Tootfinder

Raiders’ long ball connection remains a work in progress https://www.reviewjournal.com/sports/raiders/raiders-long-ball-connection-remains-a-work-in-progress-3422923/

Also, in other other #ThingUmbrella related news. My current consulting/advisor contract finishes end of the month, so I will have some time to write about various other recent updates (and unpublished work-in-progress) in early August... Please stay tuned! ✌️

WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
Dhruv Kolhatkar, Soubhagya Akkena, Edward F. Gehringer
https://arxiv.org/abs/2508.11717

WIP: Leveraging LLMs for Enforcing Design Principles in Student Code: Analysis of Prompting Strategies and RAG
This work-in-progress research-to-practice paper explores the integration of Large Language Models (LLMs) into the code-review process for open-source software projects developed in computer science and software engineering courses. The focus is on developing an automated feedback tool that evaluates student code for adherence to key object-oriented design principles, addressing the need for more effective and scalable methods to teach software design best practices. The innovative practice inv…

Did a little 9 mile loop this morning on the bike... Later on the way to work I spotted the progress of the new bike path on Lisbon Avenue. Lisbon has gone from a street I didn't even like to drive on to a street I look forward to biking on.
#mke #milwaukee

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Axel Delaval, Shujian Yang, Haicheng Wang, Han Qiu, Jialiang Lu
https://arxiv.org/abs/2508.11281

ToxiFrench: Benchmarking and Enhancing Language Models via CoT Fine-Tuning for French Toxicity Detection
Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped, primarily due to the lack of culturally relevant, large-scale datasets. In this work, we introduce TOXIFRENCH, a new public benchmark of 53,622 French online comments, constructed via a semi-automated annotation pipeline that reduces manual labeling to only 10% through high-confidence LLM-based pre-annotation and huma…

Continuing on my Meshtastic kick, (probably because I keep buying radios to play with...) This time I have a configuration guide for common settings I've found which are useful. Still a work in progress but I think most of the common options are there.
If I've forgotten something, gotten something wrong, or you have a trick I should add, let me know!

Useful Meshtastic Config Options
This guide provides a breakdown of common settings I have found to be useful for general use cases on the Meshtastic network with standard devices. For a full breakdown of all options supported in Meshtastic nodes, refer to the official documentation for Meshtastic.

WIP: Turning Fake Chips into Learning Opportunities
Haniye Mehraban, Saad Azmeen-ur-Rahman, John Hu
https://arxiv.org/abs/2507.13281 https://

WIP: Turning Fake Chips into Learning Opportunities
This work-in-progress paper presents a case study in which counterfeit TL074 operational amplifiers, discovered in a junior level electronics course, became the basis for a hands on learning experience. Counterfeit integrated circuits (IC) are increasingly common, posing a significant threat to the integrity of undergraduate electronics laboratories. Instead of simply replacing the counterfeit components, we turned the issue into a teaching moment. Students engaged in hands-on diagnostics measu…

Algorithmic Perspective on Toda's Theorem
Dror Fried, Etay Segal, Gad E. Yaron
https://arxiv.org/abs/2509.13871 https://arxiv.org/pdf/2509.13871…

Algorithmic Perspective on Toda's Theorem
Toda's Theorem is a fundamental result in computational complexity theory, whose proof relies on a reduction from a QBF problem with a constant number of quantifiers to a model counting problem. While this reduction, henceforth called Toda's reduction, is of a purely theoretical nature, the recent progress of model counting tools raises the question of whether the reduction can be utilized to an efficient algorithm for solving QBF. In this work, we address this question by looking at Toda's red…

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Liting Gao, Yi Yuan, Yaru Chen, Yuelan Cheng, Zhenbo Li, Juan Wen, Shubin Zhang, Wenwu Wang
https://arxiv.org/abs/2509.14003

RFM-Editing: Rectified Flow Matching for Text-guided Audio Editing
Diffusion models have shown remarkable progress in text-to-audio generation. However, text-guided audio editing remains in its early stages. This task focuses on modifying the target content within an audio signal while preserving the rest, thus demanding precise localization and faithful editing according to the text prompt. Existing training-based and zero-shot methods that rely on full-caption or costly optimization often struggle with complex editing or lack practicality. In this work, we p…

Integrated microheater on the 4H-silicon-carbide-on-insulator platform and its applications
Wenhan Sun, Ruixuan Wang, Jingwei Li, Haipeng Zhang, Zhensheng Jia, Qing Li
https://arxiv.org/abs/2506.15035

Integrated microheater on the 4H-silicon-carbide-on-insulator platform and its applications
Recent progress in the 4H-silicon-carbide-on-insulator (4H-SiCOI) platform has resulted in the demonstration of essential building blocks such as low-loss waveguides and microresonators. In this work, we add tunability to the 4H-SiCOI platform by integrating microheaters with compact microresonators. The strong thermo-optic effect in SiC enables a resonance tuning rate of $11.7$ pm/mW for a 36-$μ$m-radius SiC microring, with a maximum wavelength shift up to $2.4$ nm (300 GHz). The thermal time…

Extended Abstract: Towards a Performance Comparison of Syntax and Type-Directed NbE
Chester J. F. Gould, William J. Bowman
https://arxiv.org/abs/2509.13489 https://

Extended Abstract: Towards a Performance Comparison of Syntax and Type-Directed NbE
A key part of any dependent type-checker is the method for checking whether two types are equal. A common claim is that syntax-directed equality is more performant, although type-directed equality is more expressive. However, this claim is difficult to make precise, since implementations choose only one or the other approach, making a direct comparison impossible. We present some work-in-progress developing a realistic platform for direct, apples-to-apples, comparison of the two approaches, qua…

Delta Matters: An Analytically Tractable Model for $\beta$-$\delta$ Discounting Agents
Yasunori Akagi, Takeshi Kurashima
https://arxiv.org/abs/2509.13637 https://

Delta Matters: An Analytically Tractable Model for $β$-$δ$ Discounting Agents
Humans exhibit time-inconsistent behavior, in which planned actions diverge from executed actions. Understanding time inconsistency and designing appropriate interventions is a key research challenge in computer science and behavioral economics. Previous work focuses on progress-based tasks and derives a closed-form description of agent behavior, from which they obtain optimal intervention strategies. They model time-inconsistency using the $β$-$δ$ discounting (quasi-hyperbolic discounting), …

StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidance
Zefan Qu, Zhenwei Wang, Haoyuan Wang, Ke Xu, Gerhard Hancke, Rynson W. H. Lau
https://arxiv.org/abs/2509.13301

StyleSculptor: Zero-Shot Style-Controllable 3D Asset Generation with Texture-Geometry Dual Guidance
Creating 3D assets that follow the texture and geometry style of existing ones is often desirable or even inevitable in practical applications like video gaming and virtual reality. While impressive progress has been made in generating 3D objects from text or images, creating style-controllable 3D assets remains a complex and challenging problem. In this work, we propose StyleSculptor, a novel training-free approach for generating style-guided 3D assets from a content image and one or more styl…

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
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I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
1/2

Nonperturbative Heavy-Flavor Transport Approach for Hot QCD
Tharun Krishna, Ralf Rapp, Yu Fu, Steffen A. Bass, Weiyao Ke
https://arxiv.org/abs/2509.13881 https://

Nonperturbative Heavy-Flavor Transport Approach for Hot QCD
The heavy charm and bottom quarks are unique probes of the transport properties of the quark-gluon plasma (QGP) and its hadronization in high-energy nuclear collisions. A key challenge in this context is to embed the interactions of the heavy quarks in the expanding medium compatible with the strong-coupling nature of the QGP, and thus to unravel the underlying microscopic mechanisms. In the present work we progress toward this goal by combining recent $T$-matrix interactions for elastic scatte…

