Tootfinder

"Mothers, Fathers, Sisters, and Brothers need to show the battle lines are drawn, and stand together to fight for the future of victims.
Is protesting the answer? I dont know. But we’ve got to start somewhere.
To all survivors and those protesting. We stand with you in solidarity and know the fight is not over!"
-- It takes incredible courage to face down the sexual predators who have abused you.
Which is why WC, in this broken year, has selected a broken …

2025 in Review: WC’s Person of the Year
In what passes for tradition here at Wickersham’s Conscience, we spend the last week of each year thinking about the year that is ending, and making our wishes for the coming year. This is the four…

Cowboys expect $97 million star back vs Lions, mystery injury answers? https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/11/28/trevon-diggs-ir-return-cowboys-jerry-jones/87510597007/

If you’re a mediocre manager / executive / admin / leader, what this feels like is •you• have a brilliant idea that will fix things or save a bazillion dollars or boost the metrics or whatever, but all these annoying underling keep telling you that you can’t do that for reasons that you don’t understand and that make no sense to you and you suspect they’re kind of making up.
And you keep saying “Can’t you just…” and they keep giving these confusing gobbledygook answers and the result is that your brilliant leadership cannot come to fruition.
4/

The chat interface was a marketing bet. Selling #AI as if it is not auto complete. It still is.
"#ChatGPT shifted the user’s relationship to text, moving the prompt from a ‘piece of writing for the model to finish’ to a ‘question calling for an answer’."

What Was ChatGPT?
A Chatbot Optimized for Social Distance Three years after the launch of ChatGPT, we can finally speak in hindsight about what it was and how it came to be. Its meteoric rise shocked the world, gathering more users in less time than any product launch in history. But that specific

Locally compact strictly convex metric groups are abelian
Taras Banakh, Oles Mazurenko
https://arxiv.org/abs/2510.10755 https://arxiv.org/pdf/2510.10755

Locally compact strictly convex metric groups are abelian
We show that every locally compact strictly convex metric group is abelian, thus answering one problem posed by the authors in their earlir paper. To prove this theorem we first construct the isomorphic embeddings of the real line into the strictly convex metric group using its geodesic properties and charaterization of the real line as a unique not monothetic one-parametric metrizable topological group. We proceed to show that all compact subgroups in a strictly convex metric group are trivial…

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Siyoon Jin, Seongchan Kim, Dahyun Chung, Jaeho Lee, Hyunwook Choi, Jisu Nam, Jiyoung Kim, Seungryong Kim
https://arxiv.org/abs/2510.07310

MATRIX: Mask Track Alignment for Interaction-aware Video Generation
Video DiTs have advanced video generation, yet they still struggle to model multi-instance or subject-object interactions. This raises a key question: How do these models internally represent interactions? To answer this, we curate MATRIX-11K, a video dataset with interaction-aware captions and multi-instance mask tracks. Using this dataset, we conduct a systematic analysis that formalizes two perspectives of video DiTs: semantic grounding, via video-to-text attention, which evaluates whether n…

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Liwei Kang, Yue Deng, Yao Xiao, Zhanfeng Mo, Wee Sun Lee, Lidong Bing
https://arxiv.org/abs/2510.08308 h…

First Try Matters: Revisiting the Role of Reflection in Reasoning Models
Large language models have recently demonstrated significant gains in reasoning ability, often attributed to their capacity to generate longer chains of thought and engage in reflective reasoning. However, the contribution of reflections to performance improvement remains unclear. In this paper, we systematically analyze the rollouts of eight reasoning models on five mathematical datasets. We focus on reflective behaviours where the model has already produced an answer but continues reflecting …

REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
Shubham Chatterjee
https://arxiv.org/abs/2510.11592 https://arxiv.org/pd…

REGENT: Relevance-Guided Attention for Entity-Aware Multi-Vector Neural Re-Ranking
Current neural re-rankers often struggle with complex information needs and long, content-rich documents. The fundamental issue is not computational--it is intelligent content selection: identifying what matters in lengthy, multi-faceted texts. While humans naturally anchor their understanding around key entities and concepts, neural models process text within rigid token windows, treating all interactions as equally important and missing critical semantic signals. We introduce REGENT, a neural…

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Masih Aminbeidokhti, Heitor Rapela Medeiros, Eric Granger, Marco Pedersoli
https://arxiv.org/abs/2510.06982 https://…

Revisiting Mixout: An Overlooked Path to Robust Finetuning
Finetuning vision foundation models often improves in-domain accuracy but comes at the cost of robustness under distribution shift. We revisit Mixout, a stochastic regularizer that intermittently replaces finetuned weights with their pretrained reference, through the lens of a single-run, weight-sharing implicit ensemble. This perspective reveals three key levers that govern robustness: the \emph{masking anchor}, \emph{resampling frequency}, and \emph{mask sparsity}. Guided by this analysis, we…

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Wenbo Pan, Jie Xu, Qiguang Chen, Junhao Dong, Libo Qin, Xinfeng Li, Haining Yu, Xiaohua Jia
https://arxiv.org/abs/2510.01782

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks
Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal, is crucial for factual reliability. However, existing metrics fail to faithfully measure this ability. On the one hand, simple refusal-based metrics are biased by refusal rates and yield inconsistent scores when models exhibit different refusal tendencies. On the other hand, existing calibration metrics are proxy-based, capturing the performance of auxil…