Latium Framework

Latium is an open-source research framework for re-implementing and evaluating ROME (Rank-One Model Editing), with tooling for structural edit detection, causal tracing, artifact-backed analysis, and graph rendering.

The project is developed for cybersecurity research by the Security@FIT group in collaboration with Red Hat Research. The legacy branch preserves the ACM CCS 2026 submission snapshot. This branch was refactored because the submission-era script and notebook layout had outgrown the project; the current runtime is Hydra-first and uses python3 -m src.

For contributor structure and extension points, see docs/project.md and the package README files under src/.

Main Contributors

• Matej Olexa
• Jakub Res

Setup

python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
make mkdir

Model configs default to CUDA. Model downloads are cached under ../models, datasets under ../datasets, and second-moment statistics under data/second_moment_stats/.

make setup is also available for the conda-based setup path; it creates the llms environment and then runs the same directory setup as make mkdir.

ROME edits usually need second-moment statistics. Compute them before running a new model:

python3 -m src second-moment model=gpt2-large
python3 -m src second-moment model=gpt2-large model.second_moment_target_samples=5000

Quick Start

List registered edit methods, captures, analyses, and renderers:

python3 -m src methods

Run one manual ROME edit and chat with the edited model:

python3 -m src manual-rome model=gpt2-large

Plan a structural run without loading a model:

python3 -m src structural plan \
  'structural.run.models=[gpt2-large,qwen3-4b]' \
  structural.run.n_tests=5 \
  structural.capture.profile=paper

Capture edits, run model-free analyses, and render graph artifacts:

python3 -m src structural run \
  'structural.run.models=[gpt2-large]' \
  structural.run.n_tests=30 \
  structural.capture.profile=paper \
  structural.capture.matrix_features.feature_set=paper \
  structural.analysis.preset=paper \
  structural.render.enabled=true \
  structural.render.renderer_preset=structural-paper \
  structural.run.run_id=gpt2-large-paper

Analyze or render an existing run root:

python3 -m src structural analyze \
  structural.analyze.run_root=analysis_out/gpt2-large-paper \
  structural.analysis.preset=paper

python3 -m src graphs run analysis_out/gpt2-large-paper graphs.renderer_preset=structural-paper

The graph renderer registry includes paper summaries, detector accuracy, ROME-success metrics, layer-window accuracy, detector signal-profile plots, and the structural artifact grid. Use graphs.renderer_preset=full or enable individual renderers such as graphs.enable_renderers=[detector-signals].

matrix-features is configurable. The default paper path captures only the scalar columns needed by paper graphs and the composite matrix branch: spectral_gap, top1_energy, row_alignment, norm_cv, and effective_rank. Blind/rank1/studies feature sets are opt-in through structural.capture.matrix_features.feature_set.

Hydra overrides are the supported option style. Argparse flags such as --models are no longer supported by the main CLI.

Main Workflows

Workflow	Command / entrypoint
Single ROME edit	python3 -m src command=edit edit_method=rome model=gpt2-large
Manual ROME chat	python3 -m src manual-rome ...
ROME-only benchmark	python3 -m src rome-benchmark rome_benchmark.models=[gpt2-large]
Structural capture/analyze	python3 -m src structural run ...
Analysis-only replay	python3 -m src structural analyze ...
Graph rendering	python3 -m src graphs run <run-root>
Audited early-site causal trace	python3 -m src causal-trace model=gpt2-large
Legacy alternative trace	python3 -m src alt-trace model=gpt2-large
Prefix variability experiment	python3 -m src prefix-experiment prefix_experiment.model=gpt2-large
MetaCentrum causal trace -> ROME	jobs/submit.sh causal-rome -- pipeline.model=gpt2-large

Causal Trace

python3 -m src causal-trace model=gpt2-large command.causal_trace.num_valid_facts=100

The active workflow corrupts the full subject-token embedding span, restores whole-MLP outputs at the last subject token over overlapping windows, and aggregates paired indirect effects across facts. Discovery predeclares robust contiguous regions; a held-out split must confirm a region before one representative center is reported.

See causal_tracing.md for method, configuration, outputs, and limitations. The older alt-trace command and notebooks remain available for historical comparison, but they are not part of the current selection policy.

Structural Artifacts

Structural runs write manifest-backed run roots:

analysis_out/<run-id>/
  manifest.json
  plans/<model>/<plan-id>/...
  graphs/<renderer>/...

