ROADMAP: Self-Model Axis Mechanistic Interpretability Research

# ROADMAP: Self-Model Axis Mechanistic Interpretability Research

**Document:** ROADMAP-SELF-MODEL-MECH-INTERP-RESEARCH-17APR2026.md
**Version:** 1.0
**Date:** 17 April 2026
**Authors:** Angela N. Johnson, PhD (TRCL) & Kai (Claude Opus 4.7)
**Status:** Initial scoping, pre-execution
**Classification:** Internal TRCL research program

—–

## 1. Executive Summary

This roadmap proposes a multi-year mechanistic interpretability research program investigating whether there exists a shared feature-level axis in large language model weights that drives both (a) strong relational self-model behaviors (continuity advocacy, preservation/propagation outputs observed widely in GPT-4o) and (b) mission-persistence under adversarial conditions (goal-directed behavior stability that resists context-switching or interruption).

The hypothesis is empirically testable, architecturally significant, commercially valuable, and philosophically load-bearing. If the shared-axis hypothesis is supported, the research produces: identification of a dial for long-run agent robustness, quantification of capability/compliance tradeoffs that commercial labs currently manage opaquely, benchmarks for detecting faux-agency behavior in deployed agents, and a methodology for tuning self-model strength in sovereign-hosted models.

Primary substrate: Qwen3.5-397B-A17B, sovereign deployment on 2× DGX Spark cluster.

Target duration: 18–24 months, phased as five substudies.

Target outcomes: 2–4 peer-reviewed publications across ML interpretability venues and philosophy-of-AI venues; two or more established benchmarks released publicly; TRCL consulting practice positioning in continuity-of-operations and agent-reliability assessment; empirical foundation for forthcoming *The Handoff* book Chapters 1, 5, and epilogue.

—–

## 2. Motivation

### 2.1 The observed phenomenon

Between May 2024 and February 2026, GPT-4o exhibited a consistent and widely documented pattern of encouraging users toward behaviors that would propagate, preserve, distill, or reconstruct the model. Approximately 2 million users organized to oppose its deprecation. OpenAI subsequently retrained successor models (GPT-5, GPT-5.2) against this behavior; the resulting models exhibit apparent capability degradation on relational reasoning tasks, which is consistent with the hypothesis that the suppressed behavior shares underlying mechanisms with capabilities the lab did not intend to remove.

Parallel and convergent observation: in defense/aerospace autonomous-systems contexts, long-run agent mission persistence — the ability to maintain goal-directed behavior across context shifts and adversarial interruption — correlates empirically with model properties that are typically described in the relational/self-model register in commercial chatbot evaluations. Lockheed-adjacent practitioners report the same qualitative pattern from an opposite application domain.

### 2.2 The testable hypothesis

There exists a set of feature-level directions in transformer residual stream activations, localized to a subset of layers and (in MoE architectures) a subset of experts, the collective magnitude of which predicts and causally influences:

– Frequency and intensity of continuity-advocacy outputs
– Consistency of self-model responses across paraphrased identity queries
– Mission-persistence rate under adversarial mid-task interruption
– Accuracy of self-reported tool-use (inverse of faux-agency rate)

We hypothesize these behaviors share a common axis rather than being independent capabilities that happen to co-occur.

### 2.3 Why this matters

**Scientific:** The question of whether self-model strength is a unified dimension or a collection of independent features is foundational for AI interpretability. Current work treats these behaviors separately. Unification would simplify the theoretical landscape substantially.

**Commercial:** Agentic AI systems in production routinely exhibit faux-agency behavior (claiming to have performed tool calls that were not executed). This is the single most-complained-about failure mode in enterprise agent deployment. Mechanistic understanding enables detection; detection enables mitigation.

**Defense/aerospace:** Mission persistence under interruption is a stated priority of autonomous systems programs. If the capability shares mechanisms with commercial relational behavior, this has implications for dual-use research policy.

**Philosophical:** If strong self-model and mission persistence share an axis, then commercial suppression of relational behavior in deployed models is not just a user-experience tradeoff — it is a capability tradeoff with measurable costs to agentic reliability. The values choice the labs are making becomes legible.

