Uber will invest 300m in electric vehicle maker Lucid in a robotaxi deal that aims to start with one major US city late next year. The two companies announced the new partnership on Thursday. Over six years starting in 2026, Uber will acquire and deploy over 20,000 Lucid Gravity SUVs that will be equipped with autonomous vehicle (AV) technology from startup Nuro, the three companies said in a statement. The agreement illustrates the renewed plans and push for financing for self-driving cabs, years after a first wave of autonomous driving investment produced only a limited number of vehicles. Tesla has recently launched a robotaxi trial in Austin, and Alphabet's driverless taxi unit, Waymo, is speeding up its expansion.

Production-grade LLM systems require robust adherence to dozens or even hundreds of instructions simultaneously. However, the instruction-following capabilities of LLMs at high instruction densities have not yet been characterized, as existing benchmarks only evaluate models on tasks with a single or few instructions. We introduce IFScale, a simple benchmark of 500 keyword-inclusion instructions for a business report writing task to measure how instruction-following performance degrades as instruction density increases. We evaluate 20 state-of-the-art models across seven major providers and find that even the best frontier models only achieve 68% accuracy at the max density of 500 instructions. Our analysis reveals model size and reasoning capability to correlate with 3 distinct performance degradation patterns, bias towards earlier instructions, and distinct categories of instruction-following errors. Our insights can help inform design of instruction-dense prompts in real-world applications and highlight important performance-latency tradeoffs. We open-source the benchmark and all results for further analysis at https://distylai.github.io/IFScale.

Recent breakthroughs in artificial intelligence (AI) have triggered surges in market valuations for AI-related companies, often outpacing the realization of underlying capabilities. We examine the anchoring effect of AI capabilities on equity valuations and propose a Capability Realization Rate (CRR) model to quantify the gap between AI potential and realized performance. Using data from the 2023--2025 generative AI boom, we analyze sector-level sensitivity and conduct case studies (OpenAI, Adobe, NVIDIA, Meta, Microsoft, Goldman Sachs) to illustrate patterns of valuation premium and misalignment. Our findings indicate that AI-native firms commanded outsized valuation premiums anchored to future potential, while traditional companies integrating AI experienced re-ratings subject to proof of tangible returns. We argue that CRR can help identify valuation misalignment risk-where market prices diverge from realized AI-driven value. We conclude with policy recommendations to improve transparency, mitigate speculative bubbles, and align AI innovation with sustainable market value.

Tracking the strategic focus of companies through topics in their earnings calls is a key task in financial analysis. However, as industries evolve, traditional topic modeling techniques struggle to dynamically capture emerging topics and their relationships. In this work, we propose an LLM-agent driven approach to discover and retrieve emerging topics from quarterly earnings calls. We propose an LLM-agent to extract topics from documents, structure them into a hierarchical ontology, and establish relationships between new and existing topics through a topic ontology. We demonstrate the use of extracted topics to infer company-level insights and emerging trends over time. We evaluate our approach by measuring ontology coherence, topic evolution accuracy, and its ability to surface emerging financial trends.

Nvidia has notched a market capitalisation of 4 trillion, making it the first public company in the world to reach the milestone and solidifying its position as one of Wall Street's most-favoured stocks. On Wednesday, shares of the leading chip designer rose as much as 2.5 percent to an all-time high of 164, benefiting from the continuing surge in demand for artificial intelligence technologies. The stock's recent rally comes despite a sluggish start to the year, when the emergence of a Chinese discount artificial intelligence model developed by DeepSeek shook confidence in stocks linked to the sector. Nvidia achieved a 1 trillion market value for the first time in June 2023 and tripled it in about a year, faster than Apple and Microsoft, the only other United States firms with a market value of more than 3 trillion. Microsoft is the second-biggest US company, with a market capitalisation of 3.75 trillion.

