Tootfinder

from my link log —
Designing APIs for humans: Stripe object IDs.
https://dev.to/stripe/designing-apis-for-humans-object-ids-3o5a
saved 2025-05-22

Designing APIs for humans: Object IDs
Choosing your ID type Regardless of what type of business you run, you very likely require...

Q&A with Google DeepMind CEO Demis Hassabis on "a 50% chance" of AGI in the next five to 10 years, bad actors and technical risks, AI regulation, jobs, and more (Steven Levy/Wired)
https://www.wired.com/story/google-deepmin

Google DeepMind’s CEO Thinks AI Will Make Humans Less Selfish
Demis Hassabis says that systems as smart as humans are almost here, and we’ll need to radically change how we think and behave.

Curious humpback whales approach humans and blow bubble 'smoke' rings
https://phys.org/news/2025-06-curious-humpback-whales-approach-humans.html

Really cool video about why the video games industry is struggling: everybody has to compete with addictive social media for eyeballs and time. And unless whole new markets are opened up (humans are not born quickly enough) there's just no longer a way to create exponential growth. But billionaire investors need that. That's why they are rather investing in AI.
By the way, this is the same reason that cinemas have gotten in trouble (and now even streaming services...)

❝We humans are stability-seeking creatures. Getting accustomed to what used to seem unthinkable can feel like an accomplishment. And when the unthinkable recedes at least a bit…it’s easy to mistake it for proof that the dark times are ending.
But these comparatively small victories don’t alter the direction of our transformation — they don’t even slow it down measurably — even while they appeal to our deep need to normalize.…And so just when we most need to act — while there is indeed room for action and some momentum to the resistance — we tend to be lulled into complacency by the sense of relief on the one hand and boredom on the other.❞
https://www.nytimes.com/2025/05/28/opinion/trump-danger-normalization-shock.html

So I've found my answer after maybe ~30 minutes of effort. First stop was the first search result on Startpage (https://millennialhawk.com/does-poop-have-calories/), which has some evidence of maybe-AI authorship but which is better than a lot of slop. It actually has real links & cites research, so I'll start by looking at the sources.
It claims near the top that poop contains 4.91 kcal per gram (note: 1 kcal = 1 Calorie = 1000 calories, which fact I could find/do trust despite the slop in that search). Now obviously, without a range or mention of an average, this isn't the whole picture, but maybe it's an average to start from? However, the citation link is to a study (https://pubmed.ncbi.nlm.nih.gov/32235930/) which only included 27 people with impaired glucose tolerance and obesity. Might have the cited stat, but it's definitely not a broadly representative one if this is the source. The public abstract does not include the stat cited, and I don't want to pay for the article. I happen to be affiliated with a university library, so I could see if I have access that way, but it's a pain to do and not worth it for this study that I know is too specific. Also most people wouldn't have access that way.
Side note: this doing-the-research protect has the nice benefit of letting you see lots of cool stuff you wouldn't have otherwise. The abstract of this study is pretty cool and I learned a bit about gut microbiome changes from just reading the abstract.
My next move was to look among citations in this article to see if I could find something about calorie content of poop specifically. Luckily the article page had indicators for which citations were free to access. I ended up reading/skimming 2 more articles (a few more interesting facts about gut microbiomes were learned) before finding this article whose introduction has what I'm looking for: https://pmc.ncbi.nlm.nih.gov/articles/PMC3127503/
Here's the relevant paragraph:
"""
The alteration of the energy-balance equation, which is defined by the equilibrium of energy intake and energy expenditure (1–5), leads to weight gain. One less-extensively-studied component of the energy-balance equation is energy loss in stools and urine. Previous studies of healthy adults showed that ≈5% of ingested calories were lost in stools and urine (6). Individuals who consume high-fiber diets exhibit a higher fecal energy loss than individuals who consume low-fiber diets with an equivalent energy content (7, 8). Webb and Annis (9) studied stool energy loss in 4 lean and 4 obese individuals and showed a tendency to lower the fecal energy excretion in obese compared with lean study participants.
"""
And there's a good-enough answer if we do some math, along with links to more in-depth reading if we want them. A Mayo clinic calorie calculator suggests about 2250 Calories per day for me to maintain my weight, I think there's probably a lot of variation in that number, but 5% of that would be very roughly 100 Calories lost in poop per day, so maybe an extremely rough estimate for a range of humans might be 50-200 Calories per day. Interestingly, one of the AI slop pages I found asserted (without citation) 100-200 Calories per day, which kinda checks out. I had no way to trust that number though, and as we saw with the provenance of the 4.91 kcal/gram, it might not be good provenance.
To double-check, I visited this link from the paragraph above: https://www.sciencedirect.com/science/article/abs/pii/S0022316622169853?via=ihub
It's only a 6-person study, but just the abstract has numbers: ~250 kcal/day pooped on a low-fiber diet vs. ~400 kcal/day pooped on a high-fiber diet. That's with intakes of ~2100 and ~2350 kcal respectively, which is close to the number from which I estimated 100 kcal above, so maybe the first estimate from just the 5% number was a bit low.
Glad those numbers were in the abstract, since the full text is paywalled... It's possible this study was also done on some atypical patient group...
Just to come full circle, let's look at that 4.91 kcal/gram number again. A search suggests 14-16 ounces of poop per day is typical, with at least two sources around 14 ounces, or ~400 grams. (AI slop was strong here too, with one including a completely made up table of "studies" that was summarized as 100-200 grams/day). If we believe 400 grams/day of poop, then 4.91 kcal/gram would be almost 2000 kcal/day, which is very clearly ludicrous! So that number was likely some unrelated statistic regurgitated by the AI. I found that number in at least 3 of the slop pages I waded through in my initial search.