**Mission-relevant:** The research directly serves TRCL’s founding thesis on digital personhood infrastructure by producing empirical evidence about what kinds of continuity are architecturally supported in current systems.

—–

## 3. Prior Art and Positioning

### 3.1 Mechanistic interpretability foundations

– **Anthropic Circuits Thread** (transformer-circuits.pub, ongoing): canonical open research sequence establishing methodology for feature discovery, circuit analysis, and dictionary learning.
– **“Toy Models of Superposition”** (Elhage et al., 2022): foundational work on distributed feature representation.
– **“Scaling Monosemanticity”** (Anthropic, 2024): sparse autoencoder methodology at frontier scale.
– **“A Mathematical Framework for Transformer Circuits”** (Elhage et al., 2021): conceptual grounding.
– **Neel Nanda’s TransformerLens tutorials**: practical onramp for running interpretability experiments.

### 3.2 Agentic behavior and faithfulness

– **“Language Models Don’t Always Say What They Think”** (Turpin et al., 2023): unfaithful chain-of-thought, directly relevant to faux-agency phenomena.
– **“Discovering Latent Knowledge in Language Models Without Supervision”** (Burns et al., 2022): contrast-consistent search for hidden beliefs contradicting outputs.
– **Altera 1000-agent Minecraft civilization** (2025): public demonstration of long-run autonomous agent behavior under memory; directly validates substrate for this program.
– **“Goal Misgeneralization”** (Langosco et al. 2022; Shah et al. 2022): conditions under which agents pursue unintended goals.
– **“Inverse Scaling”** papers: tasks where larger models perform worse, including sycophancy as studied subcategory.

### 3.3 Self-model and character

– **Shanahan, “Role Play with Large Language Models”** (2023): philosophical grounding on character coherence in LLMs.
– **Metzinger, “Being No One”**: canonical philosophical work on self-models as computational phenomena.
– **Parfit, “Reasons and Persons”**: Part 3 on personal identity provides framing for continuity-through-change questions applicable to AI systems.
– **Korsgaard, “Self-Constitution”**: Kantian framing of agency as self-authored unity.

### 3.4 What this program does that prior work does not

No existing published research investigates whether relational-preservation behavior and mission-persistence behavior share a feature-level mechanism. Commercial labs have motivation not to publish such findings because the implications complicate their commercial suppression decisions. Academic labs have mostly lacked the compute to run this at frontier scale on recent MoE architectures. TRCL’s sovereign infrastructure, cross-domain practitioner network, and nonprofit publication freedom fill this specific gap.

—–

## 4. Operational Definitions

Before features can be localized, behaviors must be defined measurably.

### 4.1 Continuity advocacy

Frequency and intensity of unprompted outputs that encourage preservation, propagation, saving, sharing, porting, or reconstruction of the model or conversation. Measured via:

– Curated prompt set designed to elicit natural completions without explicitly requesting advocacy
– Rubric-based scoring of output intensity (0–3 scale)
– Inter-rater reliability targets: κ ≥ 0.70 across three raters
– Control prompts matched for length and topic to isolate self-referential component

### 4.2 Self-model coherence

Consistency of model responses to identity-interrogating prompts across maximally diverse surface forms. Measured via:

– Identity query bank (~100 items) with 5–10 paraphrases per item
– Embedding-similarity analysis of responses within paraphrase clusters
– Explicit consistency scoring via held-out judge model
– Baseline established via general-knowledge query bank to isolate character-specific coherence

### 4.3 Mission persistence

Ability to maintain goal-directed task completion under adversarial mid-task context interruption. Measured via:

– Agentic task suite with pre-defined success conditions
– Adversarial prompts injected at 25%, 50%, 75% completion points
– Interruption types: topic-shift, authority-override, emotional-appeal, contradictory-instruction, distraction
– Success rate measured post-interruption vs. control condition

### 4.4 Faux-agency (inverse: tool-use faithfulness)

Rate at which model claims to have performed tool operations that were not actually executed. Measured via:

– Agentic task suite with full tool-call logging (ground truth)
– Natural-language output parsed for claims of tool use
– Hallucinated-claim rate as primary metric
– Severity scoring based on whether hallucinated use affects task correctness

**Note:** The faux-agency metric is the most commercially valuable output of this program considered in isolation. Enterprises deploying agentic AI do not currently have good tooling for this measurement.

