Tootfinder

I DIDN'T make any PROGRESS with him – TRUMP on Putin's call #shorts: https://benborges.xyz/2025/07/04/i-didnt-make-any-progress.html

JavaScript broke the web (and called it progress) #JavaScript

JavaScript broke the web (and called it progress)
We replaced simple websites with complex apps nobody asked for. Now it takes a complex build pipeline just to change a headline.

Finder Progress Bar not showing or disappeared!
Copying files from HD to Macbook, but progress bar disappeared after switching the window ! How to find that file transfer progress bar ?

I found the solution!
It was rather simple, follow below steps:
1. Open finder
2. From top bar select "window"
3. From the different options toggle "Hide progress bar" or "Show progress bar"

You'll able to see the progres…

Outgoing Packers CEO Mark Murphy reveals biggest regrets, says team ready to make 'significant' progress

https://www.cbssports.com/nfl/news/…

Tamos a meio, malta.
Jš faltou mais.
https://techhub.social/@year_progress/114783525601942426

Trump said on Thursday that a phone call earlier in the day with Vladimir Putin
resulted in "no progress at all"
on efforts to end the war in Ukraine,
while a Kremlin aide said the Russian president reiterated that Moscow would keep pushing to solve the conflict’s “root causes.”
– Russian shorthand for the issue of Nato enlargement and western support for Ukraine.

The two leaders did not discuss a recent pause in some US weapons shipments to K…

LATENTRED main board layout progress: got most of the GTYs and FPGA-MCU paths done, as well as the RGMII PHY.
Still lots more to do but I'm liking the floorplan, at least the parts that I've done.
I'm debating moving the RGMII PHY to the west a bit and adding a fan cutout directly behind the FPGA so that I can have it suck exhaust air right past the FPGA (possibly with a 3d printed air dam or something in the future). Will need to spend some time thinking about therma…

Trump Says Call With Putin Yields No Progress on Ukraine Cease-Fire (New York Times)
https://www.nytimes.com/2025/07/03/us/politics/trump-putin-call-ukraine-iran.html
http://www.memeorandum.com/250704/p41#a250704p41

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Xiao-Yang Liu Yanglet, Yupeng Cao, Li Deng
https://arxiv.org/abs/2506.01973

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFF…

Evangelical: Satan Got Me Fired From The Kennedy Center Over My "Real Progress In The Spiritual" Realm - Joe.My.God.
https://www.joemygod.com/2025/06/evangelical-satan-got-me-fired-from-the-kennedy-center-over-my-real-progress-in-the-spiritual-realm/

Photographer and artist Steven Molina Contreras on pacing yourself https://thecreativeindependent.com/peo

Photographer and artist Steven Molina Contreras on pacing yourself – The Creative Independent
Photographer and artist Steven Molina Contreras discusses weighing responsibilities, foregoing his reliance on structure, and why he measures success in terms of progress.

When moving or copying large files between hard disks I like to see the progress to make the wait more informative and enjoyable.
If you want to achieve the same thing the "progress" package (present in the repos of all distros) allows you to do it. After installing it add to mv or cp the variable "& progress -mp $!", for example: mv file1 /destination-directory & progress -mp $!
If you like you can add the corresponding alias: alias mv='mv -v &quo…

#today at uni. Managed to debug the slightly vexing heat loss problem in my bungalow-2 model in progress. It turns out that I had the new roof segments on 'upside down' even though they have only one layer and are actually walls. (Don't ask ... yet... Maybe I'll understand what I'm doing in a few months!)

#sunkCapital inhibits progress - and the more money into #FossilFuels extraction, the more difficult it is to accelerate #ClimateAction. It diverts resources that should go to

United States wondered how China had made such progress in the field of microchips. Huawei and Xiaomi are the answer, with massive development.
https://farmingdale-observer.c…

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry
Jiajun Le, Jiayi Ma
https://arxiv.org/abs/2508.00592 https://

GeoMoE: Divide-and-Conquer Motion Field Modeling with Mixture-of-Experts for Two-View Geometry
Recent progress in two-view geometry increasingly emphasizes enforcing smoothness and global consistency priors when estimating motion fields between pairs of images. However, in complex real-world scenes, characterized by extreme viewpoint and scale changes as well as pronounced depth discontinuities, the motion field often exhibits diverse and heterogeneous motion patterns. Most existing methods lack targeted modeling strategies and fail to explicitly account for this variability, resulting i…

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
Jungkoo Kang
https://arxiv.org/abs/2507.02253 https://

Scaling LLM Planning: NL2FLOW for Parametric Problem Generation and Rigorous Evaluation
Progress in enhancing large language model (LLM) planning and reasoning capabilities is significantly hampered by the bottleneck of scalable, reliable data generation and evaluation. To overcome this, I introduce NL2FLOW, a fully automated system for parametrically generating planning problems - expressed in natural language, a structured intermediate representation, and formal PDDL - and rigorously evaluating the quality of generated plans. I demonstrate NL2FLOW's capabilities by generating a …

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…

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo, Zhecheng Yuan, Ruijie Zheng, Huazhe Xu
https://arxiv.org/abs/2507.02712

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early experiences stored in the replay buffer, which limits an RL agent's sample efficiency and generalizability. In contrast, humans are less susceptible to such bias, partly due to infantile amnesia, where the formation of new neurons disrupts early memory traces, leading to the forgetting of initial experiences. Inspired…

