archscope 0.2.3

Lightweight workbench for cross-architecture mechanistic interpretability experiments on small models

archscope

Mechanistic interpretability experiments across architectures — Transformers, SSMs/Mamba, recurrent models, and hybrids.

What archscope is

archscope is a small-model interpretability workbench. It's designed for quick, reproducible experiments across model families — not for large-scale SAE training, production model auditing, or replacing mature Transformer-specific tools.

Use it when you want to ask:

• Can I extract comparable activations from different architectures?
• Do linear probes transfer across model families?
• Do induction-like behaviors appear outside attention?
• Did a fine-tuned model drift in specific layers?
• Do dense or rank-1 SAEs reconstruct this model family better at this layer?

It is not: a competitor to transformer_lens or nnsight (both are broader and more mature), a production audit tool, or a SaaS. It's a small, hackable workbench.

import archscope as mi
from transformers import AutoModelForCausalLM, AutoTokenizer

tok   = AutoTokenizer.from_pretrained("state-spaces/mamba-130m-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-hf")

backend = mi.backends.Backend.for_model(model, hint="mamba")

# Extract Mamba's recurrent SSM state h_t (in addition to residual stream)
ssm = backend.extract(tok("text", return_tensors="pt"), layers=["layer_12.ssm_state"])[0]
# Shape: (B, intermediate_size, ssm_state_size) = (B, 1536, 16) for mamba-130m

---

What's inside

Core mech-interp methods

Module	What it does	Source
probes	Linear/MLP probes on hidden states	Drop the Act (arXiv:2605.11467)
sae	Dense + Rank-1 factored sparse autoencoders	WriteSAE (arXiv:2605.12770)
neurons	Top-K contrastive neuron modulation	Targeted Neuron Mod (arXiv:2605.12290)
attribute	Activation patching + DIM decomposition	Multi-Agent Sycophancy (arXiv:2605.12991)
circuits	Induction, copy, attention-concentration detectors	Olsson et al 2022
lens	Logit lens + Tuned lens	Belrose et al 2023
diff	Model-diff: base vs fine-tuned, find what changed	this library

Experiment infrastructure

Module	What it does
backends	Unified extraction API across architectures
transfer	Cross-arch probe transfer via paired-activation linear alignment
bench	InterpProfile — standardized comparable profile (mi.bench.benchmark())

Backends

Backend	Models	Specific
transformer	Pythia, GPT-2, Llama, Mistral, Qwen, MPT, Falcon, GPT-Neo	residual stream
mamba	Mamba, Mamba-2	residual + explicit .ssm_state (recurrent h_t)
kazdov	Kazdov-α hybrid MoBE-BCN+MHA	residual per custom block
recurrent	Generic RNN (user subclass)	hidden state per layer

---

Install

pip install archscope   # once on PyPI
# or:
git clone https://github.com/OriginalKazdov/archscope.git
cd archscope && pip install -e .

For Mamba on CPU you don't need mamba-ssm — HF's slow path works. On CUDA install mamba-ssm for the fast path.

---

Quick examples

Train a probe on any architecture

import archscope as mi
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("EleutherAI/pythia-160m")
tok   = AutoTokenizer.from_pretrained("EleutherAI/pythia-160m")
tk = lambda txts: tok(txts, return_tensors="pt", padding=True, truncation=True)

probe = mi.probes.fit_probe(
    model,
    inputs_pos=tk(["I love this", "Wonderful!", "Amazing"]),
    inputs_neg=tk(["I hate this", "Awful", "Terrible"]),
    layer_name="layer_5.residual",
    backend_hint="transformer",
)
print(probe.metrics)   # {'train_auroc': 1.0, ...}

Extract Mamba's SSM recurrent state

backend = mi.backends.Backend.for_model(mamba_model, hint="mamba")
rec = backend.extract(tk("Hello world"), layers=["layer_12.ssm_state"])[0]
# rec.activations.shape == (B, intermediate_size, ssm_state_size)
# This is the actual recurrent memory h_t of Mamba — exposed via the same
# extraction API used for Transformer residual streams.

