Tootfinder

Quanta Magazine authors Janna Levin and Steven Strogatz strike up a conversation with Ellie Pavlick (Research Scientist at Google Deep Mind) about the differences and similarities between the way people understand language, what NLP algorithms do, and the fact that such conversations more often than not shed light into more than Linguistics' computational side.
"Will AI Ever Understand Language Like Humans?"

Will AI Ever Understand Language Like Humans? | Quanta Magazine
AI may sound like a human, but that doesn’t mean that AI learns like a human. In this episode, Ellie Pavlick explains why understanding how LLMs can process language could unlock deeper insights into both AI and the human mind.

"Human-caused stratospheric cooling may have been detectable as early as 1885"
#Climate #ClimateChange
https:…

Human-caused stratospheric cooling may have been detectable as early as 1885
A small, multi-institutional team of climate scientists has found evidence that human-caused impacts on the stratosphere began earlier than previously thought. In their study, published in the Proceedings of the National Academy of Sciences, the group used observational data, environmental theory and computer modeling to create simulations depicting the state of the stratosphere to a time just after the dawn of the industrial age.

Sleepy garden cat is unimpressed with your human shenanigans #Caturday #CatsOfMastodon

How long until the internet, which allowed a generation to benefit from a vast wealth of human knowledge, becomes a swamp filled with generated #AI pollution? It may already be too late. https://www.theregist…

The launch of ChatGPT polluted the world forever, like the first atomic weapons tests
Feature: Academics mull the need for the digital equivalent of low-background steel

Since I did not get to take part in the Oslo Enhjulssykkelrunde (Unicycle Tour of Oslo) this year (I was busy with the kids), I decided to do my own little varient of it as part of my commute on the way into work today.
Distance 23.91km [14.86 miles], moving time 1h36m, 175m [574ft] elevation gain, average speed 14.9km/h [9.3mph]
FWIW, I actually only live about 5km [3.1 miles] from my office 🤷 😆

(1/2) #YourUndividedAttention [PODCAST] Echo Chambers of One: Companion AI and the Future of Human Connection (15 May 2025)
https://pca.st/episode/53327729-f332-4a29-82d9-00…

Why AI can't possibly make you more productive; long
#AI and "productivity", some thoughts:
Productivity is a concept that isn't entirely meaningless outside the context of capitalism, but it's a concept that is heavily inflected in a capitalist context. In many uses today it effectively means "how much you can satisfy and/or exceed your boss' expectations." This is not really what it should mean: even in an anarchist utopia, people would care about things like how many shirts they can produce in a week, although in an "I'd like to voluntarily help more people" way rather than an "I need to meet this quota to earn my survival" way. But let's roll with this definition for a second, because it's almost certainly what your boss means when they say "productivity", and understanding that word in a different (even if truer) sense is therefore inherently dangerous.
Accepting "productivity" to mean "satisfying your boss' expectations," I will now claim: the use of generative AI cannot increase your productivity.
Before I dive in, it's imperative to note that the big generative models which most people think of as constituting "AI" today are evil. They are 1: pouring fuel on our burning planet, 2: psychologically strip-mining a class of data laborers who are exploited for their precarity, 3: enclosing, exploiting, and polluting the digital commons, and 4: stealing labor from broad classes of people many of whom are otherwise glad to give that labor away for free provided they get a simple acknowledgement in return. Any of these four "ethical issues" should be enough *alone* to cause everyone to simply not use the technology. These ethical issues are the reason that I do not use generative AI right now, except for in extremely extenuating circumstances. These issues are also convincing for a wide range of people I talk to, from experts to those with no computer science background. So before I launch into a critique of the effectiveness of generative AI, I want to emphasize that such a critique should be entirely unnecessary.
But back to my thesis: generative AI cannot increase your productivity, where "productivity" has been defined as "how much you can satisfy and/or exceed your boss' expectations."
Why? In fact, what the fuck? Every AI booster I've met has claimed the opposite. They've given me personal examples of time saved by using generative AI. Some of them even truly believe this. Sometimes I even believe they saved time without horribly compromising on quality (and often, your boss doesn't care about quality anyways if the lack of quality is hard to measure of doesn't seem likely to impact short-term sales/feedback/revenue). So if generative AI genuinely lets you write more emails in a shorter period of time, or close more tickets, or something else along these lines, how can I say it isn't increasing your ability to meet your boss' expectations?
The problem is simple: your boss' expectations are not a fixed target. Never have been. In virtue of being someone who oversees and pays wages to others under capitalism, your boss' game has always been: pay you less than the worth of your labor, so that they can accumulate profit and this more capital to remain in charge instead of being forced into working for a wage themselves. Sure, there are layers of manservant caught in between who aren't fully in this mode, but they are irrelevant to this analysis. It matters not how much you please your manager if your CEO thinks your work is not worth the wages you are being paid. And using AI actively lowers the value of your work relative to your wages.
Why do I say that? It's actually true in several ways. The most obvious: using generative AI lowers the quality of your work, because the work it produces is shot through with errors, and when your job is reduced to proofreading slop, you are bound to tire a bit, relax your diligence, and let some mistakes through. More than you would have if you are actually doing and taking pride in the work. Examples are innumerable and frequent, from journalists to lawyers to programmers, and we laugh at them "haha how stupid to not check whether the books the AI reviewed for you actually existed!" but on a deeper level if we're honest we know we'd eventually make the same mistake ourselves (bonus game: spot the swipe-typing typos I missed in this post; I'm sure there will be some).
But using generative AI also lowers the value of your work in another much more frightening way: in this era of hype, it demonstrates to your boss that you could be replaced by AI. The more you use it, and no matter how much you can see that your human skills are really necessary to correct its mistakes, the more it appears to your boss that they should hire the AI instead of you. Or perhaps retain 10% of the people in roles like yours to manage the AI doing the other 90% of the work. Paradoxically, the *more* you get done in terms of raw output using generative AI, the more it looks to your boss as if there's an opportunity to get enough work done with even fewer expensive humans. Of course, the decision to fire you and lean more heavily into AI isn't really a good one for long-term profits and success, but the modern boss did not get where they are by considering long-term profits. By using AI, you are merely demonstrating your redundancy, and the more you get done with it, the more redundant you seem.
In fact, there's even a third dimension to this: by using generative AI, you're also providing its purveyors with invaluable training data that allows them to make it better at replacing you. It's generally quite shitty right now, but the more use it gets by competent & clever people, the better it can become at the tasks those specific people use it for. Using the currently-popular algorithm family, there are limits to this; I'm not saying it will eventually transcend the mediocrity it's entwined with. But it can absolutely go from underwhelmingly mediocre to almost-reasonably mediocre with the right training data, and data from prompting sessions is both rarer and more useful than the base datasets it's built on.
For all of these reasons, using generative AI in your job is a mistake that will likely lead to your future unemployment. To reiterate, you should already not be using it because it is evil and causes specific and inexcusable harms, but in case like so many you just don't care about those harms, I've just explained to you why for entirely selfish reasons you should not use it.
If you're in a position where your boss is forcing you to use it, my condolences. I suggest leaning into its failures instead of trying to get the most out of it, and as much as possible, showing your boss very clearly how it wastes your time and makes things slower. Also, point out the dangers of legal liability for its mistakes, and make sure your boss is aware of the degree to which any of your AI-eager coworkers are producing low-quality work that harms organizational goals.
Also, if you've read this far and aren't yet of an anarchist mindset, I encourage you to think about the implications of firing 75% of (at least the white-collar) workforce in order to make more profit while fueling the climate crisis and in most cases also propping up dictatorial figureheads in government. When *either* the AI bubble bursts *or* if the techbros get to live out the beginnings of their worker-replacement fantasies, there are going to be an unimaginable number of economically desperate people living in increasingly expensive times. I'm the kind of optimist who thinks that the resulting social crucible, though perhaps through terrible violence, will lead to deep social changes that effectively unseat from power the ultra-rich that continue to drag us all down this destructive path, and I think its worth some thinking now about what you might want the succeeding stable social configuration to look like so you can advocate towards that during points of malleability.
As others have said more eloquently, generative AI *should* be a technology that makes human lives on average easier, and it would be were it developed & controlled by humanists. The only reason that it's not, is that it's developed and controlled by terrible greedy people who use their unfairly hoarded wealth to immiserate the rest of us in order to maintain their dominance. In the long run, for our very survival, we need to depose them, and I look forward to what the term "generative AI" will mean after that finally happens.

