We introduce FinDebate, a multi-agent framework for financial analysis, integrating collaborative debate with domain-specific Retrieval-Augmented Generation (RAG). Five specialized agents, covering earnings, market, sentiment, valuation, and risk, run in parallel to synthesize evidence into multi-dimensional insights. To mitigate overconfidence and improve reliability, we introduce a safe debate protocol that enables agents to challenge and refine initial conclusions while preserving coherent recommendations. Experimental results, based on both LLM-based and human evaluations, demonstrate the framework's efficacy in producing high-quality analysis with calibrated confidence levels and actionable investment strategies across multiple time horizons.

Search has emerged as core infrastructure for LLM-based agents and is widely viewed as critical on the path toward more general intelligence. Finance is a particularly demanding proving ground: analysts routinely conduct complex, multi-step searches over time-sensitive, domain-specific data, making it ideal for assessing both search proficiency and knowledge-grounded reasoning. Yet no existing open financial datasets evaluate data searching capability of end-to-end agents, largely because constructing realistic, complicated tasks requires deep financial expertise and time-sensitive data is hard to evaluate. We present FinSearchComp, the first fully open-source agent benchmark for realistic, open-domain financial search and reasoning. FinSearchComp comprises three tasks -- Time-Sensitive Data Fetching, Simple Historical Lookup, and Complex Historical Investigation -- closely reproduce real-world financial analyst workflows. To ensure difficulty and reliability, we engage 70 professional financial experts for annotation and implement a rigorous multi-stage quality-assurance pipeline. The benchmark includes 635 questions spanning global and Greater China markets, and we evaluate 21 models (products) on it. Grok 4 (web) tops the global subset, approaching expert-level accuracy. DouBao (web) leads on the Greater China subset. Experimental analyses show that equipping agents with web search and financial plugins substantially improves results on FinSearchComp, and the country origin of models and tools impact performance significantly.By aligning with realistic analyst tasks and providing end-to-end evaluation, FinSearchComp offers a professional, high-difficulty testbed for complex financial search and reasoning.

Financial disclosures such as 10-K filings present challenging retrieval problems due to their length, regulatory section hierarchy, and domain-specific language, which standard retrieval-augmented generation (RAG) models underuse. We introduce FinGEAR (Financial Mapping-Guided Enhanced Answer Retrieval), a retrieval framework tailored to financial documents. FinGEAR combines a finance lexicon for Item-level guidance (FLAM), dual hierarchical indices for within-Item search (Summary Tree and Question Tree), and a two-stage cross-encoder reranker. This design aligns retrieval with disclosure structure and terminology, enabling fine-grained, query-aware context selection. Evaluated on full 10-Ks with queries aligned to the FinQA dataset, FinGEAR delivers consistent gains in precision, recall, F1, and relevancy, improving F1 by up to 56.7% over flat RAG, 12.5% over graph-based RAGs, and 217.6% over prior tree-based systems, while also increasing downstream answer accuracy with a fixed reader. By jointly modeling section hierarchy and domain lexicon signals, FinGEAR improves retrieval fidelity and provides a practical foundation for high-stakes financial analysis.

Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language analysis into disciplined, executable trades. Although reasoning LLMs have advanced in step-by-step planning and verification, their application to risk-sensitive financial decisions is underexplored. We present Trading-R1, a financially-aware model that incorporates strategic thinking and planning for comprehensive thesis composition, facts-grounded analysis, and volatility-adjusted decision making. Trading-R1 aligns reasoning with trading principles through supervised fine-tuning and reinforcement learning with a three-stage easy-to-hard curriculum. Training uses Tauric-TR1-DB, a 100k-sample corpus spanning 18 months, 14 equities, and five heterogeneous financial data sources. Evaluated on six major equities and ETFs, Trading-R1 demonstrates improved risk-adjusted returns and lower drawdowns compared to both open-source and proprietary instruction-following models as well as reasoning models. The system generates structured, evidence-based investment theses that support disciplined and interpretable trading decisions. Trading-R1 Terminal will be released at https://github.com/TauricResearch/Trading-R1.

In recent years, China's bond market has seen a surge in defaults amid regulatory reforms and macroeconomic volatility. Traditional machine learning models struggle to capture financial data's irregularity and temporal dependencies, while most deep learning models lack interpretability-critical for financial decision-making. To tackle these issues, we propose EMDLOT (Explainable Multimodal Deep Learning for Time-series), a novel framework for multi-class bond default prediction. EMDLOT integrates numerical time-series (financial/macroeconomic indicators) and unstructured textual data (bond prospectuses), uses Time-Aware LSTM to handle irregular sequences, and adopts soft clustering and multi-level attention to boost interpretability. Experiments on 1994 Chinese firms (2015-2024) show EMDLOT outperforms traditional (e.g., XGBoost) and deep learning (e.g., LSTM) benchmarks in recall, F1-score, and mAP, especially in identifying default/extended firms. Ablation studies validate each component's value, and attention analyses reveal economically intuitive default drivers. This work provides a practical tool and a trustworthy framework for transparent financial risk modeling.

Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Larry Ellison, the chair and chief technology officer of Oracle, is a supporter of Donald Trump and has regularly appeared at the White House. Oracle co-founder's shares rose by 40% in early trading, valuing his fortune at $393bn, just ahead of Musk's $384bn US tech billionaire Larry Ellison is neck-and-neck with Elon Musk in the contest to be the world's richest person after briefly overtaking the Tesla chief executive on Wednesday Ellison's wealth surged after Oracle, the business software company in which he owns a stake of 41%, reported better than expected financial results. Oracle shares rose by more than 40% in early trading, at one point valuing the business software company at approximately $960bn (£707bn) and Ellison's stake at $393bn, just ahead of Musk's fortune of $384bn, according to Bloomberg's billionaires index. However, Ellison's lead was short-lived as the stock closed at $328, a rise of 36% valuing Ellison's shareholding at $378bn and putting Musk back ahead.

London-listed miner Anglo American and Canada's Teck Resources plan to merge, marking the sector's second-biggest mergers and acquisitions deal ever and forging a new global copper-focused heavyweight. Under the proposed deal, which will require regulatory approvals and was announced on Tuesday, Anglo American shareholders will own 62.4 percent of the new company, Anglo Teck, while shareholders in Teck would hold 37.6 percent. The deal to form the world's fifth-largest copper company is also a big bet on copper by Anglo. Glencore's $90bn merger with Xstrata in 2013 remains the largest mining deal in history. Copper, used in the power and construction sectors, is set to benefit from burgeoning demand spurred by electric vehicles and artificial intelligence.

Earnings calls represent a uniquely rich and semi-structured source of financial communication, blending scripted managerial commentary with unscripted analyst dialogue. Although recent advances in financial sentiment analysis have integrated multi-modal signals, such as textual content and vocal tone, most systems rely on flat document-level or sentence-level models, failing to capture the layered discourse structure of these interactions. This paper introduces a novel multi-modal framework designed to generate semantically rich and structurally aware embeddings of earnings calls, by encoding them as hierarchical discourse trees. Each node, comprising either a monologue or a question-answer pair, is enriched with emotional signals derived from text, audio, and video, as well as structured metadata including coherence scores, topic labels, and answer coverage assessments. A two-stage transformer architecture is proposed: the first encodes multi-modal content and discourse metadata at the node level using contrastive learning, while the second synthesizes a global embedding for the entire conference. Experimental results reveal that the resulting embeddings form stable, semantically meaningful representations that reflect affective tone, structural logic, and thematic alignment. Beyond financial reporting, the proposed system generalizes to other high-stakes unscripted communicative domains such as tele-medicine, education, and political discourse, offering a robust and explainable approach to multi-modal discourse representation. This approach offers practical utility for downstream tasks such as financial forecasting and discourse evaluation, while also providing a generalizable method applicable to other domains involving high-stakes communication.

Salesforce has slashed another 4,000 jobs from its customer support workforce as the tech giant doubles down on artificial intelligence, even as the company reports strong financial results. AI agents now reportedly handle about one million customer conversations. In a recent episode of The Logan Bartlett Show, CEO Marc Benioff justified the cuts by saying he "needs less heads" as Salesforce invests heavily in AI across its operations. Earlier this year, Benioff boasted that AI was already doing 30 to 50 percent of the work, which he framed as efficiency gains – a 17 percent cost reduction achieved after shedding 1,000 people in February. On Wednesday, the Slack owner reported revenue topped 10.2bn for the quarter ending July 31, up 10 percent from the same period last year.

Chipmaker Nvidia set a fresh sales record in the second quarter, surpassing Wall Street expectations for its artificial intelligence chips. But shares of the chip giant still dropped 2.3% in after hours trading, in a sign that investors' worries of an AI bubble and the repercussions of Donald Trump's trade wars are not quelled. Nvidia's financial report was the first test of investor appetite since last week's mass AI-stock selloff, when several tech stocks saw shares tumble last week amid growing questions over whether AI-driven companies are being overvalued. On Wednesday, Nvidia reported an adjusted earnings per share of 1.08 on 46.74bn in revenue, surpassing Wall Street's projection of 1.01 in earnings per share on 46.05bn in revenue, according to Fact Set data. But investors had high expectations for the company.