Versatile Wavelength-Division Multiplexed Quantum Key Distribution Network Operating Simultaneously in the O and C Bands
Davide Scalcon, Matteo Padovan, Paolo Villoresi, Giuseppe Vallone, Marco Avesani
https://arxiv.org/abs/2507.11175

Versatile Wavelength-Division Multiplexed Quantum Key Distribution Network Operating Simultaneously in the O and C Bands
Ongoing technological progress is accelerating the commercial and global-scale deployment of Quantum Key Distribution (QKD). Its ability to enable unconditionally secure communication is expected to be a key feature of future telecommunication networks, and practical demonstrations of QKD network implementations in real-world environments are crucial for ensuring reliable adoption. In this work, we demonstrate a four-node photonic QKD network that employs versatile and cost-effective wavelength…

Clearly a work in progress. Lots of promise, but very disjointed while chemistry is built, fitness is gained, and adaptations to a faster and more physical league are happening. It's a lot of roster changes.
The defense look shaky, but often they were put in bad positions by turnovers with too many players committed. Outside backs and midfielders.
Thrilled for Chiesa to get one. Happy for Mo to continue the opening day streak.
Ekitike is my MOTM. He already looks comfor…

Embodied Navigation Foundation Model
Jiazhao Zhang, Anqi Li, Yunpeng Qi, Minghan Li, Jiahang Liu, Shaoan Wang, Haoran Liu, Gengze Zhou, Yuze Wu, Xingxing Li, Yuxin Fan, Wenjun Li, Zhibo Chen, Fei Gao, Qi Wu, Zhizheng Zhang, He Wang
https://arxiv.org/abs/2509.12129

Embodied Navigation Foundation Model
Navigation is a fundamental capability in embodied AI, representing the intelligence required to perceive and interact within physical environments following language instructions. Despite significant progress in large Vision-Language Models (VLMs), which exhibit remarkable zero-shot performance on general vision-language tasks, their generalization ability in embodied navigation remains largely confined to narrow task settings and embodiment-specific architectures. In this work, we introduce a…

Towards Automatic Evaluation and High-Quality Pseudo-Parallel Dataset Construction for Audio Editing: A Human-in-the-Loop Method
Yuhang Jia, Hui Wang, Xin Nie, Yujie Guo, Lianru Gao, Yong Qin
https://arxiv.org/abs/2508.11966

Towards Automatic Evaluation and High-Quality Pseudo-Parallel Dataset Construction for Audio Editing: A Human-in-the-Loop Method
Audio editing aims to manipulate audio content based on textual descriptions, supporting tasks such as adding, removing, or replacing audio events. Despite recent progress, the lack of high-quality benchmark datasets and comprehensive evaluation metrics remains a major challenge for both assessing audio editing quality and improving the task itself. In this work, we propose a novel approach for audio editing task by incorporating expert knowledge into both the evaluation and dataset constructio…

Statistics in 3d gravity from knots and links
Jeevan Chandra
https://arxiv.org/abs/2508.10864 https://arxiv.org/pdf/2508.10864

Statistics in 3d gravity from knots and links
In recent years, there has been remarkable progress in evaluating wormhole amplitudes in 3d Einstein gravity with negative cosmological constant and matching them to statistics of 2d CFT data. In this work, we compute non-perturbative Gaussian and non-Gaussian gravitational contributions to the OPE statistics using a framework that can systematically generate a class of such non-perturbative effects - \textit{Fragmentation of knots and links by Wilson lines}. We illustrate this idea by construc…

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Arnab Kumar Roy, Hemant Kumar Kathania, Paban Sapkota
https://arxiv.org/abs/2509.13442

Enhancing Speaker-Independent Dysarthric Speech Severity Classification with DSSCNet and Cross-Corpus Adaptation
Dysarthric speech severity classification is crucial for objective clinical assessment and progress monitoring in individuals with motor speech disorders. Although prior methods have addressed this task, achieving robust generalization in speaker-independent (SID) scenarios remains challenging. This work introduces DSSCNet, a novel deep neural architecture that combines Convolutional, Squeeze-Excitation (SE), and Residual network, helping it extract discriminative representations of dysarthric …

The Hidden Strength of Costrong Functors
Adriana Balan (Department of Mathematical Methods,Models,,Fundamental Sciences Applied in Engineering Research Center, National University of Science,Technology POLITEHNICA Bucharest), Silviu-George Pantelimon (Department of Computer Science, National University of Science,Technology POLITEHNICA Bucharest)
https://

The Hidden Strength of Costrong Functors
Strong functors and monads are ubiquitous in Computer Science. More recently, comonads have demonstrated their use in structuring context-dependent notions of computation. However, the dualisation of ``being strong'' property passed somehow unobserved so far. We argue that ``being costrong'' gives a different understanding of how functors can interact with monoidal structures. This work in progress aims to explore costrong functors and their natural properties, with an eye towards the semantics…

Speaking of new tools: Here's some other open-ended and work-in-progress tooling I published recently:
Assorted CLI utilities for data wrangling & media conversion
https://codeberg.org/thi.ng/thing-tools
This is (will be) a Swiss-army knife type multi-tool for frequent little t…

thing-tools
Assorted CLI utilities for data wrangling, dataviz & media conversion

ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement
Ali Salamatian, Amirhossein Abaskohi, Wan-Cyuan Fan, Mir Rayat Imtiaz Hossain, Leonid Sigal, Giuseppe Carenini
https://arxiv.org/abs/2509.13282

ChartGaze: Enhancing Chart Understanding in LVLMs with Eye-Tracking Guided Attention Refinement
Charts are a crucial visual medium for communicating and representing information. While Large Vision-Language Models (LVLMs) have made progress on chart question answering (CQA), the task remains challenging, particularly when models attend to irrelevant regions of the chart. In this work, we present ChartGaze, a new eye-tracking dataset that captures human gaze patterns during chart reasoning tasks. Through a systematic comparison of human and model attention, we find that LVLMs often diverge…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Chuhuai Yue, Chengqi Dong, Yinan Gao, Hang He, Jiajun Chai, Guojun Yin, Wei Lin
https://arxiv.org/abs/2508.10293 ht…

Promoting Efficient Reasoning with Verifiable Stepwise Reward
Large reasoning models (LRMs) have recently achieved significant progress in complex reasoning tasks, aided by reinforcement learning with verifiable rewards. However, LRMs often suffer from overthinking, expending excessive computation on simple problems and reducing efficiency. Existing efficient reasoning methods typically require accurate task assessment to preset token budgets or select reasoning modes, which limits their flexibility and reliability. In this work, we revisit the essence of…

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Renjie Li, Ruijie Ye, Mingyang Wu, Hao Frank Yang, Zhiwen Fan, Hezhen Hu, Zhengzhong Tu
https://arxiv.org/abs/2507.12463

MMHU: A Massive-Scale Multimodal Benchmark for Human Behavior Understanding
Humans are integral components of the transportation ecosystem, and understanding their behaviors is crucial to facilitating the development of safe driving systems. Although recent progress has explored various aspects of human behavior$\unicode{x2014}$such as motion, trajectories, and intention$\unicode{x2014}$a comprehensive benchmark for evaluating human behavior understanding in autonomous driving remains unavailable. In this work, we propose $\textbf{MMHU}$, a large-scale benchmark for hu…

Progress on the #tricycle project is mainly in thinking about and working on the subframe. One thing that I'm sure is the right decision is to give the subframe a flat mounting flange, which will bolt down onto a matching pedestal within the hull. This will make the subframe easier to fabricate, and easier to mount/dismount - which I think will be needed for any work on the wheel or tyre.

