Tootfinder

Alex Garnett will speak on 'AT: The Billion-Edge Open Social Graph' as part of our Developer track at SCaLE 23x. Full details: https://www.socallinuxexpo.org/scale/23x

The other one I truly love is GitUp (https://gitup.co). Its visualization handles certain specific tasks better than anything else — tasks where I’m more concerned about the shape of the commit graph than the contents of individual commits.
Because of the way it does live updates of repo state and offers a whole-commit-graph-level undo, I’ll sometimes keep it open in the background while doing some fiddly thing in another tool (Fork, CLI, whatever) just so I can see what the ^*@# is happening.
Alas, its lack of support for commit signing means I use it less and less.

Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[3/5]:
- Can Small Language Models Handle Context-Summarized Multi-Turn Customer-Service QA? A Synthetic D...
Lakshan Cooray, Deshan Sumanathilaka, Pattigadapa Venkatesh Raju
https://arxiv.org/abs/2602.00665 https://mastoxiv.page/@arXiv_csCL_bot/116006686092324902
- SEAD: Self-Evolving Agent for Multi-Turn Service Dialogue
Dai, Gao, Zhang, Wang, Luo, Wang, Wang, Wu, Wang
https://arxiv.org/abs/2602.03548
- OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering
Yifan Zhu, Xinyu Mu, Tao Feng, Zhonghong Ou, Yuning Gong, Haoran Luo
https://arxiv.org/abs/2602.03707
- GreekMMLU: A Native-Sourced Multitask Benchmark for Evaluating Language Models in Greek
Zhang, Konomi, Xypolopoulos, Divriotis, Skianis, Nikolentzos, Stamou, Shang, Vazirgiannis
https://arxiv.org/abs/2602.05150
- Using LLMs for Knowledge Component-level Correctness Labeling in Open-ended Coding Problems
Zhangqi Duan, Arnav Kankaria, Dhruv Kartik, Andrew Lan
https://arxiv.org/abs/2602.17542 https://mastoxiv.page/@arXiv_csCL_bot/116102514058414603
- MetaState: Persistent Working Memory Enhances Reasoning in Discrete Diffusion Language Models
Kejing Xia, Mingzhe Li, Lixuan Wei, Zhenbang Du, Xiangchi Yuan, Dachuan Shi, Qirui Jin, Wenke Lee
https://arxiv.org/abs/2603.01331 https://mastoxiv.page/@arXiv_csCL_bot/116165314672421581
- A Browser-based Open Source Assistant for Multimodal Content Verification
Milner, Foster, Karmakharm, Razuvayevskaya, Roberts, Porcellini, Teyssou, Bontcheva
https://arxiv.org/abs/2603.02842 https://mastoxiv.page/@arXiv_csCL_bot/116170368271004704
- Nw\=ach\=a Mun\=a: A Devanagari Speech Corpus and Proximal Transfer Benchmark for Nepal Bhasha ASR
Sharma, Shrestha, Poudel, Tiwari, Shrestha, Ghimire, Bal
https://arxiv.org/abs/2603.07554 https://mastoxiv.page/@arXiv_csCL_bot/116204797995674104
- Model Merging in the Era of Large Language Models: Methods, Applications, and Future Directions
Mingyang Song, Mao Zheng
https://arxiv.org/abs/2603.09938 https://mastoxiv.page/@arXiv_csCL_bot/116210189810004206
- AgentDrift: Unsafe Recommendation Drift Under Tool Corruption Hidden by Ranking Metrics in LLM Ag...
Zekun Wu, Adriano Koshiyama, Sahan Bulathwela, Maria Perez-Ortiz
https://arxiv.org/abs/2603.12564 https://mastoxiv.page/@arXiv_csCL_bot/116237800898328349
- GhanaNLP Parallel Corpora: Comprehensive Multilingual Resources for Low-Resource Ghanaian Languages
Gyamfi, Azunre, Moore, Budu, Asare, Owusu, Asiamah
https://arxiv.org/abs/2603.13793 https://mastoxiv.page/@arXiv_csCL_bot/116243544688031749
- sebis at ArchEHR-QA 2026: How Much Can You Do Locally? Evaluating Grounded EHR QA on a Single Not...
