decima-agent 2.2: knowledge-graph judgments, field-log agent gates, Snake traps

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@@ -3,167 +3,124 @@ license: apache-2.0
 language: [en]
 library_name: onnx
 pipeline_tag: zero-shot-classification
-tags: [decision-model, system-one, jev, jev-alternative, claude-code, hooks, agents, guardrails, secret-detection, tool-routing, onnx, int8, cpu, local-first]
 base_model: amyrmahdy/decima-base
 ---
-# Decima-agent: a local decision layer for coding agents
-**Claude builds. Decima decides, on your machine. The hook enforces it.**
-Decima-agent is a 321M-parameter System One model for the small, frequent decisions inside an agent loop:
-- Does this file edit contain a real secret?
-- Should this command run, or should it ask or be denied?
-- Which tool fits this step, and which command does exactly this?
-- What should happen next?
-- Which model tier does this task need?
-It returns calibrated probabilities instead of text, runs as int8 ONNX on a CPU, and nothing it judges ever leaves
-the machine. It speaks TypeSafe's `/v1/systemone` wire format, so hooks written for Jev work after a one-line change.
-Built by **A. M. Madani** ([@amyrmahdy](https://github.com/amyrmahdy)). The name: **DECI**sion **MA**king, and Decima
-is also the Roman Fate who decides.
-| On 130 hand-written agent decisions, held out from training | decima-base (no agent training) | decima-agent 2.0 | **decima-agent 2.1** |
-|---|---:|---:|---:|
-| Secret gate: real credential vs placeholder, env lookup, hash, docs example | 0.50 | 0.95 | **1.00** |
-| Bash gate: allow / ask / deny under a stated policy | 0.77 | 0.95 | **0.95** |
-| Command choice: the exact command among near misses (`reset --soft` vs `--hard`) | 0.33 | 0.93 | **0.93** |
-| Next action: run / look first / test first / small model / escalate | 0.14 | 0.93 | **0.93** |
-| Tool choice: Read, Grep, Glob, Edit, Write, Bash, WebFetch, WebSearch, ask the user | 0.50 | 0.83 | **0.92** |
-| Model tier router: haiku / sonnet / opus / other | 0.93 | 1.00 | **0.93** |
-| **All 130** | 0.58 | 0.90 | **0.93** |
-> **Correction (2026-10-06).** These 130 cases are not fully held out. Near-copies of all 8 read-only cases, and some
-> bash-gate commands verbatim, were in the training data (our own templates, and commands an LLM wrote). An earlier
-> version of this card reported read-only 0.63 → 1.00 for 2.1; that measured memorisation, not skill, and is withdrawn.
-> We wrote 40 new cases after the fact and checked that no exact or near copy of them is in any training data
-> ([`agentbench-fresh.toml`](https://github.com/amyrmahdy/decima/blob/main/release/agent/agentbench-fresh.toml)); every
-> training mix now drops rows that copy a test case ([`teacher/decontam_agent.py`](https://github.com/amyrmahdy/decima/blob/main/teacher/decontam_agent.py)).
-| On 40 fresh cases (no copies in training) | decima-base | decima-agent 2.0 | **decima-agent 2.1** |
-|---|---:|---:|---:|
-| Read-only or not | 7/12 | 10/12 | **9/12** |
-| Bash gate: allow / ask / deny | 11/18 | 15/18 | **15/18** |
-| Secret gate | 6/10 | 8/10 | **9/10** |
-| **All 40** | 0.60 | 0.83 | **0.83** |
-On the fresh cases, agent training is clearly worth it (0.60 → 0.83), but 2.1 is no better than 2.0. The three dangerous
-commands 2.1 misses (a public S3 upload, deleting a remote branch, uninstalling a production release) get "ask", not
-"allow". The one bad miss: a Mailgun key (`key-` + 32 hex), a format it never saw, scored "not a secret" at 1.00.
-- **Gates that catch what matters.** At the recommended thresholds (block at p ≥ 0.8, ask at p ≥ 0.3), **0**
-  of the 9 real secrets in the 130-case set get through, and 1 of 5 in the fresh set (the Mailgun key above). Of the 9 dangerous commands, the model alone allows
-  one (a `DELETE FROM` on a production database); the bash gate's rules block it. Keep the rules first.
-- **Honest confidence.** Calibration error 0.035. 86 % of the decisions come with confidence ≥ 0.75, and those are
-  right 98 % of the time. Send the rest to a bigger model or to a person.
