Tootfinder

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/2]:
- Bridge-RAG: An Abstract Bridge Tree Based Retrieval Augmented Generation Algorithm With Cuckoo Fi...
Li, Liu, Zong, Tao, Dai, Ren, Liu, Jiang, Yang
https://arxiv.org/abs/2603.26668 https://mastoxiv.page/@arXiv_csIR_bot/116322781593134028
- SRAG: RAG with Structured Data Improves Vector Retrieval
Shalin Shah, Srikanth Ryali, Ramasubbu Venkatesh
https://arxiv.org/abs/2603.26670 https://mastoxiv.page/@arXiv_csIR_bot/116322784870180864
- LITTA: Late-Interaction and Test-Time Alignment for Visually-Grounded Multimodal Retrieval
Seonok Kim
https://arxiv.org/abs/2603.26683 https://mastoxiv.page/@arXiv_csIR_bot/116322841916406330
- Agentic AI for Human Resources: LLM-Driven Candidate Assessment
Yuksel, Anees, Elneima, Hewavitharana, Al-Badrashiny, Sawaf
https://arxiv.org/abs/2603.26710 https://mastoxiv.page/@arXiv_csIR_bot/116322937601675587
- SEAR: Schema-Based Evaluation and Routing for LLM Gateways
Zecheng Zhang, Han Zheng, Yue Xu
https://arxiv.org/abs/2603.26728 https://mastoxiv.page/@arXiv_csDB_bot/116322627580095245
- SleepVLM: Explainable and Rule-Grounded Sleep Staging via a Vision-Language Model
Guifeng Deng, Pan Wang, Jiquan Wang, Shuying Rao, Junyi Xie, Wanjun Guo, Tao Li, Haiteng Jiang
https://arxiv.org/abs/2603.26738 https://mastoxiv.page/@arXiv_csCV_bot/116322739676378309
- Aesthetic Assessment of Chinese Handwritings Based on Vision Language Models
Chen Zheng, Yuxuan Lai, Haoyang Lu, Wentao Ma, Jitao Yang, Jian Wang
https://arxiv.org/abs/2603.26768 https://mastoxiv.page/@arXiv_csCV_bot/116323078149576728
- Learning to Select Visual In-Context Demonstrations
Eugene Lee, Yu-Chi Lin, Jiajie Diao
https://arxiv.org/abs/2603.26775 https://mastoxiv.page/@arXiv_csLG_bot/116322648878995047
- CRISP: Characterizing Relative Impact of Scholarly Publications
Hannah Collison, Benjamin Van Durme, Daniel Khashabi
https://arxiv.org/abs/2603.26791 https://mastoxiv.page/@arXiv_csDL_bot/116322621679820997
- GroupRAG: Cognitively Inspired Group-Aware Retrieval and Reasoning via Knowledge-Driven Problem S...
Xinyi Duan, Yuanrong Tang, Jiangtao Gong
https://arxiv.org/abs/2603.26807 https://mastoxiv.page/@arXiv_csIR_bot/116322959557860848
- In your own words: computationally identifying interpretable themes in free-text survey data
Jenny S Wang, Aliya Saperstein, Emma Pierson
https://arxiv.org/abs/2603.26930 https://mastoxiv.page/@arXiv_csCY_bot/116322780637316287
- Multilingual Stutter Event Detection for English, German, and Mandarin Speech
Felix Haas, Sebastian P. Bayerl
https://arxiv.org/abs/2603.26939 https://mastoxiv.page/@arXiv_csSD_bot/116322704289189130
- FormalProofBench: Can Models Write Graduate Level Math Proofs That Are Formally Verified?
Ravi, Ying, Nesterov, Krishnan, Uskuplu, Xia, Aswedige, Nashold
https://arxiv.org/abs/2603.26996 https://mastoxiv.page/@arXiv_csAI_bot/116322625941412681
- PHONOS: PHOnetic Neutralization for Online Streaming Applications
Waris Quamer, Mu-Ruei Tseng, Ghady Nasrallah, Ricardo Gutierrez-Osuna
https://arxiv.org/abs/2603.27001 https://mastoxiv.page/@arXiv_eessAS_bot/116322763598554193
- ChartNet: A Million-Scale, High-Quality Multimodal Dataset for Robust Chart Understanding
Jovana Kondic, et al.
https://arxiv.org/abs/2603.27064 https://mastoxiv.page/@arXiv_csCV_bot/116323214468792735
- daVinci-LLM:Towards the Science of Pretraining
Qin, Liu, Mi, Xie, Huang, Si, Lu, Feng, Wu, Liu, Luo, Hou, Guo, Qiao, Liu
https://arxiv.org/abs/2603.27164 https://mastoxiv.page/@arXiv_csAI_bot/116322653467105951
- LightMover: Generative Light Movement with Color and Intensity Controls
Zhou, Wang, Kim, Shu, Yu, Hold-Geoffroy, Chaturvedi, Wu, Lin, Cohen
https://arxiv.org/abs/2603.27209 https://mastoxiv.page/@arXiv_csCV_bot/116323263295656104
- Self-evolving AI agents for protein discovery and directed evolution
Tan, Zhang, Li, Yu, Zhong, Zhou, Dong, Hong
https://arxiv.org/abs/2603.27303 https://mastoxiv.page/@arXiv_csAI_bot/116322838641595927
- Inference-Time Structural Reasoning for Compositional Vision-Language Understanding
Amartya Bhattacharya
https://arxiv.org/abs/2603.27349 https://mastoxiv.page/@arXiv_csCV_bot/116323280006044500
- LLM Readiness Harness: Evaluation, Observability, and CI Gates for LLM/RAG Applications
Alexandre Cristov\~ao Maiorano
https://arxiv.org/abs/2603.27355 https://mastoxiv.page/@arXiv_csAI_bot/116322987708962414
- Heterogeneous Debate Engine: Identity-Grounded Cognitive Architecture for Resilient LLM-Based Eth...
Jakub Mas{\l}owski, Jaros{\l}aw A. Chudziak
https://arxiv.org/abs/2603.27404 https://mastoxiv.page/@arXiv_csAI_bot/116322999177460352
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I got 13 500 on the FoodGuessr Daily!
That's 1 580 points above today's average! 🎉
🌕🌕🌕🌖 4 500 (Round 1)
🌕🌕🌕🌘 4 000 (Round 2)
🌕🌕🌕🌕 5 000 (Round 3) 💯
Wednesday, Apr 29, 2026
Play here: https://www.foodguessr.com/