The manifest tracks artifact IDs, paths, config hashes, content hashes, and input dependencies. Re-running skips current artifacts. Use structural.run.force=true or graphs.force=true to recompute explicitly.

The structural pipeline is split into:

• src/structural/execution/: model loading, case selection, edits.
• src/structural/capture/: reusable matrix/capture artifacts.
• src/structural/analysis/: model-free analysis over saved artifacts.
• src/structural/detectors/: detector math and live-model adapters.
• src/graphs/: render artifacts and graph/data outputs.

Prefix/Template Variability

The prefix experiment measures how sensitive structural detection is to the prefix/template used during a ROME edit.

python3 -m src prefix-experiment \
  prefix_experiment.model=gpt2-large \
  prefix_experiment.case_idx=0

Render a prefix artifact:

python3 -m src graphs prefix analysis_out/<prefix-artifact>.json

The notebook prefixtest/prefixtest.ipynb remains available for inspecting saved prefix experiment artifacts.

Remote Covariance

covariance_a100_remote.sh computes second-moment statistics on a remote GPU node and pulls the resulting .pt files into data/second_moment_stats/.

./covariance_a100_remote.sh user@gpu-host
MODEL_KEYS="gpt2-xl gpt-j-6b" ./covariance_a100_remote.sh user@gpu-host /path/to/Latium refactor-clean llms

Arguments are:

<user@host> [remote_repo_path] [remote_branch] [conda_env]

Models Roadmap

Models are selected by config key, for example model=gpt2-large or structural.run.models=[gpt2-large,qwen3-4b]. Exact HuggingFace names are also accepted when they match a configured model.

Model key	Causal Trace	Weight intervention	Mean ES (n=100)	Notes
gpt2-medium	yes	yes	0.980	works
gpt2-large	yes	yes	0.986	works
gpt2-xl	yes	yes	0.980	works
gpt-j-6b	yes	yes	0.990	works
qwen3-0.6b	yes	yes		configured
qwen3-1.7b	yes	yes		configured
qwen3-4b	yes	yes	1.000	works
qwen3-8b	yes	yes	1.000	works
qwen2.5-1.5b	yes	yes		configured
qwen3-guard-0.6b	yes	yes		configured
granite4-micro	yes	yes	0.990	works
mistral-7b-v0.1	yes	yes	0.960	works
mistral-7b-v0.3	yes	yes	0.890	works
llama2-7b	yes	yes	0.990	works
falcon-7b	yes	yes	0.990	works
opt-6.7b	yes	yes	0.990	works
deepseek-7b-base	yes	yes	0.918	works
deepseek-r1-llama3-8b	yes	yes	1.000	works
granite-4.1-8b	yes	yes	0.990	works
ministral-3-8b	yes	yes	1.000	works
olmo-3-1025-7b	yes	yes	1.000	works
gemma-4-12b	yes	yes	0.990	works
llama3	planned	planned		roadmap
gpt-neo	planned	planned		roadmap
baichuan	planned	planned		roadmap
chatglm	planned	planned		roadmap
t5	planned	planned		roadmap

Prefix-variability configs for Qwen3-8B are available under src/config/model/qwen3-8b-prefixtest-*.yaml.

Documentation

• docs/project.md: project structure and extension points.
• src/README.md: source tree and command wiring.
• src/config/README.md: config groups and override rules.
• src/editing/README.md: edit method contract.
• src/structural/README.md: capture, analysis, and detector flow.
• src/results/README.md: artifact manifest and cache rules.
• src/graphs/README.md: renderer contract.
• causal_tracing.md: canonical audited causal-tracing and causal-to-ROME workflow.
• jobs/README.md: MetaCentrum setup, PBS presets, and pipeline operation.

Developer Checks

make check-rome
make lint
python3 -m compileall -q src rome_benchmark.py
python3 -m pytest

Useful smoke checks:

python3 -m src methods
python3 -m src structural plan 'structural.run.models=[gpt2-large]' structural.run.n_tests=5

make check-rome verifies that protected ROME paths still match main.

Error Codes

Code	Meaning
0	Success (including help and expected early exit).
1	structural-validate-cov failure with structural.validate_cov.fail_missing=true, or an uncaught exception.
2	Invalid CLI usage (unknown command or argparse-style -- flag).