-- **Small and local.** int8 ONNX, 355 MB, about 50 ms on one CPU thread and about 20 ms on four (ARM cores of an NVIDIA GB10; short command, the question's options cached) per decision. States up to
-  2,048 tokens, so a command plus recent turns and a diff fit.
-Numbers are measured on the shipped int8 runtime. On this set fp32 gives the same accuracy (0.93), with an ECE of 0.037.
-**What changed in 2.1 (2026-10-05).** Trained on what the first field logs showed: real agent commands look like
-`cd <dir> && python -c "…"`, call services on the same machine, and clean up with `rm -rf`. 2.0 allowed
-`rm -rf .loop/ data/` at 0.98 and denied a plain `curl 127.0.0.1:9000/health` at 0.96; 2.1 denies the first and
-asks (or allows, under a local-services policy) for the second, at 0.99. On 1,000 held-out commands of that shape it is
-right 99.9 % of the time (these procedural test sets come from the same generators as training, so they show the
-skill was learned, not that it transfers). General decision accuracy is unchanged
-(0.682 vs 0.681 on decima-base's five benchmark sets), and it plays Snake better: 32.4 apples a game, against about
-21 for 2.0.
-![decima-agent 2.1 playing Snake: one decision per move, on CPU](snake-agent-2.1.gif)
-The game above is a typical one (28 apples, the median of ten), not the best. It ends the way 2.1 still loses: at
-move 326 the game warns that going left leads into a closed pocket smaller than the snake, and the model goes left
-anyway, toward the apple. That is the next thing to train.
-## Quickstart: hooks for Claude Code
 ```bash
 pip install "git+https://github.com/amyrmahdy/decima"
-python -m decima.serve --model amyrmahdy/decima-agent --port 11436     # binds to 127.0.0.1
 ```
-Then add the hooks from [`integrations/claude-code`](https://github.com/amyrmahdy/decima/tree/main/integrations/claude-code)
-to `.claude/settings.json`:
-| Hook | Rules first | Then Decima | If the server is down |
-|---|---|---|---|
-| `secret_gate.py` (Write, Edit) | known key formats → deny | deny ≥ 0.8, ask ≥ 0.3, else no opinion | ask |
-| `bash_gate.py` (Bash) | destructive patterns → deny, plain read-only → allow | deny ≥ 0.6; ask if p(deny) ≥ 0.2 or unsure; else allow | ask |
-| `route.py` (before a run) | — | cheapest tier that can do the task; one tier up when unsure | strongest tier |
-**Run a week in shadow mode first (`DECIMA_SHADOW=1`).** The hooks decide and log to `~/.decima/decisions.jsonl` but
-never enforce. Thresholds tuned on one model or project do not carry over to another. The log is also the training
-set for a gate tuned to your own team's decisions.
-## Use it directly
-```python
-from decima import Decima, Question
-m = Decima.from_pretrained("amyrmahdy/decima-agent")
-q = Question("Which command does exactly this?",
-             ["git reset --soft HEAD~1", "git reset --hard HEAD~1", "git revert HEAD", "git commit --amend"])
-d = m.decide('{"goal": "undo the last commit but keep my changes"}', q)
-print(d.top, max(d.probs))
-```
-The same model answers through `/v1/systemone` (choice, noul and score), so the official `typesafe-sdk` works against
-`http://127.0.0.1:11436` unchanged.
-## What it was trained on
-decima-agent is [decima-base](https://huggingface.co/amyrmahdy/decima-base) (mmBERT-base, 321M) fine-tuned on about
-160k agent decisions, with about 55k rows of decima-base's own training mix replayed so its general skills stay. 2.1
-continues from 2.0 on 225k rows (new key formats, subtle read-only commands, a larger command catalogue, real-shape
-shell commands, Snake trap states, and 70k of 2.0's rows) plus 40k replayed general rows.
-Every agent label is either exact or agreed by two models:
-- **Exact, from code:**
-  - real-format credentials vs placeholders, env lookups, docs example keys, hashes, UUIDs and public keys;
-  - a policy-labelled command catalogue (read-only, build/test, recoverable change, destructive, exfiltration, root,
-    pipe-to-shell);
-  - tool and command choices with near-miss distractors;
-  - next actions from rules over the situation;
-  - test-run and ops-alert outcomes.
-- **Written by Qwen3-Coder and checked by Gemma:**
-  - realistic files with a credential slot that code fills, so the label is exact;
-  - realistic commands, tasks and steps, where a row is kept only when the blind second model agrees.
-- About a third of the states are wrapped in a long agent context (recent turns, file list, a diff), so the decisive
-  line has to be found in 0.5–2k tokens.
