Tootfinder

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Zaid Khan, Archiki Prasad, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
https://arxiv.org/abs/2510.12088

One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration
Symbolic world modeling requires inferring and representing an environment's transitional dynamics as an executable program. Prior work has focused on largely deterministic environments with abundant interaction data, simple mechanics, and human guidance. We address a more realistic and challenging setting, learning in a complex, stochastic environment where the agent has only "one life" to explore a hostile environment without human guidance. We introduce OneLife, a framework that models world…

Peter Thiel's Armageddon
speaking tour has—like the world—not ended yet.
For a full two years now, the billionaire has been on the circuit,
spreading his biblically inflected ideas about doomsday
through a set of variably and sometimes visibly perplexed interviewers.
He has chatted onstage with the economist podcaster Tyler Cowen about the katechon
(the scriptural term for “that which withholds” the end times);
traded some very awkward on-camera…

The Real Stakes, and Real Story, of Peter Thiel’s Antichrist Obsession
Thirty years ago, a peace-loving Austrian theologian spoke to Peter Thiel about the apocalyptic theories of Nazi jurist Carl Schmitt. They’ve been a road map for the billionaire ever since.

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Xiangxu Zhang, Lei Li, Yanyun Zhou, Xiao Zhou, Yingying Zhang, Xian Wu
https://arxiv.org/abs/2510.09275

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation
Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLMs) are fundamentally misaligned with real-world clinical practice. Most of them rely on static benchmarks derived from public medical exam items, which tend to overestimate model performance and ignore the difference between textbook cases and the ambiguous, varying conditions in the real world. Recent efforts toward dynamic evaluation offer a promi…

Interesting article from The Guardian about the idea of distinct phases or eras of human brain development.
https://www.theguardian.com/science/2025/nov/25/brain-human-cognitive-development-life-stages-cambridge-study

WhosOnFirstWatch - A Daggerheart System TTRPG Actual Play Podcast
Follows a band of broken heroes battling war, magic, and cosmic horror in the dying homebrew world of Hammanaroth—perfect for fans of gritty storytelling, high-stakes roleplay, and epic fantasy adventures....
Great Australian Pods Podcast Directory: https://www.…

Anduril cofounder Palmer Lucked defended the use of AI technology to make life-and-death decisions in war on Sunday.
A group of defense tech startups that includes Anduril,
along with traditional defense companies,
is developing autonomous AI weapons and tools for use in conflicts around the world,
-- worrying some who say the technology is not ready for such high-stakes environments.
"When it comes to life and death decision-making, I think that it is too …

Anduril's Palmer Luckey makes an ethical case for using AI in war: 'There is no moral high ground in using inferior technology'
For "life and death decision-making," Anduril cofounder Palmer Luckey says it's "too critical" to not use the most advanced technology.

StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?
Yanxu Chen, Zijun Yao, Yantao Liu, Jin Ye, Jianing Yu, Lei Hou, Juanzi Li
https://arxiv.org/abs/2510.02209

StockBench: Can LLM Agents Trade Stocks Profitably In Real-world Markets?
Large language models (LLMs) have recently demonstrated strong capabilities as autonomous agents, showing promise in reasoning, tool use, and sequential decision-making. While prior benchmarks have evaluated LLM agents in domains such as software engineering and scientific discovery, the finance domain remains underexplored, despite its direct relevance to economic value and high-stakes decision-making. Existing financial benchmarks primarily test static knowledge through question answering, bu…

When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets
Zeshi Dai, Zimo Peng, Zerui Cheng, Ryan Yihe Li
https://arxiv.org/abs/2510.00332

When Hallucination Costs Millions: Benchmarking AI Agents in High-Stakes Adversarial Financial Markets
We present CAIA, a benchmark exposing a critical blind spot in AI evaluation: the inability of state-of-the-art models to operate in adversarial, high-stakes environments where misinformation is weaponized and errors are irreversible. While existing benchmarks measure task completion in controlled settings, real-world deployment demands resilience against active deception. Using crypto markets as a testbed where $30 billion was lost to exploits in 2024, we evaluate 17 models on 178 time-anchore…

Downsized and Compromised?: Assessing the Faithfulness of Model Compression
Moumita Kamal, Douglas A. Talbert
https://arxiv.org/abs/2510.06125 https://arxi…

Downsized and Compromised?: Assessing the Faithfulness of Model Compression
In real-world applications, computational constraints often require transforming large models into smaller, more efficient versions through model compression. While these techniques aim to reduce size and computational cost without sacrificing performance, their evaluations have traditionally focused on the trade-off between size and accuracy, overlooking the aspect of model faithfulness. This limited view is insufficient for high-stakes domains like healthcare, finance, and criminal justice, w…