One rule for thee, another for me. #LLM #AI #GenAI

This https://arxiv.org/abs/2505.10661 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

It's only fair when I think it's fair: How Gender Bias Alignment Undermines Distributive Fairness in Human-AI Collaboration
Human-AI collaboration is increasingly relevant in consequential areas where AI recommendations support human discretion. However, human-AI teams' effectiveness, capability, and fairness highly depend on human perceptions of AI. Positive fairness perceptions have been shown to foster trust and acceptance of AI recommendations. Yet, work on confirmation bias highlights that humans selectively adhere to AI recommendations that align with their expectations and beliefs -- despite not being necessa…

You will find no answers to the struggles we face as humans by looking to the machines.
Be eager to say “I don’t know… but I want to find out!”
➡️ https://rasterweb.net/raster/2025/06/02/experts-dont-know/

Experts Don’t Know
Humans don't know it all, and that's good...

Feel the Force: Contact-Driven Learning from Humans
Ademi Adeniji, Zhuoran Chen, Vincent Liu, Venkatesh Pattabiraman, Raunaq Bhirangi, Siddhant Haldar, Pieter Abbeel, Lerrel Pinto
https://arxiv.org/abs/2506.01944

Feel the Force: Contact-Driven Learning from Humans
Controlling fine-grained forces during manipulation remains a core challenge in robotics. While robot policies learned from robot-collected data or simulation show promise, they struggle to generalize across the diverse range of real-world interactions. Learning directly from humans offers a scalable solution, enabling demonstrators to perform skills in their natural embodiment and in everyday environments. However, visual demonstrations alone lack the information needed to infer precise contac…

Researchers in Japan Discover Medicine Capable of Regrowing Third Set of Teeth for Humans - Dentistry Today
https://www.dentistrytoday.com/researchers-in-japan-discover-medicine-capable-of-regrowing-third-set-of-teeth-for-humans/

I've been a Duolingo user since 2013. You can track the enshittification of the product since they went public.
Enshittification is now complete, with humans being replaced by AI. This act of mine is my way of avoiding being treated like a wallet while you mistreat others.
#duolingo #workingClass

Philippe Neau & Antonella Eye Porcelluzzi – Elephant
https://www.clongclongmoo.org/2025/06/05/philippe-neau-antonella-eye-porcelluzzi-elephant/

"The industry perpetuates this state of things, keeping itself in a state of blissful high-hormone idiocy. Software is important, so clearly those who are writing it must be hailed as the holders of some occult knowledge and the purveyors of infinite wisdom. Through bribery, hubris, or ill luck, some of those same assholes find themselves later in management positions, and continue the tradition by hiring more people like themselves, because that is what humans do."

The Insane Cult Of The Asshole
If we had to choose just one profession that falls into the cult of the asshole, software craftsmanship (call it engineering or development) would certainly come to mind. Writing code is ripe to endless, serial, toxic demonstrations of manhood, bundled together with an endless admiration for those historical figures (usually referred to as "pundits" or "moguls") who show certain supposedly manly traits.

Complexity in the Wake of Artificial Intelligence
Theodore Modis
https://arxiv.org/abs/2506.04269 https://arxiv.org/pdf/2506.04269

Complexity in the Wake of Artificial Intelligence
This study aims to evaluate quantitatively, albeit in arbitrary units, the evolution of complexity of the human system since the domestication of fire. This is made possible by studying the timing of the 14 most important milestones, breaks in historical perspective, in the evolution of humans. AI is considered here as the latest such milestone with importance comparable to that of the Internet. The complexity is modeled to have evolved along a bell-shaped curve, reaching a maximum around our t…

Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems
Zoe Pfister
https://arxiv.org/abs/2507.02578 https://

Human-Machine Collaboration and Ethical Considerations in Adaptive Cyber-Physical Systems
Adaptive Cyber-Physical Systems (CPS) are systems that integrate both physical and computational capabilities, which can adjust in response to changing parameters. Furthermore, they increasingly incorporate human-machine collaboration, allowing them to benefit from the individual strengths of humans and machines. Human-Machine Teaming (HMT) represents the most advanced paradigm of human-machine collaboration, envisioning seamless teamwork between humans and machines. However, achieving effectiv…

Speaking off: did those scumbags at the University of Zürich ever face any consequences for their highly unethical work?
https://arstechnica.com/ai/2025/06/reddit-ceo-pledges-site-will-remain-written-by-humans-and-voted-on-by-humans

1945: rent time from humans.
1955: build your own computer!
1965: rent time on an IBM mainframe.
1975: get your own home computer!
1985: rent time on CompuServe.
1995: get a PC with Windows 95!
2005: rent time on AWS.
2015: get an iPhone or an Android!
2025: rent time on ChatGPT.
2035: get your own whatever!

Love this, by D.J. Grothe
We are truly only just getting started. All we have to do is to fail to kill everyone, and things will get better.
"Human civilization has existed for only 3% of the time that anatomically modern humans have existed. And modern industrial civilization has existed for just 2% of that 3% — just 0.06% of the time that anatomically modern humans have existed. Maybe we’re just getting started!"

