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🔊 #NowPlaying on #BBCRadio3:
#InTune
- Petroc interviews Self Esteem
Petroc Trelawny is joined by pop singer and musician Self Esteem who is guest curator of London Literature Festival, and there's live music from percussionist James Larter.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002kshz

Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
Xiao Yu, Baolin Peng, Michel Galley, Hao Cheng, Qianhui Wu, Janardhan Kulkarni, Suman Nath, Zhou Yu, Jianfeng Gao
https://arxiv.org/abs/2510.09577

Dyna-Mind: Learning to Simulate from Experience for Better AI Agents
Reasoning models have recently shown remarkable progress in domains such as math and coding. However, their expert-level abilities in math and coding contrast sharply with their performance in long-horizon, interactive tasks such as web navigation and computer/phone-use. Inspired by literature on human cognition, we argue that current AI agents need ''vicarious trial and error'' - the capacity to mentally simulate alternative futures before acting - in order to enhance their understanding and p…

The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research
Sergej Kucenko, Nathaniel Dennler, Fengxiang He
https://arxiv.org/abs/2510.02036 https://

The Current State of AI Bias Bounties: An Overview of Existing Programmes and Research
Current bias evaluation methods rarely engage with communities impacted by AI systems. Inspired by bug bounties, bias bounties have been proposed as a reward-based method that involves communities in AI bias detection by asking users of AI systems to report biases they encounter when interacting with such systems. In the absence of a state-of-the-art review, this survey aimed to identify and analyse existing AI bias bounty programmes and to present academic literature on bias bounties. Google, …

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic
Manuel Vargas Guzm\'an, Jakub Szymanik, Maciej Malicki
https://arxiv.org/abs/2510.09472 https://

Hybrid Models for Natural Language Reasoning: The Case of Syllogistic Logic
Despite the remarkable progress in neural models, their ability to generalize, a cornerstone for applications like logical reasoning, remains a critical challenge. We delineate two fundamental aspects of this ability: compositionality, the capacity to abstract atomic logical rules underlying complex inferences, and recursiveness, the aptitude to build intricate representations through iterative application of inference rules. In the literature, these two aspects are often confounded together un…