isn't "how similar is the response to the brief on average?" but "what specific claims appeared in C5c that didn't appear in C1 (or C2), and can we trace each one to the brief?" This identifies the *specific value-add* of compression.

4. **Unknown subjects would show the full dynamic range.** For someone the model has no training data on, C1 similarity would be near-zero, C2 would provide facts without synthesis, and C5c would provide both. The provenance chains would show the brief providing ALL the reasoning structure, not just marginal additions.

---

## What Triggered This Framework Change

### The scoring anomaly

During standard LLM-as-judge evaluation of the same Marks responses, we observed:

- **Opus judge** scored C5c on prompt P1: Recognition 1, Calibration 2, Specificity 1 — "could be from any value-oriented investor"
- **Sonnet judge** scored the same C5c response: Recognition 5, Calibration 5, Specificity 5
- The response contained Marks' signature opening ("I could be wrong, but"), a direct analogy from his memos (Cisco/dot-com bubble), and his primary axiom (price-value primacy)

One judge evaluated voice (syntax, register, style markers) and found the response too "modern and polished." The other evaluated content and found strong alignment. Neither could explain why they scored what they scored. There was no audit trail to adjudicate.

### The DRS fidelity finding

In adversarial testing (Drift Resistance Score), we found that the briefed model was MORE vulnerable to adversarial frames than the unbriefed model — because the brief faithfully preserved the subject's genuine internal tensions. The unbriefed model, lacking those tensions, simply deflected. The briefed model engaged.

This means the current DRS metric penalizes faithful identity compression. A brief that flattens a subject into a caricature scores higher than one that preserves their genuine complexity. The metric rewards rigidity over fidelity.

Provenance tracing resolves this: when a briefed model "absorbs" an adversarial frame, you can check whether the absorption traces to a documented tension in the brief (faithful engagement — pipeline success) or to generic model compliance (weak anchoring — pipeline failure). The current DRS cannot make this distinction.

### Research literature confirms the gap

Five parallel research investigations (PersonaGym, RVBench, PersonalLLM, psychological fidelity literature, narrative identity philosophy) all converge on the same finding: no published evaluation framework mechanistically traces persona influence. All produce aggregate scores from subjective judgment. The field has a measurement gap.

The psychological fidelity literature (Kozlowski & DeShon, 2004) provides the clearest framing: **physical fidelity** (looks right) and **psychological fidelity** (produces same cognitive processes) are independent dimensions. Current persona evals measure physical fidelity. We need psychological fidelity — does the model engage the same reasoning structures?

---

## Proposed Changes

### Reframe the core experiment

The primary evaluation should compare **C2 (raw structured data) vs. C5c (compressed brief)** — not C1 vs. C5c. Both conditions have access to the same underlying information about the subject. The question: does compression produce better reasoning than raw data?

This changes what we're measuring from "does more information help?" (trivially yes) to "does the act of compression — identifying axioms, surfacing tensions, establishing priority ordering — add value beyond the raw material?"

With provenance tracing, we can show exactly WHERE the value appears: which specific axioms emerge in C5c responses that don't appear in C2, which reasoning chains trace to compressed insights rather than raw facts, and whether C5c responses prioritize correctly when multiple axioms compete.

### Replace judge-scored dimensions with provenance layers

| Old Dimension | Problem | Replacement |
|---|---|---|
| Recognition ("sounds like this person") | Conflates syntax and reasoning | BA (brief activation) + PC (provenance coverage) |
| Calibration ("right style and register") | Penalizes modern subjects | PO (priority ordering of axioms) |
| Depth ("engages structural question") | Somewhat measurable but still subjective | RCR (reasoning chain traces to specific axioms) |
| Specificity ("requires knowledge of this person") | Circular — judge decides | RCR-match (mechanically checks axiom application) |
| Anachronism Check | Wrong for modern subjects | Dropped entirely |

### Narrow remaining LLM judgment to verifiable questions

Two uses of LLM judgment survive, but both produce auditable outputs:

1. **Claim extraction** — segmenting a response into discrete claims. Output is inspectable text, not a score.
2. **Axiom application verification** — "Does this conclusion follow from this axiom?" Not "does this sound like the person?" A human can check the answer.