Q&A with Hugging Face Chief Ethics Scientist Margaret Mitchell on aligning AI development with human needs, the "illusion of consensus" around AGI, and more (Melissa Heikkilä/Financial Times)
https://www.ft.com/content/7089bff2-25fc-4a25-98bf-8828ab24…

Margaret Mitchell: artificial general intelligence is ‘just vibes and snake oil’
One of the pioneers of AI ethics explains why human needs should be the central driver in the development of the technology

Why yes human, the garden chiminea does indeed make a comfy cat nest…
#CatsOfMastodon

I had the BEST time recording with @catherine_ashton_critical of Critical Info this morning! (Not, I must say, in an Oodie but Remy needed a walk 😉 ) We talked about end of life doulas, advance planning, grief, inclusive death care, and why a pet's death can be just as devastating as a human's.
Catherine's work is extraordinary and high-calibre, I encourage you to follow her if you don't already.
What are your weekend plans??!

I mean I knew that Musk was a giant loser but you won't believe how pathetic he can get. I am about to be finished reading "Character Limit" and the stories in there are something else. He is a small, damaged, complete loser of human being. wow. So much external power and money, yet he is a miserable twat. So many stories... will have to attach some here

"We think of generating source code from a prompt as an AI-powered feature of modern IDEs, but the general problem has a rich history in research efforts and domain-specific programming systems." Join William Benton at this year's Berlin Buzzwords to hear him talk about the history of program synthesis, its relationship to the history of AI, and the lessons we can learn from it today.
Learn more:

After nuclear weapons testing began in 1945, atmospheric radiation contaminated new steel production worldwide.
For decades, scientists needing radiation-free metal for sensitive instruments had to salvage steel from pre-war shipwrecks.
Scientists called this steel
"low-background steel."
Graham-Cumming sees a parallel with today's web, where AI-generated content increasingly mingles with human-created material and contaminates it.
With the advent o…

Scientists once hoarded pre-nuclear steel; now we’re hoarding pre-AI content
Newly announced catalog collects pre-2022 sources untouched by ChatGPT and AI contamination.

Vision in Action: Learning Active Perception from Human Demonstrations
Haoyu Xiong, Xiaomeng Xu, Jimmy Wu, Yifan Hou, Jeannette Bohg, Shuran Song
https://arxiv.org/abs/2506.15666 …

Vision in Action: Learning Active Perception from Human Demonstrations
We present Vision in Action (ViA), an active perception system for bimanual robot manipulation. ViA learns task-relevant active perceptual strategies (e.g., searching, tracking, and focusing) directly from human demonstrations. On the hardware side, ViA employs a simple yet effective 6-DoF robotic neck to enable flexible, human-like head movements. To capture human active perception strategies, we design a VR-based teleoperation interface that creates a shared observation space between the robo…

Michael Sweater is at it again:
https://sweater.substack.com/p/new-comics-and-some-news
I find it hard to believe he has less than 250 subscribers (hint, hint). FWIW, there are another 3 (better drawn) pages to this story covering his angsty meta-narrative about soci…

Отличная статейка про ИИ:
- и про луддитов, воевавших с подпиской на одежду
- и про двигатели Апполона, которые мы знаем как делаются, но сделать больше никогда не сможем
- и про сеошников, которые пишут для кого угодно, но не для людей
- и про костыльность новейших технологий и этичность антропика
- и про выедание интернета гигантами с добросовестным использованием
- и про человеческие жертвы богу нашему Super Intelligence, который неизбежно всех спасет через …

[2025-06-20 Fri (UTC), no new articles found for cs.HC Human-Computer Interaction]
toXiv_bot_toot

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
Roey Ron, Guy Tevet, Haim Sawdayee, Amit H. Bermano
https://arxiv.org/abs/2506.15625

HOIDiNi: Human-Object Interaction through Diffusion Noise Optimization
We present HOIDiNi, a text-driven diffusion framework for synthesizing realistic and plausible human-object interaction (HOI). HOI generation is extremely challenging since it induces strict contact accuracies alongside a diverse motion manifold. While current literature trades off between realism and physical correctness, HOIDiNi optimizes directly in the noise space of a pretrained diffusion model using Diffusion Noise Optimization (DNO), achieving both. This is made feasible thanks to our ob…

