Tootfinder

What's the angle here? California belongs to Mexico? In Communism, we will drive our own taxis? Baiting the people who love Evola with the defeat of martial order by the unwashed and unchecked proletariat? Kali Yuga fashion show? Just there for the violence? Strange times we live in.

Integrated Polarimetric Sensing and Communication with Polarization-Reconfigurable Arrays
Byunghyun Lee, Rang Liu, David J. Love, James V. Krogmeier, A. Lee Swindlehurst
https://arxiv.org/abs/2506.23410

Integrated Polarimetric Sensing and Communication with Polarization-Reconfigurable Arrays
Polarization diversity offers a cost- and space-efficient solution to enhance the performance of integrated sensing and communication systems. Polarimetric sensing exploits the signal's polarity to extract details about the target such as shape, pose, and material composition. From a communication perspective, polarization diversity can enhance the reliability and throughput of communication channels. This paper proposes an integrated polarimetric sensing and communication (IPSAC) system that j…

Skip a Layer or Loop it? Test-Time Depth Adaptation of Pretrained LLMs
Ziyue Li, Yang Li, Tianyi Zhou
https://arxiv.org/abs/2507.07996 https://arxiv.org/pdf/2507.07996 https://arxiv.org/html/2507.07996
arXiv:2507.07996v1 Announce Type: new
Abstract: Can a pretrained neural network adapt its architecture to different inputs without any finetuning? Do we need all layers for simple tasks, and are they adequate for challenging tasks? We found that the layers of a pretrained large language model (LLM) can be manipulated as separate modules to build a better and even shallower model customized for each test sample. In particular, each layer from the pretrained model can be skipped/pruned or repeated multiple times as recurrent neural networks (RNN), and stacked with others in arbitrary orders, yielding a chain-of-layers (CoLa) per sample. This compositional space greatly expands the scope of existing works on looped/recurrent pretrained modules, layer pruning, or early-exit networks. We develop a Monte Carlo Tree Search (MCTS) protocol to explore and identify the optimal CoLa for each sample from math and commonsense reasoning benchmarks. Compared to a static model of a fixed depth, CoLa allows shortcut paths (fast thinking), recurrence of the same layer(s) (slow thinking), and combining both, offering more flexible, dynamic architectures for different inputs. We conduct an extensive analysis of the MCTS-optimized CoLa, which leads to two key findings: (1) For >75% of samples with correct predictions by the original LLM, we can find shorter CoLa, suggesting a large space for improving inference efficiency; (2) For >60% of samples with originally incorrect predictions, we can identify CoLa achieving correct predictions, suggesting a large space of performance enhancement. Our results highlight the shortcomings of using a fixed architecture of pre-trained LLMs for inference on different samples and pave the way to unlock the generalization power of test-time depth adaptation.
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Primordial regular black holes as all the dark matter. III. Covariant canonical quantum gravity models
Marco Calz\`a, Davide Pedrotti, Guan-Wen Yuan, Sunny Vagnozzi
https://arxiv.org/abs/2507.02396

Primordial regular black holes as all the dark matter. III. Covariant canonical quantum gravity models
In earlier companion papers, we showed that non-singular primordial black holes (PBHs) could account for all the dark matter (DM) over a significantly wider mass range compared to Schwarzschild PBHs. Those studies, mostly based on phenomenological metrics, are now extended by considering the quantum-corrected space-time recently proposed by Zhang, Lewandowski, Ma and Yang (ZLMY), derived from an effective canonical (loop) quantum gravity approach explicitly enforcing general covariance. Unlike …

Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Albert Yu, Chengshu Li, Luca Macesanu, Arnav Balaji, Ruchira Ray, Raymond Mooney, Roberto Mart\'in-Mart\'in
https://arxiv.org/abs/2508.05535

Mixed-Initiative Dialog for Human-Robot Collaborative Manipulation
Effective robotic systems for long-horizon human-robot collaboration must adapt to a wide range of human partners, whose physical behavior, willingness to assist, and understanding of the robot's capabilities may change over time. This demands a tightly coupled communication loop that grants both agents the flexibility to propose, accept, or decline requests as they coordinate toward completing the task effectively. We apply a Mixed-Initiative dialog paradigm to Collaborative human-roBot teamin…

Adsorbate phase transitions on nanoclusters from nested sampling
Thanawitch Chatbipho, Ray Yang, Robert B. Wexler, Livia B. P\'artay
https://arxiv.org/abs/2506.01295

Adsorbate phase transitions on nanoclusters from nested sampling
Nested sampling was employed to investigate adsorption equilibria on the truncated-octahedral Lennard-Jones nanocluster LJ$_{38}$ while systematically varying adsorbate-surface well depth and Lennard-Jones size parameters. Evaluation of the canonical partition function over a wide temperature range identifies two successive phase transitions: (i) condensation of the gas phase onto the cluster surface at higher temperatures, and (ii) lateral rearrangement of the adsorbed layer at lower temperatu…

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
Satyavrat Wagle, Akshay Malhotra, Shahab Hamidi-Rad, Aditya Sant, David J. Love, Christopher G. Brinton
https://arxiv.org/abs/2506.00374

Physics-based Generative Models for Geometrically Consistent and Interpretable Wireless Channel Synthesis
In recent years, machine learning (ML) methods have become increasingly popular in wireless communication systems for several applications. A critical bottleneck for designing ML systems for wireless communications is the availability of realistic wireless channel datasets, which are extremely resource-intensive to produce. To this end, the generation of realistic wireless channels plays a key role in the subsequent design of effective ML algorithms for wireless communication systems. Generativ…