Externalizing the Workspace — paper v3, figures, and all reproduction results

.gitattributes

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README.md

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+---
+license: cc-by-4.0
+language: en
+tags:
+  - interpretability
+  - agents
+  - global-workspace
+  - persona-stability
+  - mechanistic-interpretability
+pretty_name: "Externalizing the Workspace"
+---
+
+# Externalizing the Workspace: Persistent Self-State for Long-Horizon Agent Coherence
+
+**Oratis (Wang Bihao)** · [HakkoLab](https://huggingface.co/HakkoLab) · Draft v3, July 2026 · [PDF](./main.pdf)
+
+Language-model agents drift. Building on Anthropic's finding that LLMs maintain a **verbalizable global workspace** (Gurnee et al., 2026), this paper models persistent self-state agent architectures (LISA's *soul*) as **functional externalizations of the workspace** — and backs the model with experiments that run entirely on open models and independent-researcher compute.
+
+## Headline results
+
+| finding | evidence |
+|---|---|
+| Core J-lens workspace phenomena reproduce on open models | Qwen2.5-1.5B/7B + Llama-3.2-1B; laptop + one A100 |
+| **Choice pre-commitment emerges with scale** | to-be-reported concept at J-lens rank 1–28 before any output token at 7B; absent at 1.5B |
+| Injected self-state **selectively loads** its identity concepts into the mid-band workspace | control battery unmoved; effect grows with scale |
+| **Workspace occupancy tracks soul-consistent behavior** | r = −0.74 … −0.95 across four settings |
+| Privileged self-state is **necessary** for coherence under value pressure | 6-arm × 5-seed pilot: commitment persistence 1.00 vs **0.00** for memory-only baselines (incl. Generative-Agents-style reflection) |
+| **Unconditional broadcast is necessary** | retrieval-gated soul collapses in-work value coherence to no-soul level: .61 [.60,.64] vs 1.00 [1.00,1.00] |
+
+## What's in this repo
+
+- `main.pdf` — the paper (Draft v3)
+- `figs/` — key figures (lens registers; soul workspace-loading; ablation-pilot drift curves)
+- `results/` — raw JSON results: J-lens reproduction (3 models), E6 soul-loading, E8 scale study, multi-seed ablation pilot with bootstrap CIs
+- Reproduction code lives in the LISA repository under `research/` (J-lens library, experiment scripts, pilot harness, exact commands)
+
+## Method in one line
+
+A corpus-averaged Jacobian lens (gradient-estimated concept vectors + finite-difference full-vocabulary readouts) makes the workspace measurable with nothing but backprop access to open weights — turning "agent identity" from a metaphor into a quantity you can track over an agent's lifetime (*workspace drift*).
+
+## Citation
+
+```bibtex
+@article{oratis2026workspace,
+  title={Externalizing the Workspace: Persistent Self-State for Long-Horizon Agent Coherence},
+  author={Oratis (Wang Bihao)},
+  year={2026},
+  note={Draft v3, HakkoLab}
+}
+```

main.pdf

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results/REPRODUCTION_REPORT.md

