Illinois Lawmakers Just Passed America's Strongest AI Safety Bill The bill requires companies like OpenAI, Anthropic, and Google to have third parties confirm they're following safety standards. The Illinois House of Representatives passed a bill on Wednesday requiring frontier AI labs like OpenAI, Anthropic, and Google DeepMind to have their safety practices audited by a third party. If signed into law, AI safety experts tell WIRED, it would be the nation's leading check on the power of major AI companies . The bill, SB 315, now heads to governor JB Pritzker's desk. In a post on social media on Wednesday, Pritzker said he plans to sign the bill, citing a need to hold Big Tech accountable.

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Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently. However, current robustness certification methods are only able to certify under a limited perturbation radius. Given that existingpure data-driven statistical approaches have reached a bottleneck, in this paper, we propose to integrate statistical ML modelswithknowledge (expressed aslogical rules) asareasoningcomponent using Markovlogic networks (MLN), so as to further improvethe overall certified robustness.

This raises an obvious question,how should the cost-tradeoffs be chosen so that it reflects the expert'sdecision-making?

Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. Deep neural networks (DNNs) have recently emerged as a promising solution to improve readout accuracy . Prior hardware implementations of DNN-based readout are resource-intensive and suffer from high inference latency, limiting their practical use in low-latency decoding and quantum error correction (QEC) loops. This paper proposes LUNA, a fast and efficient superconducting qubit readout accelerator that combines low-cost integrator-based preprocessing with Look-Up Table (LUT) based neural networks for classification. The architecture uses simple integrators for dimensionality reduction with minimal hardware overhead, and employs LogicNets (DNNs synthesized into LUT logic) to drastically reduce resource usage while enabling ultra-low-latency inference. We integrate this with a differential evolution based exploration and optimization framework to identify high-quality design points. Our results show up to a 10.95x reduction in area and 30% lower latency with little to no loss in fidelity compared to the state-of-the-art. LUNA enables scalable, low-footprint, and high-speed qubit readout, supporting the development of larger and more reliable quantum computing systems.

As large language models become increasingly capable, there is growing concern that they may develop reasoning processes that are encoded or hidden from human oversight. To investigate whether current interpretability techniques can penetrate such encoded reasoning, we construct a controlled testbed by fine-tuning a reasoning model (DeepSeek-R1-Distill-Llama-70B) to perform chain-of-thought reasoning in ROT-13 encryption while maintaining intelligible English outputs. We evaluate mechanistic interpretability methods--in particular, logit lens analysis--on their ability to decode the model's hidden reasoning process using only internal activations. We show that logit lens can effectively translate encoded reasoning, with accuracy peaking in intermediate-to-late layers. Finally, we develop a fully unsupervised decoding pipeline that combines logit lens with automated paraphrasing, achieving substantial accuracy in reconstructing complete reasoning transcripts from internal model representations. These findings suggest that current mechanistic interpretability techniques may be more robust to simple forms of encoded reasoning than previously understood. Our work provides an initial framework for evaluating interpretability methods against models that reason in non-human-readable formats, contributing to the broader challenge of maintaining oversight over increasingly capable AI systems.

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Breakthroughs, discoveries, and DIY tips sent every weekday. Illinois has become the first state to enact legislation banning the use of AI tools like ChatGPT for providing therapy. The bill, signed into law by Governor J.B. Pritzker last Friday, comes amid growing research showing an increase in people experimenting with AI for mental health as the country faces a shortage of access to professional therapy services. The Wellness and Oversight for Psychological Resources Act, officially called HB 1806, prohibits healthcare providers from using AI for therapy and psychotherapy services. Specifically, it prevents AI chatbots or other AI-powered tools from interacting directly with patients, making therapeutic decisions, or creating treatment plans.

Data is fundamental to the training of language models (LM). Recent research has been dedicated to data efficiency, which aims to maximize performance by selecting a minimal or optimal subset of training data. Techniques such as data filtering, sampling, and selection play a crucial role in this area. To complement it, we define Data Efficacy, which focuses on maximizing performance by optimizing the organization of training data and remains relatively underexplored. This work introduces a general paradigm, DELT, for considering data efficacy in LM training, which highlights the significance of training data organization. DELT comprises three components: Data Scoring, Data Selection, and Data Ordering. Among these components, we design Learnability-Quality Scoring (LQS), as a new instance of Data Scoring, which considers both the learnability and quality of each data sample from the gradient consistency perspective. We also devise Folding Ordering (FO), as a novel instance of Data Ordering, which addresses issues such as model forgetting and data distribution bias. Comprehensive experiments validate the data efficacy in LM training, which demonstrates the following: Firstly, various instances of the proposed DELT enhance LM performance to varying degrees without increasing the data scale and model size. Secondly, among these instances, the combination of our proposed LQS for data scoring and Folding for data ordering achieves the most significant improvement. Lastly, data efficacy can be achieved together with data efficiency by applying data selection. Therefore, we believe that data efficacy is a promising foundational area in LM training.