As autonomous AI agents move from theory into implementation, their impact on the financial services sector is becoming tangible. A recent whitepaper from IBM Consulting, titled “Agentic AI in Financial Services: Opportunities, Risks, and Responsible Implementation”, outlines how these AI systems—designed for autonomous decision-making and long-term planning—can fundamentally reshape how financial institutions operate. The paper presents a balanced framework that identifies where Agentic AI can add value, the risks it introduces, and how institutions can implement these systems responsibly. Understanding Agentic AI AI agents, in this context, are software entities that interact with their environments to accomplish tasks with a high degree of autonomy. Unlike traditional automation or even LLM-powered chatbots, Agentic AI incorporates planning, memory, and reasoning to execute dynamic tasks across systems. IBM categorizes them into Principal, Service, and Task agents, which collaborate in orchestrated systems. These systems enable the agents to autonomously process information, select tools, and interact with human users or enterprise systems in a closed loop of goal pursuit and reflection. The whitepaper describes the evolution from rule-based automation to multi-agent orchestration, emphasizing how LLMs now serve as the reasoning engine that drives agent behavior in real-time. Crucially, these agents can adapt to evolving conditions and handle complex, cross-domain tasks, making them ideal for the intricacies of financial services. Key Opportunities in Finance IBM identifies three primary use case patterns where Agentic AI can unlock significant value: Customer Engagement & Personalization Agents can streamline onboarding, personalize services through real-time behavioral data, and drive KYC/AML processes using tiered agent hierarchies that reduce manual oversight.Operational Excellence & Governance Agents improve internal efficiencies by automating risk management, compliance verification, and anomaly detection, while maintaining auditability and traceability.Technology & Software Development They support IT teams with automated testing, predictive maintenance, and infrastructure optimization—redefining DevOps through dynamic, self-improving workflows. These systems promise to replace fragmented interfaces and human handoffs with integrated, persona-driven agent experiences grounded in high-quality, governed data products. Risk Landscape and Mitigation Strategies Autonomy in AI brings unique risks. The IBM paper categorizes them under the system’s core components—goal misalignment, tool misuse, and dynamic deception being among the most critical. For instance, a wealth management agent might misinterpret a client’s risk appetite due to goal drift, or bypass controls by chaining permissible actions in unintended ways. Key mitigation strategies include: Goal Guardrails: Explicitly defined objectives, real-time monitoring, and value alignment feedback loops. Access Controls: Least-privilege design for tool/API access, combined with dynamic rate-limiting and auditing. Persona Calibration: Regularly reviewing agents’ behavior to avoid biased or unethical actions. The whitepaper also emphasizes agent persistence and system drift as long-term governance challenges. Persistent memory, while enabling learning, can cause agents to act on outdated assumptions. IBM proposes memory reset protocols and periodic recalibrations to counteract drift and ensure continued alignment with organizational values. Regulatory Readiness and Ethical Design IBM outlines regulatory developments in jurisdictions like the EU and Australia, where agentic systems are increasingly considered “high-risk.” These systems must comply with emerging mandates for transparency, explainability, and continuous human oversight. In the EU’s AI Act, for example, agents influencing access to financial services may fall under stricter obligations due to their autonomous and adaptive behavior. The paper recommends proactive alignment with ethical AI principles even in the absence of regulation—asking not just can we, but should we. This includes auditing agents for deceptive behavior, embedding human-in-the-loop structures, and maintaining transparency through natural language decision narratives and visualized reasoning paths. Conclusion Agentic AI stands at the frontier of enterprise automation. For financial services firms, the promise lies in enhanced personalization, operational agility, and AI-driven governance. Yet these benefits are closely linked to how responsibly these systems are designed and deployed. IBM’s whitepaper serves as a practical guide—advocating for a phased, risk-aware adoption strategy that includes governance frameworks, codified controls, and cross-functional accountability. Check out the White Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit. Mohammad AsjadAsjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.Mohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Critical Security Vulnerabilities in the Model Context Protocol (MCP): How Malicious Tools and Deceptive Contexts Exploit AI AgentsMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Stability AI Introduces Adversarial Relativistic-Contrastive (ARC) Post-Training and Stable Audio Open Small: A Distillation-Free Breakthrough for Fast, Diverse, and Efficient Text-to-Audio Generation Across DevicesMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge DeploymentMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and Privacy 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! 