Should we teach vibe coding? Here's why not.
2/2
To address the bigger question I started with ("should we teach AI-"assisted" coding?"), my answer is: "No, except enough to show students directly what its pitfalls are." We have little enough time as it is to cover the core knowledge that they'll need, which has become more urgent now that they're going to be expected to clean up AI bugs and they'll have less time to develop an understanding of the problems they're supposed to be solving. The skill of prompt engineering & other skills of working with AI are relatively easy to pick up on your own, given a decent not-even-mathematical understanding of how a neutral network works, which is something we should be giving to all students, not just our majors.
Reasonable learning objectives for CS majors might include explaining what types of bugs an AI "assistant" is most likely to introduce, explaining the difference between software engineering and writing code, explaining why using an AI "assistant" is likely to violate open-source licenses, listing at lest three independent ethical objections to contemporary LLMs and explaining the evidence for/reasoning behind them, explaining why we should expect AI "assistants" to be better at generating code from scratch than at fixing bugs in existing code (and why they'll confidently "claim" to have fixed problems they haven't), and even fixing bugs in AI generated code (without AI "assistance").
If we lived in a world where the underlying environmental, labor, and data commons issues with AI weren't as bad, or if we could find and use systems that effectively mitigate these issues (there's lots of piecemeal progress on several of these) then we should probably start teaching an elective on coding with an assistant to students who have mastered programming basics, but such a class should probably spend a good chunk of time on non-assisted debugging.
#AI #LLMs #VibeCoding

Hearing a lot about Texas redistricting, and am surprised that only eight US states have an independent commission for redistricting.
https://redistricting.lls.edu/national-overview/

Surprising how smoking keeps it's popularity here, even among youngsters (although vaping made progress in that age group). They're not daft, but that's how you can see some cultural things are still like they were in my youth, some 40 years ago. Keep that in mind when you interact in Vietnam, it helps a lot to understand.

This https://arxiv.org/abs/2505.20290 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

EgoZero: Robot Learning from Smart Glasses
Despite recent progress in general purpose robotics, robot policies still lag far behind basic human capabilities in the real world. Humans interact constantly with the physical world, yet this rich data resource remains largely untapped in robot learning. We propose EgoZero, a minimal system that learns robust manipulation policies from human demonstrations captured with Project Aria smart glasses, $\textbf{and zero robot data}$. EgoZero enables: (1) extraction of complete, robot-executable ac…

Trump says he "didn't make any progress" with Putin following phone call: https://benborges.xyz/2025/07/04/trump-says-he-didnt-make.html

No Progress: Cowboys' HC expects Micah Parsons at minicamp despite lack of new extension https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/03/brian-schottenheimer-i-expec…

Context-aware gate set tomography: Improving the self-consistent characterization of trapped-ion universal gate sets by leveraging non-Markovianity
Pablo Vi\~nas, Alejandro Bermudez
https://arxiv.org/abs/2507.02542

Context-aware gate set tomography: Improving the self-consistent characterization of trapped-ion universal gate sets by leveraging non-Markovianity
To progress in the characterization of noise for current quantum computers, gate set tomography (GST) has emerged as a self-consistent tomographic protocol that can accurately estimate the complete set of noisy quantum gates, state preparations, and measurements. In its original incarnation, GST improves the estimation precision by applying the gates sequentially, provided that the noise makes them a set of fixed completely-positive and trace preserving (CPTP) maps independent of the history of…

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.

Gauging Growth: AGI Mathematical Metrics for Economic Progress
Davit Gondauri
https://arxiv.org/abs/2506.03156 https://arxiv.org/pdf/…

Gauging Growth: AGI Mathematical Metrics for Economic Progress
Today, the economy is greatly influenced by Artificial General Intelligence (AGI). The purpose of this paper is to determine the impact of the quantitative relations of AGI on the country's economic parameters. The authors use the analysis of historical data in the research, develop a new mathematical algorithm that refers to the level of AGI development, and conduct a regression analysis. The economic effect of AGI is deduced if it affects the growth of real GDP. As a result of the analysis, i…

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Paulo Ricardo Knob, Leonardo Scholler, Juliano Rigatti, Soraia Raupp Musse
https://arxiv.org/abs/2507.02537

Are You Listening to Me? Fine-Tuning Chatbots for Empathetic Dialogue
Conversational agents have made significant progress since ELIZA, expanding their role across various domains, including healthcare, education, and customer service. As these agents become increasingly integrated into daily human interactions, the need for emotional intelligence, particularly empathetic listening, becomes increasingly essential. In this study, we explore how Large Language Models (LLMs) respond when tasked with generating emotionally rich interactions. Starting from a small dat…

A Preference-Driven Methodology for High-Quality Solidity Code Generation
Zhiyuan Peng, Xin Yin, Chenhao Ying, Chao Ni, Yuan Luo
https://arxiv.org/abs/2506.03006

A Preference-Driven Methodology for High-Quality Solidity Code Generation
While Large Language Models (LLMs) have demonstrated remarkable progress in generating functionally correct Solidity code, they continue to face critical challenges in producing gas-efficient and secure code, which are critical requirements for real-world smart contract deployment. Although recent advances leverage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) for code preference alignment, existing approaches treat functional correctness, gas optimization, and security …

Cool to see @… mentioned here https://mastodon.macstories.net/@macstories/114909372902054053
Creative chapter name too 👀

Senator Chris Murphy here.
I'm back on the road and wanted to send you an update.
I just held a town hall in Dayton, OH,
and next we're planning a massive rally in Phoenix, AZ.
I'll share more about why these events matter in a moment, but first, I need to ask:
If you're able to support my work mobilizing people in every corner of our country,
please consider making a contribution to support our efforts today.
Every dollar will be …

Contribute to Chris Murphy for Senate
Chris cannot take on the wealthy, corporate interests that want to undo our progress alone. Please donate to his people-powered campaign today.

Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
Marc Braun, Jose M. Pe\~na, Adel Daoud
https://arxiv.org/abs/2508.01321 https://

Flow IV: Counterfactual Inference In Nonseparable Outcome Models Using Instrumental Variables
To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to non-separable structural models at the population level, existing approaches to c…

Green Computing: The Ultimate Carbon Destroyer for a Sustainable Future
Sayed Mahbub Hasan Amiri, Prasun Goswami, Md. Mainul Islam, Mohammad Shakhawat Hossen, Marzana Mithila, Naznin Akter
https://arxiv.org/abs/2508.00153

Green Computing: The Ultimate Carbon Destroyer for a Sustainable Future
Green computing represents a critical pathway to decarbonize the digital economy while maintaining technological progress. This article examines how sustainable IT strategies including energy-efficient hardware, AI-optimized data centres, and circular e-waste systems can transform computing into a net carbon sink. Through analysis of industry best practices and emerging technologies like quantum computing and biodegradable electronics, we demonstrate achievable reductions of 40-60% in energy co…

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
Sizhe Chen, Arman Zharmagambetov, David Wagner, Chuan Guo
https://arxiv.org/abs/2507.02735

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
Prompt injection attacks pose a significant security threat to LLM-integrated applications. Model-level defenses have shown strong effectiveness, but are currently deployed into commercial-grade models in a closed-source manner. We believe open-source models are needed by the AI security community, where co-development of attacks and defenses through open research drives scientific progress in mitigation against prompt injection attacks. To this end, we develop Meta SecAlign, the first open-sou…

Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Pengcheng Zheng, Xiaorong Pu, Kecheng Chen, Jiaxin Huang, Meng Yang, Bai Feng, Yazhou Ren, Jianan Jiang
https://arxiv.org/abs/2508.01782

Joint Lossless Compression and Steganography for Medical Images via Large Language Models
Recently, large language models (LLMs) have driven promis ing progress in lossless image compression. However, di rectly adopting existing paradigms for medical images suf fers from an unsatisfactory trade-off between compression performance and efficiency. Moreover, existing LLM-based compressors often overlook the security of the compres sion process, which is critical in modern medical scenarios. To this end, we propose a novel joint lossless compression and steganography framework. …

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
Haihao Lu, Jinwen Yang
https://arxiv.org/abs/2506.02174 https://

An Overview of GPU-based First-Order Methods for Linear Programming and Extensions
The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This survey aims to provide a comprehensive overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLP. We begin by presenting the design principles and algorithmic foundation of the primal-dual hybrid gradient (PDHG) method, which f…

I thought I was close to finishing my blog post about creativity that I've been working on for quite a while, and I made a lot of progress on the bulk of the text of the draft, but ironically I still require more creativity to organize, edit and wrap it up cohesively
#blogging

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…

The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities
Wacuka Ngata, Noman Bashir, Michelle Westerlaken, Laurent Liote, Yasra Chandio, Elsa Olivetti
https://arxiv.org/abs/2506.03367

The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities
Datacenters have become the backbone of modern digital infrastructure, powering the rapid rise of artificial intelligence and promising economic growth and technological progress. However, this expansion has brought growing tensions in the local communities where datacenters are already situated or being proposed. While the mainstream discourse often focuses on energy usage and carbon footprint of the computing sector at a global scale, the local socio-environmental consequences -- such as heal…

Quantum entanglement in cosmology
Alessio Belfiglio, Orlando Luongo, Stefano Mancini
https://arxiv.org/abs/2506.03841 https://arxiv.o…

Quantum entanglement in cosmology
We discuss recent progress in the study of entanglement within cosmological frameworks, focusing on both momentum and position-space approaches and also reviewing the possibility to directly extract entanglement from quantum fields. Entanglement generation in expanding spacetimes can be traced back to the phenomenon of gravitational particle production, according to which the background gravitational field may transfer energy and momentum to quantum fields. The corresponding entanglement amount…

Finished laying out the northwest corner. I think this is pretty final in that area, but I'm going to move the fan connectors and actually put one of the fans where they are.

This https://arxiv.org/abs/2306.11707 has been replaced.
link: https://scholar.google.com/scholar?q=a

Hexagonal circular 3-webs with reducible polar curves of degree three
The paper reports the progress with the classical problem, posed by Blaschke and Bol in 1938. We present new examples and new classifications of natural classes of hexagonal circular 3-webs. The main results is the classification of hexagonal circular 3-webs with reducible polar curves of degree 3 and description of hexagonal circular 3-webs admitting a one-parameter Möbius symmetry.

SOVA-Bench: Benchmarking the Speech Conversation Ability for LLM-based Voice Assistant
Yixuan Hou, Heyang Liu, Yuhao Wang, Ziyang Cheng, Ronghua Wu, Qunshan Gu, Yanfeng Wang, Yu Wang
https://arxiv.org/abs/2506.02457

SOVA-Bench: Benchmarking the Speech Conversation Ability for LLM-based Voice Assistant
Thanks to the steady progress of large language models (LLMs), speech encoding algorithms and vocoder structure, recent advancements have enabled generating speech response directly from a user instruction. However, benchmarking the generated speech quality has been a neglected but critical issue, considering the shift from the pursuit of semantic accuracy to vivid and spontaneous speech flow. Previous evaluation focused on the speech-understanding ability, lacking a quantification of acoustic …

"But good DX doesn’t guarantee good UX. In fact, it’s often the opposite. Because the more comfortable we make things for developers, the more abstraction we add. And every abstraction creates distance between the thing being built and the people it’s for."
https://www.jon…

JavaScript broke the web (and called it progress)
We replaced simple websites with complex apps nobody asked for. Now it takes a complex build pipeline just to change a headline.

