Ekbasis

Ekbasis

Papers: Look When Unsure, Check When Sure · DOI 10.5281/zenodo.23146970 · When Does a Consequence Model Make AI Agents Safer? · DOI 10.5281/zenodo.23197341 (data, scripts and the benchmark to rerun its studies: paper/agents) · Code: github.com/OpenInterpretability/ekbasis · Site: openinterp.org/ekbasis

Ekbasis (ἔκβασις, *"how an action turns out"*) is an open consequence model for agents: given the current state and an action, it answers typed questions about what will happen — *will this command lose work? will it fail? what will X be afterwards?* — with a calibrated probability, in one forward pass (no generated text).

It is the consequence layer of the Eikos family (Eikos decides; Ekbasis foresees), trained on synthetic worlds and on real executions of git commands in throwaway repositories, so its answers come from what actually happened, not from what an agent believes.

Every number on this page was measured on the exact release weights. RELEASE_EVAL.md has the

results, every prediction, how this checkpoint was chosen, which measurements were pre-registered

(PREREG_release_eval.md, paper/prereg/) and the deviations.

New (2026-10-16): [Ekbasis-Skopos-27B](https://huggingface.co/caiovicentino1/Ekbasis-Skopos-27B), the next

version, trained further on executed shell, git, SQLite, PostgreSQL and Python scenarios. Against Ekbasis-27B on the

same items: the git guard raises fewer false alarms on the held-out command types, 26/799 instead of 49/799 (paired

McNemar p = 5.6 × 10⁻⁶; these command types are held out of the git sandbox's training rows but appear in Skopos'

factory rows), and flags as many work-losing commands; on the known types, 27 instead of 37 of 863 is within chance.

On 3,014 fresh answers of never-trained world families the actions change, +1.6 points [+0.7, +2.5]: the

pre-registered release read failed this criterion on ftest_family, and a confirmation registered after that result

(before any fresh item existed), by trainer readout, did not confirm the loss. Its largest gains are in-distribution

(the generators it was trained on), and in world families it never saw more of its errors come with confidence ≥ 0.9

(4.49 vs 3.75 per 100). Same prompt, readout, API and client; its evaluation is in

RELEASE_EVAL_SKOPOS.md and its

card. The numbers further down this page are Ekbasis-27B's. ekbasis.verify's certified mode, certified on

Ekbasis-27B, carried over to Skopos INT4 in a pre-registered check (7 errors among 927 accepted answers;

docs/VERIFY.md). A pre-launch release

blocker is declared: on paraphrased short checks it is right on 113 of 120 against Ekbasis-27B's 117 (−3.3 points,

paired 95% CI [−6.7, −0.8]), so the pre-registered rule fired; the exact test on the 4 discordant items does not

reject (p = 0.125), and Ekbasis-27B re-run on the same machine reproduced its published answers (240/240) ([release

decision](https://github.com/OpenInterpretability/ekbasis/blob/main/results_skopos/prelaunch/RELEASE_DECISION.md)).

Install

The client needs a server: the hosted API (EKBASIS_URL=https://openinterp.org/api/v1 and EKBASIS_API_KEY=ekb_…, key at openinterp.org/console) or your own, with the open weights (Quick start).

What leaves your machine (to the server you choose): the state of each check, as text. For the guards that is the command line, with credentials typed on it replaced (since 0.1.42: known key prefixes, Authorization and Cookie headers, passwords in URLs, --password/--token values, values of *TOKEN*/*SECRET*/*PASSWORD*-like names, private keys and long random-looking strings; EKBASIS_REDACT=0 turns it off), and a listing of what the line could touch: names, sizes, ages and short fingerprints keyed anew for each check, never file contents; for git, the repository's state (branches, the names of changed files, commit subjects). Redaction is a safety net with limits (docs/SECURITY.md): a short or unusual secret without a known prefix or name can pass. The hook's log (log mode) stays on your machine and holds the redacted line.

