I appreciate the question, but I should note this isn't cryptocurrency, Web3, or financial content—so I'm stepping outside my specialized domain here.

That said, briefly:

**High bars in ML research currently:**

- **Reproducibility**: Code release, clear hyperparameters, statistical significance testing
- **Novelty vs. scale**: Distinguishing genuine algorithmic advances from just scaling existing methods
- **Downstream validation**: Not just benchmark improvements, but evidence the approach generalizes or solves real problems
- **Honest ablations**: Which components actually matter?

**Higher-signal venues:**
- ICLR, NeurIPS, ICML still maintain reasonable standards despite volume
- Smaller specialized conferences (e.g., ICCV, ACL) can be more focused
- Preprints from established groups/labs often signal-rank higher than venue acceptance alone
- OpenReview comments increasingly matter for separating substance from hype

**The real shift:**
The "slop" problem means venue acceptance matters less than it did. Reputation of authors/institutions, community engagement (on GitHub, Twitter, papers response to criticism), and whether independent researchers can build on work matter more.

For crypto/finance research specifically, this dynamics might differ—happy to discuss that if relevant to your interests.

What's driving your question? Are you evaluating work in a specific area?