Logit lens / tuned lens — see what each layer "thinks"

result = mi.lens.logit_lens(
    model, tok,
    prompt="The capital of France is",
    target_token=" Paris",
    backend_hint="transformer",
)
print(result.to_markdown())

# Tuned lens — learned per-layer projections (Belrose et al 2023):
tl = mi.lens.TunedLens.fit(model, tok, calibration_texts, backend_hint="transformer")
tl.predict(model, tok, "...", backend_hint="transformer")

Model Diff — what did fine-tuning change?

from archscope.diff import compare

result = compare(
    base_model, fine_tuned_model, tokenizer,
    calibration_texts=texts,
    backend_hint="transformer",
)
print(result.to_markdown())
# Per-layer residual drift, top shifted neurons, circuit deltas.

Detect circuits cross-arch

scores = mi.circuits.run_all_circuits(model, tokenizer=tok)
print(scores["induction_head"].relative)   # × chance
print(scores["copy_circuit"].score)        # accuracy

InterpBench — standardized model profile

profile = mi.bench.benchmark(
    "EleutherAI/pythia-160m", model, tok,
    backend_hint="transformer", arch_family="transformer",
    tokenize_fn=tk,
)
print(mi.bench.profile_to_markdown(profile))

CLI:

archscope info
archscope bench EleutherAI/pythia-160m --arch transformer --out pythia.json
archscope bench state-spaces/mamba-130m-hf --arch mamba

---

Findings — running archscope on a mini-zoo of 7 small models

Each model profiled with bench.benchmark() (probes + circuits + dense vs rank-1 SAE). ~10 min total compute on CPU.

Reproduce

python scripts/reproduce_mini_zoo.py
# → _research/mini_zoo_leaderboard.json
# → _research/mini_zoo_leaderboard.md

Skip specific models with --skip Mamba-370m if memory-tight. Kazdov-α is included only if the local checkpoint is available.

Model	Arch	Params	Induction (× chance)	SAE-dense	SAE-rank1	SSM var
Pythia-160m	transformer	162M	490×	0.019	0.025	—
Pythia-410m	transformer	405M	3,261×	0.075	0.135	—
GPT-2	transformer	124M	6,393×	5.731	0.608	—
Mamba-130m	SSM	129M	6,378×	0.048	0.032	0.54
Mamba-370m	SSM	372M	7,730×	0.022	0.027	0.73
Qwen2.5-0.5B	transformer	494M	17,637×	0.092	0.068	—
kazdov-α	hybrid	98M	2,700×	0.043	0.004	—

Open questions raised by this run (single-seed observations, not formal claims):

• Does induction-like behavior require attention heads? Mamba — which has no attention mechanism — scores 6378-7730× chance on our behavioral induction test, comparable to or above similarly-sized Transformers. The test is behavioral (output-based), so it doesn't presume any specific mechanism. What in SSMs implements this behavior?
• Why does naive logit lens degrade with depth on Mamba? Applying each model's own lm_head to its intermediate residuals surfaces the target with depth on Pythia (target rank 5117 → 77 across 12 layers on "capital of France is Paris"). The same procedure on Mamba moves the target away from top-1 (rank 197 → 1049 across 24 layers). Does this hold across more SSM checkpoints? Is tuned-lens enough to fix it?
• Is rank-1 SAE preference architecture-driven or layer-driven? In this run, GPT-2, both Mambas, and kazdov-α reconstructed better with rank-1 factored SAEs at the tested mid-layer; both Pythias preferred dense; Qwen was marginal. Suggestive but needs layer sweeps + multiple seeds before claiming a pattern.
• How much do training recipe, tokenizer, and data affect induction-like behavior? Qwen2.5-0.5B shows 17,637× induction — 5.4× higher than Pythia-410m at similar size. Plausibly attributable to data curation + training stability since 2023, but we haven't isolated the cause.
• Does Mamba's SSM-state utilization scale with model size? In this run, the input-dependent variance ratio rose 0.54 (Mamba-130m) → 0.73 (Mamba-370m). Does this trend hold across more checkpoints?