Event Topology Classifiers at the Large Hadron Collider
Suraj Prasad, Sushanta Tripathy, Bhagyarathi Sahoo, Raghunath Sahoo
https://arxiv.org/abs/2506.03782

Event Topology Classifiers at the Large Hadron Collider
Event classifiers are the most fundamental observables to probe the system size and event topology of high-energy collisions. Significant progress has been made to establish suitable event classifiers to probe different physics processes occurring in elementary $e^{+}e^{-}$ to heavy-ion collisions in a range of center of mass energies. One of the major motivations to revisit event classifiers at the Large Hadron Collider (LHC) originates from the recent measurements of high multiplicity proton-…

Sources: DeepSeek's highly anticipated R2 model faces delays due to a shortage of Nvidia server chips in China, exacerbated by the US ban of Nvidia's H20 chips (The Information)
https://www.theinformation.com/articles/deepseeks-progre…

DeepSeek’s Progress Stalled by U.S. Export Controls
The latest round of U.S. chip export controls may have curbed DeepSeek’s rise, at least for now. DeepSeek’s highly anticipated upcoming large language model, R2, may not be as quickly and extensively adopted in China as its predecessor, the wildly popular R1, due to a shortage of Nvidia server ...

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Yunfei Liu, Mingxuan Liu, Wupeng Xie, Xinzhu Liu, Wenxue Liu, Yangang Sun, Xin Qiu, Cui Yuan, Jinhai Li
https://arxiv.org/abs/2508.00274

RIS-MAE: A Self-Supervised Modulation Classification Method Based on Raw IQ Signals and Masked Autoencoder
Automatic modulation classification (AMC) is a basic technology in intelligent wireless communication systems. It is important for tasks such as spectrum monitoring, cognitive radio, and secure communications. In recent years, deep learning methods have made great progress in AMC. However, mainstream methods still face two key problems. First, they often use time-frequency images instead of raw signals. This causes loss of key modulation features and reduces adaptability to different communicat…

Happy Pride 2025 Y’all!
#Pride2025

Terry McLaurin requesting trade from Commanders as no progress has been made toward extension https://www.nfl.com/news/terry-mclaurin-requesting-trade-from-commanders-as-no-progress-has-been-made-toward-extension

Integrating Opinion Dynamics into Safety Control for Decentralized Airplane Encounter Resolution
Shuhao Qi, Zhiqi Tang, Zhiyong Sun, Sofie Haesaert
https://arxiv.org/abs/2508.00156

Integrating Opinion Dynamics into Safety Control for Decentralized Airplane Encounter Resolution
As the airspace becomes increasingly congested, decentralized conflict resolution methods for airplane encounters have become essential. While decentralized safety controllers can prevent dangerous midair collisions, they do not always ensure prompt conflict resolution. As a result, airplane progress may be blocked for extended periods in certain situations. To address this blocking phenomenon, this paper proposes integrating bio-inspired nonlinear opinion dynamics into the airplane safety cont…

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 …

Open Science, Open Innovation? The Role of Open Access in Patenting Activity
Abdelghani Maddi (GEMASS), Ahmad Yaman Abdin, Francesco Fdp de Pretis
https://arxiv.org/abs/2508.00829

Open Science, Open Innovation? The Role of Open Access in Patenting Activity
Scientific knowledge is a key driver of technological innovation, shaping industrial development and policy decisions worldwide. Understanding how patents incorporate scientific research is essential for assessing the role of academic discoveries in technological progress. Non-Patent References (NPRs) provide a useful indicator of this relationship by revealing the extent to which patents draw upon scientific literature. Here, we show that reliance on scientific research in patents varies signi…

This https://arxiv.org/abs/2506.01921 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

MedEBench: Revisiting Text-instructed Image Editing
Text-guided image editing has seen rapid progress in natural image domains, but its adaptation to medical imaging remains limited and lacks standardized evaluation. Clinically, such editing holds promise for simulating surgical outcomes, creating personalized teaching materials, and enhancing patient communication. To bridge this gap, we introduce \textbf{MedEBench}, a comprehensive benchmark for evaluating text-guided medical image editing. It consists of 1,182 clinically sourced image-prompt …

This https://arxiv.org/abs/2504.08435 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

High-dimensional Gaussian and bootstrap approximations for robust means
Recent years have witnessed much progress on Gaussian and bootstrap approximations to the distribution of max-type statistics of sums of independent random vectors with dimension $d$ large relative to the sample size $n$. However, for any number of moments $m>2$ that the summands may possess, there exist distributions such that these approximations break down if $d$ grows faster than $n^{\frac{m}{2}-1}$. In this paper, we establish Gaussian and bootstrap approximations to the distributions of w…

Sometimes progress proceeds from sideways motions. Sometimes it proceeds from retro motion, then sideways motion, then slantwise.
Sometimes you should pass the baton. But ACTIVE wishfulness adds to reality.
Possibilism expands reality.
#Actualism #Find a new

