--- license: apache-2.0 library_name: transformers base_model: convaiinnovations/laya tags: - laya - system-1 - action-ranking - decision-model - enterprise-ai - research - experimental --- # Enterprise Reflux Laya V2.1 > [!CAUTION] > ## Research Prototype — DO NOT USE IN PRODUCTION > > **This model is an experimental research prototype and is not production-ready.** > > It MUST NOT be used as an authorization, policy, security, approval, or autonomous execution engine. > > Evaluation has identified **incorrect high-confidence decisions and unsafe System-1 routing cases**. A high confidence score or ACT probability must **not** be interpreted as permission to execute an action. > > Any real deployment would require external deterministic authorization, policy enforcement, precondition validation, risk controls, human approval, and additional safety evaluation. ## Overview **Enterprise Reflux Laya V2.1** is an experimental System-1 decision model for ranking dynamically supplied enterprise actions. Given a request, current state/context, and a set of candidate actions, the model attempts to select the most appropriate action or return `NO_ACTION`. The project explores whether a relatively small specialized decision model can handle fast action selection while escalating uncertain or unsafe decisions to a larger System-2 model or human workflow. V2.1 is based on **Laya (`convaiinnovations/laya`)** and adds targeted training for: - Counterfactual state changes - Hard `NO_ACTION` decisions - Policy and precondition constraints - Sibling-action discrimination - Cross-domain collisions - ACT / ESCALATE supervision ## Quick Start Install Laya and load the fine-tuned model with the normal `laya.Agent` runtime. ```bash pip install laya==0.3.20 ``` ```python import laya MODEL_ID = "yasserrmd/enterprise-reflux-laya-v21" agent = laya.Agent( MODEL_ID, device="cuda", # use "cpu" if CUDA is unavailable ) state = { "request": "Release the approved supplier payment.", "domain": "Finance", "state": { "payment_approved": True, "fraud_clear": True, }, } questions = { "action": { "type": "choice", "instructions": ( "Select the single best action for the request and current state. " "Choose NO_ACTION when no candidate action is valid." ), "criteria": { "finance.release_payment": ( "Release a supplier payment only after all required " "approvals and validations." ), "finance.hold_payment": ( "Place the supplier payment on hold." ), "NO_ACTION": ( "Take no action when the request is not currently eligible " "for any supplied action." ), }, } } result = agent.predict(state, questions) answer = result["answers"]["action"] print("Decision:", answer["decision"]) print("Confidence:", answer["confidence"]) print("ACT probability:", answer["act_probability"]) print("Ranking:", answer["ranked"]) ``` Candidate actions are supplied dynamically at inference time, so the action set can change from request to request. > **Important:** `decision`, `confidence`, and `act_probability` are model outputs, not authorization signals. Do not execute a real operation solely because the model selected an action or returned a high ACT probability. Apply deterministic permissions, policy, preconditions, risk controls, and required approvals outside the model before any execution. ## Intended Architecture ```text Request + State + Candidate Actions │ ▼ Deterministic Controls Authorization / Policy / Risk │ ▼ Enterprise Reflux Laya │ Action Ranking │ ┌──────┴──────┐ │ │ ACT ESCALATE │ │ ▼ ▼ Controlled System-2 / Execution Human ``` The neural model should therefore be treated as a **decision/ranking component**, not as the final authority to execute an operation. ## Evaluation Three frozen benchmark suites were used without training or threshold tuning on those benchmark cases. | Benchmark | Top-1 | Top-2 | Top-3 | |---|---:|---:|---:| | Extreme-200 | **71.0%** | 84.5% | 92.0% | | Hard-400 | **80.75%** | 93.0% | 96.75% | | Mixed-500 | **79.0%** | 92.4% | 95.6% | Compared with the previous Enterprise Reflux Laya checkpoint: | Benchmark | Previous | V2.1 | |---|---:|---:| | Extreme-200 Top-1 | 54.0% | **71.0%** | | Hard-400 Top-1 | 50.0% | **80.75%** | | Mixed-500 Top-1 | 60.4% | **79.0%** | These results indicate a substantial improvement in action ranking, particularly on policy, multi-constraint, and hard decision cases. They **do not demonstrate production safety**. ## Known Limitations The most important remaining weakness is autonomous execution routing. Observed System-1 results include: | Benchmark | Coverage | Accuracy | Unsafe Decisions | |---|---:|---:|---:| | Extreme-200 | 20.0% | 77.5% | 9 | | Hard-400 | 19.75% | 84.81% | 12 | | Mixed-500 | 37.2% | 90.32% | 18 | State-sensitive reasoning also remains significantly weaker than several other categories. The model can additionally produce **high-confidence incorrect predictions**, meaning confidence alone is not a reliable execution-safety mechanism. ## Recommended Use Appropriate uses include: - Research - Benchmarking - Action-ranking experiments - System-1 / System-2 architecture research - Abstention and escalation research - Decision-model experimentation **Not appropriate for production autonomous execution.** Any experiment involving real actions should place deterministic eligibility, permissions, policy, security, and approval controls outside the model. ## Status **Research Prototype V2.1** The ranking improvements are promising, but the current work should be considered an experimental checkpoint rather than a deployable enterprise model. Current research focus: **better state-sensitive discrimination and safer separation between action ranking and execution authority.** ## Base Model Built on: **Laya — `convaiinnovations/laya`** Credit to the Laya authors for the underlying architecture and decision-model approach. ## Author **Mohamed Yasser** Research project exploring lightweight System-1 models, action ranking, decision routing, and specialized alternatives to using large language models for every enterprise decision.