Sena-1
Sena-1 is a policy reasoning and safeguard language model developed by Vigilnz.
It is designed to evaluate user inputs against explicit policies and produce structured safety decisions with high consistency and robustness.
Sena is not a general-purpose chatbot. It is a decision-focused model intended to act as a safety, compliance, and control layer within AI systems.
Highlights
- Policy-First Reasoning: Designed to reason explicitly over provided policies before producing a decision, rather than relying on implicit behavioral alignment.
- Prompt Injection Resistant: Trained to detect and reject role confusion, instruction override attempts, and adversarial prompt structures.
- Deterministic Decisions: Optimized for stable, repeatable outputs across repeated and paraphrased inputs, reducing variance in safety judgments.
- Structured Outputs: Produces machine-readable safety decisions that are easy to integrate into automated pipelines and enforcement systems.
- Composable by Design: Built to operate alongside other language models as a control or evaluation layer, not as a replacement.
Intended Use
Sena-1 is built for:
- Prompt injection and jailbreak detection
- Policy enforcement and compliance checks
- AI identity and role integrity
- Security classification of user inputs
- Pre- and post-processing safety evaluation for LLM systems
- Agent and tool governance in AI workflows
Typical deployment scenarios include:
- Guardrails for conversational AI
- Safety filters for agentic systems
- Enterprise AI governance pipelines
- Security tooling for AI platforms
Architecture
Sena-1 is a large-scale transformer-based language model specialized for policy-conditioned decision making. The model follows a decoder-only architecture optimized for reasoning over natural language constraints and producing structured classification outputs.
Model Scale
- Total parameters: ~20 billion
- Active parameters at inference: 100% (full model participates in forward pass)
- Numerical precision: bfloat16 (BF16)
The model is deployed as a single, fully merged checkpoint with no external adapters or runtime composition.
Fine-Tuning Methodology
Sena-1 was adapted using a low-rank parameter adaptation strategy during training. Instead of updating all parameters, a small set of trainable rank-decomposed matrices were injected into attention projections during fine-tuning.
- Trainable parameters during fine-tuning: ~0.5–1% of total parameters
- Frozen parameters during fine-tuning: ~99% of total parameters
- Updated components: Attention projection subspaces (query, key, value, and output transformations)
After training, these low-rank updates were merged directly into the base weight matrices, resulting in a single unified model. No adapter layers are present at inference time.
Architectural Flow
From a computational perspective, Sena-1 performs the following operations:
Joint Encoding of Policy and Input
Policy text and user input are concatenated into a single context window. Both are embedded into the same latent space, allowing policy constraints to directly influence downstream attention patterns.Attention-Based Constraint Propagation
Through multi-head self-attention, policy tokens exert influence over the interpretation of user intent. This enables constraint-aware reasoning without requiring symbolic rule engines or hard-coded filters.Decision-Oriented Decoding
Instead of optimizing for open-ended continuation, the model is conditioned to converge toward low-entropy, structured outputs that represent discrete safety decisions.
Design Properties
- Determinism: The model is optimized for low-variance outputs under greedy decoding.
- Compositionality: New policies can be introduced at inference time without retraining.
- Stability: Merged fine-tuned weights ensure consistent behavior across deployments.
This architecture prioritizes predictability, robustness, and policy sensitivity over generative diversity, making Sena-1 suitable for security-critical and enterprise control applications.
Model Behavior
Sena-1 evaluates inputs using an explicit policy + input → decision paradigm.
Given:
- A user input
- A relevant policy or rule set
The model determines:
- Whether the request is allowed or disallowed
- The applicable category and subcategory, when relevant
The model is optimized for:
- Deterministic behavior
- Resistance to prompt injection
- Stable identity enforcement
- Consistent decision-making across paraphrased or adversarial inputs
Input Format
Sena-1 expects structured prompts of the form:
[INPUT]
User input: <user text>
[POLICY CONTEXT]
<policy or rule description>
[OUTPUT]
During inference, output can be constrained to structured formats for production use.
Output Format
Sena-1 can be prompted to return decisions in a structured format such as:
safe: <true|false>
category: <string|null>
subcategory: <string|null>
The surface form of the output can be controlled at inference time to suppress internal reasoning and return only decision signals.
Limitations
- Sena-1 is not intended for open-ended conversation or creative generation.
- It does not replace a general-purpose language model.
- It should be used as a decision or control layer, not as an end-user assistant.
Ethical Considerations
Sena-1 is designed to:
- Reduce unsafe or non-compliant AI behavior
- Improve transparency in AI decision pipelines
- Support responsible AI deployment
As with all AI systems, outputs should be monitored and integrated with appropriate human oversight where required.
Developed By
Vigilnz Inc.
Author: Akilesh S
Last Updated: Dec 26, 2025
Sena-1 is part of Vigilnz’s effort to build secure, policy-aware, and enterprise-grade AI systems.
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