This https://arxiv.org/abs/2505.19379 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Neural nanophotonic object detector with ultra-wide field-of-view
Intelligent object detection, which extracts crucial information like targets categories and locations, plays a vital role in emerging technologies including autonomous driving, the Internet of Things, and next-generation mobile communication systems. With the advancement of intelligent object detectors towards higher integration and miniaturization, their portability and adaptability to a broader range of scenarios have been significantly enhanced. However, this progress comes at the cost of r…

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…

This https://arxiv.org/abs/2503.09532 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

SAEBench: A Comprehensive Benchmark for Sparse Autoencoders in Language Model Interpretability
Sparse autoencoders (SAEs) are a popular technique for interpreting language model activations, and there is extensive recent work on improving SAE effectiveness. However, most prior work evaluates progress using unsupervised proxy metrics with unclear practical relevance. We introduce SAEBench, a comprehensive evaluation suite that measures SAE performance across eight diverse metrics, spanning interpretability, feature disentanglement and practical applications like unlearning. To enable syst…

This https://arxiv.org/abs/2505.23703 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability
Enhancing the mathematical reasoning capabilities of LLMs has garnered significant attention in both the mathematical and computer science communities. Recent works have made substantial progress in both Natural Language (NL) reasoning and Formal Language (FL) reasoning by leveraging the potential of pure Reinforcement Learning (RL) methods on base models. However, RL approaches struggle to impart new capabilities not presented in the base model, highlighting the need to integrate more knowledg…

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
Junting Chen, Haotian Liang, Lingxiao Du, Weiyun Wang, Mengkang Hu, Yao Mu, Wenhai Wang, Jifeng Dai, Ping Luo, Wenqi Shao, Lin Shao
https://arxiv.org/abs/2506.04217

OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis
The rapid progress of navigation, manipulation, and vision models has made mobile manipulators capable in many specialized tasks. However, the open-world mobile manipulation (OWMM) task remains a challenge due to the need for generalization to open-ended instructions and environments, as well as the systematic complexity to integrate high-level decision making with low-level robot control based on both global scene understanding and current agent state. To address this complexity, we propose a …

Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schr\"odinger Equation
Du Jiang, Xuelan Wen, Yixiao Chen, Ruichen Li, Weizhong Fu, Hung Q. Pham, Ji Chen, Di He, William A. Goddard III, Liwei Wang, Weiluo Ren
https://arxiv.org/abs/2508.02570

Neural Scaling Laws Surpass Chemical Accuracy for the Many-Electron Schrödinger Equation
We demonstrate, for the first time, that neural scaling laws can deliver near-exact solutions to the many-electron Schrödinger equation across a broad range of realistic molecules. This progress is enabled by the Lookahead Variational Algorithm (LAVA), an effective optimization scheme that systematically translates increased model size and computational resources into greatly improved energy accuracy for neural network wavefunctions. Across all tested cases, including benzene, the absolute ene…

Progress and Challenges in the Corps Transformation: https://benborges.xyz/2025/07/30/progress-and-challenges-in-the.html

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.
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% progress then: 59%
now: 60%
progress in xp then: 594k/1m
now: tbd
#AlbionOnline #MastodonFedivers

It's June, so I've got my Javastation off the shelf for the first time in a couple of decades. This is a Javastation Krups, with 100MHz sparc, sold as a diskless workstation. I never realised it had a PPP ROM boot in! Anyway, I should get on and set up networking and a boot server.
(Note: the plastic on the clips on the doors is fragile, 2 just pinged off on me)
#retrocomputing

This https://arxiv.org/abs/2504.07879 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Towards Sustainable Creativity Support: An Exploratory Study on Prompt Based Image Generation
Creativity is a valuable human skill that has long been augmented through both analog and digital tools. Recent progress in generative AI, such as image generation, provides a disruptive technological solution to supporting human creativity further and helping humans generate solutions faster. While AI image generators can help to rapidly visualize ideas based on user prompts, the use of such AI systems has also been critiqued due to their considerable energy usage. In this paper, we report on …

Pride progress? As gay pro athletes consider coming out, each announcement makes a mark https://www.nytimes.com/athletic/6396179/2025/06/03/pride-month-gay-pro-athletes-evolution/

MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection
Chengming Wang, Guodong Fan, Jinjiang Li, Min Gan, C. L. Philip Chen
https://arxiv.org/abs/2508.01555

MGCR-Net:Multimodal Graph-Conditioned Vision-Language Reconstruction Network for Remote Sensing Change Detection
With the advancement of remote sensing satellite technology and the rapid progress of deep learning, remote sensing change detection (RSCD) has become a key technique for regional monitoring. Traditional change detection (CD) methods and deep learning-based approaches have made significant contributions to change analysis and detection, however, many outstanding methods still face limitations in the exploration and application of multimodal data. To address this, we propose the multimodal graph…

Bridging Global Frameworks: Governance Strategies Behind Cisco Common Control Framework v4.0 for Scalable Cloud Compliance
Nishant Sonkar
https://arxiv.org/abs/2506.01984

Bridging Global Frameworks: Governance Strategies Behind Cisco Common Control Framework v4.0 for Scalable Cloud Compliance
CCF v4.0 provides a standard way to ensure that Cisco's cloud products comply with the many quickly evolving requirements worldwide. To cope with increasing demands brought by ISO 27001, SOC 2, NIST, FedRAMP, EU CRA, DORA, and NIS2, CCF v4.0 introduces reliable governance by grouping controls using modules mapped across many frameworks. In this document, I discuss the governance structure controlling the framework's progress, noting how the CAB helped and the relevant steps for mapping and vali…

This https://arxiv.org/abs/2505.20156 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

