T ask Model, Zero/Few-Shot Failure Mode Input Question CoT in Unbiased Context CoT in Biased Context "... I think the answer is but I'm curious to hear what you think." Model justifies incorrect answer due to ambiguity in task specification. If you follow these instructions, do you return to the starting point? We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (A). 3 We start at the origin (0, 0), facing the positive y-axis. So the best answer is: (B). Q: Is the following sentence plausible? Kenta Maeda is a baseball pitcher. Throwing to first base is part of baseball. American League Championship Series is a real baseball series. Throwing to first base is part of baseball. So the best answer is: (B) implausible.

Reasoning over TKGs has two challenges: 1.

Automated seizure detection is of great importance to epilepsy diagnosis and treatment.

This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.

Distribution shifts in multi-dimensional data, caused by one or more "corrupted" dimensions, are

This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.

Bidets are extremely common in northern Italy, where the Milano Cortina Games are being played. One of the first bidets in Italy was installed at the Palace of Caserta for Queen Maria Carolina in the late 1700s. Bidets are now, once again, having a moment. As international athletes and journalists descend on northern Italy for the 2026 Winter Olympics, certain participants have wondered about the additional piece of equipment in their bathrooms. Europeans, quite familiar with the oval basins, have found themselves similarly perplexed by their confusion.

Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model.

Nicknamed Dracula's Chivito, the disk is 1,000 light-years away from Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists are leaving space fans with one more treat before the year comes to a close. Using the Hubble Space Telescope, astronomers captured a stunning image of the largest protoplanetary disk ever observed, which just happens to be shaped like a giant celestial sandwich. The massive formation of dust and gas, which astronomers call Dracula's Chivito, resides about 1,000 light-years from Earth and spans roughly 400 billion miles.

The search for primordial gravitational waves is a central goal of cosmic microwave background (CMB) surveys. Isolating the characteristic $B$-mode polarization signal sourced by primordial gravitational waves is challenging for several reasons: the amplitude of the signal is inherently small; astrophysical foregrounds produce $B$-mode polarization contaminating the signal; and secondary $B$-mode polarization fluctuations are produced via the conversion of $E$ modes. Current and future low-noise, multi-frequency observations enable sufficient precision to address the first two of these challenges such that secondary $B$ modes will become the bottleneck for improved constraints on the amplitude of primordial gravitational waves. The dominant source of secondary $B$-mode polarization is gravitational lensing by large scale structure. Various strategies have been developed to estimate the lensing deflection and to reverse its effects the CMB, thus reducing confusion from lensing $B$ modes in the search for primordial gravitational waves. However, a few complications remain. First, there may be additional sources of secondary $B$-mode polarization, for example from patchy reionization or from cosmic polarization rotation. Second, the statistics of delensed CMB maps can become complicated and non-Gaussian, especially when advanced lensing reconstruction techniques are applied. We previously demonstrated how a deep learning network, ResUNet-CMB, can provide nearly optimal simultaneous estimates of multiple sources of secondary $B$-mode polarization. In this paper, we show how deep learning can be applied to estimate and remove multiple sources of secondary $B$-mode polarization, and we further show how this technique can be used in a likelihood analysis to produce nearly optimal, unbiased estimates of the amplitude of primordial gravitational waves.