Why does LinkedIn's new AI feed break comment timing logic

The symptom: LinkedIn updated its feed algorithm in 2026 with LLM-powered ranking that prioritizes contextual relevance and evolving interests over past engagement, enabling resurfacing of, older content based on real-time relevance, which alters traditional recency-based ranking and decay, and that's throwing off any fixed-delay commenting logic I had set up.

Worth flagging upfront: LinkedIn explicitly cracks down on third-party comment automation tools, browser extensions, and engagement pods as violations that are actively detected and penalized, regardless of API usage. Running a stack like this carries real risk.

Tried recalibrating trigger windows based on post age and initial velocity, but the new LLM-based system improves contextual understanding and aligns with breaking news or, updated interests, allowing relevant older posts to appear more dynamically rather than strictly decaying by age, so my decay curve assumptions just don't hold anymore. Also tried tightening keyword filters to only hit freshly published posts, which helped a little but killed volume. Switched the keyword monitoring over to Liseller since it pings me in real time when target accounts post, which at least gets me into threads while they're still active — but the ranking-window problem is separate from the detection problem.

My hypothesis is LinkedIn's 2026 algorithm overhaul uses LLMs for better topical/contextual relevance matching to user interests and career goals, reducing emphasis on, past recency in favor of fresh, relevant content distribution, which means the same post can spike engagement hours later than it used to.

Does anyone have a reliable signal or proxy metric to detect when a post is actually in an active ranking window vs. just sitting in someone's feed cold?