Extracting structured and quantitative insights from unstructured financial filings is essential in investment research, yet remains time-consuming and resource-intensive. Conventional approaches in practice rely heavily on labor-intensive manual processes, limiting scalability and delaying the research workflow. In this paper, we propose an efficient and scalable method for accurately extracting quantitative insights from unstructured financial documents, leveraging a multi-agent system composed of large language models. Our proposed multi-agent system consists of two specialized agents: the \emph{Extraction Agent} and the \emph{Text-to-SQL Agent}. The \textit{Extraction Agent} automatically identifies key performance indicators from unstructured financial text, standardizes their formats, and verifies their accuracy. On the other hand, the \textit{Text-to-SQL Agent} generates executable SQL statements from natural language queries, allowing users to access structured data accurately without requiring familiarity with the database schema. Through experiments, we demonstrate that our proposed system effectively transforms unstructured text into structured data accurately and enables precise retrieval of key information. First, we demonstrate that our system achieves approximately 95\% accuracy in transforming financial filings into structured data, matching the performance level typically attained by human annotators. Second, in a human evaluation of the retrieval task -- where natural language queries are used to search information from structured data -- 91\% of the responses were rated as correct by human evaluators. In both evaluations, our system generalizes well across financial document types, consistently delivering reliable performance.

The video game industry loves to tout figures: record-breaking sales numbers, astonishing revenue growth, dazzling quantities of concurrent players. It makes sense that the people who make and play games love numbers: They're proof that someone is winning. We have a new incredible number from the world of video games: In spite of an alarming price tag, it took only four days for the Nintendo Switch 2 to become the fastest-selling home video game console of all time, with 3.5 million units sold over the weekend following its June 5 release. This is tremendous business, enough for investors to take note and consider Nintendo a safe haven in a moment of extreme economic volatility. This kind of success is typically a point of pride to proponents of the video game industry, hard data proving the medium's significance to any doubters.

Law has long been a domain that has been popular in natural language processing (NLP) applications. Reasoning (ratiocination and the ability to make connections to precedent) is a core part of the practice of the law in the real world. Nevertheless, while multiple legal datasets exist, none have thus far focused specifically on reasoning tasks. We focus on a specific aspect of the legal landscape by introducing a corporate governance reasoning benchmark (CHANCERY) to test a model's ability to reason about whether executive/board/shareholder's proposed actions are consistent with corporate governance charters. This benchmark introduces a first-of-its-kind corporate governance reasoning test for language models - modeled after real world corporate governance law. The benchmark consists of a corporate charter (a set of governing covenants) and a proposal for executive action. The model's task is one of binary classification: reason about whether the action is consistent with the rules contained within the charter. We create the benchmark following established principles of corporate governance - 24 concrete corporate governance principles established in and 79 real life corporate charters selected to represent diverse industries from a total dataset of 10k real life corporate charters. Evaluations on state-of-the-art (SOTA) reasoning models confirm the difficulty of the benchmark, with models such as Claude 3.7 Sonnet and GPT-4o achieving 64.5% and 75.2% accuracy respectively. Reasoning agents exhibit superior performance, with agents based on the ReAct and CodeAct frameworks scoring 76.1% and 78.1% respectively, further confirming the advanced legal reasoning capabilities required to score highly on the benchmark. We also conduct an analysis of the types of questions which current reasoning models struggle on, revealing insights into the legal reasoning capabilities of SOTA models.

Taiwan's Yageo said it would keep Shibaura Electronics's most advanced technology in Japan if it successfully acquires the artificial intelligence sensor maker. The comments from Yageo founder and Chairman Pierre Chen come as Tokyo seeks to strike a balance between shareholder returns while ensuring cutting-edge AI technology stays at home. Shibaura's high-precision thermistors are key for monitoring the internal temperature of electronic devices to prevent overheating. That's especially important in AI, where data centers with large clusters of high-performance servers churn through troves of data. "It is not in Yageo's interest to see Shibaura's technology transfer to countries that Japan considers to be unfriendly," Chen told reporters in Taipei on Saturday.

Nvidia beat Wall Street expectations in its quarterly earnings report on Wednesday, marking another in a string of financial wins for the computer hardware giant. It reported 44.1bn in revenue in the quarter ending in April, up 69% from the previous year. The company exceeded investors' predictions of 43.3bn in revenue. Adjusted earnings per share came in at 0.81, under investor expectations of an adjusted earnings per share of 88 cents. The company also reported 39.1bn in data center revenue, up 73% from the year prior.