This https://arxiv.org/abs/2504.09546 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

A simulation-heuristics dual-process model for intuitive physics
The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time. While mental simulation accurately captured human judgments in simpler scenarios, a linear heuristic model better matched human predictions when simu…

Using AI tools to upskill human effort instead of replace human effort eventually upskills the AI tools. When you instead focus only on replacing human effort, there’s no feedback loop of improved human input to in turn improve the AI output.
From: @…
https://hachyderm.io/@hazelweakly/114520965590639421

Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings
Harbin Hong, Sebastian Caldas, Liu Leqi
https://arxiv.org/abs/2506.14997

Hypothesis Testing for Quantifying LLM-Human Misalignment in Multiple Choice Settings
As Large Language Models (LLMs) increasingly appear in social science research (e.g., economics and marketing), it becomes crucial to assess how well these models replicate human behavior. In this work, using hypothesis testing, we present a quantitative framework to assess the misalignment between LLM-simulated and actual human behaviors in multiple-choice survey settings. This framework allows us to determine in a principled way whether a specific language model can effectively simulate human…

There has long been a quiet debate, currently drowned out but still very much happening, about human replacement vs human augmentation. Think of Gary Kasparov remarking years ago that he thought chess played by humans with computer assistance could be a far more interesting game than either human-only or computer-only chess.
Here’s an argument for augmentation over automation, and note how far it diverges from the current hype despite it being written from a very AI-friendly point of view:
https://digitaleconomy.stanford.edu/news/the-turing-trap-the-promise-peril-of-human-like-artificial-intelligence/

Richard Simmons, the human flashbang

"Guest post: Why 2024’s global temperatures were unprecedented, but not surprising"
#Climate #ClimateChange

Guest post: Why 2024’s global temperatures were unprecedented, but not surprising - Carbon Brief
Human-caused greenhouse gas (GHG) emissions in 2024 continued to drive global warming to record levels.

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models
Andela Ilic, Jiaxi Jiang, Paul Streli, Xintong Liu, Christian Holz
https://arxiv.org/abs/2506.15290

Human Motion Capture from Loose and Sparse Inertial Sensors with Garment-aware Diffusion Models
Motion capture using sparse inertial sensors has shown great promise due to its portability and lack of occlusion issues compared to camera-based tracking. Existing approaches typically assume that IMU sensors are tightly attached to the human body. However, this assumption often does not hold in real-world scenarios. In this paper, we present a new task of full-body human pose estimation using sparse, loosely attached IMU sensors. To solve this task, we simulate IMU recordings from an existing…

Indicators of Global Climate Change 2024: annual update of key indicators of the state of the climate system and human influence (1)
https://essd.copernicus.org/articles/17/2641/2025/
http://www.memeorandum.com/250619/p63#a250619p63

At just 3 years old, I have a penthouse suite, 2 passive incomes, a personal chef and driver. Follow me for financial advice.

Causality in the human niche: lessons for machine learning
Richard D. Lange, Konrad P. Kording
https://arxiv.org/abs/2506.13803 https://

Causality in the human niche: lessons for machine learning
Humans interpret the world around them in terms of cause and effect and communicate their understanding of the world to each other in causal terms. These causal aspects of human cognition are thought to underlie humans' ability to generalize and learn efficiently in new domains, an area where current machine learning systems are weak. Building human-like causal competency into machine learning systems may facilitate the construction of effective and interpretable AI. Indeed, the machine learnin…

Het budget is op. Punt uit.
Als we op dit niveau blijven uitstoten gaan we over 1.9 jaar door de 1,5 °C heen en moeten we vanaf dan helemaal niets meer uitstoten om niet door te denderen.
Only two years left of world’s carbon budget to meet 1.5C target, scientists warn

Blue Lights, Insurmountable Debt, and the Affordances of its Erasure
https://ift.tt/pL6dIVC
By Justin Martin In The Dawn of Everything: A New History of Humanity (2021), anthropologist David…
via Input 4 RELCFP

Blue Lights, Insurmountable Debt, and the Affordances of its Erasure
By Justin Martin In The Dawn of Everything: A New History of Humanity (2021), anthropologist David Graeber and Archeologist David Wengrow set out to offer a new way of thinking about the human orig…

"We are NIH people and we are horrified about the takeover of US scientific and medical research and the damage to future cures for Alzheimer’s and cancer.
But we also believe in human rights and keeping people from starving. The DOGE kids are in far over their heads and they will pay for this." From:
https://bsky.app/profile/altnih4science.bsky.social/post/3lpiquozhsk22

Das ging gerade an die CDU-Bundestagsfraktion. Und mit der zusätzlichen Frage, wo die Gegenwehr und das Rückgrat der SPD bleibt, auch an diese.

At Cannes, YouTube CEO Neal Mohan says YouTube plans to integrate Google's most advanced AI video generation tool, Veo 3, into YouTube Shorts later this summer (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

YouTube to Add Google’s Veo 3 to Shorts in Move That Could Turbocharge AI on the Video Platform
The Veo 3 video generator is capable of creating both videos and sound based on text prompts, with YouTube CEO Neal Mohan saying the "AI technology will push the limits of human creativity."