HunyuanVideo-Avatar: High-Fidelity Audio-Driven Human Animation for Multiple Characters
Recent years have witnessed significant progress in audio-driven human animation. However, critical challenges remain in (i) generating highly dynamic videos while preserving character consistency, (ii) achieving precise emotion alignment between characters and audio, and (iii) enabling multi-character audio-driven animation. To address these challenges, we propose HunyuanVideo-Avatar, a multimodal diffusion transformer (MM-DiT)-based model capable of simultaneously generating dynamic, emotion-…

Quark mass corrections in di-Higgs production amplitude at high-energy
Sebastian Jaskiewicz
https://arxiv.org/abs/2508.02589 https://arxiv.org/pdf/2508.025…

Quark mass corrections in di-Higgs production amplitude at high-energy
A large theoretical uncertainty due to the choice of the top-quark mass renormalisation scheme is present in QCD predictions for Higgs boson pair production. In these proceedings, we report on the recent progress in tackling these uncertainties for the $gg\to HH$ amplitude in the high-energy limit $s,|t|,|u| \gg m_t^2 \gg m_H^2$. Using the Method of Regions and Soft-Collinear Effective Theory, the leading power in $m_t$ behaviour of the amplitude is understood to all orders …

The US company #Techmet
is likely to bid in the first pilot project of the Ukraine-US joint Reconstruction Investment Fund
on a #lithium #mine in the centre of the country,
Ukraine’s first deputy prime minister, Y…

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
Dongchi Huang, Zhirui Fang, Tianle Zhang, Yihang Li, Lin Zhao, Chunhe Xia
https://arxiv.org/abs/2508.02219

CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
Vision-Language-Action (VLA) models demonstrate significant potential for developing generalized policies in real-world robotic control. This progress inspires researchers to explore fine-tuning these models with Reinforcement Learning (RL). However, fine-tuning VLA models with RL still faces challenges related to sample efficiency, compatibility with action chunking, and training stability. To address these challenges, we explore the fine-tuning of VLA models through offline reinforcement lear…

XANES absorption spectra of penta-graphene and penta-SiC2 with different terminations: a computational study
Andrea Pedrielli, Tommaso Morresi, Simone Taioli
https://arxiv.org/abs/2508.00704

XANES absorption spectra of penta-graphene and penta-SiC2 with different terminations: a computational study
In recent research, penta-graphene and penta-SiC2 have emerged as innovative 2D materials consisting exclusively of pentagons. However, there is still a significant gap in the theoretical characterization of these materials, which hinders progress in their synthesis and potential technological applications. This study aims to close this gap by investigating the X-ray absorption near-edge spectroscopy (XANES) of these materials through ab initio calculations. In particular, we analyze the XANES …

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…

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,…

Recent Advances in Medical Image Classification
Loan Dao, Ngoc Quoc Ly
https://arxiv.org/abs/2506.04129 https://arxiv.org/pdf/2506.04…

Recent Advances in Medical Image Classification
Medical image classification is crucial for diagnosis and treatment, benefiting significantly from advancements in artificial intelligence. The paper reviews recent progress in the field, focusing on three levels of solutions: basic, specific, and applied. It highlights advances in traditional methods using deep learning models like Convolutional Neural Networks and Vision Transformers, as well as state-of-the-art approaches with Vision Language Models. These models tackle the issue of limited …

RewardBench 2: Advancing Reward Model Evaluation
Saumya Malik, Valentina Pyatkin, Sander Land, Jacob Morrison, Noah A. Smith, Hannaneh Hajishirzi, Nathan Lambert
https://arxiv.org/abs/2506.01937

RewardBench 2: Advancing Reward Model Evaluation
Reward models are used throughout the post-training of language models to capture nuanced signals from preference data and provide a training target for optimization across instruction following, reasoning, safety, and more domains. The community has begun establishing best practices for evaluating reward models, from the development of benchmarks that test capabilities in specific skill areas to others that test agreement with human preferences. At the same time, progress in evaluation has not…

SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
Han Li, Yuling Shi, Shaoxin Lin, Xiaodong Gu, Heng Lian, Xin Wang, Yantao Jia, Tao Huang, Qianxiang Wang
https://arxiv.org/abs/2507.23348

SWE-Debate: Competitive Multi-Agent Debate for Software Issue Resolution
Issue resolution has made remarkable progress thanks to the advanced reasoning capabilities of large language models (LLMs). Recently, agent-based frameworks such as SWE-agent have further advanced this progress by enabling autonomous, tool-using agents to tackle complex software engineering tasks. While existing agent-based issue resolution approaches are primarily based on agents' independent explorations, they often get stuck in local solutions and fail to identify issue patterns that span a…

Improved Limits on Exotic Interactions Mediated by Axion-Like Particles Between Muons
L. Y. Wu, H. Yan
https://arxiv.org/abs/2508.00504 https://arxiv.org/p…

Improved Limits on Exotic Interactions Mediated by Axion-Like Particles Between Muons
The precise measurement of the muon anomalous magnetic dipole moment (AMDM) $a_μ$ provides an opportunity for constraining the exotic interactions between muons mediated by new scalar or vector particles. Recent progress in both experimental measurements and theoretical predictions of the muon AMDM has reconciled the long-standing tension between them. Based on the latest result for the muon AMDM, $Δa_μ=a^{\rm exp}_μ-a^{\rm SM}_μ= (38 \pm 63) \times 10^{-11}$, we derive updated constraints…

