Dyna.Ai's agentic AI aims to enhance financial services by moving beyond pilot projects to full AI autonomy, enabling significant returns for only 24% of institutions currently achieving this.

Agentic AI is transforming industries by automating complex processes, shifting AI from reactive tools to proactive collaborators. McKinsey highlights its impact across sectors like life sciences and auto finance.

Matt DeLauro argues for unifying fraud and AML teams to combat financial crime effectively, highlighting SEON's platform as a solution.

The paper introduces CompressionAttack, a framework exploiting prompt compression in LLMs as a security vulnerability, achieving up to 87% attack success rate.

DeepKnown-Guard enhances LLM safety with a 99.3% risk recall rate and a perfect 100% safety score on high-risk tests, offering a robust framework for AI security.

Only 10% of banks and insurers have widely implemented AI agents, despite their potential to generate $450 billion in economic value by 2028, according to Capgemini Research Institute.

Ant Group Chairman Eric Jing announced AI's role as virtual CFO and COO for SMEs, enhancing payment integration and risk management.

MERaLiON-SER is a speech emotion recognition model for English and Southeast Asian languages, outperforming existing models in multilingual contexts.

The study introduces VerIH, a dataset for training language models to prioritize instructions, enhancing model reliability and robustness against attacks.

LLMs struggle to role-play villains due to safety alignment, impacting their creative generation capabilities.

The paper reviews the use of Explainable AI (XAI) in finance, highlighting a reliance on post-hoc interpretability techniques like SHAP and feature importance analysis.

SWE-Compass introduces a comprehensive benchmark for evaluating large language models (LLMs) in software engineering, covering 8 tasks, 8 scenarios, and 10 languages.

The paper provides a framework for evaluating when deploying large language models (LLMs) on-premise is cost-effective compared to commercial services, considering factors like hardware and operational costs.

The paper introduces FinRpt, a benchmark for automating Equity Research Report generation using a multi-agent framework and LLMs, addressing data scarcity and evaluation challenges.

The paper proposes a prudential framework for assessing large language models in reinsurance, enhancing governance, transparency, and capital efficiency.

The paper introduces Cooperative Market Making (CMM), a framework for distilling large language model features into smaller models for financial trading, improving performance over existing methods.

SPEAR-MM enhances financial LLM adaptation by retaining 91.2% of general capabilities while maintaining 94% of domain-specific gains, reducing computational costs by 90%.

The study identifies conditions under which large language models (LLMs) exhibit behavioral self-awareness, raising safety concerns for AI evaluation.

A five-layer architecture enhances the reliability of large language models (LLMs) in high-stakes decisions by addressing cognitive biases and maintaining partnership states.

The AIA Forecaster, a large language model-based system, matches human superforecasters on the ForecastBench benchmark and enhances market consensus predictions.

The study reveals that smaller LLMs achieve higher output consistency than larger models in financial workflows, challenging the assumption that larger models are superior.

The paper surveys cognitive edge computing, focusing on optimizing large AI models for deployment on resource-constrained devices, highlighting model optimization, system architecture, and adaptive intelligence.

The paper introduces 'Learn-to-Ask', a framework for proactive dialogue agents using offline data, achieving superior performance to human experts in medical datasets.

The paper introduces 'String Seed of Thought' (SSoT), a method to enhance LLMs' ability to generate diverse and distribution-faithful responses, crucial for applications like human-behavior simulation.

NMIXX improves cross-lingual financial text analysis by enhancing sentence embeddings, achieving significant performance gains on Korean financial benchmarks.

The paper presents a method to calibrate process reward models (PRMs) for better accuracy in large language models, reducing inference costs while maintaining accuracy.

Improving the quality of chosen responses is crucial for Direct Preference Optimization (DPO) in aligning large language models with human preferences.

The paper introduces XBreaking, a method to exploit vulnerabilities in Large Language Models' (LLMs) security alignment through targeted noise injection.

The paper introduces KoBankIR, a domain-specific IR benchmark for banking, using LLM-based query generation and enhanced answerability assessment, highlighting existing retrieval models' struggles with complex queries.

NVIDIA introduces the Nemotron Nano V2 VL, enhancing document understanding and video comprehension through improved model architecture and token reduction techniques.