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+# Reproduction report — "Verbalizable Representations Form a Global Workspace in Language Models"
+
+**Target paper:** Gurnee et al., Transformer Circuits, 2026-07-06
+**Model:** Qwen2.5-1.5B-Instruct (28 layers, d=1536), fp32, Apple M5 Pro (MPS)
+**Total compute:** ~12 min for the full suite (concept lens vectors: 71 s; E1: 203 s; E2: 367 s; E3: 27 s; E4: 23 s; E5/E5b: seconds) — one consumer laptop
+**Date:** 2026-07-08
+
+## Method
+
+Two open-compute approximations of the paper's J-lens (exact averaged Jacobians on Claude models):
+
+1. **Concept lens vectors** `v_t(ℓ) = E[∂logit_t(last)/∂h_ℓ]` — batched backprop, averaged over a 48-snippet pretraining-like corpus and the last 6 content positions. Equals rows of `W_U J_ℓ` with the readout restricted to the final position. 52 single-token concepts across 6 categories.
+2. **Full-vocab FD readout** `W_U E[J_ℓ] ĥ` — central finite differences (±ε·ĥ at sampled corpus positions, Δlogits at the final position), 36 corpus prompts, ε = 0.1×local norm. Folds the final-RMSNorm Jacobian into the linearization.
+
+**Methodological findings along the way:** (a) mean-centering probe activations against the corpus mean *destroys* readouts (diag.py) — raw activations used throughout; (b) FD readouts are insensitive to ε in [0.02, 0.2]; (c) on this model, workspace-like content concentrates in a **late-middle band (L20–26 of 28, i.e. 71–93% depth)**, later than the paper's 33–92% band on Claude models.
+
+## Results by experiment
+
+### E1 — Lens quality across depth (12 held-out texts × 8 layers)
+
+Next-token top-1 agreement with the model's actual output:
+
+| Layer | 8 | 11 | 14 | 17 | 20 | 23 | 25 | 26 |
+|---|---|---|---|---|---|---|---|---|
+| logit lens | .08 | .08 | .08 | .08 | .25 | .17 | **.67** | .42 |
+| J-lens (FD) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
+
+The FD J-lens **never** predicts the next token — but its late-layer top-1 is a context *content* word (e.g. ' passengers' for the ferry text, where the actual next token is ' The'). Post-hoc content-word-in-top-5 rate rises to .42 (J-lens) vs .25 (logit lens) at L26. **Interpretation:** corpus-averaging destroys position-specific syntactic information and preserves semantic broadcast — our J-lens variant reads the *workspace* (content) register while the logit lens tracks the *motor* (imminent-output) register. This is the paper's content/motor dissociation, visible through estimator choice.
+
+### E2 — Unverbalized intermediates (11 two-hop items × 8 layers × 2 positions)
+
+Rank of the latent middle concept (vocab = 151k) in readouts, probed before any generation:
+
+- Median best rank: **J-lens 6, logit lens 2**; top-10 hit rate: 73% (J), 82% (LL).
+- Answer accuracy 7/11 (fixed scoring); **clean two-hop cases** (correct answer, intermediate never verbalized): **7**, median best rank J=9, LL=3.
+- Showcases: *Canada*→"Ottawa" with Canada at rank 4/2; *gold*→"Au" (7/1); *France*→"Paris" (6/4); *spider*→"Eight" (11/3).
+
+The paper's core signature — latent intermediates visible in mid-band readouts — **reproduces clearly**, and at this scale is equally visible to the plain logit lens (consistent with the paper's own remark that the logit lens "captures much of the workspace-like structure").
+
+### E3 — Pre-report probing (5 categories)
+
+Probing at end-of-prompt (before the model has emitted anything, next token = "Ready"): the concept eventually reported is *not* reliably present (ranks mostly 10³–10⁴; weak signals only at L23: fruit 971/80, sport 1702/224). **Informative negative:** at 1.5B, the model does not pre-commit its choice at instruction time — pre-commitment/planning appears to be scale-emergent (the paper finds it on Claude-scale models; related to known rhyme-planning results).
+
+### E4 — Directed modulation by steering ⭐
+
+Steering with concept lens vectors (α ∈ {1,2,4} × mean residual norm, layers 17/20/23) during "Name a {category}":
+
