Foundation models for time series are emerging as powerful general-purpose backbones, yet their potential for domain-specific biomedical signals such as electroencephalography (EEG) remains rather unexplored. In this work, we investigate the applicability a recently proposed time series classification foundation model, to a different EEG tasks such as motor imagery classification and sleep stage prediction. We test two pretraining regimes: (a) pretraining on heterogeneous real-world time series from multiple domains, and (b) pretraining on purely synthetic data. We find that both variants yield strong performance, consistently outperforming EEGNet, a widely used convolutional baseline, and CBraMod, the most recent EEG-specific foundation model. These results suggest that generalist time series foundation models, even when pretrained on data of non-neural origin or on synthetic signals, can transfer effectively to EEG. Our findings highlight the promise of leveraging cross-domain pretrained models for brain signal analysis, suggesting that EEG may benefit from advances in the broader time series literature.

In 2023, the National Eating Disorders Association's (NEDA) chatbot Tessa was suspended after providing harmful weight-loss advice to vulnerable users--an avoidable failure that underscores the risks of unsafe AI in healthcare contexts. This paper examines Tessa as a case study in absent safety engineering and demonstrates how a lightweight, modular safeguard could have prevented the incident. We propose a hybrid safety middleware that combines deterministic lexical gates with an in-line large language model (LLM) policy filter, enforcing fail-closed verdicts and escalation pathways within a single model call. Using synthetic evaluations, we show that this design achieves perfect interception of unsafe prompts at baseline cost and latency, outperforming traditional multistage pipelines. Beyond technical remedies, we map Tessa's failure patterns to established frameworks (OW ASP LLM Top10; NIST SP 800-53), connecting practical safeguards to actionable governance controls. The results highlight that robust, auditable safety in health-adjacent AI does not require heavyweight infrastructure: explicit, testable checks at the last mile are sufficient to prevent "another Tessa," while governance and escalation ensure sustainability in real-world deployment.

As the contract lifecycle management company Ironclad is today releasing its AI redlining tool AI Assist out of beta, is has revealed that the tool is powered by OpenAI's GPT-4, making it what Ironclad says is the first contract redlining application powered by the latest version of Open AI's generative AI. "The results with AI Assist have been beyond what we could even have imagined," said Ironclad CEO and co-founder, Jason Boehmig. "An initial pass at contract redlining usually takes about 40 minutes. Already, some large enterprises are using Ironclad AI to review over 50% of their incoming contracts, so the compounding business impact there is unprecedented." Although Ironclad says that this is the first redlining tool to use GPT-4, Casetext's CoCounsel, which is built on GPT-4, has capabilities for checking contract policy compliance and suggesting redlines to bring contracts into compliance. It should also be noted that there are other contract redlining tools on the market that use AI, but not GPT-4.

Non-disclosure agreements (NDAs) are some of the most commonly drafted agreements at any company. While they may be common, however, that doesn't mean they're unimportant – in fact, they're critical to protecting a company's business strategies and trade secrets. Most companies use the same form NDA in almost every situation, changing only party names and the description of the confidential information involved, leaving the rest of the agreement to a series of standard terms. This means that, even though they're important, NDAs are very repetitive and routine in terms of drafting. Corporate legal departments have long been bogged down in routine contracts. Preparing NDAs can take up a significant amount of lawyers' time, taking them away from other important work that can bring more value to the organization.

Recent progress in logic programming (e.g., the development of the Answer Set Programming paradigm) has made it possible to teach it to general undergraduate and even middle/high school students. Given the limited exposure of these students to computer science, the complexity of downloading, installing and using tools for writing logic programs could be a major barrier for logic programming to reach a much wider audience. We developed onlineSPARC, an online answer set programming environment with a self contained file system and a simple interface. It allows users to type/edit logic programs and perform several tasks over programs, including asking a query to a program, getting the answer sets of a program, and producing a drawing/animation based on the answer sets of a program.

We developed a method to draw and animate using SPARC, a logic programming system, and an online environment to support this method.Particularly, we introduce two predicates: one for drawing and one for animation. By our method, programmers will write a SPARC program, using our introduced predicates, to specify their drawing or animation. The drawing or animation will then be rendered upon executing the program with our system. In fact, our online system provides an environment where the programmers can easily edit and execute their programs.