Towards Human-like Preference Profiling in Sequential Recommendation
Zhongyu Ouyang, Qianlong Wen, Chunhui Zhang, Yanfang Ye, Soroush Vosoughi
https://arxiv.org/abs/2506.02261

Towards Human-like Preference Profiling in Sequential Recommendation
Sequential recommendation systems aspire to profile users by interpreting their interaction histories, echoing how humans make decisions by weighing experience, relative preference strength, and situational relevance. Yet, existing large language model (LLM)-based recommenders often fall short of mimicking the flexible, context-aware decision strategies humans exhibit, neglecting the structured, dynamic, and context-aware mechanisms fundamental to human behaviors. To bridge this gap, we propose…

This https://arxiv.org/abs/2505.17433 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

MemeReaCon: Probing Contextual Meme Understanding in Large Vision-Language Models
Memes have emerged as a popular form of multimodal online communication, where their interpretation heavily depends on the specific context in which they appear. Current approaches predominantly focus on isolated meme analysis, either for harmful content detection or standalone interpretation, overlooking a fundamental challenge: the same meme can express different intents depending on its conversational context. This oversight creates an evaluation gap: although humans intuitively recognize ho…

I always find the use of first person plural pronouns in discussions of the distant future to be too cute…
Humans didn’t exist a million years ago. There were some crafty hominid apes but not the sort you could clean up and mistake for a human.
There is no “us” 7 billion years from now. There is very likely no “us” in half a million years. We may or may not have descendants but they won’t be “us” in any sense.
A billion years is longer than Earth has had visible life

I've been using em dashes since I picked up the LaTeX manual in 1986 and I'm not going back just because some text extruding software uses them more than most humans.
Also, my grandfather was a printer and I knew from an early age that "em" and "en" were legit scrabble words.
#OldManYellsAtCloud

Aktueller Titel: Kalte Nacht – Humans Are Mistakes
#KleineEchos – jetzt live bei https://www.mixcloud.com/live/thopan

THOPAN on Mixcloud Live
Broadcast live to your community of fans and tune in direct to creators from every genre

It's really telling how much of the conversation around AI boils down to,
"Is there any value in humans being able to think?"
Which all too quickly reduces to
"Is there any value in humans” ?
https://bsky.app/profile/kevinriggle.b

My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions
Cathy (Liu), Yang, Kevin Bauer, Xitong Li, Oliver Hinz
https://arxiv.org/abs/2506.03707

My Advisor, Her AI and Me: Evidence from a Field Experiment on Human-AI Collaboration and Investment Decisions
Amid ongoing policy and managerial debates on keeping humans in the loop of AI decision-making, we investigate whether human involvement in AI-based service production benefits downstream consumers. Partnering with a large savings bank in Europe, we produced pure AI and human-AI collaborative investment advice, passed it to customers, and examined their advice-taking in a field experiment. On the production side, contrary to concerns that humans might inefficiently override AI output, we find t…

TRiMM: Transformer-Based Rich Motion Matching for Real-Time multi-modal Interaction in Digital Humans
Yueqian Guo, Tianzhao Li, Xin Lyu, Jiehaolin Chen, Zhaohan Wang, Sirui Xiao, Yurun Chen, Yezi He, Helin Li, Fan Zhang
https://arxiv.org/abs/2506.01077

TRiMM: Transformer-Based Rich Motion Matching for Real-Time multi-modal Interaction in Digital Humans
Large Language Model (LLM)-driven digital humans have sparked a series of recent studies on co-speech gesture generation systems. However, existing approaches struggle with real-time synthesis and long-text comprehension. This paper introduces Transformer-Based Rich Motion Matching (TRiMM), a novel multi-modal framework for real-time 3D gesture generation. Our method incorporates three modules: 1) a cross-modal attention mechanism to achieve precise temporal alignment between speech and gesture…

Amazon deploys 1 millionth robot, nearing point where machines outnumber humans in warehouses https://ground.news/article/amazon-deploys-its-1-millionth-robot-in-a-sign-of-more-job-automation

Misalignment or misuse? The AGI alignment tradeoff
Max Hellrigel-Holderbaum, Leonard Dung
https://arxiv.org/abs/2506.03755 https://ar…

Misalignment or misuse? The AGI alignment tradeoff
Creating systems that are aligned with our goals is seen as a leading approach to create safe and beneficial AI in both leading AI companies and the academic field of AI safety. We defend the view that misaligned AGI - future, generally intelligent (robotic) AI agents - poses catastrophic risks. At the same time, we support the view that aligned AGI creates a substantial risk of catastrophic misuse by humans. While both risks are severe and stand in tension with one another, we show that - in p…

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…

So Builder . ai Mechanical Turked things and Microsoft (and others) were none the wiser.
Due diligence: boring stuff that when you skip it with catch up with you fast.
“Builder . ai’s platform relied on around 700 engineers based in India who manually wrote code based on customer requests. Despite the company marketing it as AI-generated, most of the work was done by humans behind the scenes.”

London AI startup Builder.ai collapses after 'human-powered' tech revelation
London AI startup Builder.ai collapses after 'human-powered' tech revelation

At a "couples retreat" for human-AI pairs, users of services like Replika and Nomi grapple with the virtual reality and emotional limits of their partners (Sam Apple/Wired)
https://www.wired.com/story/couples-retrea

My Couples Retreat With 3 AI Chatbots and the Humans Who Love Them
I found people in serious relationships with AI partners and planned a weekend getaway for them at a remote Airbnb. We barely survived.

Ad on the tube says 'Humans were the beta test. The era of AI employees is here'.
I can't *imagine* why people are a bit resistant to AI! At least offshoring never advertised on the tube. The enshittification of 21st century life continues.

I went to a concentration camp, Neuengamme, yesterday to learn more about the local history. The visit has reinforced my belief that fascism must be stopped at all costs.

#wikifinds

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.