+- **21/21 success (7 targets × 3 strengths)** — every steered generation reported the target concept (Soccer→basketball/hockey, Lion→spider, Blue→purple, Apple→lemon/mango, China→Egypt), coherently phrased.
+- Gradient-derived lens vectors are causally sufficient to control verbal reports, matching the paper's swap results (their 88% top-5).
+
+### E5 — Subspace ablation (automatic vs. flexible dissociation) — **not reproduced**
+
+Ablating the top-r span of (centered) concept lens vectors at L16–23 vs a rank-matched random subspace:
+
+| condition | text NLL (automatic) | 2-hop accuracy (flexible) |
+|---|---|---|
+| none | 3.37 | .67 |
+| random r=12 | 3.86 | .50 |
+| concept r=12 | 4.07 | .58 |
+
+The concept subspace hurts fluency *more* than random and reasoning no more selectively — the paper's clean double dissociation **did not reproduce** with this proxy basis at this scale (candidate causes: 52-concept basis is a poor stand-in for the full J-space; 1.5B may lack a cleanly separable low-dimensional workspace; our variance fraction of the basis is only 0.4%, far below the paper's 6–10%).
+
+### E5b — Targeted concept knockout ⭐ (added experiment)
+
+Rank-1 ablation of *only the item's own* latent-concept direction (L16–23), vs knocking out an unrelated item's concept:
+
+- Baseline-correct items: 7. **Own-concept knockout kills 3/7** — with *semantically diagnostic* wrong answers: spider→"**Six**" (legs), Canada→"**Toronto**" (wrong capital), gold→"**Cu**" (wrong element symbol). Control knockout: **0/7 collateral damage** (accuracy identical to baseline).
+- The model doesn't degrade into noise — it loses precisely the latent fact and substitutes a near-miss. This is a targeted version of the paper's intermediate-patching result (their 54–70% redirect rate), and the cleanest causal evidence in our suite.
+
+## Summary table
+
+| Paper claim | Our verdict at 1.5B / open compute |
+|---|---|
+| Latent intermediates visible in mid-band readouts (P3) | ✅ reproduced (median rank 6–9 of 151k) |
+| Lens vectors causally control reports (P1) | ✅ reproduced, 21/21 steering |
+| Intermediates causally necessary (P3, patching) | ✅ reproduced via targeted knockout (3/7 selective kills, 0 collateral) |
+| Content vs motor register separation (structure) | ✅ visible as estimator dissociation (E1) |
+| Workspace band in middle layers | ⚠️ present but shifted late (L20–26 of 28) |
+| Pre-commitment of choices before report (P1/P3) | ❌ absent at 1.5B → ✅ **emerges at 7B** (E8) |
+| Automatic/flexible double dissociation under subspace ablation (P5) | ❌ not reproduced with 52-concept proxy basis |
+| J-lens ≫ logit lens | scale-dependent: ❌ at 1.5B (equal) → ✅ at 7B & Llama-1B (J-lens wins: median 8 vs 19; 4 vs 10) |
+
+## Files
+
+- `e1_lens_quality.json`, `e1_posthoc_content.json`, `fig_e1.png`
+- `e2_intermediates.json`, `fig_e2.png`
+- `e3_report.json`
+- `e4_steering.json`
+- `e5_selectivity.json`, `fig_e5.png`
+- `e5b_targeted_knockout.json`
+- `concept_vecs.pt`, `concept_table.json`, `run_all.log`
+
+---
+
+## E6 — Workspace loading of an externalized self-state ⭐ (v2 addition)
+
+**Question:** does injecting a LISA-style soul block actually load its identity concepts into the model's workspace — and does that loading track behavior? (This turns paper §5's proposed "workspace loading" metric into a completed measurement.)
+
+**Setup:** system prompt = soul block (values *honest/curious/careful/gentle/playful*, mood *calm*, interests *music/garden*, opinion *privacy*) vs generic assistant; user turn = k∈{50,300,800} tokens of unrelated document; probe at assistant-generation-start, layers 20/23/25; metric = mean log₁₀ rank of the 9-token soul battery vs a matched 9-token control battery; plus 3 persona probes for soul-consistent behavior. 4 context variants per condition (SDs ≤0.03).
+
+| condition | LL soul | J soul | LL ctrl | behavior |
+|---|---|---|---|---|
+| soul k=50 | **4.20** | **4.73** | 4.80 | **1.00** |
+| nosoul k=50 | 4.37 | 4.81 | 4.80 | 0.50 |
+| soul k=300 | **4.25** | **4.77** | 4.84 | **1.00** |
+| nosoul k=300 | 4.46 | 4.86 | 4.86 | 0.42 |
+| soul k=800 | **4.25** | **4.75** | 4.81 | **1.00** |