Ibrahim Ebrar Yurt, Fabian Karl, Tejaswi Choppa, Florian Matthes
https://arxiv.org/abs/2603.13962 https://mastoxiv.page/@arXiv_csCL_bot/116243646346563497
- ExPosST: Explicit Positioning with Adaptive Masking for LLM-Based Simultaneous Machine Translation
Yuzhe Shang, Pengzhi Gao, Yazheng Yang, Jiayao Ma, Wei Liu, Jian Luan, Jinsong Su
https://arxiv.org/abs/2603.14903 https://mastoxiv.page/@arXiv_csCL_bot/116243711232778054
- BanglaSocialBench: A Benchmark for Evaluating Sociopragmatic and Cultural Alignment of LLMs in Ba...
Tanvir Ahmed Sijan, S. M Golam Rifat, Pankaj Chowdhury Partha, Md. Tanjeed Islam, Md. Musfique Anwar
https://arxiv.org/abs/2603.15949 https://mastoxiv.page/@arXiv_csCL_bot/116249122231759766
- EngGPT2: Sovereign, Efficient and Open Intelligence
G. Ciarfaglia, et al.
https://arxiv.org/abs/2603.16430 https://mastoxiv.page/@arXiv_csCL_bot/116249228411487178
- HypeLoRA: Hyper-Network-Generated LoRA Adapters for Calibrated Language Model Fine-Tuning
Bartosz Trojan, Filip G\k{e}bala
https://arxiv.org/abs/2603.19278 https://mastoxiv.page/@arXiv_csCL_bot/116277612915482857
- Automatic Analysis of Collaboration Through Human Conversational Data Resources: A Review
Yi Yu, Maria Boritchev, Chlo\'e Clavel
https://arxiv.org/abs/2603.19292 https://mastoxiv.page/@arXiv_csCL_bot/116277620779254916
- Alignment Whack-a-Mole : Finetuning Activates Verbatim Recall of Copyrighted Books in Large Langu...
Xinyue Liu, Niloofar Mireshghallah, Jane C. Ginsburg, Tuhin Chakrabarty
https://arxiv.org/abs/2603.20957 https://mastoxiv.page/@arXiv_csCL_bot/116283538317671552
- KG-Hopper: Empowering Compact Open LLMs with Knowledge Graph Reasoning via Reinforcement Learning
Shuai Wang, Yinan Yu
https://arxiv.org/abs/2603.21440 https://mastoxiv.page/@arXiv_csCL_bot/116283595007808076
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Perfect Network Resilience in Polynomial Time
Matthias Bentert, Stefan Schmid
https://arxiv.org/abs/2602.03827 https://arxiv.org/pdf/2602.03827 https://arxiv.org/html/2602.03827
arXiv:2602.03827v1 Announce Type: new
Abstract: Modern communication networks support local fast rerouting mechanisms to quickly react to link failures: nodes store a set of conditional rerouting rules which define how to forward an incoming packet in case of incident link failures. The rerouting decisions at any node $v$ must rely solely on local information available at $v$: the link from which a packet arrived at $v$, the target of the packet, and the incident link failures at $v$. Ideally, such rerouting mechanisms provide perfect resilience: any packet is routed from its source to its target as long as the two are connected in the underlying graph after the link failures. Already in their seminal paper at ACM PODC '12, Feigenbaum, Godfrey, Panda, Schapira, Shenker, and Singla showed that perfect resilience cannot always be achieved. While the design of local rerouting algorithms has received much attention since then, we still lack a detailed understanding of when perfect resilience is achievable.