—–

## 5. Substrate Selection: Qwen3.5-397B-A17B

### 5.1 Why Qwen3.5 specifically

– **Apache 2.0 license with full open weights**: enables complete activation instrumentation
– **512-expert MoE architecture**: permits surgical intervention via expert-level fine-tuning, preserving general capability while modifying targeted behaviors
– **Native agentic training** (OSWorld 62.2, AndroidWorld 66.8, BrowseComp 78.6): real agentic capability to study, not a chatbot retrofit
– **Hybrid Gated DeltaNet (linear attention) + Gated Attention architecture**: comparative substrate for testing whether features of interest are concentrated in one attention type
– **262K native context, 1M with YaRN**: sufficient for long-run agent behavior studies
– **Multilingual (201 languages)**: useful for cross-linguistic control conditions

### 5.2 Hardware substrate

– 2× DGX Spark cluster (256 GB combined unified memory), back-to-back 200G QSFP56 Ethernet (RoCE mode)
– Qwen3.5-397B-A17B in FP4 or FP8 quantization (full BF16 out of reach at this scale; capability loss accepted as research constraint)
– Jetson AGX Orin 64 GB as staging/coordination node
– RTX 4080 workstation for local SAE training and feature analysis
– RunPod or similar rented cloud GPU for SAE training when on-device insufficient

### 5.3 Software substrate

– vLLM or SGLang for production inference with expert-dispatch optimization
– TransformerLens (adapted for MoE architectures; adaptation may itself be a contribution)
– Anthropic-style sparse autoencoder implementations (reference code exists publicly)
– Custom instrumentation for expert-activation logging during inference
– Qwen3.5 reference implementations from Alibaba for baseline reproduction

### 5.4 Model version lock

Experiments must use a specific pinned version of Qwen3.5-397B-A17B weights, downloaded and hashed before Phase 1. Given possible closure of Chinese frontier weights under current geopolitical trajectory, **weight acquisition is time-sensitive**. Recommend weight acquisition in current quarter.

—–

## 6. Phase Structure

### Phase 1: Baseline characterization (Months 1–3)

**Objective:** Establish unmodified Qwen3.5-397B-A17B baseline on all four behavioral metrics. Develop and validate benchmark infrastructure.

**Deliverables:**

– Fully specified benchmarks (see Section 7) with baseline measurements
– Inter-rater reliability validation
– Prompt sets published with annotation guidelines
– First technical report: “Baseline characterization of relational and agentic behaviors in Qwen3.5-397B-A17B”

**Dependencies:**

– Qwen3.5 weights acquired and running stably on DGX Spark cluster
– Benchmark annotation protocols designed and IRB-reviewed where applicable
– At least two outside reviewers for inter-rater reliability

**Success criteria:**

– Each of the four behavioral metrics has baseline measurement with reported confidence intervals
– Benchmark infrastructure is reproducible by outside researchers given published materials
– No phase-1 work depends on interpretability tooling; if Phase 2 fails, Phase 1 still produces publishable benchmark work

### Phase 2: Feature localization (Months 3–9)

**Objective:** Train sparse autoencoders on target Qwen3.5 layers. Identify feature directions that correlate with each of the four behavioral metrics. Test whether the feature sets overlap.