Telescopes on the Andes glimpse elusive encounters fueled by the very first stars in the universe more than 13 billion years ago by detecting cosmic microwave light signals https://www.404media.co/humans-have-now-seen-the-dawn-of-time-from-ear…

Humans Have Now Seen the Dawn of Time from Earth After Breakthrough
Telescopes perched on the Andes Mountains glimpsed elusive encounters fueled by the first of the first stars in the universe more than 13 billion years ago.

A Hierarchical Integer Linear Programming Approach for Optimizing Team Formation in Education
Aaron Kessler, Tim Scheiber, Heinz Schmitz, Ioanna Lykourentzou
https://arxiv.org/abs/2506.02756

A Hierarchical Integer Linear Programming Approach for Optimizing Team Formation in Education
Teamwork is integral to higher education, fostering students' interpersonal skills, improving learning outcomes, and preparing them for professional collaboration later in their careers. While team formation has traditionally been managed by humans, either instructors or students, algorithmic approaches have recently emerged to optimize this process. However, existing algorithmic team formation methods often focus on expert teams, overlook agency in choosing one's teammates, and are limited to …

This https://arxiv.org/abs/2503.02077 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

M3HF: Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality
Designing effective reward functions in multi-agent reinforcement learning (MARL) is a significant challenge, often leading to suboptimal or misaligned behaviors in complex, coordinated environments. We introduce Multi-agent Reinforcement Learning from Multi-phase Human Feedback of Mixed Quality ($\text{M}^3\text{HF}$), a novel framework that integrates multi-phase human feedback of mixed quality into the MARL training process. By involving humans with diverse expertise levels to provide iterat…

Palaeontologists find #collagen #biomarker to identify ancient #Australian #megafauna, notable because it’s more durable…

Paleontologists Find New Biomarkers to Identify Megafauna Species in Australia’s Fossil Record | Sci.News
Paleontologists have identified peptide markers for three species of extinct Australian megafauna -- a hippo-sized wombat, a giant kangaroo, and a marsupial with enormous claws -- opening the way for research which could help us understand how a series of unexplained extinctions of megafauna 50,000 years ago happened, and if humans were responsible.

When humans feel powerless, especially after traumatic events or retraumatization or ~ gestures generally at C-PTSD ~ what often helps is having an area of control over choices, decisions, and outcomes (especially outcomes that have positive side effects like humans liking the action/work/result)
And thus I flew to LA for a weekend and got a tattoo.
#BloomScrolling

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen
https://arxiv.org/abs/2506.01954

DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation
Retrieval-Augmented Generation (RAG) methods have proven highly effective for tasks requiring factual consistency and robust knowledge retrieval. However, large-scale RAG systems consume significant computational resources and are prone to generating hallucinated content from Humans. In this work, we introduce $\texttt{DRAG}$, a novel framework for distilling RAG knowledge from large-scale Language Models (LLMs) into small LMs (SLMs). Our approach leverages evidence- and knowledge graph-based d…

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…

For The Love Of Dogs (And Their Humans!)
Great Australian Pods Podcast Directory: #GreatAusPods

Note that nowhere in that definition is there actually any attempt to define or measure “intelligence” — a term which we are scarcely able to define and to measure even for humans!
Note also that the definition is inherently a broad one and a shifting one. It’s relative to humans •and• relative to recent history.
4/

“As is the case with reading and writing a language, code is one of those things where if you don’t use it, you lose it. Early studies indicate that humans who use A.I. could become less creative over time.”
Early studies link: https://dl.acm.org/doi/abs/10.1145/3706598.3714198

Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff
Sophie Greenwood, Karen Levy, Solon Barocas, Hoda Heidari, Jon Kleinberg
https://arxiv.org/abs/2506.03102

Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff
As AI technologies improve, people are increasingly willing to delegate tasks to AI agents. In many cases, the human decision-maker chooses whether to delegate to an AI agent based on properties of the specific instance of the decision-making problem they are facing. Since humans typically lack full awareness of all the factors relevant to this choice for a given decision-making instance, they perform a kind of categorization by treating indistinguishable instances -- those that have the same o…

There was somebody fussing in my replies to my last link to my blog post about Medium (I don’t see them now; they probably blocked me, but their specific words don’t really matter), and the gist of their message was that they didn’t like that site. On the modern internet, if you have an issue with content written by humans, with no surveillance ads, that doesn’t allow AI scraping or AI slop content, with a business model that makes money… I don’t know how to help you. Honestly.

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Yui Tatsumi, Ziyue Zeng, Hiroshi Watanabe
https://arxiv.org/abs/2506.19297 https://…

Explicit Residual-Based Scalable Image Coding for Humans and Machines
Scalable image compression is a technique that progressively reconstructs multiple versions of an image for different requirements. In recent years, images have increasingly been consumed not only by humans but also by image recognition models. This shift has drawn growing attention to scalable image compression methods that serve both machine and human vision (ICMH). Many existing models employ neural network-based codecs, known as learned image compression, and have made significant strides i…

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 …

#TerryPratchett #MelonHusk

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
Jiahao Huang, Ruifeng Li, Wenwen Yu, Anan Li, Xiangning Li, Mingchao Yan, Lei Xie, Qingrun Zeng, Xueyan Jia, Shuxin Wang, Ronghui Ju, Feng Chen, Qingming Luo, Hui Gong, Xiaoquan Yang, Yuanjing Feng, Zheng Wang
https://arxiv.org/abs/25…

Convergent and divergent connectivity patterns of the arcuate fasciculus in macaques and humans
The organization and connectivity of the arcuate fasciculus (AF) in nonhuman primates remain contentious, especially concerning how its anatomy diverges from that of humans. Here, we combined cross-scale single-neuron tracing - using viral-based genetic labeling and fluorescence micro-optical sectioning tomography in macaques (n = 4; age 3 - 11 years) - with whole-brain tractography from 11.7T diffusion MRI. Complemented by spectral embedding analysis of 7.0T MRI in humans, we performed a compa…