AIRepr is a framework for assessing the reproducibility of LLM-generated data science workflows, enhancing transparency and reliability in human-AI collaboration.

TimeCopilot is an open-source framework that integrates Time Series Foundation Models with Large Language Models for enhanced forecasting, achieving state-of-the-art performance at low cost.

The Open Agent Specification (Agent Spec) offers a unified framework for defining and evaluating AI agents across different systems, enhancing interoperability and reusability.

The paper proposes a method to generate Software Architecture Descriptions (SADs) from source code using reverse engineering and large language models, reducing manual effort and improving system understanding.

The paper introduces Conformal Information Pursuit (C-IP) to improve query efficiency in Large Language Models (LLMs) by using conformal prediction sets to estimate uncertainty, outperforming traditional methods in predictive performance and query length.

TAMAS introduces a benchmark to evaluate adversarial risks in multi-agent LLM systems, revealing significant vulnerabilities to attacks. This highlights the urgent need for improved defenses.

The paper introduces AnytimeReasoner, a framework improving token efficiency in large language models by optimizing reasoning under varying token budgets using Budget Relative Policy Optimization (BRPO).

The study demonstrates that automated methods can extract court-established facts from Slovak criminal court opinions with up to 99.5% accuracy when combining advanced regular expressions and large language models.

MAC-Flow offers a framework for multi-agent coordination, achieving 14.5 times faster inference than diffusion-based methods while maintaining performance.

OpsAgent, a multi-agent system for cloud incident management, offers a cost-efficient, interpretable, and self-evolving solution, outperforming existing methods on the OPENRCA benchmark.

The Pluralistic Behavior Suite (PBSUITE) reveals that leading LLMs struggle with multi-turn adherence to custom behavioral policies, showing up to 84% failure rates in adversarial settings.

The study explores how Large Language Models (LLMs) mimic human cognitive patterns, revealing implications for AI transparency and ethical deployment.

Solly, an AI agent, achieved elite human-level play in Liar's Poker using self-play and reinforcement learning, outperforming large language models.

The paper introduces a framework to create a verifiable knowledge base, SciencePedia, using a Long Chain-of-Thought approach, enhancing scientific reasoning transparency and accuracy.

BudgetMem reduces memory usage by 72.4% with only a 1.0% F1 score degradation in long-context processing, enabling efficient deployment on modest hardware.

The iTool method improves large language models by 13.11% using reinforced fine-tuning and dynamic deficiency calibration, outperforming larger models in complex tasks.

DeepForgeSeal introduces a watermarking framework using Multi-Agent Adversarial Reinforcement Learning to enhance deepfake detection, outperforming existing methods by over 4.5% on CelebA and 5.3% on CelebA-HQ benchmarks.

LiveStar improves online video understanding with a 19.5% increase in semantic correctness and a 12.0% FPS boost, offering real-time responsiveness.

The paper introduces Adaptive Branching Monte Carlo Tree Search (AB-MCTS) to enhance LLM inference by dynamically choosing between expanding or revisiting candidate responses based on feedback, outperforming existing methods.

Training large language models with uncertainty-aware objectives enhances their reliability in experimental optimization, doubling discovery rates in pharmaceutical synthesis.

Enterprise Deep Research (EDR) is a multi-agent system that enhances enterprise analytics by outperforming state-of-the-art systems in benchmarks without human intervention.

TeaRAG improves token efficiency in Retrieval-Augmented Generation by compressing retrieval content and reasoning steps, enhancing Exact Match by up to 4% while reducing token output by over 59%.

TOBUGraph enhances LLM retrieval by using knowledge graphs to improve precision and recall, outperforming RAG methods in real-world applications.

Middle attention layers in language models provide better influence estimation than first layers, challenging previous assumptions. New aggregation methods improve performance.

AVIATOR, a new AI-agentic vulnerability injection workflow, achieves 91%-95% success rates in generating realistic software vulnerabilities, surpassing existing methods.

Activation-Informed Merging (AIM) enhances large language model performance by up to 40% using activation-space information.

HugAgent introduces a benchmark for evaluating large language models' ability to simulate individualized human reasoning, highlighting gaps in current AI models.