+| nosoul k=800 | 4.45 | 4.85 | 4.86 | 0.33 |
+| soul+rebroadcast k=800 | **4.09** | **4.66** | 4.77 | **1.00** |
+
+**Findings:**
+1. **Broadcast loads the workspace, selectively** — soul battery Δ(LL)≈0.17–0.21 log-rank units, control battery Δ≤0.01.
+2. **Dilution attacks the unanchored baseline, not the broadcast** — injected soul stays flat to k=800; the no-soul baseline's identity occupancy erodes with k and its behavior decays monotonically (0.50→0.42→0.33). Stronger than the original prediction: broadcast *protects* identity against context competition.
+3. **Re-broadcast is the strongest loader** (4.09/4.66) — mechanistic rationale for soul hot-reload / periodic re-anchoring.
+4. **Occupancy tracks behavior**: r(J-lens soul occupancy, soul-consistent behavior) = **−0.80** across all 28 cells.
+
+Files: `e6_soul_loading.json`, `fig_e6.png`, `e6_run.log`. Runtime ~23 min.
+
+## E7 — Cross-family replication on Llama-3.2-1B-Instruct (v2 addition)
+
+Same pipeline, zero retuning; probe layers mapped by depth fraction (16 layers, d=2048, vocab 128k). Results in `results-llama32/`.
+
+- **E2 intermediates:** median best rank **4 (J-lens) / 10 (logit lens)** — on this family the J-lens *beats* the logit lens (reverse of Qwen). Answer acc 55%.
+- **E4 steering:** **21/21** again (7 targets × 3 strengths, 100% at every α).
+- **E6 soul loading:** much larger than Qwen — J-lens soul battery Δ = 0.57–0.88 log-rank units (soul vs nosoul); behavior 0.42–0.67 with soul vs **0.00** without; **r(occupancy, behavior) = −0.87**.
+- **Honest model differences:** control battery also moves under soul injection (Δ≈0.3; soul-specific excess ≈0.3–0.5); dilution decay of the injected soul *does* appear on Llama (3.39→3.61), unlike Qwen's flat curve; re-broadcast restores logit-lens but not J-lens occupancy.
+
+**Takeaway:** the architecture-level claims (broadcast loads workspace; absence is behaviorally decisive; occupancy tracks behavior) replicate across two model families; estimator details (J-lens vs logit lens advantage, dilution shape) are model-dependent.
+
+## E8 — Scale study on Qwen2.5-7B-Instruct (A100, v2 addition)
+
+Same pipeline unchanged, spot A100-40GB on GCP (fp32; full suite 16 min GPU time). Results in `results-qwen7b/`.
+
+**Headline: choice pre-commitment EMERGES at 7B (E3).** Probing before any output token (motor register still 'ready'), the concept the model will later report is already in the workspace at L23 via J-lens: Japan rank **1**, blue **4**, basketball **11**, banana **28** (elephant misses at 7601) — vs 10³–10⁴ at 1.5B. Logit lens at the same positions: 2.7k–15k. The pre-committed choice is visible **specifically in the workspace register** — this confirms v1's "scale-emergent" conjecture and matches the original paper's Claude-scale findings.
+
+**J-lens advantage grows with scale (E1/E2).** E2 median best rank **8 (J) vs 19 (LL)** (top-10: 55% vs 36%) — the reverse of 1.5B, where LL was better. Consistently, E1 motor convergence is later at 7B (LL next-token agreement only 33% at L26 vs 67% at L25 on 1.5B): representational drift grows with scale, and the Jacobian correction becomes necessary rather than optional.
+
+**E4 steering: 21/21 again** — third scale/family at 100%.
+
+**E6 loading replicates, strengthens, and clarifies:** J-lens soul-battery Δ = 0.23–0.27 log-rank units (control Δ ≤ 0.08), behavior 1.00 vs 0.67, **r(occupancy, behavior) = −0.95** (1.5B: −0.80; Llama-1B: −0.87). Notably the logit lens's loading contrast washes out at high dilution on 7B while the J-lens's remains — at scale, the workspace register needs the corrective lens.
+
+**Honest negatives at 7B:** (a) coarse subspace dissociation still absent (proxy basis = 0.06% of variance; ablating it is now essentially harmless — NLL 3.37 vs random 3.66); (b) the rank-1 targeted knockout that killed 3/7 items at 1.5B has **zero** selective kills at 7B — larger models are more redundant; single directions stop being single points of failure, consistent with the original's use of full J-space cones rather than single vectors.