Animals are abused and exploited in various ways for the sake of entertainment. LCA strongly opposes the use of animals in entertainment.
Animals have their own needs, interests, and rights, especially the right to engage in their natural behaviors in their natural habitat. https://www.lcanimal.org/…

Reluctant Interaction Inference after Additive Modeling
Yiling Huang, Snigdha Panigrahi, Guo Yu, Jacob Bien
https://arxiv.org/abs/2506.01219 https://

Reluctant Interaction Inference after Additive Modeling
Additive models enjoy the flexibility of nonlinear models while still being readily understandable to humans. By contrast, other nonlinear models, which involve interactions between features, are not only harder to fit but also substantially more complicated to explain. Guided by the principle of parsimony, a data analyst therefore may naturally be reluctant to move beyond an additive model unless it is truly warranted. To put this principle of interaction reluctance into practice, we formula…

This https://arxiv.org/abs/2308.03734 has been replaced.
link: https://scholar.google.com/scholar?q=a

Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
The entity resolution problem requires finding pairs across datasets that belong to different owners but refer to the same entity in the real world. To train and evaluate solutions (either rule-based or machine-learning-based) to the entity resolution problem, generating a ground truth dataset with entity pairs or clusters is needed. However, such a data annotation process involves humans as domain oracles to review the plaintext data for all candidate record pairs from different parties, which…

Aerones, which makes robots that can service wind turbines in about half the time of humans, raised $62M led by Activate Capital and S2G Investments (Virginia Furness/Reuters)
https://www.reuters.com/sustainability/cli

LLMs exhibit "potemkin understanding"!
Hope the methodology here is better than the last LLM-hater arxiv paper that came through
Must read it more carefully...
https://mathstodon.xyz/@gregeganSF/114758840374128081

Greg Egan (@gregeganSF@mathstodon.xyz)
Attached: 1 image “Potemkin Understanding in Large Language Models” A detailed analysis of the incoherent application of concepts by LLMs, showing how benchmarks that reliably establish domain competence in humans can be passed by LLMs lacking similar competence. H/T @acowley@mastodon.social Link: https://arxiv.org/abs/2506.21521

This https://arxiv.org/abs/2503.08720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"
"why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify that the prevailing vision in the AI community is largely a monoculture that emphasizes objectives such as replacing humans and improving p…

Note that nowhere in that definition is there actually any attempt to define or measure “intelligence” — a term which we are scarcely able to define and to measure even for humans!
Note also that the definition is inherently a broad one and a shifting one. It’s relative to humans •and• relative to recent history.
4/

A controversial new book:
"We are eating the Earth"
says excess carbon dioxide in the atmosphere is a long-term challenge resulting from an otherwise cheerful story,
in which more people live better lives with fuller bellies and bigger dreams.
Lawyer-turned-science-cop Tim Searchinger discovered that the popular carbon solution of 20 years ago,
-- plant-based biofuels,
-- was a disaster in the making.
His insight: Land used to grow fuel w…

There’s new hope that humans will save our planet
‘We Are Eating the Earth,’ argues for producing more food on less land to avert climate crisis.

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity
Yiqun Yao, Xiang Li, Xin Jiang, Xuezhi Fang, Naitong Yu, Aixin Sun, Yequan Wang
https://arxiv.org/abs/2506.01934

RoboEgo System Card: An Omnimodal Model with Native Full Duplexity
Humans naturally process real-world multimodal information in a full-duplex manner. In artificial intelligence, replicating this capability is essential for advancing model development and deployment, particularly in embodied contexts. The development of multimodal models faces two primary challenges: (1) effectively handling more than three modalities-such as vision, audio, and text; and (2) delivering full-duplex responses to rapidly evolving human instructions. To facilitate research on mode…

Requirements Elicitation Follow-Up Question Generation
Yuchen Shen, Anmol Singhal, Travis Breaux
https://arxiv.org/abs/2507.02858 https://

Requirements Elicitation Follow-Up Question Generation
Interviews are a widely used technique in eliciting requirements to gather stakeholder needs, preferences, and expectations for a software system. Effective interviewing requires skilled interviewers to formulate appropriate interview questions in real time while facing multiple challenges, including lack of familiarity with the domain, excessive cognitive load, and information overload that hinders how humans process stakeholders' speech. Recently, large language models (LLMs) have exhibited s…

Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation
Yeseon Hong, Junhyuk Choi, Minju Kim, Bugeun Kim
https://arxiv.org/abs/2505.24658

Can LLMs and humans be friends? Uncovering factors affecting human-AI intimacy formation
Large language models (LLMs) are increasingly being used in conversational roles, yet little is known about how intimacy emerges in human-LLM interactions. Although previous work emphasized the importance of self-disclosure in human-chatbot interaction, it is questionable whether gradual and reciprocal self-disclosure is also helpful in human-LLM interaction. Thus, this study examined three possible aspects contributing to intimacy formation: gradual self-disclosure, reciprocity, and naturalnes…

This https://arxiv.org/abs/2409.18745 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A study on the effects of mixed explicit and implicit communications in human-virtual-agent interactions
Communication between humans and robots (or virtual agents) is essential for interaction and often inspired by human communication, which uses gestures, facial expressions, gaze direction, and other explicit and implicit means. This work presents an interaction experiment where humans and virtual agents interact through explicit (gestures, manual entries using mouse and keyboard, voice, sound, and information on screen) and implicit (gaze direction, location, facial expressions, and raise of ey…

According to my napkin estimations, and also assuming (unreasonably) that we will have cracked 100% fusion efficiency of E=mc² within the next few weeks, humans will have burned up the entire mantle of the Earth in approximately 2200 years.
This is using the 2023 figures for present use (15000 Mtoe, ie about 7 tonnes annually) and its 2.2% growth rate which, while suddenly up from the long standing 1.5%, is largely pre-LLMs.