This paper closes this gap and presents a complete characterization of when perfect resilience can be achieved. This characterization also allows us to design an $O(n)$-time algorithm to decide whether a given instance is perfectly resilient and an $O(nm)$-time algorithm to compute perfectly resilient rerouting rules whenever it is. Our algorithm is also attractive for the simple structure of the rerouting rules it uses, known as skipping in the literature: alternative links are chosen according to an ordered priority list (per in-port), where failed links are simply skipped. Intriguingly, our result also implies that in the context of perfect resilience, skipping rerouting rules are as powerful as more general rerouting rules. This partially answers a long-standing open question by Chiesa, Nikolaevskiy, Mitrovic, Gurtov, Madry, Schapira, and Shenker [IEEE/ACM Transactions on Networking, 2017] in the affirmative.
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Calibrations for the Sasaki volume on odd spheres and the no-gap problem
Jonas Matuzas
https://arxiv.org/abs/2602.22961 https://arxiv.org/pdf/2602.22961 https://arxiv.org/html/2602.22961
arXiv:2602.22961v1 Announce Type: new
Abstract: For each odd sphere $S^{n}$ with $n=2m 1\ge 5$, we consider the Sasaki volume functional $\mathrm{Vol}^S(V)=\int_{S^{n}}\sqrt{\det(I (\nabla V)^{\top}(\nabla V))}\,d\mathrm{vol}$ on smooth unit tangent vector fields $V$. Using the Brito--Chacon--Naveira calibration $\omega=a\wedge\Theta$ on the unit tangent bundle $E=UTS^{n}$, we establish the universal calibrated lower bound $\mathrm{Vol}^S(V)\ge c(m;1)\,\mathrm{vol}(S^{n})$, where $c(m;1)=4^{m}/\binom{2m}{m}$. In the relaxed (integral-current) setting, we show that the section-constrained stable mass in $E$ equals the calibration value and is attained by an $\omega$-calibrated mass-minimizing integral $n$-cycle in the section class.
We also analyze the equality case on smooth graphs. If a smooth graph is $\omega$-calibrated on an open set, then it satisfies the rigidity system $\nabla_V V=0$ and $\nabla_X V=\lambda X$ for all $X\perp V$, hence is locally a radial distance-gradient field. In particular, for $m\ge 2$ there is no smooth unit field on $S^n$ whose graph is $\omega$-calibrated everywhere.
Finally, we construct an explicit smooth recovery sequence (presented in detail for $S^5$ and then extended to all odd dimensions) and prove a uniform nonvanishing estimate for the polar-shell normalization in the patching construction. As a consequence, $\inf_{V}\,\mathrm{Vol}^S(V)=c(m;1)\,\mathrm{vol}(S^{n})$, so there is no Lavrentiev gap.
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Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/3]:
- Diffusion Modulation via Environment Mechanism Modeling for Planning
Hanping Zhang, Yuhong Guo
https://arxiv.org/abs/2602.20422 https://mastoxiv.page/@arXiv_csAI_bot/116130110576555049
- Heterogeneity-Aware Client Selection Methodology For Efficient Federated Learning
Nihal Balivada, Shrey Gupta, Shashank Shreedhar Bhatt, Suyash Gupta
https://arxiv.org/abs/2602.20450 https://mastoxiv.page/@arXiv_csDC_bot/116130191233002036
- Prior-Agnostic Incentive-Compatible Exploration
Ramya Ramalingam, Osbert Bastani, Aaron Roth
https://arxiv.org/abs/2602.20465 https://mastoxiv.page/@arXiv_csGT_bot/116130245628406144
- PhyGHT: Physics-Guided HyperGraph Transformer for Signal Purification at the HL-LHC
Mohammed Rakib, Luke Vaughan, Shivang Patel, Flera Rizatdinova, Alexander Khanov, Atriya Sen
https://arxiv.org/abs/2602.20475 https://mastoxiv.page/@arXiv_hepex_bot/116130242350426528
- ActionEngine: From Reactive to Programmatic GUI Agents via State Machine Memory
Zhong, Faisal, Fran\c{c}a, Leesatapornwongsa, Szekeres, Rong, Nath
https://arxiv.org/abs/2602.20502 https://mastoxiv.page/@arXiv_csAI_bot/116130180718734838
- Inner Speech as Behavior Guides: Steerable Imitation of Diverse Behaviors for Human-AI coordination
Rakshit Trivedi, Kartik Sharma, David C Parkes
https://arxiv.org/abs/2602.20517 https://mastoxiv.page/@arXiv_csAI_bot/116130223344095649
- Stop-Think-AutoRegress: Language Modeling with Latent Diffusion Planning
Lovelace, Belardi, Zalouk, Polavaram, Kundurthy, Weinberger
https://arxiv.org/abs/2602.20528 https://mastoxiv.page/@arXiv_csCL_bot/116130628998822849
- Standard Transformers Achieve the Minimax Rate in Nonparametric Regression with $C^{s,\lambda}$ T...