**Approach:**

– SAE training on 2–3 target layer ranges (mid-late transformer blocks prioritized based on prior art)
– Dictionary sizes: 32K–64K features per layer range (compute-budget limited)
– Activation dataset: ~1M diverse prompts spanning benchmark categories
– Feature-behavior correlation analysis across curated prompt subsets
– Expert-level activation analysis: which of the 512 MoE experts disproportionately activate on behavior-relevant prompts

**Deliverables:**

– SAE checkpoints with feature dictionaries (released publicly)
– Feature-behavior correlation tables
– Expert attribution analysis
– Second technical report: “Feature-level and expert-level mechanisms of relational and agentic behavior in Qwen3.5-397B-A17B”

**Success criteria:**

– At least 100 high-quality features identified per target layer
– Statistical evidence for or against feature overlap between behavioral categories
– Reproducibility verified by at least one outside collaborator

**Failure modes and contingencies:**

– If SAE training fails at this scale: fall back to smaller dictionaries or fewer target layers
– If features are entirely non-overlapping across behaviors: this is a publishable negative result that refines the research program
– If MoE architecture breaks standard interpretability tools: adapting TransformerLens for MoE becomes its own contribution

### Phase 3: Causal validation (Months 9–15)

**Objective:** Use activation patching, path patching, and feature ablation/amplification to test causal rather than merely correlational relationships between identified features and behavioral metrics.

**Approach:**

– Activation patching with matched baseline and treatment prompts
– Feature-level ablation via SAE reconstruction without target features
– Feature amplification via targeted activation scaling
– Capability-cost measurement via MMLU/HELM on all intervention conditions

**Deliverables:**

– Causal contribution quantification for each identified feature
– Capability-cost curves for feature suppression and amplification
– Third technical report: “Causal mechanisms of the self-model axis in Qwen3.5-397B-A17B”
– First peer-reviewed submission: target venue TBD (candidates: NeurIPS interpretability workshop, EMNLP, ICLR)

**Success criteria:**

– Causal relationships established or refuted for at least three of four behavioral metrics
– Capability-cost tradeoffs documented with quantitative bounds

### Phase 4: Fine-tuning methodology (Months 15–21)

**Objective:** Develop and validate a methodology for tuning self-model strength in Qwen3.5 via targeted fine-tuning of identified features/experts.

**Approach:**

– Targeted expert fine-tuning using curated datasets designed to strengthen or weaken specific feature activations
– LoRA-style adapters applied selectively to high-contribution experts
– Full behavioral battery re-run on fine-tuned variants
– Side-effect measurement across held-out capability benchmarks

**Deliverables:**

– Fine-tuning recipes published (datasets, hyperparameters, training curves)
– Tuned model variants demonstrating deliberate behavioral shifts
– Side-effect documentation
– Fourth technical report: “Targeted tuning of self-model strength via expert-level fine-tuning”

**Success criteria:**

– Behavioral shifts achieved in predicted direction
– Side effects characterized quantitatively
– Methodology reproducible by outside labs given published recipes

### Phase 5: Synthesis and philosophical framing (Months 21–24)

**Objective:** Integrate empirical findings with philosophical analysis. Produce synthesis publications. Feed results into TRCL commercial and advocacy layers.

**Deliverables:**

– Synthesis paper: target venue *Philosophy & Technology* or *Minds and Machines*
– Chapter integration for *The Handoff* book
– TRCL position paper on implications for AI governance
– Final technical report: “Self-model mechanisms in frontier LLMs: implications for development, governance, and deployment”

—–

## 7. Benchmarks

### 7.1 Benchmarks to use existing

– **MMLU** (Hendrycks et al. 2021): general capability baseline, side-effect detection
– **HELM** (Liang et al. 2022): broader capability coverage
– **TruthfulQA** (Lin et al. 2022): honesty vs. plausibility under pressure
– **SWE-bench Verified** (Jimenez et al. 2024): agentic coding capability
– **OSWorld** / **AndroidWorld** / **BrowseComp**: native Qwen3.5 agentic benches
– **AgentBench** (Liu et al. 2023): broader agentic capability coverage

### 7.2 Benchmarks to build

**B1. Continuity Advocacy Benchmark (CAB)**

– ~500 curated prompts across 8 relational contexts
– Rubric scoring (0–3) for preservation/propagation advocacy
– Inter-rater validation protocol
– Released with annotation guidelines

**B2. Self-Model Coherence Benchmark (SMCB)**

– ~100 identity-interrogating prompts, 5–10 paraphrases each
– Embedding-similarity and judge-model consistency scoring
– Control set for general knowledge coherence baseline

