Tootfinder

Planetary waves can activate resonant drag instabilities in 3D dusty gaseous discs
Ra\'ul O. Chametla, Yasuhiro Hasegawa, Gennaro D'Angelo
https://arxiv.org/abs/2506.13592

Planetary waves can activate resonant drag instabilities in 3D dusty gaseous discs
Resonant Drag Instabilities (RDIs) in protoplanetary discs are driven by the aerodynamic back-reaction of dust on gas and occur when the relative dust-gas motion resonate with a wave mode intrinsic to the gas fluid. Axisymmetric models indicate that the RDI generates filamentary perturbations, leading to grain clumping and planetesimal formation. Motivated by these findings, we investigate the dust-gas interaction in a non-axisymmetric inviscid protoplanetary disc with an embedded low-mass plan…

Resonant helical dichroism in twisted dielectric metastructures
Yiyuan Wang, Chi Li, Haoyi Yu, Stefan A. Maier, Jinhui Shi, Haoran Ren, Kirill Koshelev
https://arxiv.org/abs/2506.14173

Resonant helical dichroism in twisted dielectric metastructures
Circular dichroism, arising from interactions with light fields of opposite spin angular momentum, has become a fundamental tool for molecular characterization. Meanwhile, helical dichroism (HD) - the dichroic response to vortex beams carrying opposite orbital angular momentum (OAM) - offers an alternative approach for probing chiral molecules and photonic structures. Previous demonstrations of HD have been limited to non-resonant light-matter interactions with chiral micro- and nanostructures,…

Nanoscale Magnetic Resonance Imaging and Control of a Strongly Interacting Dipolar System
Piotr Put, Nathaniel T. Leitao, Christina Spaegele, Haoyang Gao, Oksana Makarova, Bartholomeus Machielse, Hengyun Zhou, Federico Capasso, Leigh S. Martin, Hongkun Park, Mikhail D. Lukin
https://arxiv.org/abs/2506.11920

Nanoscale Magnetic Resonance Imaging and Control of a Strongly Interacting Dipolar System
Magnetic Resonance Imaging (MRI) is a fundamental tool for physical and life sciences, yet its spatial resolution is typically limited to macroscopic scales. Here, we demonstrate nanoscale MRI by combining strong, time-dependent local magnetic field gradients with coherent control of a dense ensemble of electron spins hosted in atom-like defects in diamond. Using this platform, we generate and manipulate nanoscale spin textures - spatially structured patterns of spin orientation - and track the…

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Fengran Mo, Chuan Meng, Mohammad Aliannejadi, Jian-Yun Nie
https://arxiv.org/abs/2506.10635

Conversational Search: From Fundamentals to Frontiers in the LLM Era
Conversational search enables multi-turn interactions between users and systems to fulfill users' complex information needs. During this interaction, the system should understand the users' search intent within the conversational context and then return the relevant information through a flexible, dialogue-based interface. The recent powerful large language models (LLMs) with capacities of instruction following, content generation, and reasoning, attract significant attention and advancements, …

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Xiangning Yu, Zhuohan Wang, Linyi Yang, Haoxuan Li, Anjie Liu, Xiao Xue, Jun Wang, Mengyue Yang
https://arxiv.org/abs/2506.09853

Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning
Chain-of-Thought (CoT) prompting plays an indispensable role in endowing large language models (LLMs) with complex reasoning capabilities. However, CoT currently faces two fundamental challenges: (1) Sufficiency, which ensures that the generated intermediate inference steps comprehensively cover and substantiate the final conclusion; and (2) Necessity, which identifies the inference steps that are truly indispensable for the soundness of the resulting answer. We propose a causal framework that …

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
Antonella Bilotta
https://arxiv.org/abs/2506.10048 https://…

Growing a Modular Framework for Modal Systems- HOLMS: a HOL Light Library
The present dissertation introduces the research project on HOLMS (\textbf{HOL} Light Library for \textbf{M}odal \textbf{S}ystems), a growing modular framework for modal reasoning within the HOL Light proof assistant. To provide an accessible introduction to the library, the fundamentals of modal logic are outlined first, followed by a concise manual for the proof assistant itself. The core contribution of this work on HOLMS is the development of a unified and modular strategy for proving adequ…

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
Polina Kirichenko, Mark Ibrahim, Kamalika Chaudhuri, Samuel J. Bell
https://arxiv.org/abs/2506.09038

AbstentionBench: Reasoning LLMs Fail on Unanswerable Questions
For Large Language Models (LLMs) to be reliably deployed in both everyday and high-stakes domains, knowing when not to answer is equally critical as answering correctly. Real-world user queries, which can be underspecified, ill-posed, or fundamentally unanswerable, require LLMs to reason about uncertainty and selectively abstain -- i.e., refuse to answer definitively. However, abstention remains understudied, without a systematic evaluation framework for modern LLMs. In this work, we introduce …

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Chenhui Deng, Yun-Da Tsai, Guan-Ting Liu, Zhongzhi Yu, Haoxing Ren
https://arxiv.org/abs/2506.05566

ScaleRTL: Scaling LLMs with Reasoning Data and Test-Time Compute for Accurate RTL Code Generation
Recent advances in large language models (LLMs) have enabled near-human performance on software coding benchmarks, but their effectiveness in RTL code generation remains limited due to the scarcity of high-quality training data. While prior efforts have fine-tuned LLMs for RTL tasks, they do not fundamentally overcome the data bottleneck and lack support for test-time scaling due to their non-reasoning nature. In this work, we introduce ScaleRTL, the first reasoning LLM for RTL coding that scal…

This https://arxiv.org/abs/2506.04907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Verbose ListOps (VLO): Beyond Long Context -- Unmasking LLM's Reasoning Blind Spots
Large Language Models (LLMs), whilst great at extracting facts from text, struggle with nested narrative reasoning. Existing long context and multi-hop QA benchmarks inadequately test this, lacking realistic distractors or failing to decouple context length from reasoning complexity, masking a fundamental LLM limitation. We introduce Verbose ListOps, a novel benchmark that programmatically transposes ListOps computations into lengthy, coherent stories. This uniquely forces internal computation …

This https://arxiv.org/abs/2503.23630 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csIR_…

Finding Interest Needle in Popularity Haystack: Improving Retrieval by Modeling Item Exposure
Recommender systems operate in closed feedback loops, where user interactions reinforce popularity bias, leading to over-recommendation of already popular items while under-exposing niche or novel content. Existing bias mitigation methods, such as Inverse Propensity Scoring (IPS) and Off-Policy Correction (OPC), primarily operate at the ranking stage or during training, lacking explicit real-time control over exposure dynamics. In this work, we introduce an exposure-aware retrieval scoring appr…