How popular media gets love wrong
Now a bit of background about why I have this "engineered" model of love:
First, I'm a white straight cis man. I've got a few traits that might work against my relationship chances (e.g., neurodivergence; I generally fit pretty well into the "weird geek" stereotype), but as I was recently reminded, it's possible my experience derives more from luck than other factors, and since things are tilted more in my favor than most people on the planet, my advice could be worse than useless if it leads people towards strategies that would only have worked for someone like me. I don't *think* that's the case, but it's worth mentioning explicitly.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
When I first started dating my now-wife, we were both in graduate school. I was 26, and had exactly zero dating/romantic experience though that point in my life. In other words, a pretty stereotypical "incel" although I definitely didn't subscribe to incel ideology at all. I felt lonely, and vaguely wanted a romantic relationship (I'm neither aromantic nor asexual), but had never felt socially comfortable enough to pursue one before. I don't drink and dislike most social gatherings like parties or bars; I mostly hung around the fringes of the few college parties I attended, and although I had a reasonable college social life in terms of friends, I didn't really do anything to pursue romance, feeling too awkward to know where to start. I had the beginnings of crushes in both high school and college, but never developed a really strong crush, probably correlated with not putting myself in many social situations outside of close all-male friend gatherings. I never felt remotely comfortable enough to act on any of the proto-crushes I did have. I did watch porn and masturbate, so one motivation for pursuing a relationship was physical intimacy, but loneliness was as much of a motivating factor, and of course the social pressure to date was a factor too, even though I'm quite contrarian.
I'm lucky in that I had some mixed-gender social circles already like intramural soccer and a graduate-student housing potluck. Graduate school makes a *lot* more of these social spaces accessible, so I recognize that those not in school of some sort have a harder time of things, especially if like me they don't feel like they fit in in typical adult social spaces like bars.
However, at one point I just decided that my desire for a relationship would need action on my part and so I'd try to build a relationship and see what happened. I worked up my courage and asked one of the people in my potluck if she'd like to go for a hike (pretty much clearly a date but not explicitly one; in retrospect not the best first-date modality in a lot of ways, but it made a little more sense in our setting where we could go for a hike from our front door). To emphasize this point: I was not in love with (or even infatuated with) my now-wife at that point. I made a decision to be open to building a relationship, but didn't follow the typical romance story formula beyond that. Now of course, in real life as opposed to popular media, this isn't anything special. People ask each other out all the time just because they're lonely, and some of those relationships turn out fine (although many do not).
I was lucky in that some aspects of who I am and what I do happened to be naturally comforting to my wife (natural advantage in the "appeal" model of love) but of course there are some aspects of me that annoy my wife, and we negotiate that. In the other direction, there's some things I instantly liked about my wife, and other things that still annoy me. We've figured out how to accept a little, change a little, and overall be happy with each other (though we do still have arguments; it's not like the operation/construction/maintenance of the "love mechanism" is always perfectly smooth). In particular though, I approached the relationship with the attitude of "I want to try to build a relationship with this person," at first just because of my own desires for *any* relationship, and then gradually more and more through my desire to build *this specific* relationship as I enjoyed the rewards of companionship.
So for example, while I think my wife is objectively beautiful, she's also *subjectively* very beautiful *to me* because having decided to build a relationship with her, I actively tried to see her as beautiful, rather than trying to judge whether I wanted a relationship with her based on her beauty. In other words, our relationship is more causative of her beauty-to-me than her beauty-to-me is causative of our relationship. This is the biggest way I think the "engineered" model of love differs from the "fire" and "appeal" models: you can just decide to build love independent of factors we typically think of as engendering love (NOT independent of your partner's willingness to participate, of course), and then all of those things like "thinking your partner is beautiful" can be a result of the relationship you're building. For sure those factors might affect who is willing to try building a relationship with you in the first place, but if more people were willing to jump into relationship building (not necessarily with full commitment from the start) without worrying about those other factors, they might find that those factors can come out of the relationship instead of being prerequisites for it. I think this is the biggest failure of the "appeal" model in particular: yes you *do* need to do things that appeal to your partner, but it's not just "make myself lovable" it's also: is your partner putting in the effort to see the ways that you are beautiful/lovable/etc., or are they just expecting you to become exactly some perfect person they've imagined (and/or been told to desire by society)? The former is perfectly possible, and no less satisfying than the latter.
To cut off my rambling a bit here, I'll just add that in our progress from dating through marriage through staying-married, my wife and I have both talked at times explicitly about commitment, and especially when deciding to get married, I told her that I knew I couldn't live up to the perfect model of a husband that I'd want to be, but that if she wanted to deepen our commitment, I was happy to do that, and so we did. I also rearranged my priorities at that point, deciding that I knew I wanted to prioritize this relationship above things like my career or my research interests, and while I've not always been perfect at that in my little decisions, I've been good at holding to that in my big decisions at least. In the end, *once we had built a somewhat-committed relationship*, we had something that we both recognized was worth more than most other things in life, and that let us commit even more, thus getting even more out of it in the long term. Obviously you can't start the first date with an expectation of life-long commitment, and you need to synchronize your increasing commitment to a relationship so that it doesn't become lopsided, which is hard. But if you take the commitment as an active decision and as the *precursor* to things like infatuation, attraction, etc., you can build up to something that's incredibly strong and rewarding.
I'll follow this up with one more post trying to distill some advice from my ramblings.
#relationships #love

I need to show progress for an action in a shell script I made for work. I am not a dev and I am not going to vibe code something to replace the script, so the "UI" is just the dialog utililty using ncurses. Sadly this does not have a spinner, which is what I actually want. I want this, as I do not know in advance how long extraction of archives it extracts will take. Dialog expects you to give it progress updates up to 100%.

Cowboys Coach Speaks Out On New QB’s Progress https://heavy.com/sports/nfl/dallas-cowboys/joe-milton-doing-well-progress/?adt_ei=[email]

Dolphins QB Tua Tagovailoa says relationship with WR Tyreek Hill 'still a work in progress' https://www.nfl.com/news/dolphins-qb-tua-tagovailoa-says-relationship-with-wr-tyreek-hill-still-a-work-in-progress

AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications
Fabio Tarocco, Pi A. B. Haase, Fabijan Pavo\v{s}evi\'c, Vijay Krishna, Leonardo Guidoni, Stefan Knecht, Martina Stella
https://arxiv.org/abs/2…

AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications
The selection of a balanced active space is a critical step in multi-reference quantum chemistry calculations, particularly for systems with strong electron correlation. Likewise, active space selection is a key to unlock the potential of contemporary quantum computing in quantum chemistry. Albeit recent progress, there remains a lack of a unified, robust, and fully automated framework for active space selection that performs reliably across a wide range of molecular systems. In this work, we…

Evaluating LLMs on Chinese Idiom Translation
Cai Yang, Yao Dou, David Heineman, Xiaofeng Wu, Wei Xu
https://arxiv.org/abs/2508.10421 https://arxiv.org/pdf/…

Evaluating LLMs on Chinese Idiom Translation
Idioms, whose figurative meanings usually differ from their literal interpretations, are common in everyday language, especially in Chinese, where they often contain historical references and follow specific structural patterns. Despite recent progress in machine translation with large language models, little is known about Chinese idiom translation. In this work, we introduce IdiomEval, a framework with a comprehensive error taxonomy for Chinese idiom translation. We annotate 900 translation p…

Causal evidence of racial and institutional biases in accessing paywalled articles and scientific data
Hazem Ibrahim, Fengyuan Liu, Khalid Mengal, Aaron R. Kaufman, Yasir Zaki, Talal Rahwan
https://arxiv.org/abs/2509.08299

Causal evidence of racial and institutional biases in accessing paywalled articles and scientific data
Scientific progress fundamentally depends on researchers' ability to access and build upon the work of others. Yet, a majority of published work remains behind expensive paywalls, limiting access to universities that can afford subscriptions. Furthermore, even when articles are accessible, the underlying datasets could be restricted, available only through a "reasonable request" to the authors. One way researchers could overcome these barriers is by relying on informal channels, such as emailin…

Thanking the @… folks for the excellent work they do, and especially for their upcoming support for security certificates for IP addresses which is nothing short of revolutionary for the future of the (Small) Web.

