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EVIDENCE AND EVALUATION / Undominated.ai

Undominated · BitNet ternary weight audit

Audits BitNet b1.58 ternary weight representation and addition-only kernel energy reduction factors.

“Audits BitNet b1.58 ternary weight representation and addition-only kernel energy reduction factors.”

01 / THE REASONING

Why this made the selection.

  • Pairs a bounded workflow with an offline Python checker and explicitly synthetic example inputs.
  • Created for recurring evidence and comparison failures: missing facts remain unknown and every conclusion keeps its scope.

02 / THE REVIEW RECORD

What we actually inspected.

Source review has boundaries.
A clear record is more useful than a “safe” badge.

Material inspected

  • Original definition and MIT licence
  • Bundled checker source, input contract and synthetic examples

Our findings

  • First-party resource authored by Undominated.ai; this is our own verification record, not an independent endorsement.
  • The download preserves the complete source and supporting files. Its manifest hashes identify the exact bytes.
  • The checker type-checks every declared field, recomputes only identities that match the bundled synthetic example, and does not emit a compliance or release flag.

Not established by this review

  • Model compliance with these instructions on arbitrary tasks
  • Native integration in every agent host
  • Public GitHub source identity
  • Remote installation through the standard Skills CLI
  • The new npm resource CLI installation

The review applies to the material and revision named here. A newer upstream release can change its behavior.

03 / PUT IT TO WORK

Add a skill to your workflow.

Upstream setup instructions ↗
  1. Download the complete bundle from https://undominated.ai/resources/skills/undominated-bitnet-ternary-weight-audit/bundle.zip and extract it into a new directory.
  2. Place the extracted directory at .agents/skills/undominated-bitnet-ternary-weight-audit/ or your host's documented skills directory. Preserve SKILL.md, scripts, examples and licence files together.
  3. Run the documented synthetic example from the skill directory with Python 3.10 or later before using your own evidence.

Before you start

  • An Agent Skills compatible host
  • Python 3.10 or later for the optional checker

THE COMPLETE REVIEWED DEFINITION

Read it before you reuse it.

Original source bytes, with attribution.
Review the host-specific setup notes above.

---
name: undominated-bitnet-ternary-weight-audit
description: "Audits BitNet b1.58 ternary weight representation and addition-only kernel energy reduction factors. Recomputed fields must match the other inputs. A pass is not a compliance, safety, or price verdict."
license: MIT
metadata:
  author: Undominated.ai
  version: "1.1.0"
---

# BitNet ternary weight audit

## Description
Audits BitNet b1.58 ternary weight representation and addition-only kernel energy reduction factors.

## Category
`serving`

## Tags
`serving`, `bitnet`, `ternary`, `energy`, `quantization`

## When to Use
Audit BitNet b1.58 model deployments to verify energy efficiency gains and multiplication-free kernel throughput.

## Operational Limitations
Evaluates observed energy reduction factors; does not synthesize CUDA assembly directly.

## Verification Script
Run the deterministic verification checker:
```bash
python3 scripts/check.py examples/synthetic.json
```
Exit codes:
- `0`: Check passed and thresholds satisfied.
- `1`: Threshold breach or operational violation detected.
- `2`: Schema error or invalid usage.

## What the checker recomputes

- `factor` = 1 * baseline_fp16_energy_joules / gemm_energy_joules, then the original `<` check against `target_energy_reduction_factor`. `baseline_fp16_energy_joules` cannot exceed `gemm_energy_joules`.

Every other declared field is type-checked. A negative value is rejected except for deltas, drifts, and gains. A type check does not, by itself, prove the measurement.

`population` and `observedAt` are required. The bundled examples are synthetic fixtures, not market measurements.

```bash
python3 scripts/check.py examples/synthetic.json
python3 scripts/check.py examples/contradiction.json
python3 scripts/check.py examples/broken.json
```

Exit 0 is a pass, exit 1 is a review, and exit 2 is invalid input. A pass does not certify a provider, a regulation, a release, or a price.

The download contains SKILL.md. Extract the whole bundle; the supporting files are required. Inspect the included MANIFEST.json for file hashes.

By Undominated.ai. Exact upstream source ↗ · Licence · Attribution

SHA-256 7eccbd02af9c3f3d6274bbd9e80d5dcb3a8224fe1067eddb62e520f70bae184f

Read the applicable licence
MIT License

Copyright (c) 2026 Undominated.ai

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

04 / FOLLOW THE EVIDENCE

The source trail.

Our notes are separate from the original resource.
Check upstream before adopting a new version.

  1. https://undominated.ai/resources/skills/undominated-bitnet-ternary-weight-audit/definition.md

    Supports: Workflow and input requirements, Stated limitations

  2. MIT licence ↗Checked

    https://undominated.ai/resources/skills/undominated-bitnet-ternary-weight-audit/LICENSE.txt

    Supports: Redistribution terms and attribution

  3. Complete source bundle ↗Checked

    https://undominated.ai/resources/skills/undominated-bitnet-ternary-weight-audit/bundle.zip

    Supports: Full local source, supporting files and integrity manifest

Evidence & Ask