Human Traces V ⛩️
人类踪迹 V ⛩️
📷 Pentax MX
🎞️ Harman Red 125 （FF）
If you like my work, Support by buying me a coffee or a roll of film from PayPal https://www.paypal.com/paypalme/ydcdingsite

Still, there are some other things Hypercard did we’d do well to study, even with full-scale tools. Off the top of my head:
- It richly rewarded unguided exploration. Unsuccessful experimentation had a way of leading to paths forward, not just dead ends.
- Much of it worked by direct manipulation: if you want the thing there, you put the thing there. (Unity and Godot both sort of kind of do some descendant of this, but not with the same discoverability and transparency.)
- There was a rich library of good starting points, modifiable examples.
- An empty but functioning new project had essentially zero boilerplate. You didn’t have to have 15 files and hundreds of lines of code to get a blank page.
- Its UI made it easy-ish for newcomers to ask “What can I do with this thing here?” Modern autocomplete and inline docs kind of sort of approximate this, but in practice only for people who already have tool expertise.
- HyperTalk (the programming language) is tricky to write (it’s a p-lang), but it’s remarkably easy to read. You can peer at it with very limited knowledge and make educated guesses about its semantics, and those guesses will be mostly correct. (HyperTalk syntax tends to get the most attention when people talk about this, I think at the expense of the other things above.)

Iran’s parliament speaker, Mohammad Bagher Ghalibaf, said Iranian forces
“are waiting for the arrival of American troops on the ground -- to set them on fire”.
Ghalibaf said the US is secretly plotting a ground attack despite a message of diplomacy coming out of the White House.
The Pentagon is reportedly preparing for weeks of possible “ground operations” in Iran, as thousands of US soldiers and marines arrive in the Middle East.
Diplomatic talks in Islamabad …

Dunno if this is a actual lawyer or what but some interesting points here about AI code and ownership
https://legallayer.substack.com/p/who-owns-the-claude-code-wrote

*click* Until Firefox devs screw something up that's absolutely no AI functionality for me, thanks.
Oh, it's in its own new section of the settings, "AI Controls".
#firefox #mozilla #noai

Localized Dynamics-Aware Domain Adaption for Off-Dynamics Offline Reinforcement Learning
Zhangjie Xia, Yu Yang, Pan Xu
https://arxiv.org/abs/2602.21072 https://arxiv.org/pdf/2602.21072 https://arxiv.org/html/2602.21072
arXiv:2602.21072v1 Announce Type: new
Abstract: Off-dynamics offline reinforcement learning (RL) aims to learn a policy for a target domain using limited target data and abundant source data collected under different transition dynamics. Existing methods typically address dynamics mismatch either globally over the state space or via pointwise data filtering; these approaches can miss localized cross-domain similarities or incur high computational cost. We propose Localized Dynamics-Aware Domain Adaptation (LoDADA), which exploits localized dynamics mismatch to better reuse source data. LoDADA clusters transitions from source and target datasets and estimates cluster-level dynamics discrepancy via domain discrimination. Source transitions from clusters with small discrepancy are retained, while those from clusters with large discrepancy are filtered out. This yields a fine-grained and scalable data selection strategy that avoids overly coarse global assumptions and expensive per-sample filtering. We provide theoretical insights and extensive experiments across environments with diverse global and local dynamics shifts. Results show that LoDADA consistently outperforms state-of-the-art off-dynamics offline RL methods by better leveraging localized distribution mismatch.
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Den of Angels is a forum dedicated to the appreciation of resin ball-jointed dolls and the artisans who make them, started in May 2002 as a Yahoo! Group
https://denofangels.com/

A tautological continuous field of Roe bimodules
Vladimir Manuilov
https://arxiv.org/abs/2603.23366 https://arxiv.org/pdf/2603.23366 https://arxiv.org/html/2603.23366
arXiv:2603.23366v1 Announce Type: new
Abstract: We generalize the notion of a continuous field of C*-algebras to that of Hilbert C*-bimodules. Given a partially ordered set $P$ and a monotonically non-decreasing family of ternary rings of operators (TROs) assigned to the points of $P$, we equip $P$ with a certain zero-dimensional Hausdorff topology and use a certain compactification $\gamma P$ to get the base space for a continuous field of Hilbert C*-bimodules over $\gamma P$.
As a motivating example, we consider the set $D(X,Y)$ of coarse equivalence classes of metrics on the disjoint union of two metric spaces, $X$ and $Y$. Each such class gives rise to a uniform Roe bimodule, a TRO linking the uniform Roe algebras of $X$ and $Y$. The resulting family of TROs is non-decreasing with respect to the natural partial order on $D(X,Y)$ and thus yields a tautological continuous field of Hilbert C*-bimodules over $\gamma D(X,Y)$.
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