Getting ready to issue IP address certificates
Happy to see the progress on this. Thought you might be interested in seeing how important this feature is going to be for the Small Web: It’s going to be invaluable for helping decentralise the web. Thank you for the work you do 🙂

Scaling Learned Image Compression Models up to 1 Billion
Yuqi Li, Haotian Zhang, Li Li, Dong Liu, Feng Wu
https://arxiv.org/abs/2508.09075 https://arxiv.or…

Scaling Learned Image Compression Models up to 1 Billion
Recent advances in large language models (LLMs) highlight a strong connection between intelligence and compression. Learned image compression, a fundamental task in modern data compression, has made significant progress in recent years. However, current models remain limited in scale, restricting their representation capacity, and how scaling model size influences compression performance remains unexplored. In this work, we present a pioneering study on scaling up learned image compression mode…

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
Boris Bolliet, Aleksandra Kusiak, Fiona McCarthy, Alina Sabyr, Kristen Surrao, Jens Chluba, Carmen Embil Villagra, Simone Ferraro, Boryana Hadzhiyska, Dongwon Han, J. Colin Hill, Juan Francisco Mac\'ias-P\'erez, Mathew Madhavacheril, Abhishek Maniyar, Yogesh Mehta, Shivam Pandey, Emmanuel Schaan, Blake Sherwin, Alessio Spurio Mancini…

CLASS_SZ II: Notes and Examples of Fast and Accurate Calculations of Halo Model, Large Scale Structure and Cosmic Microwave Background Observables
These notes are very much work-in-progress and simply intended to showcase, in various degrees of details (and rigour), some of the cosmology calculations that class_sz can do. We describe the class_sz code in C, Python and Jax. Based on the Boltzmann code class, it can compute a wide range of observables relevant to current and forthcoming CMB and Large Scale Structure surveys. This includes galaxy shear and clustering, CMB lensing, thermal and kinetic Sunyaev and Zeldovich observables, Cosmic…

2-dimensional TFTs via modular $\infty$-operads
Jan Steinebrunner
https://arxiv.org/abs/2506.22104 https://arxiv.org/pdf/2506.22104…

2-dimensional TFTs via modular $\infty$-operads
This lecture series is based on joint work in progress with Shaul Barkan, as well as work in progress of the author. The five sections of these notes correspond to the five lectures, but more details have been added. $2$-dimensional topological field theories ($2$D TFTs) valued in vector spaces are commutative Frobenius algebras. The goal of this lecture series is to generalize from the $1$-category of vector spaces to any symmetric monoidal $\infty$-category $\mathcal{C}$, i.e. to study symm…

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Wasi Uddin Ahmad, Somshubra Majumdar, Aleksander Ficek, Sean Narenthiran, Mehrzad Samadi, Jocelyn Huang, Siddhartha Jain, Vahid Noroozi, Boris Ginsburg
https://arxiv.org/abs/2507.09075

OpenCodeReasoning-II: A Simple Test Time Scaling Approach via Self-Critique
Recent advancements in reasoning-based Large Language Models (LLMs), particularly their potential through test-time scaling, have created significant opportunities for distillation in code generation and critique. However, progress in both areas fundamentally depends on large-scale, high-quality datasets. In this work, we introduce OpenCodeReasoning-II, a dataset consists of 2.5M question-solution-critique triples (approx. 35K unique programming questions), making it nearly twice the size of th…

Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Shanle Zheng, Keqin Bao, Jizhi Zhang, Yang Zhang, Fuli Feng, Xiangnan He
https://arxiv.org/abs/2507.07064

Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
LLM-based recommender systems have made significant progress; however, the deployment cost associated with the large parameter volume of LLMs still hinders their real-world applications. This work explores parameter pruning to improve parameter efficiency while maintaining recommendation quality, thereby enabling easier deployment. Unlike existing approaches that focus primarily on inter-layer redundancy, we uncover intra-layer redundancy within components such as self-attention and MLP modules…

Proceedings 14th International Workshop on Trends in Functional Programming in Education
Rose Bohrer (AIST, Tokyo, JP)
https://arxiv.org/abs/2508.02305 https://

Proceedings 14th International Workshop on Trends in Functional Programming in Education
The goal of TFPIE is to gather researchers, teachers and professionals that use, or are interested in the use of, functional programming in education. TFPIE aims to be a venue where novel ideas, classroom-tested ideas and work-in-progress on the use of functional programming in education are discussed. The one-day workshop will foster a spirit of open discussion by having a review process for publication after the workshop.

As a liberal/progressive I'd like to remind us (myself included) that people who work with their hands are often as skilled as, or even more skilled then, those of us with advanced university degrees.
If we want to make political progress we must assure that that progress is for us all, white collar, blue collar: in other works for everyone.

Reliable Magnetometry for Antiferromagnets and Thin Films: Correcting Substrate Artifacts in Mn3Sn/MgO Systems
Katarzyna Gas, Maciej Sawicki
https://arxiv.org/abs/2507.01385

Reliable Magnetometry for Antiferromagnets and Thin Films: Correcting Substrate Artifacts in Mn3Sn/MgO Systems
The rapid progress in antiferromagnetic and altermagnetic spintronics has led to increased interest in magnetic materials with vanishing net magnetization but strong spin-dependent transport properties. As thin films of such materials become central to device concepts, precise magnetic characterization is essential, for quantify intrinsic moments, and interpret transport signatures such as the anomalous Hall effect. In this work, we show that commercial MgO substrates, commonly used in epitaxia…

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
Yasong Feng, Yifan Jiang, Tianyu Wang, Zhiliang Ying
https://arxiv.org/abs/2506.23335

Breaking a Logarithmic Barrier in the Stopping Time Convergence Rate of Stochastic First-order Methods
This work provides a novel convergence analysis for stochastic optimization in terms of stopping times, addressing the practical reality that algorithms are often terminated adaptively based on observed progress. Unlike prior approaches, our analysis: 1. Directly characterizes convergence in terms of stopping times adapted to the underlying stochastic process. 2. Breaks a logarithmic barrier in existing results. Key to our results is the development of a Grönwall-type argument tailored to such…

Progress! Got the STM32H750 test board to the point of being able to light up some LEDs on GPIOs before jumping into an infinite loop.
Next step will be all of the RCC and PWR config to get it operational at the desired VOS and clock configuration, at which point I can start thinking about bringing up a UART.
But that will probably have to wait till tomorrow given that it's 1 in the morning and I have work tomorrow.