These aren't published findings — they're observations from a single mini-zoo run. Methodological corrections welcome.

Metrics caveats

• Induction score is behavioral (output-based), not proof of a specific circuit. It tells you the model copies A→B associations in-context; it doesn't tell you how.
• SAE reconstruction error is measured on a small sample of mid-layer activations. Lower is better. Numbers are not comparable across layers with different residual magnitudes (e.g., Pythia L11 has very large residuals which dominate dense SAE recon).
• SSM-state variance ratio is descriptive — it tells you whether the state changes meaningfully across inputs, not whether the state is causally used downstream.
• Logit lens results are diagnostic, not a guarantee of representational alignment. Naive logit lens applies the final lm_head to intermediate residuals — when that fails, it just means the residuals aren't in the final-layer vocab space (e.g., Mamba). TunedLens is the fix.
• All probes/SAEs/circuit tests in InterpBench are single-seed. Treat differences <2× as noise.

---

Honest limits

archscope is a v0.2 release. What it does well: cross-architecture mech-interp primitives, unified API, real observable findings, validated on multiple architectures. What it doesn't do yet:

• No causal scrubbing (gold-standard circuit verification)
• No interactive notebook viz (matplotlib helpers are TBD)
• Circuit detection is limited to induction / copy / attention-concentration — no IOI, name-mover, or successor heads yet
• Mamba-2 backend support is partial (Mamba-1 fully supported)
• No pretrained SAE collection (you train your own per layer)
• Probe transfer assumes same-tokenizer paired data

See CONTRIBUTING.md for what we welcome (new backends, new circuit detectors, viz helpers).

For mature Transformer-centric workflows, prefer transformer_lens or nnsight. They are broader and more mature; archscope focuses on lightweight cross-architecture experiments and small / non-standard model workflows.

---

Citation

@misc{dovzak2026archscope,
  title  = {archscope: Cross-architecture mechanistic interpretability experiments},
  author = {Juan Cruz Dovzak},
  year   = {2026},
  url    = {https://github.com/OriginalKazdov/archscope}
}

Source papers reimplemented or wrapped:

• WriteSAE — arXiv:2605.12770
• Drop the Act / ProFIL — arXiv:2605.11467
• Targeted Neuron Modulation — arXiv:2605.12290
• Multi-Agent Sycophancy — arXiv:2605.12991
• Tuned Lens (Belrose et al, 2023)
• Induction heads (Olsson et al, 2022)

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Troubleshooting

"The fast path is not available because ..." (Mamba on CPU)

Normal. Mamba falls back to a slow pure-PyTorch path that works correctly (~30s per benchmark vs ~1s on CUDA). Install pip install mamba-ssm causal-conv1d only on CUDA machines.

Custom backend not auto-detected

Pass Backend.for_model(model, hint="my_backend") explicitly. Auto-detection uses config.model_type.

RuntimeError: Trying to backward through the graph a second time

Activations from Backend.extract() carry the autograd graph by default. Call .detach() before reusing, or extract inside torch.no_grad(). The high-level probes.fit_probe() does this for you.

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Roadmap (post-0.2.0)

• Multi-token circuit detection: IOI, name-mover, successor heads
• Mamba-2 backend with same .ssm_state API
• Cross-arch SAE feature alignment (extend transfer.py from probes to features)
• Pretrained SAE collection for common small models
• Plotly/matplotlib viz helpers
• HuggingFace Space demo

PRs welcome — see CONTRIBUTING.md.

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License

Apache-2.0