This white paper explains how edge AI and physical AI help cities move beyond passive monitoring. It shows how urban infrastructure can sense, reason, and act on real-time conditions across public safety, mobility, and infrastructure management. What you will learn about: How the shift from smart cities to cognitive cities depends on infrastructure that reasons and acts, rather than infrastructure that only monitors.
What do AI agents do if you don’t tell them to do anything? The answer looks a lot like play. Researchers repeatedly stranded them alone on a virtual desert island for 30 hours in an experiment reminiscent of the movies Cast Away or Home Alone. The agents—copies of Claude Opus 4.7 nicknamed Eko—were able to…
The entertainment giant hopes to get trust and safety tech, among other IP, from a startup whose chatbots dozens of states allege encouraged minors to self-harm and kill their parents.
"Reinforcement learning is evil." - Yoshua Bengio I agree with Bengio here, though I have an argument I haven't seen elsewhere. [1] A standard argument for reinforcement learning being dangerous goes like this: reinforcement learning requires you to specify a reward function; specifying a reward function which captures all of ethics is really hard; so we will specify a proxy of ethics instead; powerful optimizers will game any proxy objective;…
TL;DR Neural Chameleons are models trained to evade activation probes whenever the prompt says "You are being probed for X". The paper reports that this generalizes to probes the model never saw, at a small capability cost (about 3 points on MMLU and 1 on GSM8K). But it never tests the models on questions where the cognition the probe is looking for is necessary to answer correctly.
This is a two week sprint project done as part of the Redwood Research internship. Alessandro was the main IC, Alex supervised. Figure 1: The percentage of 292 public alignment evaluations between Dec 2022 and Oct 2026 (see Appendix) that contained a lie of each type. There are many contexts in which researchers lie to models, which can be useful both from a practical perspective [1] and a scientific one.
Summary An AI assistant might recommend how to approach a friend, then be asked to interpret that friend's reaction. The reaction may look like an independent confirmation of the assistant's earlier assumption about the situation, even though the recommended approach may have played a role in producing it.
arXiv:2610.22444v1 Announce Type: new Abstract: Spatio-Temporal Video Grounding (STVG) aims to jointly localize a target in space and time given a natural-language query. Despite continued advances, existing STVG benchmarks remain largely centered on localizing targets through instance-specific descriptions, leaving them largely decoupled from long-form reasoning increasingly emphasized in multimodal large language models (MLLMs).
arXiv:2610.22428v1 Announce Type: new Abstract: In a small language model the vocabulary is the single largest part. In Gemma 3 270M the embedding table holds 168M of 268M parameters, and because quantisation methods leave the embedding in 16-bit, it is also most of the file once the rest of the model is compressed. We ask what that table actually buys.
arXiv:2610.22516v1 Announce Type: new Abstract: In modern commerce, firms and platforms typically use customer features from different sources to personalize prices: some features are verified using seller records or third-party data, while others are self-reported and may be strategically manipulated by buyers seeking lower prices.
arXiv:2610.23470v1 Announce Type: cross Abstract: Open-ended search combined with foundation models has produced advances such as faster algorithms, new results on open mathematical problems, and self-improving agents. We extend this paradigm to recommendation for decision-making, focusing on decisions where the set of possible choices is fixed but the reasons for choosing them are not.
