Computing such neural set divergence require aligning nodes and edges of the two graphs.

Late interaction methods compute representations for the query and corpus graphs separately, and compare these representations using simple similarity functions at the last stage, leading to highly scalable systems. Early interaction methods combine information from both graphs right from the input stages, are usually considerablymoreaccurate,butslower.

Section 4.2 demonstrates addressthechallenges (Figure 1 contains of Scatterbrain).

Recent works proposed various linear self-attention mechanisms, scaling only asO(L)for serial computation. We conduct a thorough complexity analysis of Performers,aclass which includes most recent linear Transformer mechanisms.

Transformer architectures have become very popular yet the original implementation requires $O(L^2)$ in serial time and memory as functions of input length $L$. Recent works proposed various linear self-attention mechanisms, scaling only as $O(L)$ for serial computation. We conduct a thorough complexity analysis of Performers, a class which includes most recent linear Transformer mechanisms. We note a remarkable computational flexibility: the gradient computation can be performed with no approximations using sublinear memory as a function of $L$ (in addition to negligible storage for the input sequence), at a cost of greater time complexity in the parallel setting. In the extreme case, a Performer consumes only $O(1)$ memory, and still requires $O(L)$ time. Due to complete backward-compatibility, this discovered time-memory tradeoff can be used for fine-tuning on low-memory devices in a decentralized fashion without any server computations.

A bill focused on how someone's name or likeness can be used after their death was also passed. New York is taking steps to regulate the use of AI in the state's entertainment industry. NY State Governor Kathy Hochul passed two pieces of legislation on Thursday that forces certain productions to disclose the use of AI-generated performers, and defines rules around how someone's likeness can be used after their death. Assembly Bill A8887B, now known as S.8420-A, specifically covers the use of AI performers in advertisements. Per Hochul's announcement, the law requires persons who produce or create an advertisement to identify if it includes AI generated synthetic performers.

A video game at the centre of a debate over artificial intelligence (AI) is in the running for the top prize at next year's Bafta Game Awards. Arc Raiders, from Swedish developer Embark Studios, has been a smash-hit since its October launch, selling more than four million copies. But the multiplayer shooter has been criticised for using text-to-speech tools to create additional lines, based on dialogue previously recorded by the game's actors. It is one of 10 titles longlisted for the prestigious best game award, with a shortlist to be announced in the run-up to April's annual ceremony. Other games up for the top prize include blockbusters Ghost of Yōtei and Death Stranding 2, indie games Hollow Knight: Silksong and Hades II, and indie adventure Blue Prince.

Assessment of medication history-taking has traditionally relied on human observation, limiting scalability and detailed performance data. While Generative AI (GenAI) platforms enable extensive data collection and learning analytics provide powerful methods for analyzing educational traces, these approaches remain largely underexplored in pharmacy clinical training. This study addresses this gap by applying learning analytics to understand how students develop clinical communication competencies with GenAI-powered virtual patients -- a crucial endeavor given the diversity of student cohorts, varying language backgrounds, and the limited opportunities for individualized feedback in traditional training settings. We analyzed 323 students' interaction logs across Australian and Malaysian institutions, comprising 50,871 coded utterances from 1,487 student-GenAI dialogues. Combining Epistemic Network Analysis to model inquiry co-occurrences with Sequential Pattern Mining to capture temporal sequences, we found that high performers demonstrated strategic deployment of information recognition behaviors. Specifically, high performers centered inquiry on recognizing clinically relevant information, integrating rapport-building and structural organization, while low performers remained in routine question-verification loops. Demographic factors including first-language background, prior pharmacy work experience, and institutional context, also shaped distinct inquiry patterns. These findings reveal inquiry patterns that may indicate clinical reasoning development in GenAI-assisted contexts, providing methodological insights for health professions education assessment and informing adaptive GenAI system design that supports diverse learning pathways.