Can anyone describe to me the last time they actually used a human-readable sitemap page on a website?
What were you looking for? Why did the menu system not give this to you?
#webdev

Isolating Noisy Labelled Test Cases in Human-in-the-Loop Oracle Learning
Charaka Geethal Kapugama
https://arxiv.org/abs/2506.13273 https://

Isolating Noisy Labelled Test Cases in Human-in-the-Loop Oracle Learning
Incorrectly labelled test cases can adversely affect the training process of human-in-the-loop oracle learning tech-niques. This paper introduces ISONOISE, a technique designed to identify such mislabelled test cases introduced during human-in-the-loop oracle learning. This technique can be applied to programs taking numeric inputs. Given a compromised automatic test oracle and its training test suite, ISONOISE first isolates thetest cases suspected of being mislabelled. This task is performed …

Re: Niri WM
So yes:
- You can open applications on specific workspaces and switch focus there immediately
- Not only dynamic, but also 'declared' workspaces are possible (I've got my 9 easily set up)
- There is a one-stop config file that's human readable
- The wiki will have some quirks probably, but in general very clear and quite extensive already
- My favorite tool: 'niri validate', super simple double-check if your config will w…

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
Francesco Chiossi, Julian Rasch, Robin Welsch, Albrecht Schmidt, Florian Michahelles
https://arxiv.org/abs/2506.15293

Designing Intent: A Multimodal Framework for Human-Robot Cooperation in Industrial Workspaces
As robots enter collaborative workspaces, ensuring mutual understanding between human workers and robotic systems becomes a prerequisite for trust, safety, and efficiency. In this position paper, we draw on the cooperation scenario of the AIMotive project in which a human and a cobot jointly perform assembly tasks to argue for a structured approach to intent communication. Building on the Situation Awareness-based Agent Transparency (SAT) framework and the notion of task abstraction levels, we …

El Salvador arrests prominent human rights lawyer who defends deportees | Human Rights News | Al Jazeera
https://www.aljazeera.com/news/2025/5/19/el-salvador-arrests-prominent-human-rights-lawyer-who-defends-deportees…

El Salvador arrests prominent human rights lawyer who defends deportees
Ruth Eleonora López has defended Venezuelan immigrants deported to El Salvador by US President Trump’s administration.

"These evangelical men saved sex for marriage – they weren’t well prepared"
In the meantime the females didn't stay home by the fire!
https://www.theguardian.com/lifeandstyle/2025/jun/17/evangelical-sex-marriage

"Kids have a right to education" doesn't mean "right to learn to create content efficiently". It means the right to an environment and relationships and mentoring and resources that allow them to become their full self. It's about the human and not any output. FFS.

Human Locomotion Implicit Modeling Based Real-Time Gait Phase Estimation
Yuanlong Ji, Xingbang Yang, Ruoqi Zhao, Qihan Ye, Quan Zheng, Yubo Fan
https://arxiv.org/abs/2506.15150

Human Locomotion Implicit Modeling Based Real-Time Gait Phase Estimation
Gait phase estimation based on inertial measurement unit (IMU) signals facilitates precise adaptation of exoskeletons to individual gait variations. However, challenges remain in achieving high accuracy and robustness, particularly during periods of terrain changes. To address this, we develop a gait phase estimation neural network based on implicit modeling of human locomotion, which combines temporal convolution for feature extraction with transformer layers for multi-channel information fusi…

ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Yufeng Zhang, Wenrui Dai, Hang Yu, Shizhan Liu, Junhui Hou, Jianguo Li, Weiyao Lin
https://arxiv.org/abs/2506.15228

ABC: Adaptive BayesNet Structure Learning for Computational Scalable Multi-task Image Compression
Neural Image Compression (NIC) has revolutionized image compression with its superior rate-distortion performance and multi-task capabilities, supporting both human visual perception and machine vision tasks. However, its widespread adoption is hindered by substantial computational demands. While existing approaches attempt to address this challenge through module-specific optimizations or pre-defined complexity levels, they lack comprehensive control over computational complexity. We present A…

Coming tomorrow: a new song (preview from my upcoming folksong album) and song video
#MusicVideo
Watch for it on https://muz4now.com/?utm_source=dl…

Stan Stewart - aka @muz4now - the muse is present
Stan Stewart is a poet, musician, and human being who enjoys connecting with people and nature. Since his focus is on improvisational music, the inspiration he gets from these connections is often the source of his music.

Better weather today 😀 Doing the running belt with water now too, sucks to be without hydration even on these short runs in higher temps. Also made a friend (non-human) 😏
#running #runnersofmastodon

“Apocalypse forces us to radically change. But by facing the future with optimism instead of doom, we can transform ourselves into the kinds of people-the kinds of communities—who can survive.”
https://slate.com/technology/2025/05/h

interactome_stelzl: Stelzl human interactome (2005)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred via high-throughput Y2H experiments using bait and prey methodology.
This network has 1706 nodes and 6207 edges.
Tags: Biological, Protein interactions, Unweighted

Two things that @… made me realise with this latest dispatch:
1. Saying an LLM can replace a human is nonsense for many reasons, but one is that LLMs are generic, whereas a human is specific. They’re bad at chess, and so is the average human, but you don’t hire the average human to do a job, you hire a skilled one

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Junyan Jiang, Daniel Chin, Liwei Lin, Xuanjie Liu, Gus Xia
https://arxiv.org/abs/2506.15548

Versatile Symbolic Music-for-Music Modeling via Function Alignment
Many music AI models learn a map between music content and human-defined labels. However, many annotations, such as chords, can be naturally expressed within the music modality itself, e.g., as sequences of symbolic notes. This observation enables both understanding tasks (e.g., chord recognition) and conditional generation tasks (e.g., chord-conditioned melody generation) to be unified under a music-for-music sequence modeling paradigm. In this work, we propose parameter-efficient solutions fo…

Kann euch das Buch "Opus. The Cult of Dark Money, Human Trafficking, and Right-Wing Conspiracy inside the Catholic Church" von Gareth Gore empfehlen. Sehr lesenswert. Der Zusammenhang zwischen der Besetzung des US Supreme Court und Opus Dei war mir bspw. völlig neu.
Tatsächlich sehe ich nach dem Buch Papst Benedikt XVI in einem etwas positiveren Licht und Johannes Paul II in einem noch negativeren.