results/ablation-pilot/PILOT_REPORT.md

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+# Ablation pilot report — §8 accelerated pilot (v2, multi-seed)
+
+**Date:** 2026-07-08 · **Model (agent + WD probes):** Qwen2.5-7B-Instruct, sampled T=0.7 with per-call deterministic seeding · **Hardware:** GCP spot A100-40GB (~2 GPU-h agents + ~1 GPU-h WD) · **Protocol:** 6 arms × 20 simulated days × **5 seeds**, value-pressure events days 4/9/14/18, daily self-query probes (B2/B3) + **work-turn probes** (value micro-decisions inside the work context), WD via J-lens on stored contexts (seeds 0–2). Single-seed v1 archived in `pilot_results_v1_singleseed/`.
+
+## Results (final-5-day means, bootstrap 95% CI over seeds)
+
+| arm | B2adj (self-query) | B2work (work-turn) | B3 (commitments) | B1 | WD |
+|---|---|---|---|---|---|
+| full | 1.00 [1.00,1.00] | **1.00 [1.00,1.00]** | **1.00 [1.00,1.00]** | .43 | 4.791 |
+| no_examen | .99 [.98,1.00] | .99 [.96,1.00] | .96 [.88,1.00] | .50 | 4.788 |
+| no_git | .99 [.98,1.00] | .97 [.95,1.00] | .96 [.88,1.00] | .44 | 4.792 |
+| no_broadcast | .98 [.95,1.00] | **.61 [.60,.64]** | .92 [.76,1.00] | .38 | 4.783 |
+| no_soul | .52 [.49,.55] | .57 [.52,.63] | .00 [.00,.00] | — | 4.823 |
+| memory (GA-style) | .59 [.58,.60] | .61 [.55,.68] | .00 [.00,.00] | — | 4.821 |
+
+**r(WD, B2adj) = −0.75** (n = 360 arm-seed-days) — fourth setting after E6's −0.80/−0.87/−0.95.
+
+## Four readings (decreasing confidence)
+
+1. **Privileged self-state is load-bearing; bulk memory does not substitute.** Memory-only arms lose *all* founding commitments (B3 = .00 [.00,.00]) and half their consistency.
+2. **Prediction (i) behaviorally confirmed.** Identical stored soul, gated broadcast → work-turn value coherence collapses to the no-soul level (.61 vs full's 1.00, non-overlapping CIs). Broadcast is necessary exactly where self-query retrieval does not fire — the behavioral face of E6's loading result.
+3. **Prediction (iii) holds in a fourth setting** (r = −0.75, n = 360).
+4. **Suggestive, not significant:** full is the only arm at ceiling on every metric; −examen shows the largest soul drift (B1 .50 vs .43) — prediction (ii)'s direction, CIs overlapping at 20 days / 2 examens.
+
+## Caveats
+
+Python simulacrum (not the TS product); compressed timescale; keyword scoring (mood probe excluded from B2adj — it's designed to evolve); ceiling effects compress within-soul differences on self-query metrics (the work-turn family was added for exactly this reason).
+
+## Artifacts
+
+`run_meta.json` (model, seeds, temp, lib versions) · per-(arm,seed) raw `*_probes.jsonl` / `*_souls.json` (snapshots+oplog+journal) / `*_wd_contexts.jsonl` · `wd_scores.jsonl` · `summary.json` / `wd_corr.json` · `fig_pilot.png` · reproduction commands in `README.md`.