Hydrodynamic Effects in Cryogenic Buffer Gas Cells: Design Insights from Hybrid Simulations
Nick Vogeley, Bernd Bauerhenne, Daqing Wang
https://arxiv.org/abs/2508.04364 https://…

Hydrodynamic Effects in Cryogenic Buffer Gas Cells: Design Insights from Hybrid Simulations
Cryogenic buffer gas beam sources have become an essential tool for experiments requiring cold molecular beams with low forward velocities. Although recent experimental advances have led to significant progress in source optimization, numerical studies remain limited due to the challenges posed by the large parameter ranges required to describe both the dense buffer gas and the dilute seed molecules. In this work, we report a numerical evaluation of cryogenic buffer gas beam cells operating in …

Early sneak peek of #WIP composer UI relayout.
(still on my local dev, not dev site)
#PhanpySocialDev #design

My latest for The Register, on the multi-decade challenge of improving security in the core of the Internet https://www.theregister.com/2025/08/27/systems_approach_securing_internet_infrastructure/?td=rt-3a

BGP’s security problems are notorious. Attempts to fix that are a work in progress
Systems Approach: Securing internet infrastructure remains a challenging endeavour

Judgment as Coordination: A Joint Systems View of Visualization Design Practice
Paul C. Parsons, Arran Ridley
https://arxiv.org/abs/2507.01209 https://

Judgment as Coordination: A Joint Systems View of Visualization Design Practice
Professional visualization design has become an increasingly important area of inquiry, yet much of the field's discourse remains anchored in researcher-centered contexts. Studies of design practice often focus on individual designers' decisions and reflections, offering limited insight into the collaborative and systemic dimensions of professional work. In this paper, we propose a systems-level reframing of design judgment grounded in the coordination and adaptation that sustain progress amid …

Post-AGB Binaries as Interacting Systems
Hans Van Winckel
https://arxiv.org/abs/2507.02514 https://arxiv.org/pdf/2507.02514

Post-AGB Binaries as Interacting Systems
We present recent progress in our understanding of the physical interaction mechanisms at work in evolved binaries of low-to-intermediate initial mass, which are surrounded by a stable disc of gas and dust. These systems are known as post-asymptotic giant-branch (post-AGB) binaries, but recently, it has been shown that some systems are too low in luminosity and should be considered as post-red-giant branch (post-RGB) instead. While the systems are currently well within their Roche lobe, they st…

Domain Wall Engineering in Graphene-Based Josephson Junctions
Xia'an Du, Junjie Qi, Hua Jiang, X. C. Xie
https://arxiv.org/abs/2509.02082 https://arxiv…

Domain Wall Engineering in Graphene-Based Josephson Junctions
Recent progress has enabled the controlled fabrication of domain walls (DWs) in graphene, which host topological kink states. Meanwhile, reliable techniques for constructing graphene-based Joseph- son junctions have been established. While the experimental prerequisites for combining DWs with Josephson junctions are now available, this direction remains largely unexplored. In this work, we theoretically investigate transport properties in graphene-based Josephson junctions mediated by topologic…

An Update on the Raiders' Training Camp Progress https://www.si.com/nfl/raiders/las-vegas-geno-smith-training-camp-jakobi-meyers-pete-carroll-brock-bowers

A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
Martin Bubel, Tobias Seidel, Michael Bortz
https://arxiv.org/abs/2509.02242 https://arxiv.o…

A Machine Learning-Fueled Modelfluid for Flowsheet Optimization
Process optimization in chemical engineering may be hindered by the limited availability of reliable thermodynamic data for fluid mixtures. Remarkable progress is being made in predicting thermodynamic mixture properties by machine learning techniques. The vast information provided by these prediction methods enables new possibilities in process optimization. This work introduces a novel modelfluid representation that is designed to seamlessly integrate these ML-predicted data directly into…

Firing up SWTOR on my Linux rig for tonight's stream! My character's Force powers are strong, but my ability to avoid standing in fire is... a work in progress. Wish me luck (and minimal rezzing)! #MMORPG #Gaming 🐧 🧙‍♂️

High-temperature growth of ultra thin NbTiN films on lithium niobate for integrated single photon detection
Sjoerd Telkamp, Odiel Hooybergs, Myriam Rihani, Giovanni Finco, Tummas Napoleon Arge, Robin N. D\"ur, Victor Mougel, Daniel Scheffler, Filip Krizek, Rachel Grange, Robert J. Chapman, Werner Wegscheider
https://arxiv.org/abs/2509…

High-temperature growth of ultra thin NbTiN films on lithium niobate for integrated single photon detection
Lithium niobate-on-insulator (LNOI) is an emerging photonic platform with high potential for scalable quantum information processing due to its strong second-order nonlinearity. However, little progress has been made in developing on-chip single-photon detectors on LNOI. Niobium titanium nitride (NbTiN) superconducting nanowire single-photon detectors (SNSPDs) are a promising candidate for this application. In this work, we use DC reactive magnetron sputtering to grow high-quality NbTiN thin fi…

Historical Tech Tree
The tech tree is an interactive visualization of technological history from 3 million years ago to today. A work in progress, it currently contains 1988 technologies and 2369 connections between them.
⏳ https://www.historicaltechtree.com

Historical Tech Tree
Interactive visualization of technological history

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, Conghui He, Weijia Li
https://arxiv.org/abs/2508.09987

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Recently, GPT-4o has garnered significant attention for its strong performance in image generation, yet open-source models still lag behind. Several studies have explored distilling image data from GPT-4o to enhance open-source models, achieving notable progress. However, a key question remains: given that real-world image datasets already constitute a natural source of high-quality data, why should we use GPT-4o-generated synthetic data? In this work, we identify two key advantages of syntheti…

Measurement and preparation protocols for quantum field theory on curved spacetimes
Christopher J. Fewster
https://arxiv.org/abs/2508.21426 https://arxiv.o…

Measurement and preparation protocols for quantum field theory on curved spacetimes
In this conference proceedings contribution, I describe work in progress concerning two problems in the measurement theory of quantum fields. First, it is proved that all local observables can be obtained from local measurement schemes. Second, I describe a protocol for preparing a Hadamard local product state of given Hadamard states relative to the local algebras of specified spacelike separated regions.

Exponential mixing for the stochastic Kuramoto-Sivashinsky equation on the 1D torus
Peng Gao, Hung D. Nguyen
https://arxiv.org/abs/2508.01794 https://arxiv…

Exponential mixing for the stochastic Kuramoto-Sivashinsky equation on the 1D torus
In this paper, we study the large-time behaviors of the Kuramoto-Sivashinsky equation (KSE) on the 1D torus while being subjected to random perturbation via additive Gaussian noise. It is well-known that under suitable assumptions on the stochastic forcing, the KSE admits a unique invariant probability measure. In this work, we make further progress on the topic of ergodicity by addressing the issue of convergence rate toward equilibrium. In comparison with the previous results, we can prove th…

Spotted this Progress Pride Graffiti at the weekend in a carpark that I often use. There's some awesome work there!
#Graffiti #ProgressPride #Pride

Is the medical image segmentation problem solved? A survey of current developments and future directions
Guoping Xu, Jayaram K. Udupa, Jax Luo, Songlin Zhao, Yajun Yu, Scott B. Raymond, Hao Peng, Lipeng Ning, Yogesh Rathi, Wei Liu, You Zhang
https://arxiv.org/abs/2508.20139

Is the medical image segmentation problem solved? A survey of current developments and future directions
Medical image segmentation has advanced rapidly over the past two decades, largely driven by deep learning, which has enabled accurate and efficient delineation of cells, tissues, organs, and pathologies across diverse imaging modalities. This progress raises a fundamental question: to what extent have current models overcome persistent challenges, and what gaps remain? In this work, we provide an in-depth review of medical image segmentation, tracing its progress and key developments over the …

Energy-Based Flow Matching for Generating 3D Molecular Structure
Wenyin Zhou, Christopher Iliffe Sprague, Vsevolod Viliuga, Matteo Tadiello, Arne Elofsson, Hossein Azizpour
https://arxiv.org/abs/2508.18949

Energy-Based Flow Matching for Generating 3D Molecular Structure
Molecular structure generation is a fundamental problem that involves determining the 3D positions of molecules' constituents. It has crucial biological applications, such as molecular docking, protein folding, and molecular design. Recent advances in generative modeling, such as diffusion models and flow matching, have made great progress on these tasks by modeling molecular conformations as a distribution. In this work, we focus on flow matching and adopt an energy-based perspective to improv…

WFC/WFD: Web Fuzzing Commons, Dataset and Guidelines to Support Experimentation in REST API Fuzzing
Omur Sahin, Man Zhang, Andrea Arcuri
https://arxiv.org/abs/2509.01612 https:/…