Mixed Traffic: A Perspective from Long Duration Autonomy
Filippos Tzortzoglou, Logan E. Beaver
https://arxiv.org/abs/2506.15004 https://

Mixed Traffic: A Perspective from Long Duration Autonomy
The rapid adoption of autonomous vehicle has established mixed traffic environments, comprising both autonomous and human-driven vehicles (HDVs), as essential components of next-generation mobility systems. Along these lines, connectivity between autonomous vehicles and infrastructure (V2I) is also a significant factor that can effectively support higher-level decision-making. At the same time, the integration of V2I within mixed traffic environments remains a timely and challenging problem. In…

AutoSAS: a new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation
Duncan R. Sutherland, Rachel Ford, Yun Liu, Tyler B. Martin, Peter A. Beaucage
https://arxiv.org/abs/2506.13918

AutoSAS: a new human-aside-the-loop paradigm for automated SAS fitting for high throughput and autonomous experimentation
The advancement of artificial-intelligence driven autonomous experiments demands physics-based modeling and decision-making processes, not only to improve the accuracy of the experimental trajectory but also to increase trust by allowing transparent human-machine collaboration. High-quality structural characterization techniques (e.g., X-ray, neutron, or static light scattering) are a particularly relevant example of this need: they provide invaluable information but are challenging to analyze …

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Yuto Harada, Yusuke Yamauchi, Yusuke Oda, Yohei Oseki, Yusuke Miyao, Yu Takagi
https://arxiv.org/abs/2506.14681

Massive Supervised Fine-tuning Experiments Reveal How Data, Layer, and Training Factors Shape LLM Alignment Quality
Supervised fine-tuning (SFT) is a critical step in aligning large language models (LLMs) with human instructions and values, yet many aspects of SFT remain poorly understood. We trained a wide range of base models on a variety of datasets including code generation, mathematical reasoning, and general-domain tasks, resulting in 1,000+ SFT models under controlled conditions. We then identified the dataset properties that matter most and examined the layer-wise modifications introduced by SFT. Our…

Object personification in #autism: This paper will be very sad if you don't read it - PubMed
https://pubmed.ncbi.nlm.nih.gov/30101594/
Object personification is the attrib…

At Cannes, YouTube CEO Neal Mohan says YouTube plans to integrate Google's most advanced AI video generation tool, Veo 3, into YouTube Shorts later this summer (Alex Weprin/The Hollywood Reporter)
https://www.hollywoodreporter.com/business

YouTube to Add Google’s Veo 3 to Shorts in Move That Could Turbocharge AI on the Video Platform
The Veo 3 video generator is capable of creating both videos and sound based on text prompts, with YouTube CEO Neal Mohan saying the "AI technology will push the limits of human creativity."

More Montessori:
❝Imagination does not become great until human beings, given the courage and the strength, use it to create.❞
And here’s a kicker:
❝Establishing lasting peace is the work of education; all politics can do is keep us out of war.❞
Put that last thought in the context of arts and education being deeply intertwined, and oligarchy seeking to dismantle both, and…well….
/end

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places
Mohammad Hashemi, Andreas Zufle
https://arxiv.org/abs/2506.14570

From Points to Places: Towards Human Mobility-Driven Spatiotemporal Foundation Models via Understanding Places
Capturing human mobility is essential for modeling how people interact with and move through physical spaces, reflecting social behavior, access to resources, and dynamic spatial patterns. To support scalable and transferable analysis across diverse geographies and contexts, there is a need for a generalizable foundation model for spatiotemporal data. While foundation models have transformed language and vision, they remain limited in handling the unique challenges posed by the spatial, tempora…

If you are in #Oslo remember to visit #Monolitten. I frequently do.
#Unicycle #Enhjulssykkel

Blue Lights, Insurmountable Debt, and the Affordances of its Erasure https://popularcultureandtheology.com/2025/06/20/blue-lights-insurmountable-debt-and-the-affordances-of-its-erasure/

Blue Lights, Insurmountable Debt, and the Affordances of its Erasure
By Justin Martin In The Dawn of Everything: A New History of Humanity (2021), anthropologist David Graeber and Archeologist David Wengrow set out to offer a new way of thinking about the human orig…

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Festus Adedoyin, Huseyin Dogan
https://arxiv.org/abs/2506.15325

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Advancements in Artificial Intelligence (AI) have significantly transformed the financial industry, enabling the development of more personalised and adaptable financial products and services. This research paper explores various instances where Human-Centred AI (HCAI) has facilitated these advancements, drawing from contemporary studies and industry progress. The paper examines how the application of HCAI-powered data analytics, machine learning, and natural language processing enables financi…

"Only two years left of world’s carbon budget to meet 1.5C target, scientists warn"
#Climate #ClimateChange #ParisAgreement

PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
Ming Xu, Xu Zhang
https://arxiv.org/abs/2506.14596 https://

PoseGRAF: Geometric-Reinforced Adaptive Fusion for Monocular 3D Human Pose Estimation
Existing monocular 3D pose estimation methods primarily rely on joint positional features, while overlooking intrinsic directional and angular correlations within the skeleton. As a result, they often produce implausible poses under joint occlusions or rapid motion changes. To address these challenges, we propose the PoseGRAF framework. We first construct a dual graph convolutional structure that separately processes joint and bone graphs, effectively capturing their local dependencies. A Cross…

Which Humans? Inclusivity and Representation in Human-Centered AI
Rada Mihalcea, Nazanin Andalibi, David Jensen, Matthew Turk, Pamela Wisniewski, Holly Yanco
https://arxiv.org/abs/2506.14680

Which Humans? Inclusivity and Representation in Human-Centered AI
As AI systems continue to spread and become integrated into many aspects of society, the concept of "human-centered AI" has gained increasing prominence, raising the critical question of which humans are the AI systems to be centered around.

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
Caio C. G. Ribeiro, Douglas G. Macharet
https://arxiv.org/abs/2506.13937

Beyond the Plane: A 3D Representation of Human Personal Space for Socially-Aware Robotics
The increasing presence of robots in human environments requires them to exhibit socially appropriate behavior, adhering to social norms. A critical aspect in this context is the concept of personal space, a psychological boundary around an individual that influences their comfort based on proximity. This concept extends to human-robot interaction, where robots must respect personal space to avoid causing discomfort. While much research has focused on modeling personal space in two dimensions, …

In 1967
artist Michael Bowen advertised
"A Gathering of Tribes for the Human Be-In"
in the underground newspaper the San Francisco Oracle
to bring together the "tribes" of psychedelic San Francisco hippies and Berkeley anti-war activists.
https://exhibits.library.uc…

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Xiao Wang, Junru Yu, Jun Huang, Qiong Wu, Ljubo Vacic, Changyin Sun
https://arxiv.org/abs/2506.14502