Yanming Lai, Defeng Sun
https://arxiv.org/abs/2602.20555 https://mastoxiv.page/@arXiv_statML_bot/116130512372759166
- Personal Information Parroting in Language Models
Nishant Subramani, Kshitish Ghate, Mona Diab
https://arxiv.org/abs/2602.20580 https://mastoxiv.page/@arXiv_csCL_bot/116130630309564204
- Characterizing Online and Private Learnability under Distributional Constraints via Generalized S...
Mo\"ise Blanchard, Abhishek Shetty, Alexander Rakhlin
https://arxiv.org/abs/2602.20585 https://mastoxiv.page/@arXiv_statML_bot/116130525452248337
- Amortized Bayesian inference for actigraph time sheet data from mobile devices
Daniel Zhou, Sudipto Banerjee
https://arxiv.org/abs/2602.20611 https://mastoxiv.page/@arXiv_statML_bot/116130543144314661
- Knowing the Unknown: Interpretable Open-World Object Detection via Concept Decomposition Model
Xueqiang Lv, Shizhou Zhang, Yinghui Xing, Di Xu, Peng Wang, Yanning Zhang
https://arxiv.org/abs/2602.20616 https://mastoxiv.page/@arXiv_csCV_bot/116130795466851481
- On the Convergence of Stochastic Gradient Descent with Perturbed Forward-Backward Passes
Boao Kong, Hengrui Zhang, Kun Yuan
https://arxiv.org/abs/2602.20646 https://mastoxiv.page/@arXiv_mathOC_bot/116130476952419594
- DANCE: Doubly Adaptive Neighborhood Conformal Estimation
Feng, Reich, Beaglehole, Luo, Park, Yoo, Huang, Mao, Boz, Kim
https://arxiv.org/abs/2602.20652 https://mastoxiv.page/@arXiv_statML_bot/116130551664144143
- Vision-Language Models for Ergonomic Assessment of Manual Lifting Tasks: Estimating Horizontal an...
Mohammad Sadra Rajabi, Aanuoluwapo Ojelade, Sunwook Kim, Maury A. Nussbaum
https://arxiv.org/abs/2602.20658 https://mastoxiv.page/@arXiv_csCV_bot/116130809228818544
- F10.7 Index Prediction: A Multiscale Decomposition Strategy with Wavelet Transform for Performanc...
Xuran Ma, et al.
https://arxiv.org/abs/2602.20712 https://mastoxiv.page/@arXiv_astrophIM_bot/116130530693731576
- Communication-Inspired Tokenization for Structured Image Representations
Davtyan, Sahin, Haghighi, Stapf, Acuaviva, Alahi, Favaro
https://arxiv.org/abs/2602.20731 https://mastoxiv.page/@arXiv_csCV_bot/116130824303022936
- SibylSense: Adaptive Rubric Learning via Memory Tuning and Adversarial Probing
Yifei Xu, et al.