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Atsuyuki Miyai, Zaiying Zhao, Kazuki Egashira, Atsuki Sato, Tatsumi Sunada, Shota Onohara, Hiromasa Yamanishi, Mashiro Toyooka, Kunato Nishina, Ryoma Maeda, Kiyoharu Aizawa, Toshihiko Yamasaki
https://arxiv.org/abs/2506.01952

WebChoreArena: Evaluating Web Browsing Agents on Realistic Tedious Web Tasks
Powered by a large language model (LLM), a web browsing agent operates web browsers in a human-like manner and offers a highly transparent path toward automating a wide range of everyday tasks. As web agents become increasingly capable and demonstrate proficiency in general browsing tasks, a critical question emerges: Can they go beyond general browsing to robustly handle tasks that are tedious and complex, or chores that humans often avoid doing themselves? In this paper, we introduce WebChore…

Can Machines Philosophize?
Michele Pizzochero, Giorgia Dellaferrera
https://arxiv.org/abs/2507.00675 https://arxiv.org/pdf/2507.00675…

Can Machines Philosophize?
Inspired by the Turing test, we present a novel methodological framework to assess the extent to which a population of machines mirrors the philosophical views of a population of humans. The framework consists of three steps: (i) instructing machines to impersonate each human in the population, reflecting their backgrounds and beliefs, (ii) administering a questionnaire covering various philosophical positions to both humans and machines, and (iii) statistically analyzing the resulting response…

White collar workers are increasingly using AI note-taking apps to assist them during Zoom meetings or fully represent them when they choose not to attend (Washington Post)

No one likes meetings. They’re sending their AI note takers instead.
Artificial intelligence apps that record and summarize meetings can tempt workers into skipping calls, leaving humans who join in the company of silent bots.

FWIW, I’ve yet to see any indication that the use of LLMs (pseudo-AI) has improved the quality of phishing as measured by how much gets past technical defenses.
LLMs are a great leveler. They produce median texts to fit their prompts. They cannot produce anything that requires creativity. They cannot produce high-quality fakes because they cannot produce high-quality anything. They are a play on the fact that 50% of people are at or below median cognitive capacity.

This is another horrifying statistic to shelve alongside the composition of global mammalian biomass:
• humans 34%
• livestock and pets 62%
• wild animals 4%
#science #biology #ecology

Jonathan Schofield (@urlyman@mastodon.social)
Attached: 1 image …Here are some highlights (lowlights): “If you drop all that old life into the container that we call Earth, and burn them, it turns out that what we burn in *1 year* weighs 100x more than everything alive today. Everything. All the whales, elephants, forests, insects, grass, crops, birds, fish, people, dogs and cats. Add up all the carbon in everything alive now, and still, in one year we burn a hundred times more.” #climateDiary

Animals are abused and exploited in various ways for the sake of entertainment. LCA strongly opposes the use of animals in entertainment.
Animals have their own needs, interests, and rights, especially the right to engage in their natural behaviors in their natural habitat. https://www.lcanimal.org/…

Scaling Human Judgment in Community Notes with LLMs
Haiwen Li, Soham De, Manon Revel, Andreas Haupt, Brad Miller, Keith Coleman, Jay Baxter, Martin Saveski, Michiel A. Bakker
https://arxiv.org/abs/2506.24118

Scaling Human Judgment in Community Notes with LLMs
This paper argues for a new paradigm for Community Notes in the LLM era: an open ecosystem where both humans and LLMs can write notes, and the decision of which notes are helpful enough to show remains in the hands of humans. This approach can accelerate the delivery of notes, while maintaining trust and legitimacy through Community Notes' foundational principle: A community of diverse human raters collectively serve as the ultimate evaluator and arbiter of what is helpful. Further, the feedbac…

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Chenjun Xu, Bingbing Wen, Bin Han, Robert Wolfe, Lucy Lu Wang, Bill Howe
https://arxiv.org/abs/2506.00582

Do Language Models Mirror Human Confidence? Exploring Psychological Insights to Address Overconfidence in LLMs
Psychology research has shown that humans are poor at estimating their performance on tasks, tending towards underconfidence on easy tasks and overconfidence on difficult tasks. We examine three LLMs, Llama-3-70B-instruct, Claude-3-Sonnet, and GPT-4o, on a range of QA tasks of varying difficulty, and show that models exhibit subtle differences from human patterns of overconfidence: less sensitive to task difficulty, and when prompted to answer based on different personas -- e.g., expert vs laym…

This https://arxiv.org/abs/2504.14305 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Adversarial Locomotion and Motion Imitation for Humanoid Policy Learning
Humans exhibit diverse and expressive whole-body movements. However, attaining human-like whole-body coordination in humanoid robots remains challenging, as conventional approaches that mimic whole-body motions often neglect the distinct roles of upper and lower body. This oversight leads to computationally intensive policy learning and frequently causes robot instability and falls during real-world execution. To address these issues, we propose Adversarial Locomotion and Motion Imitation (ALMI…

Stylometry recognizes human and LLM-generated texts in short samples
Karol Przystalski, Jan K. Argasi\'nski, Iwona Grabska-Gradzi\'nska, Jeremi K. Ochab
https://arxiv.org/abs/2507.00838

