Tootfinder

Structural Code Search using Natural Language Queries
Ben Limpanukorn, Yanjun Wang, Zach Patterson, Pranav Garg, Murali Krishna Ramanathan, Xiaofei Ma, Anoop Deoras, Miryung Kim
https://arxiv.org/abs/2507.02107

Structural Code Search using Natural Language Queries
Searching code is a common task that developers perform to understand APIs, learn common code patterns, and navigate code. Currently, developers most commonly search using keywords and regular expressions that are easy to use and widely available. Beyond keywords and regular expressions, structural code search tools allow developers to search for code based on its syntactic structure. This has numerous applications ranging from bug finding to systematically refactoring code. However, these stru…

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Wei Chow, Yuan Gao, Linfeng Li, Xian Wang, Qi Xu, Hang Song, Lingdong Kong, Ran Zhou, Yi Zeng, Yidong Cai, Botian Jiang, Shilin Xu, Jiajun Zhang, Minghui Qiu, Xiangtai Li, Tianshu Yang, Siliang Tang, Juncheng Li
https://arxiv.org/abs/2506.03144

MERIT: Multilingual Semantic Retrieval with Interleaved Multi-Condition Query
Semantic retrieval is crucial for modern applications yet remains underexplored in current research. Existing datasets are limited to single languages, single images, or singular retrieval conditions, often failing to fully exploit the expressive capacity of visual information as evidenced by maintained performance when images are replaced with captions. However, practical retrieval scenarios frequently involve interleaved multi-condition queries with multiple images. Hence, this paper introduc…

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Yufa Zhou, Shaobo Wang, Xingyu Dong, Xiangqi Jin, Yifang Chen, Yue Min, Kexin Yang, Xingzhang Ren, Dayiheng Liu, Linfeng Zhang
https://arxiv.org/abs/2506.00577

Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding generalization requirements. This paper explores whether post-training techniques, specifically Supervised Fine-Tuning (SFT) and Reinforcement Learning with Verifiable Rewards (RLVR), can effectively $\textit{generalize}$ to multi-agent scenarios. We use economic reasoning as a testbed, leveraging its strong foundations in m…

State Similarity in Modular Superconducting Quantum Processors with Classical Communications
Bujiao Wu, Changrong Xie, Peng Mi, Zhiyi Wu, Zechen Guo, Peisheng Huang, Wenhui Huang, Xuandong Sun, Jiawei Zhang, Libo Zhang, Jiawei Qiu, Xiayu Linpeng, Ziyu Tao, Ji Chu, Ji Jiang, Song Liu, Jingjing Niu, Yuxuan Zhou, Yuxuan Du, Wenhui Ren, Youpeng Zhong, Tongliang Liu, Dapeng Yu

State Similarity in Modular Superconducting Quantum Processors with Classical Communications
As quantum devices continue to scale, distributed quantum computing emerges as a promising strategy for executing large-scale tasks across modular quantum processors. A central challenge in this paradigm is verifying the correctness of computational outcomes when subcircuits are executed independently following circuit cutting. Here we propose a cross-platform fidelity estimation algorithm tailored for modular architectures. Our method achieves substantial reductions in sample complexity compar…

🇺🇦 #NowPlaying on #BBC6Music's #NewMusicFix
Leon Vynehall:
🎵 Mirror's Edge (feat. Poison Anna)
#LeonVynehall
https://leonvynehall.bandcamp.com/track/mirrors-edge-ft-poison-anna
https://open.spotify.com/track/456tPmLLrNb6ZRTeyS2h80

Graph neural network model for the era of large atomistic models
Duo Zhang, Anyang Peng, Chun Cai, Wentao Li, Yuanchang Zhou, Jinzhe Zeng, Mingyu Guo, Chengqian Zhang, Bowen Li, Hong Jiang, Tong Zhu, Weile Jia, Linfeng Zhang, Han Wang
https://arxiv.org/abs/2506.01686

Graph neural network model for the era of large atomistic models
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the development of large models, suggesting that their generalizability in downstream tasks consistently improves with increased model size, expanded training datasets, and larger computational budgets. In this study, we present DPA3, a multi-layer graph neural network f…

from my link log —
Towards fearless SIMD in Rust, 7 years later.
https://linebender.org/blog/towards-fearless-simd/
saved 2025-03-30

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Timothy Disher, Chris Cameron, Brian Hutton
https://arxiv.org/abs/2506.22154

Bayesian non-parametric lumping and splitting of nodes in Network Meta-Analysis under heterogeneity
Network meta-analysis (NMA) synthesizes evidence for multiple treatments, but decisions on node formation can have important statistical implications including bias or inflated uncertainty. Existing data-driven methods often lack flexibility or fail to fully account for node uncertainty and adjust for between-trial heterogeneity simultaneously. We introduce a Bayesian non-parametric framework using a Dirichlet process prior with a regularized horseshoe base measure. This data-driven approach al…

🇺🇦 #NowPlaying on #BBC6Music's #LoudAndProud
TAAHLIAH:
🎵 Heavenrise
#TAAHLIAH
https://taahliah.bandcamp.com/track/heavenrise
https://open.spotify.com/track/3h5TUyJlnfFnU5E9gi8EUf

🇺🇦 #NowPlaying on #BBC6Music's #LoudAndProud
TAAHLIAH:
🎵 Heavenrise
#TAAHLIAH
https://taahliah.bandcamp.com/track/heavenrise
https://open.spotify.com/track/3h5TUyJlnfFnU5E9gi8EUf