Latent Coherence
arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments s
This explanation quotes or summarizes the cited source record. Saving creates a bookmark on this device, with no automatic account sync.
- First observed in our collection
- Oct 26, 2026, 4:00 AM UTC
- Earliest cited publication
- Oct 26, 2026, 4:00 AM UTC
- Last observed
- Oct 26, 2026, 4:00 AM UTC
Evidence in the last complete seven days
- Source mentions
- 0 Previous seven days 1
- Distinct sources
- 0 Previous 1
- Comparable growth
- Not comparable
- Ranking appearances
- 0
Collection observations and undated records are separate from dated publication counts. Observed in 7d：0 · Undated records：0
Collection coverage · Comparable windowsCurrent / previous observed days 7 / 7
Counts use complete UTC days. First observed means first seen by this collection, not when a term was coined.
Current window：2026-10-30 → 2026-11-06 (end excluded) · History 18 days
| Source | Current days | Previous days | Last success |
|---|---|---|---|
| arXiv | 6 | 4 | Nov 6, 2026, 12:30 AM UTC |
| arXiv | 7 | 3 | Nov 6, 2026, 5:15 AM UTC |
| Ask HN | 7 | 4 | Nov 6, 2026, 3:45 PM UTC |
| Ask HN | 7 | 4 | Nov 6, 2026, 3:30 PM UTC |
| Ask HN | 7 | 4 | Nov 6, 2026, 4:00 PM UTC |
| Ask HN | 7 | 4 | Nov 6, 2026, 4:00 PM UTC |
| GitHub | 7 | 4 | Nov 6, 2026, 12:45 PM UTC |
| GitHub Issues | 7 | 4 | Nov 6, 2026, 4:00 PM UTC |
| GitHub Releases | 7 | 2 | Nov 6, 2026, 12:15 PM UTC |
| GitHub | 7 | 4 | Nov 6, 2026, 2:30 PM UTC |
| GitHub Issues | 7 | 4 | Nov 6, 2026, 4:45 PM UTC |
| GitHub Releases | 7 | 4 | Nov 6, 2026, 4:45 PM UTC |
| GitHub | 7 | 4 | Nov 6, 2026, 2:30 PM UTC |
| GitHub Issues | 7 | 4 | Nov 6, 2026, 5:00 PM UTC |
| GitHub Releases | 7 | 4 | Nov 6, 2026, 4:45 PM UTC |
| GitHub Discovery | 1 | 0 | Nov 6, 2026, 4:00 PM UTC |
| GitHub Trending | 7 | 4 | Nov 6, 2026, 1:15 PM UTC |
| 7 | 3 | Nov 6, 2026, 8:30 AM UTC | |
| Hugging Face | 7 | 3 | Nov 6, 2026, 8:30 AM UTC |
| Hugging Face Spaces | 1 | 0 | Nov 6, 2026, 3:30 PM UTC |
| Hacker News | 7 | 4 | Nov 6, 2026, 1:15 PM UTC |
| Hacker News | 7 | 4 | Nov 6, 2026, 4:15 PM UTC |
| Hacker News | 7 | 4 | Nov 6, 2026, 4:15 PM UTC |
| Hacker News | 1 | 0 | Nov 6, 2026, 4:00 PM UTC |
| Show HN | 1 | 0 | Nov 6, 2026, 3:45 PM UTC |
| MIT News | 7 | 3 | Nov 6, 2026, 5:45 PM UTC |
| OpenAI | 7 | 4 | Nov 6, 2026, 11:45 AM UTC |
| Product Hunt | 7 | 4 | Nov 6, 2026, 1:15 PM UTC |
| 量子位 | 6 | 4 | Nov 4, 2026, 10:15 AM UTC |
| OpenRouter | 1 | 0 | Nov 6, 2026, 6:36 PM UTC |
| GitHub Trending | 7 | 7 | Nov 6, 2026, 1:15 PM UTC |
| Hugging Face Models | 1 | 0 | Nov 6, 2026, 6:36 PM UTC |
| HF Daily Papers | 1 | 0 | Nov 6, 2026, 6:36 PM UTC |
| OpenRouter | 1 | 0 | Nov 6, 2026, 6:36 PM UTC |
| Product Hunt | 7 | 7 | Nov 6, 2026, 1:15 PM UTC |
| SEC EDGAR | 7 | 4 | Nov 6, 2026, 10:45 AM UTC |
| Sequoia Capital | 7 | 3 | Nov 6, 2026, 6:45 AM UTC |
| Simon Willison | 1 | 0 | Nov 6, 2026, 3:15 PM UTC |
| TechCrunch | 7 | 4 | Nov 6, 2026, 3:00 PM UTC |
| Contracts Finder | 7 | 4 | Nov 6, 2026, 8:00 AM UTC |
| V2EX | 7 | 4 | Nov 6, 2026, 3:00 PM UTC |
- Counts describe monitored public sources, not the whole web or search demand.
Daily history · up to 90 days
Bars show dated source mentions. The latest UTC day may be incomplete. Zero mentions and missing collection coverage are different conditions.
View accessible daily data
| UTC date | Mentions | Sources | Observations | Ranking appearances |
|---|---|---|---|---|
| 2026-10-23 | 0 | 0 | 0 | 0 |
| 2026-10-24 | 0 | 0 | 0 | 0 |
| 2026-10-25 | 0 | 0 | 0 | 0 |
| 2026-10-26 | 1 | 1 | 1 | 0 |
| 2026-10-27 | 0 | 0 | 0 | 0 |
| 2026-10-28 | 0 | 0 | 0 | 0 |
| 2026-10-29 | 0 | 0 | 0 | 0 |
| 2026-10-30 | 0 | 0 | 0 | 0 |
| 2026-10-31 | 0 | 0 | 0 | 0 |
| 2026-11-01 | 0 | 0 | 0 | 0 |
| 2026-11-02 | 0 | 0 | 0 | 0 |
| 2026-11-03 | 0 | 0 | 0 | 0 |
| 2026-11-04 | 0 | 0 | 0 | 0 |
| 2026-11-05 | 0 | 0 | 0 | 0 |
| 2026-11-06 | 0 | 0 | 0 | 0 |
Source evidence
Sources in the evidence shown：arXiv 1
arXiv · Published Oct 26, 2026
Latent Coherence: Do Frontier Language Models' Probability Judgments Obey the Law of Total Probability?
arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments s
View source record →
Review priority 11 / 100
This score orders research candidates. It is not search volume, a forecast or verified commercial demand.
Evidence
0 / 30Distinct dated articles or first observations; repeat crawling adds nothing.
Publisher diversity
0 / 25Conservative owner grouping; syndicated copies count once.
Comparable change
0 / 20Comparable complete weeks only; no percentage from a zero baseline.
Recent evidence
2.6 / 15Latest publication or first observation, not repeated fetch time.
Name specificity
8.5 / 10Explicit entities and technical concepts outrank ambiguous acronyms.
Limitations and interpretation
- First observation is a collection milestone, not proof that a word or product is new to the world.
- Counts describe monitored public sources, not the whole web or search demand.
- First observed here is not the invention date of a name or idea.
- Candidates come from public titles, summaries and entity URLs, not verified full-text analysis.
- Owner grouping is conservative; undisclosed syndication may remain.
- Sources behind this term changed or lack a comparable baseline.
- The previous week is too small for a reliable percentage change.
- Current evidence comes from one publisher or owner.