A federal agent pointed a gun at a combat veteran’s face and said “
"bang, bang” and
“you’re dead, liberal”
during an immigration raid last week in Chicago,
the veteran is alleging in a new court filing.
https://…
WaPo Publisher Will Lewis appeared at NFL Honors in San Francisco Thursday, after failing to show for a Zoom call in which WaPo announced historic layoffs (Drew Lerner/Awful Announcing)
https://awfulannouncing.com/newspapers/will-lewis-nfl…
This week in small town paper headlines:
Vegan Circus serves up cruelty-free carnival delights
Joey Chang to bring unique cello-beatbox performance to Nevada City
911 caller reports truck with flames coming from undercarriage
Hospital announces their first baby of 2026 (Jan 5)
Oklahoma instructor removed from teaching for failing a screed of an essay
https://apnews.com/article/oklahoma-bible-essay-instructor-fulneckydei-f37ba4b8afdc9d9aba3b73b124f13b9b
A graduate student at the University of Oklahoma
is the latest in a growing list of college instructors to face disciplinary action
after being targeted by Turning Point USA,
the right-wing campus pressure group founded by the late Charlie Kirk.
Mel Curth, a trans psychology instructor who recently won the Department of Psychology’s Outstanding Graduate Teaching Award,
gave a zero to junior Samantha Fulnecky’s reaction paper for an assignment on “gender typicali…
Follow up reporting on that breaking and entering by CBP in #Utah that led to warrantless kidnapping of the owner as well as the employee they were following.
Thank you, @… for the detailed reporting. Our local paper has removed the paywall on all reporting for 2026, so don…
Someone really doesn't like their Bändchen checked...
ACAB = all creatures are bändchened!
#39c3
Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
https://arxiv.org/abs/2512.05271 https://arxiv.org/pdf/2512.05271 https://arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
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