The EU publishes the Artificial Intelligence Act, laying down harmonised rules on artificial intelligence and amending certain Union legislative acts.
The Regulation amends several existing EU regulations to incentivise defence-related investment in the EU budget in order to implement the ReArm Europe Plan.
The Council of the European Union adopted a decision concluding, on behalf of the EU, the Council of Europe Framework Convention on Artificial Intelligence and Human Rights, Democracy and the Rule of Law.
The Council amends Regulation (EU) 2021/1173 establishing the European High Performance Computing Joint Undertaking. The update modifies the legal framework governing the Joint Undertaking.
The Council adopted a decision amending Decision (CFSP) 2022/2269, updating Union support for implementing the project “Promoting Responsible Innovation in Artificial Intelligence for Peace and Security.”
Recent multimodal large language models (MLLMs) have shown strong potential for autonomous driving scene understanding, yet existing methods still face a fundamental trade-off between temporal reasoning and spatial precision. Models that rely on single-frame or low-resolution inputs often miss small, distant, or partially occluded hazards, while language-centric driving models frequently provide l
Knowledge graphs (KGs) can be enhanced through rule mining; however, the resulting logical rules are often difficult for humans to interpret due to their inherent complexity and the idiosyncratic labeling conventions of individual KGs. This work presents Rule2Text, a comprehensive framework that leverages large language models (LLMs) to generate natural language explanations for mined logical rule
Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators. However, automatically constructing such pipelines is computationally difficult because operator sequences are combinatorial and end-to-end evaluation is expensive. Existing state-of-the-art (SOTA) Multi-DQN method
The energy cost of Large Language Model (LLM) inference is rapidly becoming a barrier to sustainable and scalable deployment. Although modern serving architectures expose distinct prefill and decode behaviors, existing systems fail to exploit these phase differences for energy-efficient serving under strict latency SLOs. This paper introduces VoltanaLLM, the first system that explicitly targets an
Sparse autoencoders (SAEs) are increasingly used to extract interpretable concepts from vision and vision language models, yet existing evaluation methods largely rely on proxy metrics or qualitative inspection rather than measuring semantic correspondence. We present a human-grounded evaluation framework that quantifies alignment between SAE latents and human-annotated concepts, without requiring
Capital is concentrating in a small number of outsized bets, with late-stage and strategic transactions dominating the board. The pattern points to continued appetite for infrastructure-scale exposure and for companies already large enough to attract acquisition-style capital.
Capital is concentrating in a small number of outsized bets, with late-stage and strategic transactions dominating the board. The pattern points to continued appetite for infrastructure-scale exposure and for companies already large enough to attract acquisition-style capital.
Capital is concentrating in a small number of outsized bets, with late-stage and strategic transactions dominating the board. The pattern points to continued appetite for infrastructure-scale exposure and for companies already large enough to attract acquisition-style capital.
Named moves are clustered around company formation and senior sales leadership, which makes the desk more about organizational shape than churn. A new startup team is being assembled while a commercial leader exits a major lab, both of which can alter go-to-market and execution.
Named moves are clustered around company formation and senior sales leadership, which makes the desk more about organizational shape than churn. A new startup team is being assembled while a commercial leader exits a major lab, both of which can alter go-to-market and execution.
Named moves are clustered around company formation and senior sales leadership, which makes the desk more about organizational shape than churn. A new startup team is being assembled while a commercial leader exits a major lab, both of which can alter go-to-market and execution.
The benchmark feed is thin but still useful as a snapshot of where one model family is being measured across core reasoning and knowledge tasks. Without a broader leaderboard shift, the main signal is continued attention to standardized evaluation surfaces rather than a new frontier score.
The benchmark feed is thin but still useful as a snapshot of where one model family is being measured across core reasoning and knowledge tasks. Without a broader leaderboard shift, the main signal is continued attention to standardized evaluation surfaces rather than a new frontier score.
The benchmark feed is thin but still useful as a snapshot of where one model family is being measured across core reasoning and knowledge tasks. Without a broader leaderboard shift, the main signal is continued attention to standardized evaluation surfaces rather than a new frontier score.