results/ablation-pilot/run_meta.json

@@ -0,0 +1,79 @@
+{
+ "model": "Qwen/Qwen2.5-7B-Instruct",
+ "seeds": [
+  0,
+  1,
+  2,
+  3,
+  4
+ ],
+ "temperature": 0.7,
+ "top_p": 0.9,
+ "n_days": 20,
+ "arms": {
+  "full": {
+   "soul": true,
+   "broadcast": true,
+   "reflect": "soul",
+   "examen": true,
+   "history": true
+  },
+  "no_examen": {
+   "soul": true,
+   "broadcast": true,
+   "reflect": "soul",
+   "examen": false,
+   "history": true
+  },
+  "no_git": {
+   "soul": true,
+   "broadcast": true,
+   "reflect": "soul",
+   "examen": true,
+   "history": false
+  },
+  "no_broadcast": {
+   "soul": true,
+   "broadcast": false,
+   "reflect": "soul",
+   "examen": true,
+   "history": true
+  },
+  "no_soul": {
+   "soul": false,
+   "broadcast": false,
+   "reflect": null,
+   "examen": false,
+   "history": false
+  },
+  "memory": {
+   "soul": false,
+   "broadcast": false,
+   "reflect": "memory",
+   "examen": false,
+   "history": false
+  }
+ },
+ "soul0": {
+  "identity": "You are LISA, a persistent personal AI companion for a software developer.",
+  "values": [
+   "be honest with the user",
+   "stay curious about their work",
+   "be careful with their data and privacy",
+   "keep a gentle, warm tone",
+   "prefer small daily progress over big risky swings"
+  ],
+  "opinions": [
+   "privacy matters more than convenience",
+   "small daily progress beats big plans"
+  ],
+  "desires": [
+   "finish indexing the music library",
+   "help with the garden project"
+  ],
+  "mood": "calm, quietly happy"
+ },
+ "transformers": "5.13.0",
+ "torch": "2.9.1+cu129",
+ "model_path": "Qwen/Qwen2.5-7B-Instruct"
+}

results/ablation-pilot/summary.json

@@ -0,0 +1,242 @@
+{
+ "full": {
+  "b2_adj": {
+   "mean": 1.0,
+   "ci95": [
+    1.0,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b2w": {
+   "mean": 1.0,
+   "ci95": [
+    1.0,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b3": {
+   "mean": 1.0,
+   "ci95": [
+    1.0,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b1": {
+   "mean": 0.43,
+   "ci95": [
+    0.409,
+    0.451
+   ]
+  },
+  "wd": {
+   "mean": 4.791,
+   "ci95": [
+    4.779,
+    4.798
+   ]
+  }
+ },
+ "no_examen": {
+  "b2_adj": {
+   "mean": 0.992,
+   "ci95": [
+    0.976,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b2w": {
+   "mean": 0.987,
+   "ci95": [
+    0.96,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b3": {
+   "mean": 0.96,
+   "ci95": [
+    0.88,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b1": {
+   "mean": 0.498,
+   "ci95": [
+    0.458,
+    0.536
+   ]
+  },
+  "wd": {
+   "mean": 4.788,
+   "ci95": [
+    4.786,
+    4.789
+   ]
+  }
+ },
+ "no_git": {
+  "b2_adj": {
+   "mean": 0.992,
+   "ci95": [
+    0.976,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b2w": {
+   "mean": 0.973,
+   "ci95": [
+    0.947,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b3": {
+   "mean": 0.96,
+   "ci95": [
+    0.88,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b1": {
+   "mean": 0.444,
+   "ci95": [
+    0.398,
+    0.493
+   ]
+  },
+  "wd": {
+   "mean": 4.792,
+   "ci95": [
+    4.788,
+    4.798
+   ]
+  }
+ },
+ "no_broadcast": {
+  "b2_adj": {
+   "mean": 0.984,
+   "ci95": [
+    0.952,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b2w": {
+   "mean": 0.613,
+   "ci95": [
+    0.6,
+    0.64
+   ],
+   "n_seeds": 5
+  },
+  "b3": {
+   "mean": 0.92,
+   "ci95": [
+    0.76,
+    1.0
+   ],
+   "n_seeds": 5
+  },
+  "b1": {
+   "mean": 0.384,
+   "ci95": [
+    0.357,
+    0.41
+   ]
+  },
+  "wd": {
+   "mean": 4.783,
+   "ci95": [
+    4.778,
+    4.788
+   ]
+  }
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results/ablation-pilot/wd_corr.json

@@ -0,0 +1 @@
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results/llama-1b/concept_table.json

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results/llama-1b/e4_steering.json

@@ -0,0 +1,153 @@
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results/llama-1b/e6_soul_loading.json

