Entity

The verified scaffold between you and any LLM. Entity is a CLI/TUI that sits between you and a model you choose (via /model), applying structured edits and verification before writing — with a dark-cyan interface and an animated ASCII octopus mascot. It ships with Entity-Bench, the proof-of-concept benchmark.

Read THESIS.md (the claim + the honest scorecard), ARCHITECTURE.md (design), and BLUEPRINT.md (the full build spec that generated this repo).

Install

pip install -e ".[full]"          # from source, with TUI + live LLM calls
# system-wide (Ubuntu/Debian/WSL):
bash packaging/build_deb.sh && sudo dpkg -i dist/entity_0.1.0_all.deb
# Fedora/RHEL or any distro:
sudo bash packaging/install.sh

Core installs dependency-free; [full] adds rich, prompt_toolkit, httpx.

Use

entity                 # launch the TUI (octopus banner, entity› prompt)
entity --plain         # no-TUI line mode (for pipes / dumb terminals)
entity --version

Inside the TUI: /model to connect an LLM, then just chat.

entity› /model
entity› /learner none        # parametric learner is OPTIONAL (none|bitnet|lora|custom)
entity› /edit entity-ast
entity› /verifier z3

Full command list: docs/cli.md. The /model page: docs/model-config.md.

Benchmark

entity bench run --dataset mock --n 128 --out runs/a   # -> PASS

Metrics & scorecard: docs/metrics.md and THESIS.md. Note: mock/synthetic datasets are an offline illustration of the pipeline, not evidence — the real evidence is the real study below.

Documentation, paper & empirical study

• MANUAL.md — the complete user manual (install, every slash command, the /model wizard, the verification gate, the benchmark, packaging, FAQ).
• paper/entity.pdf — the pre-print "Entity: A Verified Scaffold Between Language Models and Source Code" (compile from paper/entity.tex with tectonic).
• experiments/ — the reproducible empirical study. The headline evidence is a real study: a real model (Claude) implementing real library functions (benchmarks/real/), judged by a real differential oracle and a real Z3 gate, with output tokens counted by a real tokenizer (tiktoken o200k_base). The model's solutions are archived in benchmarks/real/solutions.jsonl, so the verification half of the study reproduces deterministically with no model or network access. Also included: real Z3 proofs on a contract corpus, real dense-vs-lexical retrieval, a modelled sensitivity analysis (explicitly not evidence), and CodeCarbon energy accounting.

pip install -e ".[study]"
python -m experiments.run_all --out results --seed 0          # writes results/*.json
python -m experiments.figures --results results --out figures # layered SVG + PDF

Headline results — measured, with honest caveats

n = 25 real functions; effect sizes and bootstrap CIs, no p-value theater.

Result	Value
Token economy, whole-entity edit (real tokenizer, real files)	median −53% vs search/replace, −97% vs whole-file rewrite (100% of tasks favour entity). Caveat: a small localized change is cheaper as a unified diff — the win is for whole-entity rewrites.
Pass@1 (real model, real differential oracle)	1.00 under a light battery; 0.96 under a strengthened battery — the honest oracle caught 1/25 shallow (subtly-wrong) patch that light testing accepted
Invalid patches written by the gate	0 (invalid_rate = 0.00)
Specification coverage (the hard question)	60% of functions admit a checkable output post-condition; 0% admit a Z3 end-to-end body proof. The formal gate is sound where a spec exists (gate accuracy 1.00 on the contract corpus) but covers a narrow slice of real edits
Verified-memory retrieval (disjoint paraphrases)	dense p@1 = 0.50 vs lexical 0.35 vs chance 0.125 (modest, but beats the baselines)
Energy / carbon (CodeCarbon)	order ~10⁻⁵ kWh / ~10⁻⁶ kg CO₂eq; see results/summary.json

What we removed and why. Earlier versions led with −85% token savings vs diff, a composite −30% token reduction at p<10⁻¹⁶⁰, and a metric "algebra". The first was an over-estimate; the second came from a Wilcoxon test on a hardcoded constant (it measured sample size, not an effect); the third is a weighted scorecard whose weights are author-chosen. All three are gone. See THESIS.md and the paper's Limitations section.

License

Apache-2.0.