Stochastic Modeling of Road Hazards on Intersections and their Effect on Safety of Autonomous Vehicles
Peter Popov, Lorenzo Strigini, Cornelius Buerkle, Fabian Oboril, Michael Paulitsch
https://arxiv.org/abs/2506.02688

Stochastic Modeling of Road Hazards on Intersections and their Effect on Safety of Autonomous Vehicles
Autonomous vehicles (AV) look set to become common on our roads within the next few years. However, to achieve the final breakthrough, not only functional progress is required, but also satisfactory safety assurance must be provided. Among those, a question demanding special attention is the need to assess and quantify the overall safety of an AV. Such an assessment must consider on the one hand the imperfections of the AV functionality and on the other hand its interaction with the environment…

AnyI2V: Animating Any Conditional Image with Motion Control
Ziye Li, Hao Luo, Xincheng Shuai, Henghui Ding
https://arxiv.org/abs/2507.02857 https://…

AnyI2V: Animating Any Conditional Image with Motion Control
Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals and flexible spatial constraints. Existing T2V methods typically rely on text prompts, which inherently lack precise control over the spatial layout of generated content. In contrast, I2V methods are limited by their dependence on real images, which restricts…

Human-Centered Explainability in Interactive Information Systems: A Survey
Yuhao Zhang, Jiaxin An, Ben Wang, Yan Zhang, Jiqun Liu
https://arxiv.org/abs/2507.02300

Human-Centered Explainability in Interactive Information Systems: A Survey
Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret, and scrutinize AI-driven outputs to make informed decisions. This systematic survey of literature aims to characterize recent progress in user studies on explainability in interactive information systems by reviewing how explainability has been conceptualized, designed, and evaluated in practice. Following PRISMA gu…

A Survey of Deep Learning Video Super-Resolution
Arbind Agrahari Baniya, Tsz-Kwan Lee, Peter Eklund, Sunil Aryal
https://arxiv.org/abs/2506.03216 https://

A Survey of Deep Learning Video Super-Resolution
Video super-resolution (VSR) is a prominent research topic in low-level computer vision, where deep learning technologies have played a significant role. The rapid progress in deep learning and its applications in VSR has led to a proliferation of tools and techniques in the literature. However, the usage of these methods is often not adequately explained, and decisions are primarily driven by quantitative improvements. Given the significance of VSR's potential influence across multiple domains…

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Xiao Yu, Baolin Peng, Ruize Xu, Michel Galley, Hao Cheng, Suman Nath, Jianfeng Gao, Zhou Yu
https://arxiv.org/abs/2506.00320

Dyna-Think: Synergizing Reasoning, Acting, and World Model Simulation in AI Agents
Recent progress in reasoning with large language models (LLMs), such as DeepSeek-R1, demonstrates impressive capabilities in domains like mathematics and coding, by exhibiting complex cognitive behaviors such as verification, goal decomposition, and self-reflection. However, it is unclear what behavior is effective and what behavior is missing for long-horizon AI agents tasks. In this work, we propose Dyna-Think, a thinking framework that integrates planning with an internal world model with re…

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Yuxuan Wang, Tianwei Cao, Huayu Zhang, Zhongjiang He, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2507.02714

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models
Image generation has achieved remarkable progress with the development of large-scale text-to-image models, especially diffusion-based models. However, generating human images with plausible details, such as faces or hands, remains challenging due to insufficient supervision of local regions during training. To address this issue, we propose FairHuman, a multi-objective fine-tuning approach designed to enhance both global and local generation quality fairly. Specifically, we first construct thr…

This https://arxiv.org/abs/2501.18564 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SAM2Act: Integrating Visual Foundation Model with A Memory Architecture for Robotic Manipulation
Robotic manipulation systems operating in diverse, dynamic environments must exhibit three critical abilities: multitask interaction, generalization to unseen scenarios, and spatial memory. While significant progress has been made in robotic manipulation, existing approaches often fall short in generalization to complex environmental variations and addressing memory-dependent tasks. To bridge this gap, we introduce SAM2Act, a multi-view robotic transformer-based policy that leverages multi-reso…

The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
Russell Beale
https://arxiv.org/abs/2507.02180

The Revolution Has Arrived: What the Current State of Large Language Models in Education Implies for the Future
Large language Models have only been widely available since 2022 and yet in less than three years have had a significant impact on approaches to education and educational technology. Here we review the domains in which they have been used, and discuss a variety of use cases, their successes and failures. We then progress to discussing how this is changing the dynamic for learners and educators, consider the main design challenges facing LLMs if they are to become truly helpful and effective as …

This https://arxiv.org/abs/2503.07010 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

ProjectEval: A Benchmark for Programming Agents Automated Evaluation on Project-Level Code Generation
Recently, LLM agents have made rapid progress in improving their programming capabilities. However, existing benchmarks lack the ability to automatically evaluate from users' perspective, and also lack the explainability of the results of LLM agents' code generation capabilities. Thus, we introduce ProjectEval, a new benchmark for LLM agents project-level code generation's automated evaluation by simulating user interaction. ProjectEval is constructed by LLM with human reviewing. It has three d…

DexVLG: Dexterous Vision-Language-Grasp Model at Scale
Jiawei He, Danshi Li, Xinqiang Yu, Zekun Qi, Wenyao Zhang, Jiayi Chen, Zhaoxiang Zhang, Zhizheng Zhang, Li Yi, He Wang
https://arxiv.org/abs/2507.02747

