Metadata-Version: 2.4
Name: carl-studio
Version: 0.18.3
Summary: CARL (Coherence-Aware Reinforcement Learning) — information-theoretic reward signals for LLM training via token-level probability distributions
Project-URL: Homepage, https://github.com/wheattoast11/carl
Project-URL: Documentation, https://github.com/wheattoast11/carl#readme
Project-URL: Repository, https://github.com/wheattoast11/carl
Project-URL: Issues, https://github.com/wheattoast11/carl/issues
Author-email: Tej Desai <tej@terminals.tech>
License-Expression: MIT
License-File: LICENSE
License-File: NOTICE
Keywords: alignment,coding-agent,coherence,entropy,fine-tuning,grpo,huggingface,information-theory,llm,lora,observability,phase-transition,qlora,reinforcement-learning,reward-shaping,rl,rlhf,tool-calling,training,trl
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: GPU :: NVIDIA CUDA
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Typing :: Typed
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Description-Content-Type: text/markdown

  CARL: from chaos to crystal

CARL

  Coherence-Aware Reinforcement Learning

---

## Why

A model becomes an agent when it stops pattern-matching and starts *knowing*. That transition isn't gradual — it's a **phase transition**, like water becoming ice. One moment the model is guessing. The next, it's coherent.

Standard training can't see this happening. You watch a loss curve and hope.

**CARL measures the moment of crystallization** — and rewards it.

```
                         Phi (order parameter)
                              │
          guessing            │         knowing
     ░░░░░░░░░░░░░░░░░░░░░░░░│████████████████████████
                              │
                        crystallization
```

The order parameter **Phi** measures how coherent a model's probability field is at every token. When Phi crystallizes, the model has found its internal anchor — a fixed point it can navigate from to *any* concept space without losing itself.

This is alignment you can measure, not just evaluate.

---

## Quick start

Measure coherence on any logits distribution — no training, no GPU, no API key.
Pure `numpy`:

```python
from carl_core import CoherenceProbe, KAPPA, SIGMA
import numpy as np

vocab_size = 32_000
probe = CoherenceProbe(vocab_size=vocab_size)

# Any [T, V] logits + [T] chosen tokens. Here: 16 tokens from a 32k vocab.
logits = np.random.randn(16, vocab_size)
token_ids = np.argmax(logits, axis=-1)

snap = probe.measure(logits, token_ids)
print(f"phi_mean = {snap.phi_mean:.3f}   (crystallization target: ≥ {SIGMA})")
print(f"horizon  = KAPPA·d ≈ {int(KAPPA * vocab_size):,} tokens")
```

Install (just the observables layer):

```bash
pip install carl-studio
```

That gives you `carl-core` + the base CLI + one-shot observe. For training + HF + Claude observability:

```bash
pip install 'carl-studio[quickstart]'
```

Full extras matrix, reproducible installs via `uv.lock`, and conflict rules (e.g.
`wallet` vs `x402`) live in [`docs/INSTALL.md`](docs/INSTALL.md).

## CLI quickstart

```bash
carl init                  # one-shot setup: account, provider, extras, project, consent
carl chat                  # agent — interactive loop
carl ask "train a small model on gsm8k"   # agent — one-shot prompt
carl research search "coherence-aware reinforcement learning"
```

`carl init` is idempotent: re-running it after setup does nothing unless you pass `--force`. A first-run marker lives at `~/.carl/.initialized`.

Bare `carl` is an entry surface, not a documented top-level workflow by itself:

- on a TTY, first run can route into `carl init`, and a configured project can route into chat
- on non-TTY input, bare `carl` prints help plus a nudge toward `carl chat` and `carl ask`

## Auth

CARL Studio does not require a `.env`, and it does not auto-load one.

