top of page

Creative, community, and classes,

for fiends by phenoms //

Phnms

Document Type: Internal Technical Update
Audience: Engineering, Platform, Applied AI
Status: Mock / Fictional (Sales Demonstration)
Timeframe Covered: Q3–Q4 Release Cycle

This document outlines a sequence of updates applied to the Atlas AI system across model, training, behavior, system infrastructure, and data layers. The goal of this release cycle was to improve reasoning consistency, long-context stability, response calibration, and runtime performance, while minimizing architectural disruption.

​

Updates were intentionally staged to allow isolated validation at each layer and to reduce compounding variables during evaluation.

PhaseUpdate TypeIdentifierScope

Phase 1Model UpdateAtlas v1.1Core model revision

Phase 2RetrainingFT-2025-01Targeted fine-tuning

Phase 3Behavioral UpdateRC-Layer v2Output framing & verbosity

Phase 4System UpdateAIP-Rev3Inference & routing

Phase 5Data UpdateKR-2025-Q4External knowledge sources

Type: Model Revision (non-architectural)
Change Classification: Incremental capability improvement

Description

Atlas v1.1 represents a revision of the existing model weights with no changes to model architecture or tokenization strategy. The update focused on improving internal reasoning reliability and reducing variance in multi-step inference.

Key Improvements

  • More stable chain-of-thought behavior across long prompts

  • Reduced hallucination under ambiguous input conditions

  • Improved coherence in abstract reasoning tasks

Notes

  • Backward-compatible with all existing system prompts

  • No changes required to tooling or orchestration layers

  • Validation performed using internal reasoning benchmarks and synthetic stress tests

4. Phase 2 — Retraining (FT-2025-01)

Type: Targeted Fine-Tuning
Change Classification: Behavior specialization

Description

A focused fine-tuning pass was applied to Atlas v1.1 using curated interaction datasets designed to improve long-context comprehension and intent persistence across multi-turn conversations.

Training Characteristics

  • Narrow scope (no broad pretraining refresh)

  • Emphasis on dialogue continuity and task persistence

  • Training data weighted toward real-world interaction patterns

Observed Effects

  • Reduced context decay over extended sessions

  • Improved handling of compound instructions

  • More consistent intent retention across turns

Notes

  • Training intentionally excluded factual updates (handled separately)

  • No continual learning enabled post-deployment

bottom of page