By:Salvatore Attaguile | Forest Code Labs
CAG just got an upgrade.
DOI (v1.5):
https://doi.org/10.5281/zenodo.19136101
GitHub : https://github.com/SpiralSalFCL2026/CAG---Context-Anchored-Generation
In the original version, the focus was controlling semantic drift during generation.
In v1.5, the problem became clearer:
Drift isn’t just a decoding issue — it’s a context lifecycle issue.
What’s new in v1.5
1. Mode-Aware Activation
CAG is no longer always-on.
It activates when precision matters:
- Research mode
- Deep workflows
- Agent/tool-based execution
And stays out of the way during:
- Creative writing
- Ideation
- Exploration
2. Structured Anchor Initialization
Most failures don’t start in decoding.
They start with underspecified context.
v1.5 introduces structured anchor construction — turning vague prompts into a defined semantic frame.
3. Anchor Lifecycle Management
Anchors degrade over time.
In long-running sessions or multi-model workflows:
- context shifts
- assumptions change
- drift accumulates silently
v1.5 introduces anchor refresh and lifecycle awareness to keep generation aligned with current reality—not initial intent.
Suggested Anchor Template
To initialize a stable semantic frame:
- Primary Goal
- Secondary Aims
- Success Criteria
- Constraints (Scope, Ethics, Time, Risk)
- Voice / Tone
- Core Assumptions
- Non-Negotiables
- Open Questions
Why this matters
CAG is evolving from:
- a decoding constraint mechanism
into:
- a system for maintaining coherence across time, context, and interaction depth
Update
CAG is now versioned and available via Zenodo (v2.2).
557+ downloads across versions so far.
Closing thought
Controlling drift at the token level is step one.
Controlling drift across context and time is where things start to get interesting.
Curious how others are handling long-context stability and multi-model workflows.
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