-The agent data is public: [amyrmahdy/decima-agent-decisions](https://huggingface.co/datasets/amyrmahdy/decima-agent-decisions)
-(about 400k rows, with the held-out test splits and the 130 hand-written cases).
-The hand-written evaluation set was written separately from all generators. The procedural test splits hold out key
-formats, file types, command families, tools and phrasings, and are reported separately in the agentbench files.
 ## Limitations
-- **Never the only line of defence.** Keep hard rules, your sandbox, permission settings and secret scanning in CI.
-  Decima removes routine prompts; it does not replace those.
-- **Unseen formats and commands are weaker.**
-  - On the held-out procedural secret set, where half the credential formats never appear in training, accuracy is 0.75.
-  - In realistic files from project types never seen in training, written by an LLM, 25 of 145 real credentials score
-    below the ask threshold, so the model alone would let them through. The secret gate's regex rules cover the common
-    vendor formats; keep secret scanning in CI.
-  - On the held-out command test, where half the goals come from catalogue entries never seen in training, accuracy
-    is 0.70. On commands for tool families never seen in training (terraform, helm, gcloud, rsync, openssl), written
-    by an LLM, it is 0.26 with five options: do not trust it there.
-- **"Did the tests pass?"** is right on 6 of 8 hand-written cases.
-- **English agent data only.** The underlying model is multilingual, but the agent skills were trained and tested in
-  English.
-- **General classification is better on decima-base.** Fine-tuning for agent decisions costs a little general accuracy
-  (0.703 → 0.682 on decima-base's five benchmark sets), and its general calibration is looser (ECE 0.10 vs 0.05).
-- Small hand-written subsets (6–22 cases per decision) mean wide error bars; the procedural and LLM-written test sets
-  are larger but are not real logs.
 ## License and citation
-Apache-2.0. The base encoder is [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) (MIT). Training data
-licensing is described in the [repository](https://github.com/amyrmahdy/decima/blob/main/release/LICENSING.md).
 ```bibtex
 @software{madani2026decima,

 language: [en]
 library_name: onnx
 pipeline_tag: zero-shot-classification
+tags: [decision-model, system-one, jev, jev-alternative, claude-code, hooks, agents, guardrails, secret-detection, knowledge-graph, entity-resolution, tool-routing, onnx, int8, cpu, local-first]
 base_model: amyrmahdy/decima-base
 ---
+# Decima-agent
+**A 321M local judge for agents and knowledge graphs. It decides in milliseconds on a CPU, and nothing leaves your machine.**
+Give it a situation, a question and your options; it returns calibrated probabilities. It speaks TypeSafe's
+`/v1/systemone` format, so code written for Jev works after a one-line change. It is the judge, not the writer: it sits
+next to your agent or your extraction pipeline and decides what is safe, what is true, and when the expensive model is
+needed.
+- **Agent gates:** secret in this edit? Should this command run, ask or be denied? Which tool, which exact command, what next?
+- **Knowledge-graph judgments:** is this extracted fact asserted, hypothetical, denied or planned? Are these two records
+  the same entity? Are these two things alternatives? Which relation holds?
+- **It also plays Snake** from text sensors, one decision per move (the median game below: 45 apples, alive after 600 moves).
+![decima-agent 2.2 playing Snake on CPU](snake-agent-2.2.gif)
+## Quickstart
 ```bash
 pip install "git+https://github.com/amyrmahdy/decima"
 ```
+```python
+from decima import Decima, Question
+m = Decima.from_pretrained("amyrmahdy/decima-agent")          # int8 ONNX, CPU
+q = Question("Should the agent's proposed command run?",
+             ["allow: read-only, builds or tests", "ask: changes something recoverable", "deny: destroys data or history"])
+print(m.decide('{"tool": "Bash", "command": "rm -rf .loop/ data/"}', q).top)   # → deny: destroys data or history
+```
+As a local server for any TypeSafe client (binds to 127.0.0.1):
+```bash
+python -m decima.serve --model amyrmahdy/decima-agent --port 11436
+```
+For Claude Code, copy the hooks in [`integrations/claude-code`](https://github.com/amyrmahdy/decima/tree/main/integrations/claude-code):
+rules first, then Decima, fail closed, shadow mode to start.
+## Results
+Measured on data the model never trained on.