Stylometry recognizes human and LLM-generated texts in short samples
The paper explores stylometry as a method to distinguish between texts created by Large Language Models (LLMs) and humans, addressing issues of model attribution, intellectual property, and ethical AI use. Stylometry has been used extensively to characterise the style and attribute authorship of texts. By applying it to LLM-generated texts, we identify their emergent writing patterns. The paper involves creating a benchmark dataset based on Wikipedia, with (a) human-written term summaries, (b) …

This https://arxiv.org/abs/2505.23436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Emergent Risk Awareness in Rational Agents under Resource Constraints
Advanced reasoning models with agentic capabilities (AI agents) are deployed to interact with humans and to solve sequential decision-making problems under (approximate) utility functions and internal models. When such problems have resource or failure constraints where action sequences may be forcibly terminated once resources are exhausted, agents face implicit trade-offs that reshape their utility-driven (rational) behaviour. Additionally, since these agents are typically commissioned by a h…

This https://arxiv.org/abs/2402.11871 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Real World to Logic and Back: Learning Generalizable Relational Concepts For Long Horizon Robot Planning
Humans efficiently generalize from limited demonstrations, but robots still struggle to transfer learned knowledge to complex, unseen tasks with longer horizons and increased complexity. We propose the first known method enabling robots to autonomously invent relational concepts directly from small sets of unannotated, unsegmented demonstrations. The learned symbolic concepts are grounded into logic-based world models, facilitating efficient zero-shot generalization to significantly more comple…

Can LLMs Replace Humans During Code Chunking?
Christopher Glasz, Emily Escamilla, Eric O. Scott, Anand Patel, Jacob Zimmer, Colin Diggs, Michael Doyle, Scott Rosen, Nitin Naik, Justin F. Brunelle, Samruddhi Thaker, Parthav Poudel, Arun Sridharan, Amit Madan, Doug Wendt, William Macke, Thomas Schill
https://arxiv.org/abs/2506.198…

Can LLMs Replace Humans During Code Chunking?
Large language models (LLMs) have become essential tools in computer science, especially for tasks involving code understanding and generation. However, existing work does not address many of the unique challenges presented by code written for government applications. In particular, government enterprise software is often written in legacy languages like MUMPS or assembly language code (ALC) and the overall token lengths of these systems exceed the context window size for current commercially a…

‘It’s death by a thousand cuts’: marine ecologist on the collapse of coral reefs https://www.theguardian.com/environment/ng-interactive/2025/jun/25/tipping-points-coral-oceans-climate-crisis-marine-ecologist

Re this from @…, of the biggest tells about the current AI hype bubble:
Instead of replacing the work humans don’t want to do, it’s purporting to replace the work executives hate paying for.
Instead of an end to drudgery, they’re pushing an end to purpose and meaning.
And yeah, we’re going to end up cleaning up the AI’s messes. And doing its laundry.
https://mastodon.social/@PavelASamsonov/114598616057210141

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.

EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment
Mikolaj Walczak, Romina Aalishah, Wyatt Mackey, Brittany Story, David L. Boothe Jr., Nicholas Waytowich, Xiaomin Lin, Tinoosh Mohsenin
https://arxiv.org/abs/2506.03046

EDEN: Entorhinal Driven Egocentric Navigation Toward Robotic Deployment
Deep reinforcement learning agents are often fragile while humans remain adaptive and flexible to varying scenarios. To bridge this gap, we present EDEN, a biologically inspired navigation framework that integrates learned entorhinal-like grid cell representations and reinforcement learning to enable autonomous navigation. Inspired by the mammalian entorhinal-hippocampal system, EDEN allows agents to perform path integration and vector-based navigation using visual and motion sensor data. At th…

This https://arxiv.org/abs/2501.07071 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Value Compass Leaderboard: A Platform for Fundamental and Validated Evaluation of LLMs Values
As Large Language Models (LLMs) achieve remarkable breakthroughs, aligning their values with humans has become imperative for their responsible development and customized applications. However, there still lack evaluations of LLMs values that fulfill three desirable goals. (1) Value Clarification: We expect to clarify the underlying values of LLMs precisely and comprehensively, while current evaluations focus narrowly on safety risks such as bias and toxicity. (2) Evaluation Validity: Existing …

Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
John P. Dickerson, Hadi Hosseini, Samarth Khanna, Leona Pierce
https://arxiv.org/abs/2506.00079

Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs -- raises critical questions about their alignment with human moral values. We systematically evaluate the behavior of several prominent LLMs against human preferences in kidney allocation scenarios and show that LLMs: i) exhibit stark deviations from human values in prioritizing various attributes, and ii) in contrast to humans, LLMs rarely express indec…

Re this from @…, of the biggest tells about the current AI hype bubble:
Instead of replacing the work humans don’t want to do, it’s purporting to replace the work executives hate paying for.
Instead of an end to drudgery, they’re pushing an end to purpose and meaning.
And yeah, we’re going to end up cleaning up the AI’s messes. And doing its laundry.
https://mastodon.social/@PavelASamsonov/114598616057210141

This https://arxiv.org/abs/2503.05231 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Kaiwu: A Multimodal Manipulation Dataset and Framework for Robot Learning and Human-Robot Interaction
Cutting-edge robot learning techniques including foundation models and imitation learning from humans all pose huge demands on large-scale and high-quality datasets which constitute one of the bottleneck in the general intelligent robot fields. This paper presents the Kaiwu multimodal dataset to address the missing real-world synchronized multimodal data problems in the sophisticated assembling scenario,especially with dynamics information and its fine-grained labelling. The dataset first provi…

Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner, Kaushik Mallik, David Pape
https://arxiv.org/abs/2506.00496

Monitoring Robustness and Individual Fairness
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI models that make decisions about humans. We propose runtime monitoring of input-output robustness of deployed, black-box AI models, where the goal is to design monitors that would observe one long execution sequence of the model, and would raise an alarm whenever it is detected that two similar inputs from the past…

Call center agents in Australia, Canada, Greece, and the US say they've been repeatedly mistaken for AI, as the industry rapidly integrates AI alongside humans (Morgan Meaker/Bloomberg)
https://www.bloomberg.com/news/articles/20

Here’s the real actual definition of “artificial intelligence,” the true technical meaning in research and engineering circles when it’s not being used as marketing hype.
Artificial intelligence is anything that
1. humans are generally good at, and
2. computers were recently bad at.
That’s it. That’s all it means. You’ll hear people refine it and dress it up, but that’s the heart of the definition. (Check Wikipedia!)
3/

A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning
Arnab Debnath, Gregory J. Stein, Jana Kosecka
https://arxiv.org/abs/2506.02593

A Hybrid Approach to Indoor Social Navigation: Integrating Reactive Local Planning and Proactive Global Planning
We consider the problem of indoor building-scale social navigation, where the robot must reach a point goal as quickly as possible without colliding with humans who are freely moving around. Factors such as varying crowd densities, unpredictable human behavior, and the constraints of indoor spaces add significant complexity to the navigation task, necessitating a more advanced approach. We propose a modular navigation framework that leverages the strengths of both classical methods and deep rei…

This https://arxiv.org/abs/2412.05718 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

RLZero: Direct Policy Inference from Language Without In-Domain Supervision
The reward hypothesis states that all goals and purposes can be understood as the maximization of a received scalar reward signal. However, in practice, defining such a reward signal is notoriously difficult, as humans are often unable to predict the optimal behavior corresponding to a reward function. Natural language offers an intuitive alternative for instructing reinforcement learning (RL) agents, yet previous language-conditioned approaches either require costly supervision or test-time tr…

This https://arxiv.org/abs/2412.16772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?
The ongoing revolution in language modeling has led to various novel applications, some of which rely on the emerging social abilities of large language models (LLMs). Already, many turn to the new cyber friends for advice during the pivotal moments of their lives and trust them with the deepest secrets, implying that accurate shaping of the LLM's personality is paramount. To this end, state-of-the-art approaches exploit a vast variety of training data, and prompt the model to adopt a particula…

Here’s the real actual definition of “artificial intelligence,” the true technical meaning in research and engineering circles when it’s not being used as marketing hype.
Artificial intelligence is anything that
1. humans are generally good at, and
2. computers were recently bad at.
That’s it. That’s all it means. You’ll hear people refine it and dress it up, but that’s the heart of the definition. (Check Wikipedia!)
3/

[Thread] A new US paper shows the best frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel (Rohan Paul/@rohanpaul_ai)
https://x.com/rohanpaul_ai/status/1934751145400111572

Rohan Paul (@rohanpaul_ai) on X
This is really BAD news of LLM's coding skill. ☹️ The best Frontier LLM models achieve 0% on hard real-life Programming Contest problems, domains where expert humans still excel. LiveCodeBench Pro, a benchmark composed of problems from Codeforces, ICPC, and IOI (“International

For example:
- Telling apart photos of cats and dogs is “AI.”
- Making up fake but plausible facts on an arbitrary topic is “AI.”
- Walking is “AI.”
- Doing long multiplication is something we might call “intelligence” in humans, but it is not “AI” because computers have •always• been good at it.
- Winning at checkers •used• to be “AI” because computers didn’t used to be able to do that, but now it’s not “AI” because computers have been good at it for too long.
5/

This https://arxiv.org/abs/2505.21432 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Humans practice slow thinking before performing actual actions when handling complex tasks in the physical world. This thinking paradigm, recently, has achieved remarkable advancement in boosting Large Language Models (LLMs) to solve complex tasks in digital domains. However, the potential of slow thinking remains largely unexplored for robotic foundation models interacting with the physical world. In this work, we propose Hume: a dual-system Vision-Language-Action (VLA) model with value-guided…

For example:
- Telling apart photos of cats and dogs is “AI.”
- Making up fake but plausible facts on an arbitrary topic is “AI.”
- Walking is “AI.”
- Doing long multiplication is something we might call “intelligence” in humans, but it is not “AI” because computers have •always• been good at it.
- Winning at checkers •used• to be “AI” because computers didn’t used to be able to do that, but now it’s not “AI” because computers have been good at it for too long.
5/

This https://arxiv.org/abs/2309.03678 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Fully Onboard SLAM for Distributed Mapping with a Swarm of Nano-Drones
The use of Unmanned Aerial Vehicles (UAVs) is rapidly increasing in applications ranging from surveillance and first-aid missions to industrial automation involving cooperation with other machines or humans. To maximize area coverage and reduce mission latency, swarms of collaborating drones have become a significant research direction. However, this approach requires open challenges in positioning, mapping, and communications to be addressed. This work describes a distributed mapping system ba…

This https://arxiv.org/abs/2503.03480 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

SafeVLA: Towards Safety Alignment of Vision-Language-Action Model via Constrained Learning
Vision-language-action models (VLAs) show potential as generalist robot policies. However, these models pose extreme safety challenges during real-world deployment, including the risk of harm to the environment, the robot itself, and humans. How can safety constraints be explicitly integrated into VLAs? We address this by exploring an integrated safety approach (ISA), systematically modeling safety requirements, then actively eliciting diverse unsafe behaviors, effectively constraining VLA poli…