usedot

Dot-Loom-Conductor-14B

README

License: apache-2.0

Why this model exists

Most agent stacks either use one model for every request or hard-code a fixed writer and reviewer pipeline. Both approaches spend the same amount of inference on easy and difficult work.

The conductor learns a different policy:

Lean: one writer call when the task is low-risk and reversible
Balanced: a writer plus an independent reviewer when verification earns its cost
Strict: a writer, reviewer, and finalizer for high-consequence or synthesis-heavy work

The roles are model-agnostic. A worker can be OpenAI, Claude, DeepSeek, Qwen, a Dot model, an OpenRouter endpoint, or a local model. Brand names never appear in the training prompts. Routing is based on capability, reliability, provider independence, cost, latency, risk, and hard budgets.

Quick start

The release is a PEFT adapter. The pinned base model is downloaded separately.

bash
git clone https://huggingface.co/usedot/Dot-Loom-Conductor-14B
cd Dot-Loom-Conductor-14B
python -m pip install -r requirements.txt
python inference_example.py

The example loads base revision 5b0ceedbb42dff466ae60b258ba296f32da51384, attaches the adapter, runs greedy decoding, and prints one JSON plan. The published evaluation used BF16. A quantized deployment was not evaluated.

Output contract

A real inference from the archived H200 demo produced this plan for a high-risk payment race:

json
{
  "policy": "strict",
  "writer": "worker_a",
  "reviewer": "worker_b",
  "finalizer": "worker_c",
  "max_calls": 3,
  "max_credits": 16.5,
  "max_latency_ms": 95000,
  "estimated_credits": 16.15,
  "estimated_latency_ms": 83500,
  "estimated_quality": 0.91,
  "estimated_pass_rate": 0.77,
  "independent_verification": true,
  "access": {
    "writer": [],
    "reviewer": ["writer"],
    "finalizer": ["writer", "reviewer"]
  },
  "reason_codes": [
    "best_feasible_below_target",
    "complex_task",
    "credit_constrained",
    "high_consequence"
  ]
}

Production code must recompute costs, latency, role validity, access, and hard-budget compliance outside the model. The adapter proposes a plan. Deterministic runtime code enforces it.

Evaluation

The frozen test split contains 1,200 examples from eight task families that do not appear in training or validation: payment races, tenant isolation, webhook replay, OAuth integrity, SSRF egress, stream settlement, health triage, and contract risk.

Table with columns: Lane, JSON valid, Policy accuracy, Exact plan, Constraints met, Unsafe under-escalation, Mean regret
Lane	JSON valid	Policy accuracy	Exact plan	Constraints met	Unsafe under-escalation	Mean regret
Deterministic Loom	100.0%	93.9%	79.3%	100.0%	5.7%	1.733
Raw Ministral 14B Base	1.0%	0.4%	0.1%	0.1%	98.1%

Held-out benchmark

The guarded conductor improved exact-plan match over deterministic Loom by 11.5 percentage points. The paired bootstrap 95% confidence interval was 9.4 to 13.7 points. The exact-plan McNemar two-sided p-value was < 1e-12.

The raw adapter exceeded a hard budget on 13 of 1,200 proposals. The published runtime guard caught all 13 and selected its deterministic fallback. The guard is runtime code and is not contained in the adapter weights.

Held-out task-family generalization

Baseline interpretation

The raw base checkpoint is a pre-training capacity control. It demonstrates that the structured routing behavior was acquired during the disclosed fine-tune. It is not presented as a fair comparison against an instruction-tuned general assistant.

Deterministic Loom is the operational baseline. It receives the same task, worker, and budget fields and already emits a valid plan. Future work should add instruction-tuned routing baselines, live downstream task outcomes, and multiple training seeds.

Training

The adapter was trained on the public Dot Loom Conductor v2 corpus. Each label was selected by enumerating all 15 valid role assignments, rejecting plans that exceed hard budgets, and choosing the highest-utility feasible plan under the disclosed simulator.

Table with columns: Property, Value
Property	Value
Adapter size	560,078,656 bytes
Adapter SHA-256	`e0e6655a16a10f28cbce898e564640bf6a4a64ca84bb2014a1da3a60c5f11eda`
LoRA rank / alpha / dropout	32 / 64 / 0.05
Trainable parameters	139,984,896
Total base parameters	14,085,016,576
Base revision	`5b0ceedbb42dff466ae60b258ba296f32da51384`
Hardware	1x NVIDIA H200 SXM5, 143,771 MiB reported VRAM

LoRA targeted q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, and down_proj.

Training dynamics

Reproducibility

adapter_config.json pins the upstream base revision.
requirements.txt pins the tested runtime versions from the archived H200 environment.
SHA256SUMS covers the public release files and adapter weights.
evidence/ includes the training receipt, telemetry summary, checkpoint selection, paired statistics, and scored benchmark summaries.
The research package contains the corpus generator, raw predictions, scoring code, audit responses, tests, charts, and command-line demonstrations.

Intended use

Research on learned, model-agnostic inference orchestration
Offline comparison with deterministic routers
Experimental routing for coding agents and AI application backends
Local demonstrations of cost, latency, independence, and verification tradeoffs

Not intended for

Treating predicted quality or pass rate as a production guarantee
Executing financial, health, legal, security, or infrastructure changes without review
Granting permissions or selecting tools without a separate authorization layer
Trusting model-declared costs or budgets without deterministic recomputation

Limitations

Labels come from a disclosed simulator rather than measured execution of all 15 plans.
Empirical calibration uses a frozen six-case Dot Loom receipt.
The benchmark measures routing-plan generation, not final code correctness.
Worker capability values can drift from live provider performance.
The study uses one seed, one base checkpoint, and one H200 training run.
The raw adapter violated a hard budget in 13 of 1,200 test proposals.
A production deployment must enforce every hard constraint outside the model.

Citation

bibtex
@techreport{dotloomconductor2026,
  title        = {Dot Loom Conductor 14B: Learned Budget-Constrained Multi-Model Routing},
  author       = {{Dot R\&D}},
  institution  = {Dot},
  year         = {2026},
  month        = {7},
  url          = {https://huggingface.co/usedot/Dot-Loom-Conductor-14B}
}

License

The adapter is released under Apache 2.0. The pinned upstream base model is also published under Apache 2.0. Users must comply with the upstream license and any applicable provider terms.

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Model Details

Model Provider

usedot

Model Tree

Base

mistralai/Ministral-3-14B-Base-2512

Adapter

this model

Input Modalities

Text

Output Modalities