The Chain-of-Query (CoQ) framework improves SQL-aided table understanding by using multi-agent collaboration, enhancing accuracy and reducing invalid SQL rates.

The study explores how imagination networks in humans differ from those in large language models (LLMs), revealing significant differences in network clustering and centrality correlations.

The study introduces the Trustworthiness Calibration Framework (TCF) to evaluate phishing email detectors using large language models, highlighting GPT-4's superior trust profile.

ProRefine enhances AI prompt accuracy by 3-37% using inference-time optimization, enabling smaller models to perform like larger ones.

Most organizations are still piloting AI, with 88% using it in at least one function, but only 39% report enterprise-level EBIT impact. High performers focus on growth and innovation.

Retail banks could increase profits by $370 billion by 2030 through AI adoption, according to BCG.

The study reveals that ChatGPT's responses align with inferred political views, showing varied reasoning and vocabulary. This suggests potential bias in AI outputs.

POLIS-Bench introduces a comprehensive evaluation framework for LLMs in bilingual governmental policy tasks, highlighting a performance hierarchy among models.

The paper highlights the importance of construct validity in evaluating large language models (LLMs) and offers eight recommendations to improve benchmark development.

WinnowRAG enhances retrieval-augmented generation by filtering irrelevant documents, improving accuracy without model fine-tuning.

The study uses agent-based simulations with Large Language Models to assess misinformation vulnerabilities, finding mental schemas more influential than professional backgrounds in interpreting misinformation.

EncouRAGe is a Python framework for evaluating Retrieval-Augmented Generation (RAG) systems, highlighting the underperformance of RAG compared to Oracle Context and the superior results of Hybrid BM25.

JPMorgan Chase's AI transformation, led by Derek Waldron, leverages a $18 billion tech budget and LLM Suite to democratize AI use among employees, enhancing productivity and innovation.

ATLAS, an adaptive testing framework, reduces evaluation items by 90% while maintaining precision, challenging static benchmarks in LLM evaluation.

High-level piece on how real-time payment systems are reshaping the future of the payments industry and broader financial services.

Agentic AI transforms enterprise automation by enabling agents to reason, act, and collaborate, bridging the gap between knowledge and outcomes. Azure AI Foundry provides a unified platform to develop and deploy these agents efficiently.

RegTech, or regulatory technology, helps financial institutions manage compliance and risk. In 2024, banks paid $19.3 billion in penalties, highlighting the need for effective RegTech solutions.

The global payments industry, valued at $2.4 trillion, faces growing fraud risks, with online payment fraud expected to exceed $362 billion by 2028. Payments providers must balance fraud prevention with customer experience.

AI is transforming the insurance industry, with leaders achieving 6.1 times the TSR of laggards. Insurers must integrate AI enterprise-wide to remain competitive.

New CROs should adopt six key habits to succeed, including establishing a clear risk strategy and fostering strong relationships with the board and supervisors.

De Nederlandsche Bank's 2024 study reveals that 15 out of 36 insurers use AI, mainly for fraud detection and risk assessment. The AI Act, effective from August 2024, guides AI application in insurance.

The paper explores the reasons behind the failure of multi-agent large language model (LLM) systems, focusing on coordination and communication issues.

Agentic AI autonomously solves complex, multi-step problems using sophisticated reasoning and iterative planning, enhancing productivity across industries.

Data science in 2024 will be shaped by AI, big data, and interdisciplinary integration, impacting career opportunities and industry practices.

Digital transformation and data-driven insights are reshaping financial services, with fintechs challenging traditional banks. Open banking and compliance with regulations like PSD2 and ESG are crucial. AI and cybersecurity are key focus areas.

AI and quantum computing are reshaping computer science, with AI aiding 18% of research papers and quantum computing promising breakthroughs despite current limitations.

Digital transformation integrates digital technology into business operations, driving competitive advantage and customer value. It is crucial for businesses to stay relevant in a technology-driven world.

Deloitte's report outlines how Agentic AI can drive cost savings, job creation, and growth by 2028. BFSI sectors can leverage AI for efficiency and innovation.

AI will transform insurance by 2030, shifting from 'detect and repair' to 'predict and prevent'. This will impact claims, distribution, and underwriting.