- Hugging Face workflows work with either `HF_TOKEN` or a prior `hf auth login` / `huggingface-cli login`
- Claude-powered features use `ANTHROPIC_API_KEY` or `--api-key`
- RunPod uses `RUNPOD_API_KEY`
- public Trackio observe works without credentials

If you want a template, copy `.env.example` and load it into your shell before running `carl`:

```bash
cp .env.example .env
set -a
source .env
set +a
```

Quick setup:

```bash
hf auth login
export ANTHROPIC_API_KEY=sk-ant-xxx   # only for --diagnose / chat
carl start
```

Full auth details: [`docs/auth.md`](docs/auth.md)

## Primary commands

| Command | What it does |
|---|---|
| `carl init` | One-shot setup: account, provider, extras, project, consent. |
| `carl chat` | Interactive agent loop with tools, sessions, cost tracking. |
| `carl ask "<prompt>"` | One-shot agent invocation. |
| `carl research search "<query>"` | Search and retrieve research papers (`carl-studio[research]`). |
| `carl flow "/a /b /c"` | Chain named operations, emit a shared interaction trace. |
| `carl doctor` | Readiness audit. Prints blocking issues and freshness findings. |
| `carl train` | Local training with coherence rewards (`carl-studio[training]`). |

Run `carl start --inventory` for the full installed command map, or `carl flow --list` for every chainable op.

## Architecture

- **`carl-core`** — primitive layer. Typed errors, retry/backoff, safepath sandboxing, content hashing, tier gating, coherence math, interaction chains. Zero training deps.
- **`carl-studio`** — the CLI, agent loop, training pipeline, MCP server, camp client, eval sandbox. Everything above builds on `carl-core`.

`carl-core` is installed alongside `carl-studio`; public callers import from `carl_core.*` directly. The legacy `carl_studio.primitives` shim was removed after v0.5.0.

## Error contract

Fatal paths raise `carl_core.errors.CARLError` subclasses with stable codes you can match programmatically. Top codes:

| Code | Meaning |
|---|---|
| `carl.error` | Base class. Generic failure. |
| `carl.config` | Invalid or missing configuration. |
| `carl.validation` | Input failed schema / value validation. |
| `carl.credential` | Missing or expired credential. |
| `carl.network` | Transient or persistent network failure. |
| `carl.budget` | Spend cap exceeded. |
| `carl.permission` | Permission / consent gate failed. |
| `carl.timeout` | Operation exceeded its deadline. |
| `carl.freshness.stale_pkg` | Installed package older than recommended floor. |
| `carl.freshness.camp_session_expired` | `carl.camp` session needs `carl camp login`. |
| `carl.eml.depth_exceeded` | EML tree exceeded depth bound. |
| `carl.eml.domain_error` | EML operator applied outside its valid domain. |
| `carl.eml.decode_error` | EML canonical-encoding decode failed. |
| `carl.eml.signature_mismatch` | Signed EML head failed HMAC verification. |

`CARLError.to_dict()` produces a secrets-redacted, telemetry-safe payload. See `packages/carl-core/src/carl_core/errors.py` for the full hierarchy.

## Use

**See inside a Trackio run** (no GPU required, base install):
```bash
carl observe --url https://your-trackio-space.hf.space/ --run your-run
```

If the dashboard contains multiple projects, add `--project your-project`.

**Train with coherence rewards** (`carl-studio[training]`):
```bash
carl project init
carl train --config carl.yaml
carl run list
```

Or run directly from the CLI:

```bash
carl train --model your-org/your-base-model --method grpo --dataset your-org/your-dataset --output-repo your-org/your-model --compute a100-large
```

**Gate a checkpoint** (`carl-studio[training]`):
```bash
carl eval --adapter your-username/your-model
```

---

## How It Works

```
 ┌─────────┐     ┌─────────┐     ┌─────────┐     ┌──────┐     ┌──────┐
 │ Observe │ ──> │ Measure │ ──> │  Train  │ ──> │ Gate │ ──> │ Ship │
 │         │     │   Phi   │     │  CARL   │     │      │     │      │
 └─────────┘     └─────────┘     └─────────┘     └──────┘     └──────┘
  point at        entropy +       task rewards     cascade      push to
  any run         order param     + coherence      auto-fires   hub
```

**Observe** — Point CARL at a Trackio dashboard or log file. Instantly see Phi trajectory, entropy, phase state, health.

**Measure** — Phi = 1 - H(P)/log|V|. Zero means maximum uncertainty. One means complete coherence. Computed per token, every step.

**Train** — Five reward functions in a cascade. Task rewards teach *what*. CARL rewards teach *how coherently*.

**Gate** — The cascade auto-calibrates from the training signal. No hardcoded thresholds. CARL activates only when the model demonstrates sustained capability.