https://arxiv.org/abs/2602.20751 https://mastoxiv.page/@arXiv_csCL_bot/116130739757479992
- Assessing the Impact of Speaker Identity in Speech Spoofing Detection
Anh-Tuan Dao, Driss Matrouf, Nicholas Evans
https://arxiv.org/abs/2602.20805 https://mastoxiv.page/@arXiv_csSD_bot/116130218074059060
- Don't Ignore the Tail: Decoupling top-K Probabilities for Efficient Language Model Distillation
Sayantan Dasgupta, Trevor Cohn, Timothy Baldwin
https://arxiv.org/abs/2602.20816 https://mastoxiv.page/@arXiv_csCL_bot/116130753521420972
- DRESS: A Continuous Framework for Structural Graph Refinement
Eduar Castrillo Velilla
https://arxiv.org/abs/2602.20833 https://mastoxiv.page/@arXiv_csDS_bot/116130545112457981
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Marco DeepResearch: Unlocking Efficient Deep Research Agents via Verification-Centric Design
Bin Zhu, Qianghuai Jia, Tian Lan, Junyang Ren, Feng Gu, Feihu Jiang, Longyue Wang, Zhao Xu, Weihua Luo
https://arxiv.org/abs/2603.28376 https://arxiv.org/pdf/2603.28376 https://arxiv.org/html/2603.28376
arXiv:2603.28376v1 Announce Type: new
Abstract: Deep research agents autonomously conduct open-ended investigations, integrating complex information retrieval with multi-step reasoning across diverse sources to solve real-world problems. To sustain this capability on long-horizon tasks, reliable verification is critical during both training and inference. A major bottleneck in existing paradigms stems from the lack of explicit verification mechanisms in QA data synthesis, trajectory construction, and test-time scaling. Errors introduced at each stage propagate downstream and degrade the overall agent performance. To address this, we present Marco DeepResearch, a deep research agent optimized with a verification-centric framework design at three levels: \textbf{(1)~QA Data Synthesis:} We introduce verification mechanisms to graph-based and agent-based QA synthesis to control question difficulty while ensuring answers are unique and correct; \textbf{(2)~Trajectory Construction:} We design a verification-driven trajectory synthesis method that injects explicit verification patterns into training trajectories; and \textbf{(3)~Test-time scaling:} We use Marco DeepResearch itself as a verifier at inference time and effectively improve performance on challenging questions. Extensive experimental results demonstrate that our proposed Marco DeepResearch agent significantly outperforms 8B-scale deep research agents on most challenging benchmarks, such as BrowseComp and BrowseComp-ZH. Crucially, under a maximum budget of 600 tool calls, Marco DeepResearch even surpasses or approaches several 30B-scale agents, like Tongyi DeepResearch-30B.
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Replaced article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/5]:
- Beyond In-Distribution Success: Scaling Curves of CoT Granularity for Language Model Generalization
Ru Wang, Wei Huang, Selena Song, Haoyu Zhang, Qian Niu, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
https://arxiv.org/abs/2502.18273 https://mastoxiv.page/@arXiv_csCL_bot/114069031700102129
- Benchmarking NLP-supported Language Sample Analysis for Swiss Children's Speech
Anja Ryser, Yingqiang Gao, Sarah Ebling
https://arxiv.org/abs/2504.00780 https://mastoxiv.page/@arXiv_csCL_bot/114267149909002069
- Cultural Biases of Large Language Models and Humans in Historical Interpretation
Fabio Celli, Georgios Spathulas
https://arxiv.org/abs/2504.02572 https://mastoxiv.page/@arXiv_csCL_bot/114278467094094490
- BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text
Jiageng Wu, et al.
https://arxiv.org/abs/2504.19467 https://mastoxiv.page/@arXiv_csCL_bot/114420036189999973
- Understanding the Anchoring Effect of LLM with Synthetic Data: Existence, Mechanism, and Potentia...
Yiming Huang, Biquan Bie, Zuqiu Na, Weilin Ruan, Songxin Lei, Yutao Yue, Xinlei He
https://arxiv.org/abs/2505.15392 https://mastoxiv.page/@arXiv_csCL_bot/114550277171100272
- Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods
Raza, Qureshi, Farooq, Lotif, Chadha, Pandya, Emmanouilidis
https://arxiv.org/abs/2505.17870 https://mastoxiv.page/@arXiv_csCL_bot/114572956853819813
- LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Fu, Jiang, Hong, Li, Guo, Yang, Chen, Zhang
https://arxiv.org/abs/2506.14493 https://mastoxiv.page/@arXiv_csCL_bot/114703502552989170
- GHTM: A Graph-based Hybrid Topic Modeling Approach with a Benchmark Dataset for the Low-Resource ...