WFC/WFD: Web Fuzzing Commons, Dataset and Guidelines to Support Experimentation in REST API Fuzzing
Fuzzing REST APIs is an important research problem, with practical applications and impact in industry. As such, a lot of research work has been carried out on this topic in the last few years. However, there are three major issues that hinder further progress: how to deal with API authentication; how to catalog and compare different fault types found by different fuzzers; and what to use as case study to facilitate fair comparisons among fuzzers. To address these important challenges, we prese…

The results of the development of the SPHERE-3 detector for studying the PCR mass composition in the 1-1000 PeV energy range. The status of 2025
D. V. Chernov, E. A. Bonvech, O. V. Cherkesova, E. L. Entina, V. I. Galkin, V. A. Ivanov, T. A. Kolodkin, N. O. Ovcharenko, D. A. Podgrudkov, T. M. Roganova, M. D. Ziva
https://arxiv.or…

The results of the development of the SPHERE-3 detector for studying the PCR mass composition in the 1-1000 PeV energy range. The status of 2025
The SPHERE-3 setup is destined to register the EAS Cherenkov light, both the direct and the reflected from the snow surface. A set of methods and new approaches in the measurement technique enable substantial progress in the study of the primary cosmic ray composition in the energy range 1--1000 PeV. The present work reveals the current status of the project development and detector performance modelling.

The Graph Structure of a Class of Permutation Maps over Ring $\mathbb{Z}_{p^k}$
Kai Tan, Chengqing Li
https://arxiv.org/abs/2506.20118 https://

The Graph Structure of a Class of Permutation Maps over Ring $\mathbb{Z}_{p^k}$
Understanding the periodic and structural properties of permutation maps over residue rings such as $\mathbb{Z}_{p^k}$ is a foundational challenge in algebraic dynamics and pseudorandom sequence analysis. Despite notable progress in characterizing global periods, a critical bottleneck remains: the lack of explicit tools to analyze local cycle structures and their evolution with increasing arithmetic precision. In this work, we propose a unified analytical framework to systematically derive the …

Lower Bounds against the Ideal Proof System in Finite Fields
Tal Elbaz, Nashlen Govindasamy, Jiaqi Lu, Iddo Tzameret
https://arxiv.org/abs/2506.17210 https…

Lower Bounds against the Ideal Proof System in Finite Fields
Lower bounds against strong algebraic proof systems and specifically fragments of the Ideal Proof System (IPS), have been obtained in an ongoing line of work. All of these bounds, however, are proved only over large (or characteristic $0$) fields, yet finite fields are the more natural setting for propositional proof complexity, especially for progress toward lower bounds for Frege systems such as $AC^0[p]$-Frege. This work establishes lower bounds against fragments of IPS over fixed finite fie…

Polynomial Chaos Expansion for Operator Learning
Himanshu Sharma, Luk\'a\v{s} Nov\'ak, Michael D. Shields
https://arxiv.org/abs/2508.20886 https://…

Polynomial Chaos Expansion for Operator Learning
Operator learning (OL) has emerged as a powerful tool in scientific machine learning (SciML) for approximating mappings between infinite-dimensional functional spaces. One of its main applications is learning the solution operator of partial differential equations (PDEs). While much of the progress in this area has been driven by deep neural network-based approaches such as Deep Operator Networks (DeepONet) and Fourier Neural Operator (FNO), recent work has begun to explore traditional machine …

Constraint Maps: Insights and Related Themes
Alessio Figalli, Andr\'e Guerra, Sunghan Kim, Henrik Shahgholian
https://arxiv.org/abs/2506.23608 https://…

Constraint Maps: Insights and Related Themes
This paper explores recent progress related to constraint maps. Building on the exposition in [14], our goal is to provide a clear and accessible account of some of the more intricate arguments behind the main results in this work. Along the way, we include several new results of independent value. In particular, we give optimal geometric conditions on the target manifold that guarantee a unique continuation result for the projected image map. We also prove that the gradient of a minimizing har…

@… Do share screenshots, etc. if you’re up for it. I’d love to see your work in progress. 😄

A Large-Scale Study of Floating-Point Usage in Statically Typed Languages
Andrea Gilot, Tobias Wrigstad, Eva Darulova
https://arxiv.org/abs/2509.04936 https://

A Large-Scale Study of Floating-Point Usage in Statically Typed Languages
Reasoning about floating-point arithmetic is notoriously hard. While static and dynamic analysis techniques or program repair have made significant progress, more work is still needed to make them relevant to real-world code. On the critical path to that goal is understanding what real-world floating-point code looks like. To close that knowledge gap, this paper presents the first large-scale empirical study of floating-point arithmetic usage in statically typed languages across public GitHub r…

EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control
Delin Qu, Haoming Song, Qizhi Chen, Zhaoqing Chen, Xianqiang Gao, Xinyi Ye, Qi Lv, Modi Shi, Guanghui Ren, Cheng Ruan, Maoqing Yao, Haoran Yang, Jiacheng Bao, Bin Zhao, Dong Wang
https://arxiv.org/abs/2508.21112…

EmbodiedOneVision: Interleaved Vision-Text-Action Pretraining for General Robot Control
The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general-purpose embodied intelligent systems. Recent vision-language-action (VLA) models, which are co-trained on large-scale robot and visual-text data, have demonstrated notable progress in general robot control. However, they still fail to achieve human-level flexibility in interleaved reasoning and interaction. In this work, introduce EO-Robotics, consists of EO-1 model…

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
Andrey Sakhovskiy, Elena Tutubalina
https://arxiv.org/abs/2509.07588 htt…

BALI: Enhancing Biomedical Language Representations through Knowledge Graph and Language Model Alignment
In recent years, there has been substantial progress in using pretrained Language Models (LMs) on a range of tasks aimed at improving the understanding of biomedical texts. Nonetheless, existing biomedical LLMs show limited comprehension of complex, domain-specific concept structures and the factual information encoded in biomedical Knowledge Graphs (KGs). In this work, we propose BALI (Biomedical Knowledge Graph and Language Model Alignment), a novel joint LM and KG pre-training method that au…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Yajie Hao, Qiming Ding, Xiaoting Wang, Xiao Yuan
https://arxiv.org/abs/2509.07460 ht…

Large-scale Efficient Molecule Geometry Optimization with Hybrid Quantum-Classical Computing
Accurately and efficiently predicting the equilibrium geometries of large molecules remains a central challenge in quantum computational chemistry, even with hybrid quantum-classical algorithms. Two major obstacles hinder progress: the large number of qubits required and the prohibitive cost of conventional nested optimization. In this work, we introduce a co-optimization framework that combines Density Matrix Embedding Theory (DMET) with Variational Quantum Eigensolver (VQE) to address these l…

First-principles phonon physics using the Pheasy code
Changpeng Lin, Jian Han, Ben Xu, Nicola Marzari
https://arxiv.org/abs/2508.01020 https://arxiv.org/pd…

First-principles phonon physics using the Pheasy code
Parameter-free calculations of lattice dynamics from first principles have achieved significant progress in the past decades, with a wealth of applications in thermodynamics, phase transitions, and transport properties of materials. Current approaches to derive the interatomic force constants (IFCs) of lattice potential become challenging and sometimes infeasible when going beyond third-order anharmonicity, due to the combinatorial explosion in the number of higher-order IFCs. In this work, we …

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Song Wang, Zhen Tan, Zihan Chen, Shuang Zhou, Tianlong Chen, Jundong Li
https://arxiv.org/abs/2506.17784

AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction
Recent progress in large language model (LLM)-based multi-agent collaboration highlights the power of structured communication in enabling collective intelligence. However, existing methods largely rely on static or graph-based inter-agent topologies, lacking the potential adaptability and flexibility in communication. In this work, we propose a new framework that rethinks multi-agent coordination through a sequential structure rather than a graph structure, offering a significantly larger topo…