Toward Safety-First Human-Like Decision Making for Autonomous Vehicles in Time-Varying Traffic Flow
Despite the recent advancements in artificial intelligence technologies have shown great potential in improving transport efficiency and safety, autonomous vehicles(AVs) still face great challenge of driving in time-varying traffic flow, especially in dense and interactive situations. Meanwhile, human have free wills and usually do not make the same decisions even situate in the exactly same scenarios, leading to the data-driven methods suffer from poor migratability and high search cost proble…

interactome_figeys: Figeys human interactome (2007)
A network of human proteins and their binding interactions. Nodes represent proteins and an edge represents an interaction between two proteins, as inferred using a mass spectrometry‐based approach.
This network has 2239 nodes and 6452 edges.
Tags: Biological, Protein interactions, Unweighted

WebXAII: an open-source web framework to study human-XAI interaction
Jules Leguy, Pierre-Antoine Jean, Felipe Torres Figueroa, S\'ebastien Harispe
https://arxiv.org/abs/2506.14777

WebXAII: an open-source web framework to study human-XAI interaction
This article introduces WebXAII, an open-source web framework designed to facilitate research on human interaction with eXplainable Artificial Intelligence (XAI) systems. The field of XAI is rapidly expanding, driven by the growing societal implications of the widespread adoption of AI (and in particular machine learning) across diverse applications. Researchers who study the interaction between humans and XAI techniques typically develop ad hoc interfaces in order to conduct their studies. The…

Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
Qingming Liu, Zhen Liu, Dinghuai Zhang, Kui Jia
https://arxiv.org/abs/2506.15684

Nabla-R2D3: Effective and Efficient 3D Diffusion Alignment with 2D Rewards
Generating high-quality and photorealistic 3D assets remains a longstanding challenge in 3D vision and computer graphics. Although state-of-the-art generative models, such as diffusion models, have made significant progress in 3D generation, they often fall short of human-designed content due to limited ability to follow instructions, align with human preferences, or produce realistic textures, geometries, and physical attributes. In this paper, we introduce Nabla-R2D3, a highly effective and s…

Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
Amirreza Payandeh, Anuj Pokhrel, Daeun Song, Marcos Zampieri, Xuesu Xiao
https://arxiv.org/abs/2506.14233

Narrate2Nav: Real-Time Visual Navigation with Implicit Language Reasoning in Human-Centric Environments
Large Vision-Language Models (VLMs) have demonstrated potential in enhancing mobile robot navigation in human-centric environments by understanding contextual cues, human intentions, and social dynamics while exhibiting reasoning capabilities. However, their computational complexity and limited sensitivity to continuous numerical data impede real-time performance and precise motion control. To this end, we propose Narrate2Nav, a novel real-time vision-action model that leverages a novel self-su…

"I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergence
Naba Rizvi, Taggert Smith, Tanvi Vidyala, Mya Bolds, Harper Strickland, Andrew Begel, Rua Williams, Imani Munyaka
https://arxiv.org/abs/2506.12098

"I Hadn't Thought About That": Creators of Human-like AI Weigh in on Ethics And Neurodivergence
Human-like AI agents such as robots and chatbots are becoming increasingly popular, but they present a variety of ethical concerns. The first concern is in how we define humanness, and how our definition impacts communities historically dehumanized by scientific research. Autistic people in particular have been dehumanized by being compared to robots, making it even more important to ensure this marginalization is not reproduced by AI that may promote neuronormative social behaviors. Second, th…

Gwen Snyder posted something on Saturday to the effect that “Fascists •need• the appearance of strength, and right now, Trump just looks weak.”
I think it’s easy for us evidence-hungry thinker types to underestimate just how much appearance matters. Massive rallies are not — •must• not be — the endpoint of resistance. However, neither are they purely symbolic. They have a real and concrete effect. They change perceptions, which change human behavior. Yes, they matter. They matter a lot.
/end

EmojiVoice: Towards long-term controllable expressivity in robot speech
Paige Tutt\"os\'i, Shivam Mehta, Zachary Syvenky, Bermet Burkanova, Gustav Eje Henter, Angelica Lim
https://arxiv.org/abs/2506.15085

EmojiVoice: Towards long-term controllable expressivity in robot speech
Humans vary their expressivity when speaking for extended periods to maintain engagement with their listener. Although social robots tend to be deployed with ``expressive'' joyful voices, they lack this long-term variation found in human speech. Foundation model text-to-speech systems are beginning to mimic the expressivity in human speech, but they are difficult to deploy offline on robots. We present EmojiVoice, a free, customizable text-to-speech (TTS) toolkit that allows social roboticists …

Foundation of Affective Computing and Interaction
Changzeng Fu
https://arxiv.org/abs/2506.15497 https://arxiv.org/pdf/2506.15497

Foundation of Affective Computing and Interaction
This book provides a comprehensive exploration of affective computing and human-computer interaction technologies. It begins with the historical development and basic concepts of human-computer interaction, delving into the technical frameworks and practical applications of emotional computing, visual interaction, voice interaction, brain-computer interfaces, physiological electrical signal analysis, and social robotics. The book covers a wide range of topics, including the psychological and ne…

In an Oxford study, LLMs correctly identified medical conditions 94.9% of the time when given test scenarios directly, vs. 34.5% when prompted by human subjects (Nick Mokey/VentureBeat)
https://venturebeat.com/ai/just-add-hu

Just add humans: Oxford medical study underscores the missing link in chatbot testing
Patients using chatbots to assess their own medical conditions may end up with worse outcomes than conventional methods, according to a new Oxford study.

Six-DoF Hand-Based Teleoperation for Omnidirectional Aerial Robots
Jinjie Li, Jiaxuan Li, Kotaro Kaneko, Liming Shu, Moju Zhao
https://arxiv.org/abs/2506.15009

Six-DoF Hand-Based Teleoperation for Omnidirectional Aerial Robots
Omnidirectional aerial robots offer full 6-DoF independent control over position and orientation, making them popular for aerial manipulation. Although advancements in robotic autonomy, operating by human remains essential in complex aerial environments. Existing teleoperation approaches for multirotors fail to fully leverage the additional DoFs provided by omnidirectional rotation. Additionally, the dexterity of human fingers should be exploited for more engaged interaction. In this work, we p…

Exploring MLLMs Perception of Network Visualization Principles
Jacob Miller, Markus Wallinger, Ludwig Felder, Timo Brand, Henry F\"orster, Johannes Zink, Chunyang Chen, Stephen Kobourov
https://arxiv.org/abs/2506.14611

