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Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[6/6]:
- Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning
Chi-Pin Huang, Yunze Man, Zhiding Yu, Min-Hung Chen, Jan Kautz, Yu-Chiang Frank Wang, Fu-En Yang
https://arxiv.org/abs/2601.09708 https://mastoxiv.page/@arXiv_csCV_bot/115898618760721320
- Universality of Many-body Projected Ensemble for Learning Quantum Data Distribution
Quoc Hoan Tran, Koki Chinzei, Yasuhiro Endo, Hirotaka Oshima
https://arxiv.org/abs/2601.18637 https://mastoxiv.page/@arXiv_quantph_bot/115967001797773134
- FROST: Filtering Reasoning Outliers with Attention for Efficient Reasoning
Haozheng Luo, Zhuolin Jiang, Md Zahid Hasan, Yan Chen, Soumalya Sarkar
https://arxiv.org/abs/2601.19001 https://mastoxiv.page/@arXiv_csCL_bot/115972068838908815
- Analysis of Shuffling Beyond Pure Local Differential Privacy
Shun Takagi, Seng Pei Liew
https://arxiv.org/abs/2601.19154 https://mastoxiv.page/@arXiv_csDS_bot/115971701218309765
- CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models
Weining Fu, Kai Shu, Kui Xu, Qiangfeng Cliff Zhang
https://arxiv.org/abs/2602.02620
- XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas
Sultana, Afsar, Rahu, Singh, Shula, Combs, Forchetti, Asari
https://arxiv.org/abs/2602.04819
- Flow-Based Conformal Predictive Distributions
Trevor Harris
https://arxiv.org/abs/2602.07633 https://mastoxiv.page/@arXiv_statML_bot/116045671088130364
- GOT-Edit: Geometry-Aware Generic Object Tracking via Online Model Editing
Shih-Fang Chen, Jun-Cheng Chen, I-Hong Jhuo, Yen-Yu Lin
https://arxiv.org/abs/2602.08550 https://mastoxiv.page/@arXiv_csCV_bot/116046486984991360
- UI-Venus-1.5 Technical Report
Venus Team, et al.
https://arxiv.org/abs/2602.09082 https://mastoxiv.page/@arXiv_csCV_bot/116050980295461008
- The Wisdom of Many Queries: Complexity-Diversity Principle for Dense Retriever Training
Xincan Feng, Noriki Nishida, Yusuke Sakai, Yuji Matsumoto
https://arxiv.org/abs/2602.09448 https://mastoxiv.page/@arXiv_csIR_bot/116051022881293649
- Intent Laundering: AI Safety Datasets Are Not What They Seem
Shahriar Golchin, Marc Wetter
https://arxiv.org/abs/2602.16729 https://mastoxiv.page/@arXiv_csCR_bot/116101884238965526
- The Metaphysics We Train: A Heideggerian Reading of Machine Learning
Heman Shakeri
https://arxiv.org/abs/2602.19028 https://mastoxiv.page/@arXiv_csCY_bot/116125225694943789
- Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
David Schmotz, Luca Beurer-Kellner, Sahar Abdelnabi, Maksym Andriushchenko
https://arxiv.org/abs/2602.20156 https://mastoxiv.page/@arXiv_csCR_bot/116125330557447048
- A Very Big Video Reasoning Suite
Maijunxian Wang, et al.
https://arxiv.org/abs/2602.20159 https://mastoxiv.page/@arXiv_csCV_bot/116125664801070747
toXiv_bot_toot

How Larry Ellison is helping his son David build a media empire, including making the case to Trump for why Paramount, not Netflix, should acquire WBD (Theodore Schleifer/New York Times)
https://www.nytime…

A Father and Son’s $108 Billion Hostile Bid for Warner Bros. Discovery
Larry and David Ellison didn’t always have a close relationship. Now they’re one of the most intriguing partnerships in business.

Series A, Episode 08 - Duel
BLAKE: Yes.
AVON: Then I agree. [Lets go of Blake]
JENNA: Deactivating.
BLAKE: Vila, Gan, Jenna: we're going for a ram, take out the command ship.
GAN: A ram?!?
BLAKE: I don't see that there's any other hope for us.
https://blake.torpidity.net/m/108/173

Jets RB Breece Hall to receive tag if sides do not agree to contract by March 3

https://www.cbssports.com/nfl/news/jets-rb-breece-hall-franchise-tag/

How Larry Ellison is helping his son David build a media empire, including making the case to Trump for why Paramount, not Netflix, should acquire WBD (Theodore Schleifer/New York Times)
https://www.nytime…

A Father and Son’s $108 Billion Hostile Bid for Warner Bros. Discovery
Larry and David Ellison didn’t always have a close relationship. Now they’re one of the most intriguing partnerships in business.

Ich habe #ausGründen ChatGPT nach einer praktischen #Formel gefragt, nach der ich die benötigte #Länge für ein

ChatGPT - Geschenkband: Länge berechnen
Shared via ChatGPT

I messaged my doctor's office about the extreme pain I am in. From the response:
"You should not be in excruciating pain, that is not reasonable."
I agree! Sadly they have no solution besides refilling my prescriptions, telling me to continue what I am doing, and telling me to wait a week for the next appointment.
(Luckily my daughter was just in Illinois.)

Merry Christmas 🎄 to all of you!
Whether you celebrate or not, whether you believe in God or not, whether we agree in opinions or not. I simply wish you all the best.
Let it be some time of tolerance and peace.
And tell your loved ones about the great people on the Fediverse 😉

physics_collab: Multilayer physicist collaborations (2015)
Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network.
This n…

Sequential Counterfactual Inference for Temporal Clinical Data: Addressing the Time Traveler Dilemma
Jingya Cheng, Alaleh Azhir, Jiazi Tian, Hossein Estiri
https://arxiv.org/abs/2602.21168 https://arxiv.org/pdf/2602.21168 https://arxiv.org/html/2602.21168
arXiv:2602.21168v1 Announce Type: new
Abstract: Counterfactual inference enables clinicians to ask "what if" questions about patient outcomes, but standard methods assume feature independence and simultaneous modifiability -- assumptions violated by longitudinal clinical data. We introduce the Sequential Counterfactual Framework, which respects temporal dependencies in electronic health records by distinguishing immutable features (chronic diagnoses) from controllable features (lab values) and modeling how interventions propagate through time. Applied to 2,723 COVID-19 patients (383 Long COVID heart failure cases, 2,340 matched controls), we demonstrate that 38-67% of patients with chronic conditions would require biologically impossible counterfactuals under naive methods. We identify a cardiorenal cascade (CKD -> AKI -> HF) with relative risks of 2.27 and 1.19 at each step, illustrating temporal propagation that sequential -- but not naive -- counterfactuals can capture. Our framework transforms counterfactual explanation from "what if this feature were different?" to "what if we had intervened earlier, and how would that propagate forward?" -- yielding clinically actionable insights grounded in biological plausibility.
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