Raiders Confident in Group That is a Work in Progress https://www.si.com/nfl/raiders/news/raiders-training-camp-elandon-roberts-devin-white-pete-carroll

WebArXiv: Evaluating Multimodal Agents on Time-Invariant arXiv Tasks
Zihao Sun, Meng Fang, Ling Chen
https://arxiv.org/abs/2507.00938 https://

WebArXiv: Evaluating Multimodal Agents on Time-Invariant arXiv Tasks
Recent progress in large language models (LLMs) has enabled the development of autonomous web agents capable of navigating and interacting with real websites. However, evaluating such agents remains challenging due to the instability and inconsistency of existing benchmarks, which often rely on dynamic content or oversimplified simulations. In this work, we introduce WebArXiv, a static and time-invariant benchmark comprising 275 web-based tasks grounded in the arXiv platform. WebArXiv ensures r…

Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamics
Polina V. Banushkina, Sergei V. Krivov
https://arxiv.org/abs/2508.07326 https:…

Nonparametric Reaction Coordinate Optimization with Histories: A Framework for Rare Event Dynamics
Rare but critical events in complex systems, such as protein folding, chemical reactions, disease progression, and extreme weather or climate phenomena, are governed by complex, high-dimensional, stochastic dynamics. Identifying an optimal reaction coordinate (RC) that accurately captures the progress of these dynamics is crucial for understanding and simulating such processes. This work introduces a nonparametric RC optimization framework that incorporates trajectory histories, enabling robust…

Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
Adwait Chandorkar, Hasan Tercan, Tobias Meisen
https://arxiv.org/abs/2508.00744 https://

Rethinking Backbone Design for Lightweight 3D Object Detection in LiDAR
Recent advancements in LiDAR-based 3D object detection have significantly accelerated progress toward the realization of fully autonomous driving in real-world environments. Despite achieving high detection performance, most of the approaches still rely on a VGG-based or ResNet-based backbone for feature exploration, which increases the model complexity. Lightweight backbone design is well-explored for 2D object detection, but research on 3D object detection still remains limited. In this work,…

More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research
Trent D. Buskirk, Florian Keusch, Leah von der Heyde, Adam Eck
https://arxiv.org/abs/2509.03391

More Parameters Than Populations: A Systematic Literature Review of Large Language Models within Survey Research
Survey research has a long-standing history of being a human-powered field, but one that embraces various technologies for the collection, processing, and analysis of various behavioral, political, and social outcomes of interest, among others. At the same time, Large Language Models (LLMs) bring new technological challenges and prerequisites in order to fully harness their potential. In this paper, we report work-in-progress on a systematic literature review based on keyword searches from mult…

AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code
Nadeen Fathallah, Daniel Hern\'andez, Steffen Staab
https://arxiv.org/abs/2507.19549

AccessGuru: Leveraging LLMs to Detect and Correct Web Accessibility Violations in HTML Code
The vast majority of Web pages fail to comply with established Web accessibility guidelines, excluding a range of users with diverse abilities from interacting with their content. Making Web pages accessible to all users requires dedicated expertise and additional manual efforts from Web page providers. To lower their efforts and promote inclusiveness, we aim to automatically detect and correct Web accessibility violations in HTML code. While previous work has made progress in detecting certain…

Time-Masked Transformers with Lightweight Test-Time Adaptation for Neural Speech Decoding
Ebrahim Feghhi, Shreyas Kaasyap, Nima Hadidi, Jonathan C. Kao
https://arxiv.org/abs/2507.02800

Time-Masked Transformers with Lightweight Test-Time Adaptation for Neural Speech Decoding
Speech neuroprostheses aim to restore communication for people with severe paralysis by decoding speech directly from neural activity. To accelerate algorithmic progress, a recent benchmark released intracranial recordings from a paralyzed participant attempting to speak, along with a baseline decoding algorithm. Prior work on the benchmark showed impressive accuracy gains. However, these gains increased computational costs and were not demonstrated in a real-time decoding setting. Here, we mak…

Various thi.ng updates, bug fixes, additions and new version of https://github.com/thi-ng/zig-thing/ — now fully compatible with current Zig v0.14.1
On a more diary/devlog note: I also updated several of my Zig based work-in-progress art pieces to the latest version (some of them not touc…

Understanding the Drag Torque in Common Envelope Evolution
Soumik Bhattacharyya, Luke Chamandy, Eric G. Blackman, Adam Frank, Baowei Liu
https://arxiv.org/abs/2506.19547

Understanding the Drag Torque in Common Envelope Evolution
Common envelope (CE) evolution is largely governed by the drag torque applied on the inspiralling stellar components by the envelope. Previous work has shown that idealized models of the torque based on a single body moving in rectilinear motion through an unperturbed atmosphere can be highly inaccurate. Progress requires new models for the torque that account for binarity. Toward this end we perform a new 3D global hydrodynamic CE simulation with the mass of the companion point particle set eq…

Semantic Concentration for Self-Supervised Dense Representations Learning
Peisong Wen, Qianqian Xu, Siran Dai, Runmin Cong, Qingming Huang
https://arxiv.org/abs/2509.09429 https…

Semantic Concentration for Self-Supervised Dense Representations Learning
Recent advances in image-level self-supervised learning (SSL) have made significant progress, yet learning dense representations for patches remains challenging. Mainstream methods encounter an over-dispersion phenomenon that patches from the same instance/category scatter, harming downstream performance on dense tasks. This work reveals that image-level SSL avoids over-dispersion by involving implicit semantic concentration. Specifically, the non-strict spatial alignment ensures intra-instance…

Decoding the Poetic Language of Emotion in Korean Modern Poetry: Insights from a Human-Labeled Dataset and AI Modeling
Iro Lim, Haein Ji, Byungjun Kim
https://arxiv.org/abs/2509.03932

Decoding the Poetic Language of Emotion in Korean Modern Poetry: Insights from a Human-Labeled Dataset and AI Modeling
This study introduces KPoEM (Korean Poetry Emotion Mapping) , a novel dataset for computational emotion analysis in modern Korean poetry. Despite remarkable progress in text-based emotion classification using large language models, poetry-particularly Korean poetry-remains underexplored due to its figurative language and cultural specificity. We built a multi-label emotion dataset of 7,662 entries, including 7,007 line-level entries from 483 poems and 615 work-level entries, annotated with 44 f…

URSA: The Universal Research and Scientific Agent
Michael Grosskopf, Russell Bent, Rahul Somasundaram, Isaac Michaud, Arthur Lui, Nathan Debardeleben, Earl Lawrence
https://arxiv.org/abs/2506.22653

URSA: The Universal Research and Scientific Agent
Large language models (LLMs) have moved far beyond their initial form as simple chatbots, now carrying out complex reasoning, planning, writing, coding, and research tasks. These skills overlap significantly with those that human scientists use day-to-day to solve complex problems that drive the cutting edge of research. Using LLMs in "agentic" AI has the potential to revolutionize modern science and remove bottlenecks to progress. In this work, we present URSA, a scientific agent ecosystem for…

GEAR: Gaze-Enabled Human-Robot Collaborative Assembly
Asad Ali Shahid, Angelo Moroncelli, Drazen Brscic, Takayuki Kanda, Loris Roveda
https://arxiv.org/abs/2507.18947 https://…

GEAR: Gaze-Enabled Human-Robot Collaborative Assembly
Recent progress in robot autonomy and safety has significantly improved human-robot interactions, enabling robots to work alongside humans on various tasks. However, complex assembly tasks still present significant challenges due to inherent task variability and the need for precise operations. This work explores deploying robots in an assistive role for such tasks, where the robot assists by fetching parts while the skilled worker provides high-level guidance and performs the assembly. We intr…

ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical Knowledge
Zihan Zhao, Bo Chen, Ziping Wan, Lu Chen, Xuanze Lin, Shiyang Yu, Situo Zhang, Da Ma, Zichen Zhu, Danyang Zhang, Huayang Wang, Zhongyang Dai, Liyang Wen, Xin Chen, Kai Yu
https://arxiv.org/abs/2507.21990

ChemDFM-R: An Chemical Reasoner LLM Enhanced with Atomized Chemical Knowledge
While large language models (LLMs) have achieved impressive progress, their application in scientific domains such as chemistry remains hindered by shallow domain understanding and limited reasoning capabilities. In this work, we focus on the specific field of chemistry and develop a Chemical Reasoner LLM, ChemDFM-R. We first construct a comprehensive dataset of atomized knowledge points to enhance the model's understanding of the fundamental principles and logical structure of chemistry. Then,…

RAAG: Ratio Aware Adaptive Guidance
Shangwen Zhu, Qianyu Peng, Yuting Hu, Zhantao Yang, Han Zhang, Zhao Pu, Ruili Feng, Fan Cheng
https://arxiv.org/abs/2508.03442 https://

RAAG: Ratio Aware Adaptive Guidance
Flow-based generative models have recently achieved remarkable progress in image and video synthesis, with classifier-free guidance (CFG) becoming the standard tool for high-fidelity, controllable generation. However, despite their practical success, little is known about how guidance interacts with different stages of the sampling process-especially in the fast, low-step regimes typical of modern flow-based pipelines. In this work, we uncover and analyze a fundamental instability: the earliest…

Emergent statistical mechanics in holographic random tensor networks
Shozab Qasim, Jens Eisert, Alexander Jahn
https://arxiv.org/abs/2508.16570 https://arx…

Emergent statistical mechanics in holographic random tensor networks
Recent years have enjoyed substantial progress in capturing properties of complex quantum systems by means of random tensor networks (RTNs), which form ensembles of quantum states that depend only on the tensor network geometry and bond dimensions. Of particular interest are RTNs on hyperbolic geometries, with local tensors typically chosen from the unitary Haar measure, that model critical boundary states of holographic bulk-boundary dualities. In this work, we elevate static pictures of ensem…

Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases
Zimin Chen, Yue Pan, Siyu Lu, Jiayi Xu, Claire Le Goues, Martin Monperrus, He Ye
https://arxiv.org/abs/2507.19942

Prometheus: Unified Knowledge Graphs for Issue Resolution in Multilingual Codebases
Language model (LM) agents, such as SWE-agent and OpenHands, have made progress toward automated issue resolution. However, existing approaches are often limited to Python-only issues and rely on pre-constructed containers in SWE-bench with reproduced issues, restricting their applicability to real-world and work for multi-language repositories. We present Prometheus, designed to resolve real-world issues beyond benchmark settings. Prometheus is a multi-agent system that transforms an entire co…

Multimodal Mathematical Reasoning with Diverse Solving Perspective
Wenhao Shi, Zhiqiang Hu, Yi Bin, Yang Yang, See-Kiong Ng, Heng Tao Shen
https://arxiv.org/abs/2507.02804

Multimodal Mathematical Reasoning with Diverse Solving Perspective
Recent progress in large-scale reinforcement learning (RL) has notably enhanced the reasoning capabilities of large language models (LLMs), especially in mathematical domains. However, current multimodal LLMs (MLLMs) for mathematical reasoning often rely on one-to-one image-text pairs and single-solution supervision, overlooking the diversity of valid reasoning perspectives and internal reflections. In this work, we introduce MathV-DP, a novel dataset that captures multiple diverse solution tra…

Raiders Could Face Patriots Without Star Player After Injury Update https://heavy.com/sports/nfl/las-vegas-raiders/las-vegas-patriots-christian-gonzalez-update/?adt_ei=[email]

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Pranay Dugar, Mohitvishnu S. Gadde, Jonah Siekmann, Yesh Godse, Aayam Shrestha, Alan Fern
https://arxiv.org/abs/2508.14098

No More Marching: Learning Humanoid Locomotion for Short-Range SE(2) Targets
Humanoids operating in real-world workspaces must frequently execute task-driven, short-range movements to SE(2) target poses. To be practical, these transitions must be fast, robust, and energy efficient. While learning-based locomotion has made significant progress, most existing methods optimize for velocity-tracking rather than direct pose reaching, resulting in inefficient, marching-style behavior when applied to short-range tasks. In this work, we develop a reinforcement learning approach…

FlowETL: An Autonomous Example-Driven Pipeline for Data Engineering
Mattia Di Profio, Mingjun Zhong, Yaji Sripada, Marcel Jaspars
https://arxiv.org/abs/2507.23118 https://

FlowETL: An Autonomous Example-Driven Pipeline for Data Engineering
The Extract, Transform, Load (ETL) workflow is fundamental for populating and maintaining data warehouses and other data stores accessed by analysts for downstream tasks. A major shortcoming of modern ETL solutions is the extensive need for a human-in-the-loop, required to design and implement context-specific, and often non-generalisable transformations. While related work in the field of ETL automation shows promising progress, there is a lack of solutions capable of automatically designing a…

Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy
Yuhao Liu, Tengfei Wang, Fang Liu, Zhenwei Wang, Rynson W. H. Lau
https://arxiv.org/abs/2506.22432

Shape-for-Motion: Precise and Consistent Video Editing with 3D Proxy
Recent advances in deep generative modeling have unlocked unprecedented opportunities for video synthesis. In real-world applications, however, users often seek tools to faithfully realize their creative editing intentions with precise and consistent control. Despite the progress achieved by existing methods, ensuring fine-grained alignment with user intentions remains an open and challenging problem. In this work, we present Shape-for-Motion, a novel framework that incorporates a 3D proxy for …

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Wanyue Zhang, Yibin Huang, Yangbin Xu, JingJing Huang, Helu Zhi, Shuo Ren, Wang Xu, Jiajun Zhang
https://arxiv.org/abs/2509.02359

Why Do MLLMs Struggle with Spatial Understanding? A Systematic Analysis from Data to Architecture
Spatial understanding is essential for Multimodal Large Language Models (MLLMs) to support perception, reasoning, and planning in embodied environments. Despite recent progress, existing studies reveal that MLLMs still struggle with spatial understanding. However, existing research lacks a comprehensive and systematic evaluation of these limitations, often restricted to isolated scenarios, such as single-view or video. In this work, we present a systematic analysis of spatial understanding from…

Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal
Yang Wang, Chenghao Xiao, Yizhi Li, Stuart E. Middleton, Noura Al Moubayed, Chenghua Lin
https://arxiv.org/abs/2507.21750

Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal
Pre-trained language models (PLMs) have driven substantial progress in natural language processing but remain vulnerable to adversarial attacks, raising concerns about their robustness in real-world applications. Previous studies have sought to mitigate the impact of adversarial attacks by introducing adversarial perturbations into the training process, either implicitly or explicitly. While both strategies enhance robustness, they often incur high computational costs. In this work, we propose …

OpenNav: Open-World Navigation with Multimodal Large Language Models
Mingfeng Yuan, Letian Wang, Steven L. Waslander
https://arxiv.org/abs/2507.18033 https://

OpenNav: Open-World Navigation with Multimodal Large Language Models
Pre-trained large language models (LLMs) have demonstrated strong common-sense reasoning abilities, making them promising for robotic navigation and planning tasks. However, despite recent progress, bridging the gap between language descriptions and actual robot actions in the open-world, beyond merely invoking limited predefined motion primitives, remains an open challenge. In this work, we aim to enable robots to interpret and decompose complex language instructions, ultimately synthesizing a…