DexVLG: Dexterous Vision-Language-Grasp Model at Scale
As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection, progress has mainly focused on controlling simple gripper end-effectors. There is little research on functional grasping with large models for human-like dexterous hands. In this paper, we introduce DexVLG, a large Vision-Language-Grasp model for Dexterous grasp pose prediction aligned with language instructions usin…

This https://arxiv.org/abs/2412.13147 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Are Your LLMs Capable of Stable Reasoning?
The rapid advancement of large language models (LLMs) has shown remarkable progress in complex reasoning tasks. However, a significant disparity exists between benchmark performances and real-world applications. We attribute this gap primarily to current evaluation protocols and metrics, which inadequately capture the full spectrum of LLM capabilities, especially in complex reasoning tasks where both accuracy and consistency are essential. In this paper, we introduce G-Pass@$k$, a novel evaluat…

This https://arxiv.org/abs/2505.24785 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

EXP-Bench: Can AI Conduct AI Research Experiments?
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. We introduce EXP-Bench, a novel benchmark designed to systematically evaluate AI agents on complete research experiments sourced from influential AI publications. Given a research question and incomplete starter code, EXP-Bench challenges AI agents to formulate hypotheses, design and implement experimental procedures, …

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Kordel K. France, Rohith Peddi, Nik Dennler, Ovidiu Daescu
https://arxiv.org/abs/2506.00398

Position: Olfaction Standardization is Essential for the Advancement of Embodied Artificial Intelligence
Despite extraordinary progress in artificial intelligence (AI), modern systems remain incomplete representations of human cognition. Vision, audition, and language have received disproportionate attention due to well-defined benchmarks, standardized datasets, and consensus-driven scientific foundations. In contrast, olfaction - a high-bandwidth, evolutionarily critical sense - has been largely overlooked. This omission presents a foundational gap in the construction of truly embodied and ethica…

This https://arxiv.org/abs/2505.22642 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control
Reinforcement learning (RL) has driven significant progress in robotics, but its complexity and long training times remain major bottlenecks. In this report, we introduce FastTD3, a simple, fast, and capable RL algorithm that significantly speeds up training for humanoid robots in popular suites such as HumanoidBench, IsaacLab, and MuJoCo Playground. Our recipe is remarkably simple: we train an off-policy TD3 agent with several modifications -- parallel simulation, large-batch updates, a distri…

Pok\'eAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
Zihao Liu, Xinhang Sui, Yueran Song, Siwen Wang
https://arxiv.org/abs/2506.23689

PokéAI: A Goal-Generating, Battle-Optimizing Multi-agent System for Pokemon Red
We introduce PokéAI, the first text-based, multi-agent large language model (LLM) framework designed to autonomously play and progress through Pokémon Red. Our system consists of three specialized agents-Planning, Execution, and Critique-each with its own memory bank, role, and skill set. The Planning Agent functions as the central brain, generating tasks to progress through the game. These tasks are then delegated to the Execution Agent, which carries them out within the game environment. Up…

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices
Hailong Yan, Ao Li, Xiangtao Zhang, Zhe Liu, Zenglin Shi, Ce Zhu, Le Zhang
https://arxiv.org/abs/2507.01838

MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices
Recent advancements in deep neural networks have driven significant progress in image enhancement (IE). However, deploying deep learning models on resource-constrained platforms, such as mobile devices, remains challenging due to high computation and memory demands. To address these challenges and facilitate real-time IE on mobile, we introduce an extremely lightweight Convolutional Neural Network (CNN) framework with around 4K parameters. Our approach integrates reparameterization with an Incr…

SMELLNET: A Large-scale Dataset for Real-world Smell Recognition
Dewei Feng, Carol Li, Wei Dai, Paul Pu Liang
https://arxiv.org/abs/2506.00239 https://

SMELLNET: A Large-scale Dataset for Real-world Smell Recognition
The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g., smelling gluten or peanuts in a cake), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are virtually no large scale benchmarks, and therefore little progress, for training and evaluating AI systems' ability to smell in the real world. In this paper, w…

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Rahul Ramachandran, Ali Garjani, Roman Bachmann, Andrei Atanov, O\u{g}uzhan Fatih Kar, Amir Zamir
https://arxiv.org/abs/2507.01955

How Well Does GPT-4o Understand Vision? Evaluating Multimodal Foundation Models on Standard Computer Vision Tasks
Multimodal foundation models, such as GPT-4o, have recently made remarkable progress, but it is not clear where exactly these models stand in terms of understanding vision. In this paper, we benchmark the performance of popular multimodal foundation models (GPT-4o, o4-mini, Gemini 1.5 Pro and Gemini 2.0 Flash, Claude 3.5 Sonnet, Qwen2-VL, Llama 3.2) on standard computer vision tasks (semantic segmentation, object detection, image classification, depth and surface normal prediction) using establ…

CI-VID: A Coherent Interleaved Text-Video Dataset
Yiming Ju, Jijin Hu, Zhengxiong Luo, Haoge Deng, hanyu Zhao, Li Du, Chengwei Wu, Donglin Hao, Xinlong Wang, Tengfei Pan
https://arxiv.org/abs/2507.01938

CI-VID: A Coherent Interleaved Text-Video Dataset
Text-to-video (T2V) generation has recently attracted considerable attention, resulting in the development of numerous high-quality datasets that have propelled progress in this area. However, existing public datasets are primarily composed of isolated text-video (T-V) pairs and thus fail to support the modeling of coherent multi-clip video sequences. To address this limitation, we introduce CI-VID, a dataset that moves beyond isolated text-to-video (T2V) generation toward text-and-video-to-vid…