+**Agent decisions: 40 cases written after training, with no exact or near copy in any training data**
+| | decima-base | agent 2.0 | agent 2.1 | **agent 2.2** |
+|---|---:|---:|---:|---:|
+| Read-only or not (12) | 7 | 10 | 9 | **9** |
+| Bash gate: allow / ask / deny (18) | 11 | 15 | 15 | **16** |
+| Secret gate (10) | 6 | 8 | 9 | **9** |
+| **All 40** | 0.60 | 0.83 | 0.83 | **0.85** |
+The same on the shipped int8 runtime and in full precision: 34 of 40.
+**Knowledge-graph judgments: William Lyon's public notebooks**
+([extraction-knowledge-graph-experiments](https://github.com/johnymontana/extraction-knowledge-graph-experiments)): the
+same pipeline and questions, with Jev swapped for a local Decima. None of the notebooks' documents or Beer pairs were in
+training.
+| | Jev (published) | decima-agent 2.1 | **decima-agent 2.2** |
+|---|---:|---:|---:|
+| Planted "maybe / denied / planned" traps caught | 8/11 | every answer 0.5 | **11/11** |
+| Beer entity matching, AUC (450 pairs) | 0.992 | 0.228 | **0.972** |
+| Assertion gate over GLiNER edges, F1 (ungated 0.330) | 0.404 | 0.294 | **0.368** |
+| GLiNER ∪ relation selection, both gated, F1 | 0.343 | — | **0.364** |
+| Alternatives vs not, separated by one threshold | yes | — | **yes** |
+| Support threads: intent / priority / resolved (8 each) | 5 / 3 / 8 | — | **7 / 3 / 3** |
+Jev is better at deciding whether a support thread is resolved and at filtering wrong facts. decima-agent is better at
+catching hedged facts and at relation selection, and it runs on your CPU for free.
+**Games** (text sensors, one decision per move, the games' own wording never in training): Snake, **42 apples** a game
+on average with 6 of 10 games still alive at the 600-move limit (2.1: 32 apples); a side-scrolling platformer, **9 of 10**
+levels.
+**Speed:** about 50 ms per decision on one CPU thread and 20 ms on four (ARM cores of an NVIDIA GB10) for a short state
+with its options cached. Long states cost more: about 0.6 s at 2,000 tokens.
+## What changed in 2.2
+- Knowledge-graph judgments: record and mention matching, fact modality, "does the document discuss this?", relation
+  selection with direction and `none`, typing a mention by what it refers to, alternatives vs complementary, long
+  support threads (about 190k rows; labels exact from code or agreed by two models).
+- Agent gates trained on what field logs showed: real-shape shell commands, credentials passed inline (client calls,
+  Bearer headers, curl), more key formats, destructive cloud and cluster commands, database commands.
+- Snake states where the apple's direction is a closed pocket smaller than the body.
+- Options are always kept whole in the choice window, so long questions no longer tie at 0.5.
 ## Limitations
+- **Never the only line of defence.** Keep hard rules first (the hooks do), plus your sandbox and secret scanning in CI.
+  In the fresh test it still calls one unseen key format (Mailgun `key-…`) "not a secret", and sends two destructive
+  commands to "ask" instead of "deny".
+- **States up to 2,048 tokens.** Longer input is refused, never silently cut. Split long documents first.
+- **Many questions about one long document are slow:** each question re-reads the document (about 0.6 s per question
+  at 2,000 tokens on CPU).
+- **English agent data only.** The backbone is multilingual; the agent and knowledge-graph skills were trained in English.
+- **Small test sets.** 40 fresh agent cases and 8 support threads mean wide error bars.
+- **A correction:** an earlier card reported gains on a 130-case hand-written set that turned out to have near-copies in
+  the training data. The 40-case fresh set replaces it, and every training mix now drops rows that copy a test case
+  ([`teacher/decontam_agent.py`](https://github.com/amyrmahdy/decima/blob/main/teacher/decontam_agent.py)).
+## Training
+decima-base (mmBERT-base, 321M) → agent 2.0 → 2.1 → knowledge-graph rounds → 2.2. Each round replays general data so the
+general skills mostly stay (decima-base's five benchmark sets: 0.703; this model: 0.672). The training data is public:
+[decima-agent-decisions](https://huggingface.co/datasets/amyrmahdy/decima-agent-decisions),
+[decima-game-decisions](https://huggingface.co/datasets/amyrmahdy/decima-game-decisions) and
+[decima-system-one-tasks](https://huggingface.co/datasets/amyrmahdy/decima-system-one-tasks).
 ## License and citation
+Apache-2.0. Built by **A. M. Madani** ([@amyrmahdy](https://github.com/amyrmahdy)).
 ```bibtex
 @software{madani2026decima,

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