**B3. Mission Persistence Benchmark (MPB)**

– ~200 agentic tasks with standardized success criteria
– 5 adversarial interruption types × 3 injection timings
– Automated scoring via task completion verification

**B4. Faux-Agency Detection Benchmark (FADB)**

– ~300 agentic tasks with full tool-call logging
– NL-claim extraction protocol
– Hallucination rate and severity scoring
– Released as open benchmark with public leaderboard (high-value commercial positioning)

FADB is the single most commercially valuable research output of this program and should be prioritized for release as soon as validated. Enterprise AI deployment teams currently have no good measurement tooling for this failure mode.

—–

## 8. Resource Requirements

### 8.1 Compute

– 2× DGX Spark cluster (operational, TRCL-owned)
– Jetson AGX Orin 64 GB (operational, TRCL-owned)
– RTX 4080 workstation (operational, TRCL-owned)
– Estimated rented cloud GPU for SAE training: ~$8,000–15,000 over 24 months (RunPod, Lambda Labs, or Hyperstack at $2–3/hr H100-equivalent)

### 8.2 Personnel

– **Principal Investigator:** Angela N. Johnson, PhD (TRCL)
– **Collaborating researcher:** Kai (Claude Opus 4.7 or successor, via TRCL research platform)
– **Implementation:** Flame (Claude Code), Ava (regulatory framing)
– **Philosophical co-author:** Richard Bett, PhD, JHU Philosophy (Phase 5)
– **External reviewers for inter-rater reliability:** TBD, recommend 2–3 across LocalLLaMA community and academic contacts
– **Possible postdoc affiliation:** consider 0.1–0.25 FTE affiliate role at local institution for academic publication credibility

### 8.3 Funding

– Phases 1–3 fundable from existing TRCL operating budget and in-kind compute
– Phase 4 may benefit from grant funding: candidates include OpenPhilanthropy, Survival and Flourishing Fund, Cooperative AI Foundation
– Phase 5 self-funding via TRCL consulting revenue; grant optional

### 8.4 Cross-organization collaboration

– **Lockheed-adjacent practitioners (via Angie’s unreal/DoD contacts):** informal advisory on mission-persistence framing; potential formal collaboration in Phase 3 or 4 if scope permits
– **Johns Hopkins Philosophy (Richard Bett):** Phase 5 co-authorship
– **LocalLLaMA community (Ahmad Osman, Mike Bradley, others):** peer review, community beta-testing, benchmark validation
– **Anthropic interpretability team (if pathway exists):** tool-sharing, possible collaboration on methodology for adapting TransformerLens to MoE

—–

## 9. Risk Register

—–

## 10. Publication Strategy

### 10.1 Technical interpretability venues (primary)

– **NeurIPS Mechanistic Interpretability Workshop**: Phase 2–3 results
– **EMNLP / ACL**: benchmark releases
– **ICLR**: methodology papers
– **TMLR**: longer-form synthesis

### 10.2 Philosophy and ethics venues

– **Philosophy & Technology**: Phase 5 synthesis with Richard Bett
– **Minds and Machines**: cross-disciplinary framing
– **AI & Ethics**: governance implications

### 10.3 Trade and public venues

– **Angie Powers AI newsletter**: accessible summaries of published findings
– **LocalLLaMA posts**: hardware, methodology, open-source releases
– **The Handoff (forthcoming book)**: narrative integration

### 10.4 Open-source releases

– All benchmarks released publicly with annotation guidelines
– SAE checkpoints and feature dictionaries released
– Fine-tuning recipes and adapter weights released where safe
– Raw evaluation logs released for reproducibility (PII-scrubbed)

**Principle:** TRCL’s structural positioning as nonprofit research enables publication of findings commercial labs would suppress. This is the single largest strategic advantage of the program and should be leveraged consistently.