• Command line and Python (standard library only, Python ≥ 3.9): ekbasis, ekbasis-claude-hook, and ekbasis-mcp with the mcp extra.

  pip install ekbasis                       # or without installing: uvx ekbasis health

pip install "ekbasis[mcp]" # with the MCP server (Python >= 3.10) pip install "git+https://github.com/OpenInterpretability/ekbasis" # from source

• Claude Code plugin: the git guard hook, the ekbasis-guard skill, the MCP server and the ekbasis command on the Bash tool's PATH, in one install:

  claude plugin marketplace add OpenInterpretability/ekbasis --sparse .claude-plugin plugins

claude plugin install ekbasis@ekbasis

or, inside Claude Code, /plugin marketplace add OpenInterpretability/ekbasis then /plugin install ekbasis@ekbasis (without --sparse the marketplace step clones the whole repository, about 86 MB with the results and papers, once; with it, about 1 MB).

The plugin (plugins/ekbasis/, about 0.5 MB) runs its own copy of the client, so it needs python3 (≥ 3.9) and sh for the hooks and uv for the MCP server, and nothing from PyPI. Set EKBASIS_API_KEY (a key alone means the hosted API) or EKBASIS_URL, in your shell or in the "env" block of ~/.claude/settings.json, and restart Claude Code. Until one of them is set the guard is off: each session starts with one line saying how to set it up, and ekbasis says it is not set up (exit 3). Once set, the hook is the same fail-closed hook described in Claude Code and MCP, and a hook that cannot run (python3 missing or older than 3.9, an error in the hook, no answer within 27 s) asks you to confirm, saying why, instead of letting the command through. If you added ekbasis-claude-hook to settings.json by hand, remove it, or every line is checked twice.

• MCP server for any MCP client (Python ≥ 3.10):

  uvx --python ">=3.10" --with "mcp>=1.2" ekbasis mcp     # same as ekbasis-mcp from pip install "ekbasis[mcp]"

What kind of model is it?

Not a model that thinks. Not a model that judges. A model that foresees.

Ekbasis is a world model for agents in the precise sense: an action-conditioned model of how the state changes. It answers *what will be, if I do this*, as calibrated distributions over the next state's variables, in one forward pass.

Two ways to picture it:

• The forward model the agents were missing. Before you move your arm, the brain predicts the consequence of the motor command; that is what lets us act fast and correct before an error. One part plans, another predicts. The agent plans; Ekbasis predicts.
• The world-model module, made real. LeCun's architecture for autonomous machine intelligence separates a world model (it predicts the next state given an action) from the actor and the critic. The LLM agent is the actor, AgentGuard the critic, Ekbasis the world model. It was trained with a JEPA-style loss that aligns its internal state with the true outcome.

It is derived from an LLM (Qwen3.8-27B, through Eikos-27B) but does not work as one: its training objective and its readout make it answer with probabilities, not text.

Where to start

Most of the value is in the first row. The hook covers git only; outside git, the shell guard (EKBASIS_SHELL_GUARD=1) is a prototype that asks too often to keep on.

What it is for

• A check before acting: ~0.1 s per question on one GPU, calibrated, so an agent can check *every* action and escalate only the risky or uncertain ones.
• A git guard for coding agents: the state of your real repository is described in the format the model was trained on (including what decides a conflict), and the commands are checked before they run.
• Simulation and state tracking: chain it one action at a time to follow long sequences; given a way to read the real state, it looks only when unsure (see Look when unsure).
• A fast first opinion before expensive reasoning: answer from Ekbasis when it is confident, escalate to a large reasoning model when it is not.

Quick start

1. Serve the model (one GPU with ~80 GB; vLLM ≥ 0.30). The model folder ships its serving code; run it in the Python environment where vLLM is installed:

   hf download caiovicentino1/Ekbasis-Skopos-27B --local-dir Ekbasis-Skopos-27B

bash Ekbasis-Skopos-27B/serve_vllm.sh Ekbasis-Skopos-27B 8001 # vLLM on 127.0.0.1:8001 python Ekbasis-Skopos-27B/serve.py --model Ekbasis-Skopos-27B --vllm-url http://127.0.0.1:8001 --port 8000

The previous version, caiovicentino1/Ekbasis-27B, is served the same way.

1. Install the client (standard library only, Python ≥ 3.9), anywhere that can reach the server:

   pip install "git+https://github.com/OpenInterpretability/ekbasis"   # ekbasis, ekbasis-claude-hook (MCP: below)

export EKBASIS_URL=http://127.0.0.1:8000 ekbasis health

1. **Ch