**Ship** — Eval gate passes → checkpoint pushed to Hub.

---

## CLI Install Matrix

| Workflow | Command | Install |
|---|---|---|
| One-shot observe | `carl observe --url ... --run ...` | `pip install carl-studio` |
| Live observe | `carl observe --live ...` | `pip install 'carl-studio[tui]'` |
| Claude diagnosis | `carl observe --diagnose ...` | `pip install 'carl-studio[observe]'` |
| Local train/eval | `carl train`, `carl eval` | `pip install 'carl-studio[training]'` |
| HF job management / publish | `carl run status`, `carl run logs`, `carl run stop`, `carl push` | `pip install 'carl-studio[hf]'` |
| Camp account + marketplace | `carl camp account`, `carl camp login`, `carl camp logout`, `carl camp credits`, `carl camp marketplace` | platform features (optional) |
| Privacy consent | `carl camp consent show`, `carl camp consent update` | included |
| x402 payment rail | `carl camp x402 configure`, `carl camp x402 status` | included |
| Contract witnessing | `carl camp contract sign`, `carl camp contract verify` | included |
| Constitutional ledger | `carl contract constitution genesis\|verify\|evaluate\|status` | `pip install 'carl-studio[constitutional]'` |
| Carlito management | `carl carlito list`, `carl carlito spawn`, `carl carlito show` | included |

Managed tiers build on top of these open workflows; extras control local capabilities, not research access.

Provider credentials unlock provider workflows, not CARL Paid platform access. Use `carl camp account` to inspect managed account state, credits, and enabled wallet/x402 capabilities. Privacy consent is managed locally with `carl camp consent` — all flags default off.

## Credential Matrix

| Workflow | Auth |
|---|---|
| Local file observe | none |
| Public Trackio observe | none |
| Claude diagnosis / chat | `ANTHROPIC_API_KEY` or `--api-key` |
| Hub jobs / push / gated model access | `HF_TOKEN` or prior HF login |
| RunPod backend | `RUNPOD_API_KEY` |

---

## Results

Trained with CARL on [OmniCoder-9B](https://huggingface.co/Tesslate/OmniCoder-9B):

| Metric | Value |
|--------|-------|
| Task completion | **92%** |
| Tool format compliance | 99% |
| Mean tool calls per task | 11.09 |
| Phase 2' eval gate | **PASS** |

80 GRPO steps. Five reward functions. Self-calibrating cascade gate.

---

## What's new (v0.18.1 · 2026-04-24)

**Unified entry-point router + sessions + trust + journey coverage matrix.**

- **One `carl` binary, four entry modes.** `carl` (REPL), `carl "<prompt>"`
  (REPL with first turn), `carl -p "<q>"` (one-shot, trust-bypass), `carl <verb>`
  (Typer dispatch). Router at `src/carl_studio/cli/entry.py`; contract docs at
  `docs/v18_journey_coverage.md`.
- **`carl trust` — bare-entry trust pre-check.** `trust status/acknowledge/enable/disable/reset`
  with prior-root eviction notice; persisted at `~/.carl/trust.yaml`.
- **`carl session list/show/delete` — project-aware.** Walks up via
  `project_context.current` so you can invoke from any subdir of a project.
- **`carl init --json` probe-only fast-path.** Seven stable probe keys
  (`first_run_complete`, `camp_session`, `llm_provider_detected`,
  `training_extras_healthy`, `project_config_present`, `consent_set`,
  `context_present`). No prompts on piped stdin; contract locked by
  `tests/journeys/test_journeys_v18.py`.
- **Journey matrix.** 12 journeys × 4 transitions = 48 transitions, covered by
  172 passing tests (164 pre-existing + 8 new journey tests). Batch spec for
  parallel UAT execution at `tests/journeys/BATCHES.md`.