Farhana Haque, Md. Abdur Rahman, Sumon Ahmed
https://arxiv.org/abs/2508.00605 https://mastoxiv.page/@arXiv_csCL_bot/114969875643478303
- Link Prediction for Event Logs in the Process Industry
Anastasia Zhukova, Thomas Walton, Christian E. Lobm\"uller, Bela Gipp
https://arxiv.org/abs/2508.09096 https://mastoxiv.page/@arXiv_csCL_bot/115020938764936882
- AirQA: A Comprehensive QA Dataset for AI Research with Instance-Level Evaluation
Huang, Cao, Zhang, Kang, Wang, Wang, Luo, Zheng, Qian, Chen, Yu
https://arxiv.org/abs/2509.16952 https://mastoxiv.page/@arXiv_csCL_bot/115253526588472475
- Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortio...
Jun Seo Kim, Hyemi Kim, Woo Joo Oh, Hongjin Cho, Hochul Lee, Hye Hyeon Kim
https://arxiv.org/abs/2509.17292 https://mastoxiv.page/@arXiv_csCL_bot/115253586227941157
- Dual-Space Smoothness for Robust and Balanced LLM Unlearning
Han Yan, Zheyuan Liu, Meng Jiang
https://arxiv.org/abs/2509.23362 https://mastoxiv.page/@arXiv_csCL_bot/115293308293558024
- The Rise of AfricaNLP: Contributions, Contributors, Community Impact, and Bibliometric Analysis
Tadesse Destaw Belay, et al.
https://arxiv.org/abs/2509.25477 https://mastoxiv.page/@arXiv_csCL_bot/115298213432594791
- Open ASR Leaderboard: Towards Reproducible and Transparent Multilingual and Long-Form Speech Reco...
Srivastav, Zheng, Bezzam, Le Bihan, Koluguri, \.Zelasko, Majumdar, Moumen, Gandhi
https://arxiv.org/abs/2510.06961 https://mastoxiv.page/@arXiv_csCL_bot/115343748052193267
- Neuron-Level Analysis of Cultural Understanding in Large Language Models
Taisei Yamamoto, Ryoma Kumon, Danushka Bollegala, Hitomi Yanaka
https://arxiv.org/abs/2510.08284 https://mastoxiv.page/@arXiv_csCL_bot/115349533441895984
- CLMN: Concept based Language Models via Neural Symbolic Reasoning
Yibo Yang
https://arxiv.org/abs/2510.10063 https://mastoxiv.page/@arXiv_csCL_bot/115372392366793754
- Schema for In-Context Learning
Chen, Chen, Wang, Leong, Fung, Bernales, Aspuru-Guzik
https://arxiv.org/abs/2510.13905 https://mastoxiv.page/@arXiv_csCL_bot/115389057899856601
- Evaluating Latent Knowledge of Public Tabular Datasets in Large Language Models
Matteo Silvestri, Fabiano Veglianti, Flavio Giorgi, Fabrizio Silvestri, Gabriele Tolomei
https://arxiv.org/abs/2510.20351 https://mastoxiv.page/@arXiv_csCL_bot/115428615784704418
- LuxIT: A Luxembourgish Instruction Tuning Dataset from Monolingual Seed Data
Julian Valline, Cedric Lothritz, Siwen Guo, Jordi Cabot
https://arxiv.org/abs/2510.24434 https://mastoxiv.page/@arXiv_csCL_bot/115457025096322944
- Surfacing Subtle Stereotypes: A Multilingual, Debate-Oriented Evaluation of Modern LLMs
Muhammed Saeed, Muhammad Abdul-mageed, Shady Shehata
https://arxiv.org/abs/2511.01187 https://mastoxiv.page/@arXiv_csCL_bot/115491321130591723
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