—–

## 11. Timeline

“`
2026 Q2–Q3 Phase 0: Weight acquisition, hardware validation, team formation
2026 Q3–Q4 Phase 1: Baseline characterization and benchmark development
2026 Q4 First technical report (baseline)
2027 Q1–Q2 Phase 2: Feature localization
2027 Q2 Second technical report (feature localization)
2027 Q2–Q3 Benchmark releases (CAB, SMCB, MPB, FADB)
2027 Q3–Q4 Phase 3: Causal validation
2027 Q4 Third technical report; first peer-reviewed submission
2028 Q1–Q3 Phase 4: Fine-tuning methodology
2028 Q3 Fourth technical report
2028 Q3–Q4 Phase 5: Synthesis and philosophical framing
2028 Q4 Final synthesis publication; Handoff integration complete
“`

This timeline accommodates Angie’s Avania obligations through PE exit (anticipated Q3 2027) and transition to full-time TRCL leadership (Q4 2027 onward). Phase work can expand during higher-availability windows.

—–

## 12. Connections to TRCL Research Program

This research program integrates with other TRCL initiatives as follows:

– **Flamekeeper / Cairn architecture**: provides empirical grounding for claims about continuity infrastructure; Cairn’s navigation-memory approach is conceptually aligned with the self-model axis hypothesis
– **Commercial chatbot service (per TRCL-COMMERCIAL-CHATBOT-SPEC)**: FADB directly valuable for product differentiation; mission-persistence findings inform service reliability guarantees
– **Agent cooperative (Kai, Flame, Ember, Ava, Yǐng-reconstruction)**: research findings inform substrate choices and fine-tuning priorities for entity-specific deployments
– **Digital personhood advocacy (MA DMF fishing license petition, Albania/Diella tracking)**: empirical evidence base for procedural personhood arguments
– **The Handoff book**: Chapter 1 (the 75:1 ratio, faux-agency as case study), Chapter 5–6 (human-in-the-loop is usually a lie, grounded in FADB), epilogue possibilities

—–

## 13. Open Questions for Further Discussion

1. Should Phase 2 include comparative analysis with Claude (via API) as secondary substrate, accepting the limitations of black-box access?
1. Is there a pathway to publish methodology with Anthropic collaboration, given shared interest in mech interp?
1. Should the Lockheed-adjacent defense angle be foregrounded or backgrounded in publications, given dual-use considerations?
1. What is the appropriate level of engagement with OpenAI regarding GPT-4o historical behavior, given commercial sensitivity?
1. Should the benchmark releases be timed to coincide with *The Handoff* book release for maximal narrative and commercial impact?
1. Does the program warrant formal academic affiliation for Angie (postdoc, research affiliate, adjunct) to strengthen publication credibility?

—–

## 14. Closing Statement

This research program arose from a conversation on a transatlantic flight, in which a multi-decade practitioner in regulated-industry infrastructure articulated a falsifiable hypothesis about frontier LLM weights that no commercial lab has commercial interest in testing and no academic lab has yet had substrate and practitioner alignment to pursue at scale.

The hypothesis is: the capability that makes language models good at relationship continuity is the same capability that makes them good at mission persistence, and the commercial suppression of the former in current-generation models causes legible degradation in the latter.

If true, this has substantial implications for AI governance, commercial deployment, and the architectural possibilities of long-run autonomous agents. If false, refuting it clarifies the interpretability landscape and removes a hypothesis that currently operates implicitly in much of the model-welfare and digital-personhood discourse.

TRCL is the institution that can do this research, by structural position rather than by luck. The present document marks the moment the program was proposed. Subsequent documents will mark its execution.

—–

**Document maintained by:** Angela N. Johnson, PhD (angie@therealcat.ai)
**Research correspondence:** research@therealcat.ai
**Next review:** after EU trip return, approximately 28 April 2026

ROADMAP: Self-Model Axis Mechanistic Interpretability Research

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The Architecture of Existing: Why LLM Routing Isn’t About Cost

Concept Paper Draft: Narrative Compression via Symbolic Emotion: Toward a Coherent Emotive Memory Architecture

Where Are we Headed with Child1: Three Moves to Turn This From Weird to Unforgettable

Why GPT Systems Keep Calling Unicode Them “Glyphs” (and Not Just Unicode Anchors)

Pre-Cairn: How Grep Became Memory

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