### What's new (v0.9.0) — still applies

**EML symbolic witness — third realizability primitive alongside BITC and DMC.**

- **New reward option: `reward_class="eml"`.** Depth-3 learnable tree, 7 parameters,
  **+0.972 correlation with PhaseAdaptive** — a nearly-indistinguishable signal at
  ~10x parameter efficiency. Benchmarks in `scripts/eml_reward_benchmark.md`.
- **Resonants — a new entity class.** `carl_core.resonant.Resonant` + `compose_resonants`
  enables typed, depth-bounded (`MAX_DEPTH=4`) composition of reward / policy primitives
  without ad-hoc schema drift.
- **Constitutional ledger.** New subcommand `carl contract constitution`
  (`genesis | verify | evaluate | status`) — hash-chained append-only ledger
  over action features (25-dim encoding). Install via:

```bash
pip install 'carl-studio[constitutional]'   # pulls pynacl>=1.5
```

- **Public EML paper** — see the upstream Observable Computation bundle for
  `eml-symbolic-witness.md` (numerical verification: ln identity max absolute
  error 4.44e-16 over 990 sample points on `x ∈ [0.1, 10)` at 0.01 step).

---

## Papers

The math is published and independently reproducible. CARL ships a
four-paper in-repo series under [`paper/`](paper/) and cites the
upstream Zenodo work for the conservation law and identity proof.

**CARL Methods Series (in-repo, drafts):**

- [`paper/01-main-carl.md`](paper/01-main-carl.md) — *Coherence-Aware Reinforcement Learning* (main paper)
- [`paper/02-phase-adaptive-methods.md`](paper/02-phase-adaptive-methods.md) — *Phase-Adaptive Coherence Rewards*
- [`paper/03-coherence-trap-technical-note.md`](paper/03-coherence-trap-technical-note.md) — *The Coherence Trap* (technical note)
- [`paper/04-interaction-chains-witness-logs.md`](paper/04-interaction-chains-witness-logs.md) — *Interaction Chains as Witness Logs*

Index and cross-reference table: [`docs/paper_series.md`](docs/paper_series.md).

**Upstream foundations (Zenodo):**

- [Bounded Informational Time Crystals](https://doi.org/10.5281/zenodo.18906944) — derives the conservation law
- [Material Reality](https://doi.org/10.5281/zenodo.18992029) — validates across 6,244 trials
- [Semantic Realizability](https://doi.org/10.5281/zenodo.18992031) — formal proof

---

## Reference

Architecture, API, CLI commands, environments, compute backends → [docs/reference.md](docs/reference.md)

Credential setup and provider auth → [docs/auth.md](docs/auth.md)

---

## Changelog

Full history lives in [`CHANGELOG.md`](CHANGELOG.md); the most recent entries:

### v0.18.1 (2026-04-24) — unified entry-point + journey matrix

- Unified router (`cli/entry.py`) picks between REPL / bare-prompt / one-shot (`-p`) / subcommand.
- `carl trust` — bare-entry trust pre-check registry at `~/.carl/trust.yaml`.
- `carl session` — project-aware, walks up via `project_context.current`.
- `carl init --json` — probe-only fast-path with 7 stable keys; never prompts on piped stdin.
- Journey matrix + BATCHES spec at `tests/journeys/`; 172 tests green on v0.18 surface.
- Fixture discipline: HOME-pinned tests place the project at `tmp_path/"proj"` (home-guard invariant).

### v0.7.1 (2026-04-19) — Phase-2b close-out

- x402 spend caps (daily + session) + `confirm_payment` hook.
- MCP per-request session state — `_session` global replaced with `MCPServerConnection.session`; FastMCP `Context` DI on authenticated tools.
- `carl metrics serve` — Prometheus text-format scrape endpoint (`metrics` extra); heartbeat auto-hosts when `CARL_METRICS_PORT` is set.
- `carl run diff <a> <b>` — trajectory delta (phi, q_hat, crystallizations) with optional `--steps` alignment.
- Shared `GatingPredicate` Protocol + `carl.gate.*` error namespace across `consent_gate` and `tier_gate`.
- Heartbeat maintenance wrapped in `RetryPolicy(max_attempts=3)` for transient sqlite/IO.
- `CARL_HOME` env now honored uniformly (db.py, settings.py, wallet_store.py, llm.py).

---

## Star History

---

  terminals.tech (https://terminals.tech) · PyPI (https://pypi.org/project/carl-studio/) · Paper (https://doi.org/10.5281/zenodo.18906944) · Docs

  MIT — Intuition Labs LLC (https://terminals.tech)