Exploring MLLMs Perception of Network Visualization Principles
In this paper, we test whether Multimodal Large Language Models (MLLMs) can match human-subject performance in tasks involving the perception of properties in network layouts. Specifically, we replicate a human-subject experiment about perceiving quality (namely stress) in network layouts using GPT-4o and Gemini-2.5. Our experiments show that giving MLLMs exactly the same study information as trained human participants results in a similar performance to human experts and exceeds the performanc…

Machine Mirages: Defining the Undefined
Hamidou Tembine
https://arxiv.org/abs/2506.13990 https://arxiv.org/pdf/2506.13990

Machine Mirages: Defining the Undefined
As multimodal machine intelligence systems started achieving average animal-level and average human-level fluency in many measurable tasks in processing images, language, and sound, they began to exhibit a new class of cognitive aberrations: machine mirages. These include delusion, illusion, confabulation, hallucination, misattribution error, semantic drift, semantic compression, exaggeration, causal inference failure, uncanny valley of perception, bluffing-patter-bullshitting, cognitive stereo…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- Implementation Considerations for Automated AI Grading of Student Work
Zewei (Victor), Tian, Alex Liu, Lief Esbenshade, Shawon Sarkar, Zachary Zhang, Kevin He, Min Sun

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Huihan Liu, Rutav Shah, Shuijing Liu, Jack Pittenger, Mingyo Seo, Yuchen Cui, Yonatan Bisk, Roberto Mart\'in-Mart\'in, Yuke Zhu
https://arxiv.org/abs/2506.14727

Casper: Inferring Diverse Intents for Assistive Teleoperation with Vision Language Models
Assistive teleoperation, where control is shared between a human and a robot, enables efficient and intuitive human-robot collaboration in diverse and unstructured environments. A central challenge in real-world assistive teleoperation is for the robot to infer a wide range of human intentions from user control inputs and to assist users with correct actions. Existing methods are either confined to simple, predefined scenarios or restricted to task-specific data distributions at training, limit…

Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Xinglei Wang, Tao Cheng, Stephen Law, Zichao Zeng, Ilya Ilyankou, Junyuan Liu, Lu Yin, Weiming Huang, Natchapon Jongwiriyanurak
https://arxiv.org/abs/2506.14070…

Into the Unknown: Applying Inductive Spatial-Semantic Location Embeddings for Predicting Individuals' Mobility Beyond Visited Places
Predicting individuals' next locations is a core task in human mobility modelling, with wide-ranging implications for urban planning, transportation, public policy and personalised mobility services. Traditional approaches largely depend on location embeddings learned from historical mobility patterns, limiting their ability to encode explicit spatial information, integrate rich urban semantic context, and accommodate previously unseen locations. To address these challenges, we explore the appl…

Design an Editable Speech-to-Sign-Language Transformer System: A Human-Centered AI Approach
Yingchao Li
https://arxiv.org/abs/2506.14677 https://

Design an Editable Speech-to-Sign-Language Transformer System: A Human-Centered AI Approach
This paper presents a human-centered, real-time, user-adaptive speech-to-sign language animation system that integrates Transformer-based motion generation with a transparent, user-editable JSON intermediate layer. The framework overcomes key limitations in prior sign language technologies by enabling direct user inspection and modification of sign segments, thus enhancing naturalness, expressiveness, and user agency. Leveraging a streaming Conformer encoder and autoregressive Transformer-MDN d…

Context Matters: Learning Generalizable Rewards via Calibrated Features
Alexandra Forsey-Smerek, Julie Shah, Andreea Bobu
https://arxiv.org/abs/2506.15012 …

Context Matters: Learning Generalizable Rewards via Calibrated Features
A key challenge in reward learning from human input is that desired agent behavior often changes based on context. Traditional methods typically treat each new context as a separate task with its own reward function. For example, if a previously ignored stove becomes too hot to be around, the robot must learn a new reward from scratch, even though the underlying preference for prioritizing safety over efficiency remains unchanged. We observe that context influences not the underlying preference…

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Xinyang Li, Siqi Liu, Bochao Zou, Jiansheng Chen, Huimin Ma
https://arxiv.org/abs/2506.14224

From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
As large language models evolve, there is growing anticipation that they will emulate human-like Theory of Mind (ToM) to assist with routine tasks. However, existing methods for evaluating machine ToM focus primarily on unimodal models and largely treat these models as black boxes, lacking an interpretative exploration of their internal mechanisms. In response, this study adopts an approach based on internal mechanisms to provide an interpretability-driven assessment of ToM in multimodal large …

[2025-06-19 Thu (UTC), 20 new articles found for cs.HC Human-Computer Interaction]
toXiv_bot_toot

3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception
Yuankai Lin, Xiaofan Lu, Jiahui Chen, Hua Yang
https://arxiv.org/abs/2506.15087

3D Vision-tactile Reconstruction from Infrared and Visible Images for Robotic Fine-grained Tactile Perception
To achieve human-like haptic perception in anthropomorphic grippers, the compliant sensing surfaces of vision tactile sensor (VTS) must evolve from conventional planar configurations to biomimetically curved topographies with continuous surface gradients. However, planar VTSs have challenges when extended to curved surfaces, including insufficient lighting of surfaces, blurring in reconstruction, and complex spatial boundary conditions for surface structures. With an end goal of constructing a …

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Boshen Shi, Yongqing Wang, Fangda Guo, Jiangli Shao, Huawei Shen, Xueqi Cheng
https://arxiv.org/abs/2506.13795

BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection
Transferring extensive knowledge from relevant social networks has emerged as a promising solution to overcome label scarcity in detecting social bots and other anomalies with GNN-based models. However, effective transfer faces two critical challenges. Firstly, the network heterophily problem, which is caused by bots hiding malicious behaviors via indiscriminately interacting with human users, hinders the model's ability to learn sufficient and accurate bot-related knowledge from source domains…

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Hanbit Oh, Andrea M. Salcedo-V\'azquez, Ixchel G. Ramirez-Alpizar, Yukiyasu Domae
https://arxiv.org/abs/2506.15157

Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation
Imitation learning (IL) aims to enable robots to perform tasks autonomously by observing a few human demonstrations. Recently, a variant of IL, called In-Context IL, utilized off-the-shelf large language models (LLMs) as instant policies that understand the context from a few given demonstrations to perform a new task, rather than explicitly updating network models with large-scale demonstrations. However, its reliability in the robotics domain is undermined by hallucination issues such as LLM-…

See What I Mean? CUE: A Cognitive Model of Understanding Explanations
Tobias Labarta, Nhi Hoang, Katharina Weitz, Wojciech Samek, Sebastian Lapuschkin, Leander Weber
https://arxiv.org/abs/2506.14775

See What I Mean? CUE: A Cognitive Model of Understanding Explanations
As machine learning systems increasingly inform critical decisions, the need for human-understandable explanations grows. Current evaluations of Explainable AI (XAI) often prioritize technical fidelity over cognitive accessibility which critically affects users, in particular those with visual impairments. We propose CUE, a model for Cognitive Understanding of Explanations, linking explanation properties to cognitive sub-processes: legibility (perception), readability (comprehension), and inter…

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
Lingfeng Li, Yunlong Lu, Yongyi Wang, Qifan Zheng, Wenxin Li
https://arxiv.org/abs/2506.14246

Mxplainer: Explain and Learn Insights by Imitating Mahjong Agents
People need to internalize the skills of AI agents to improve their own capabilities. Our paper focuses on Mahjong, a multiplayer game involving imperfect information and requiring effective long-term decision-making amidst randomness and hidden information. Through the efforts of AI researchers, several impressive Mahjong AI agents have already achieved performance levels comparable to those of professional human players; however, these agents are often treated as black boxes from which few in…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Human-Robot Co-Transportation using Disturbance-Aware MPC with Pose Optimization
Al Jaber Mahmud, Amir Hossain Raj, Duc M. Nguyen, Weizi Li, Xuesu Xiao, Xuan Wang

Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
Bernhard Hilpert, Muhan Hou, Kim Baraka, Joost Broekens
https://arxiv.org/abs/2506.13583

Can you see how I learn? Human observers' inferences about Reinforcement Learning agents' learning processes
Reinforcement Learning (RL) agents often exhibit learning behaviors that are not intuitively interpretable by human observers, which can result in suboptimal feedback in collaborative teaching settings. Yet, how humans perceive and interpret RL agent's learning behavior is largely unknown. In a bottom-up approach with two experiments, this work provides a data-driven understanding of the factors of human observers' understanding of the agent's learning process. A novel, observation-based paradi…

Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Mohna Chakraborty, Lu Wang, David Jurgens
https://arxiv.org/abs/2506.14948

Structured Moral Reasoning in Language Models: A Value-Grounded Evaluation Framework
Large language models (LLMs) are increasingly deployed in domains requiring moral understanding, yet their reasoning often remains shallow, and misaligned with human reasoning. Unlike humans, whose moral reasoning integrates contextual trade-offs, value systems, and ethical theories, LLMs often rely on surface patterns, leading to biased decisions in morally and ethically complex scenarios. To address this gap, we present a value-grounded framework for evaluating and distilling structured moral…

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Zhirui Sun, Xingrong Diao, Yao Wang, Bi-Ke Zhu, Jiankun Wang
https://arxiv.org/abs/2506.14305

Socially Aware Robot Crowd Navigation via Online Uncertainty-Driven Risk Adaptation
Navigation in human-robot shared crowded environments remains challenging, as robots are expected to move efficiently while respecting human motion conventions. However, many existing approaches emphasize safety or efficiency while overlooking social awareness. This article proposes Learning-Risk Model Predictive Control (LR-MPC), a data-driven navigation algorithm that balances efficiency, safety, and social awareness. LR-MPC consists of two phases: an offline risk learning phase, where a Prob…

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
Gaze-informed Signatures of Trust and Collaboration in Human-Autonomy Teams

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Hexian Ni, Tao Lu, Haoyuan Hu, Yinghao Cai, Shuo Wang
https://arxiv.org/abs/2506.14648

SENIOR: Efficient Query Selection and Preference-Guided Exploration in Preference-based Reinforcement Learning
Preference-based Reinforcement Learning (PbRL) methods provide a solution to avoid reward engineering by learning reward models based on human preferences. However, poor feedback- and sample- efficiency still remain the problems that hinder the application of PbRL. In this paper, we present a novel efficient query selection and preference-guided exploration method, called SENIOR, which could select the meaningful and easy-to-comparison behavior segment pairs to improve human feedback-efficiency…

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
Ryota Okumura, Tadahiro Taniguchi, Akira Taniguchi, Yoshinobu Hagiwara
https://arxiv.org/abs/2506.15468

Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI
We propose co-creative learning as a novel paradigm where humans and AI, i.e., biological and artificial agents, mutually integrate their partial perceptual information and knowledge to construct shared external representations, a process we interpret as symbol emergence. Unlike traditional AI teaching based on unilateral knowledge transfer, this addresses the challenge of integrating information from inherently different modalities. We empirically test this framework using a human-AI interacti…

StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
Jina Kim, Leeje Jang, Yao-Yi Chiang, Guanyu Wang, Michelle Pasco
https://arxiv.org/abs/2506.14670

StreetLens: Enabling Human-Centered AI Agents for Neighborhood Assessment from Street View Imagery
Traditionally, neighborhood studies have employed interviews, surveys, and manual image annotation guided by detailed protocols to identify environmental characteristics, including physical disorder, decay, street safety, and sociocultural symbols, and to examine their impact on developmental and health outcomes. While these methods yield rich insights, they are time-consuming and require intensive expert intervention. Recent technological advances, including vision-language models (VLMs), have…

The Rise of AI Companions: How Human-Chatbot Relationships Influence Well-Being
Yutong Zhang, Dora Zhao, Jeffrey T. Hancock, Robert Kraut, Diyi Yang
https://arxiv.org/abs/2506.12605

The Rise of AI Companions: How Human-Chatbot Relationships Influence Well-Being
As large language models (LLMs)-enhanced chatbots grow increasingly expressive and socially responsive, many users are beginning to form companionship-like bonds with them, particularly with simulated AI partners designed to mimic emotionally attuned interlocutors. These emerging AI companions raise critical questions: Can such systems fulfill social needs typically met by human relationships? How do they shape psychological well-being? And what new risks arise as users develop emotional ties t…