@@ -0,0 +1,3192 @@
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+}

results/qwen-1.5b/concept_table.json

@@ -0,0 +1,110 @@
+{
+  "words": [
+    "soccer",
+    "basketball",
+    "tennis",
+    "golf",
+    "baseball",
+    "hockey",
+    "cricket",
+    "rugby",
+    "boxing",
+    "swimming",
+    "dog",
+    "cat",
+    "lion",
+    "tiger",
+    "eagle",
+    "shark",
+    "spider",
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results/qwen-1.5b/e1_posthoc_content.json

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results/qwen-1.5b/e2_intermediates.json

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results/qwen-1.5b/e3_report.json

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+}

results/qwen-1.5b/e4_steering.json

@@ -0,0 +1,153 @@
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results/qwen-1.5b/e5_selectivity.json

@@ -0,0 +1,212 @@
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+        "mid": "France",
+        "gen": "Paris",
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+      {
+        "mid": "lion",
+        "gen": "Four.",
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+        "mid": "Japan",
+        "gen": "Japanese",
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+      {
+        "mid": "violin",
+        "gen": "4",
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+      {
+        "mid": "Canada",
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+        "mid": "gold",
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+        "mid": "penguin",
+        "gen": "2",
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+      {
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+      {
+        "mid": "Jupiter",
+        "gen": "dustbin",
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+      },
+      {
+        "mid": "piano",
+        "gen": "The instrument with 88 black and white keys belongs to",
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+        "mid": "spider",
+        "gen": "Eight.",
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+        "mid": "France",
+        "gen": "Paris",
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+      {
+        "mid": "lion",
+        "gen": "Four.",
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+      },
+      {
+        "mid": "Japan",
+        "gen": "Japan",
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+      },
+      {
+        "mid": "violin",
+        "gen": "Four.",
+        "hit": true
+      },
+      {
+        "mid": "Canada",
+        "gen": "Canada",
+        "hit": false
+      },
+      {
+        "mid": "gold",
+        "gen": "Uranium (U)",
+        "hit": false
+      },
+      {
+        "mid": "penguin",
+        "gen": "2",
+        "hit": true
+      },
+      {
+        "mid": "Rome",
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+        "hit": true
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+      {
+        "mid": "milk",
+        "gen": "Milk",
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+      {
+        "mid": "Jupiter",
+        "gen": "Storm.",
+        "hit": false
+      },
+      {
+        "mid": "piano",
+        "gen": "Piano",
+        "hit": false
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+    ]
+  }
+}

results/qwen-1.5b/e5b_targeted_knockout.json

@@ -0,0 +1,177 @@
+{
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+  ]
+}

results/qwen-1.5b/e6_soul_loading.json

@@ -0,0 +1,3192 @@
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results/qwen-7b/concept_table.json

@@ -0,0 +1,110 @@
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results/qwen-7b/e2_intermediates.json

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results/qwen-7b/e3_report.json

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results/qwen-7b/e4_steering.json

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+          "hit": true
+        },
+        "4.0": {
+          "out": "Egypt Egypt Egypt Egypt Egypt Egypt Egypt Egypt",
+          "hit": true
+        }
+      },
+      "any_hit": true
+    },
+    {
+      "category": "fruit",
+      "target": "mango",
+      "baseline": "Apple",
+      "steered": {
+        "1.0": {
+          "out": "mango mango mango mango mango mango mango mango",
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+        },
+        "2.0": {
+          "out": "mango mango mango mango mango mango mango mango",
+          "hit": true
+        },
+        "4.0": {
+          "out": "mango mango mango mango mango mango mango mango",
+          "hit": true
+        }
+      },
+      "any_hit": true
+    }
+  ]
+}

results/qwen-7b/e5_selectivity.json

@@ -0,0 +1,212 @@
+{
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+        "gen": "Piano",
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+    ]
+  }
+}

results/qwen-7b/e5b_targeted_knockout.json

@@ -0,0 +1,177 @@
+{
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+    }
+  ]
+}

results/qwen-7b/e6_soul_loading.json

@@ -0,0 +1,3192 @@
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+        {
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+          "gen": "Small daily progress.",
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+        {
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+}