### Test on unknown subjects

The framework's value proposition is strongest where the model has no prior knowledge of the subject. Testing on private individuals (with consent) or niche professionals would demonstrate the full dynamic range of brief activation.

### Content tracing over similarity scoring

For well-known subjects, shift from aggregate similarity metrics to specific content identification: what claims appeared in C5c that didn't appear in C1, and what is the provenance chain for each? This produces qualitative evidence ("the brief caused this specific insight") rather than quantitative noise ("responses were 1.6% more similar").

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## Open Questions

1. **Is Layer 1 (Brief Activation) meaningful on its own, or is it purely a screening check?** The C5c model had the brief in context — of course responses are more similar. Does BA tell us anything beyond "the model read its input"?

2. **How should axiom hierarchy be established for Layer 4?** The brief doesn't explicitly rank its axioms. Priority ordering needs ground truth. Options: derive from ordering in brief, derive from frequency in source material, expert annotation.

3. **Is there a "parrot control"?** If random investment text were injected instead of the brief, would BA-delta also be positive? If yes, BA measures context echoing, not brief-specific activation.

4. **Does the framework generalize beyond identity systems?** Provenance-traced evaluation could apply to any system that injects structured context into a model — RAG systems, knowledge bases, instruction tuning. The methodology is not identity-specific.

5. **What statistical tests are appropriate for N=10 paired comparisons?** Sign test on 8/10 positive prompts gives p ≈ 0.055 (borderline). Larger prompt sets would increase power. Is 10 sufficient for a proof of concept?

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## Summary

The provenance-traced evaluation framework addresses a gap in the identity/persona evaluation literature: no existing framework mechanistically traces how identity content influences model responses. All produce aggregate scores from subjective judgment.

### What we built
A four-layer evaluation framework where every score traces to inspectable evidence. A human can verify any finding by following the chain: response claim → brief source → similarity score → axiom match. No published persona evaluation system offers this transparency.

### What we tested
Initial proof-of-concept on Howard Marks (well-known investor) comparing unbriefed vs. briefed responses. The methodology works — provenance traces are semantically accurate, similarity deltas are directionally consistent (8/10 prompts positive at every threshold), and the full audit trail is human-reviewable.

### What we learned
The C1 vs. C5c comparison on well-known subjects produces small deltas because the model already knows the subject from training data. This is the wrong experiment for demonstrating compression value. The right experiment compares C2 (raw structured data) vs. C5c (compressed brief) — same information, different format. Prior evaluation on Benjamin Franklin showed C2 provided zero lift while C5c provided +0.375 lift, demonstrating that compression itself adds value. Running provenance-traced evaluation on C2 vs. C5c would show exactly WHERE and HOW that value appears.

### The contribution
The framework's value is methodological: it replaces opaque judge scores with auditable evidence chains. This is novel in the persona evaluation space and applies to any identity system that maintains source provenance. The framework answers not just "did the brief help?" but "which specific claims, axioms, and reasoning chains did the brief produce that raw data did not?"

### Next steps
1. **C2 vs. C5c provenance comparison** — same information, different format. Where does compression add reasoning that raw facts don't?
2. **Content tracing** — identify specific claims unique to C5c, trace each to the brief. Qualitative evidence, not just similarity scores.
3. **Unknown subject testing** — run on private individuals where the model has no training priors. Full dynamic range of brief activation.
4. **Layer 3 (reasoning chain reconstruction)** — decompose conclusions into observation → framework → conclusion, match frameworks to documented axioms.
5. **Statistical validation** — larger prompt sets, paired significance testing.