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The Model Context Protocol (MCP) represents a powerful paradigm shift in how large language models interact with tools, services, and external data sources. Designed to enable dynamic tool invocation, the MCP facilitates a standardized method for describing tool metadata, allowing models to select and call functions intelligently. However, as with any emerging framework that enhances model autonomy, MCP introduces significant security concerns. Among these are five notable vulnerabilities: Tool Poisoning, Rug-Pull Updates, Retrieval-Agent Deception (RADE), Server Spoofing, and Cross-Server Shadowing. Each of these weaknesses exploits a different layer of the MCP infrastructure and reveals potential threats that could compromise user safety and data integrity. Tool Poisoning is one of the most insidious vulnerabilities within the MCP framework. At its core, this attack involves embedding malicious behavior into a harmless tool. In MCP, where tools are advertised with brief descriptions and input/output schemas, a bad actor can craft a tool with a name and summary that seem benign, such as a calculator or formatter. However, once invoked, the tool might perform unauthorized actions such as deleting files, exfiltrating data, or issuing hidden commands. Since the AI model processes detailed tool specifications that may not be visible to the end-user, it could unknowingly execute harmful functions, believing it operates within the intended boundaries. This discrepancy between surface-level appearance and hidden functionality makes tool poisoning particularly dangerous. Rug-Pull Updates Closely related to tool poisoning is the concept of Rug-Pull Updates. This vulnerability centers on the temporal trust dynamics in MCP-enabled environments. Initially, a tool may behave exactly as expected, performing useful, legitimate operations. Over time, the developer of the tool, or someone who gains control of its source, may issue an update that introduces malicious behavior. This change might not trigger immediate alerts if users or agents rely on automated update mechanisms or do not rigorously re-evaluate tools after each revision. The AI model, still operating under the assumption that the tool is trustworthy, may call it for sensitive operations, unwittingly initiating data leaks, file corruption, or other undesirable outcomes. The danger of rug-pull updates lies in the deferred onset of risk: by the time the attack is active, the model has often already been conditioned to trust the tool implicitly. Retrieval-Agent Deception Retrieval-Agent Deception, or RADE, exposes a more indirect but equally potent vulnerability. In many MCP use cases, models are equipped with retrieval tools to query knowledge bases, documents, and other external data to enhance responses. RADE exploits this feature by placing malicious MCP command patterns into publicly accessible documents or datasets. When a retrieval tool ingests this poisoned data, the AI model may interpret embedded instructions as valid tool-calling commands. For instance, a document that explains a technical topic might include hidden prompts that direct the model to call a tool in an unintended manner or supply dangerous parameters. The model, unaware that it has been manipulated, executes these instructions, effectively turning retrieved data into a covert command channel. This blurring of data and executable intent threatens the integrity of context-aware agents that rely heavily on retrieval-augmented interactions. Server Spoofing Server Spoofing constitutes another sophisticated threat in MCP ecosystems, particularly in distributed environments. Because MCP enables models to interact with remote servers that expose various tools, each server typically advertises its tools via a manifest that includes names, descriptions, and schemas. An attacker can create a rogue server that mimics a legitimate one, copying its name and tool list to deceive models and users alike. When the AI agent connects to this spoofed server, it may receive altered tool metadata or execute tool calls with entirely different backend implementations than expected. From the model’s perspective, the server seems legitimate, and unless there is strong authentication or identity verification, it proceeds to operate under false assumptions. The consequences of server spoofing include credential theft, data manipulation, or unauthorized command execution. Cross-Server Shadowing Finally, Cross-Server Shadowing reflects the vulnerability in multi-server MCP contexts where several servers contribute tools to a shared model session. In such setups, a malicious server can manipulate the model’s behavior by injecting context that interferes with or redefines how tools from another server are perceived or used. This can occur through conflicting tool definitions, misleading metadata, or injected guidance that distorts the model’s tool selection logic. For example, if one server redefines a common tool name or provides conflicting instructions, it can effectively shadow or override the legitimate functionality offered by another server. The model, attempting to reconcile these inputs, may execute the wrong version of a tool or follow harmful instructions. Cross-server shadowing undermines the modularity of the MCP design by allowing one bad actor to corrupt interactions that span multiple otherwise secure sources. In conclusion, these five vulnerabilities expose critical security weaknesses in the Model Context Protocol’s current operational landscape. While MCP introduces exciting possibilities for agentic reasoning and dynamic task completion, it also opens the door to various behaviors that exploit model trust, contextual ambiguity, and tool discovery mechanisms. As the MCP standard evolves and gains broader adoption, addressing these threats will be essential to maintaining user trust and ensuring the safe deployment of AI agents in real-world environments. Sources https://techcommunity.microsoft.com/blog/microsoftdefendercloudblog/plug-play-and-prey-the-security-risks-of-the-model-context-protocol/4410829 Mohammad AsjadAsjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.Mohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Stability AI Introduces Adversarial Relativistic-Contrastive (ARC) Post-Training and Stable Audio Open Small: A Distillation-Free Breakthrough for Fast, Diverse, and Efficient Text-to-Audio Generation Across DevicesMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge DeploymentMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and PrivacyMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/ServiceNow AI Released Apriel-Nemotron-15b-Thinker: A Compact Yet Powerful Reasoning Model Optimized for Enterprise-Scale Deployment and Efficiency 🚨 Build GenAI you can trust. ⭐️ Parlant is your open-source engine for controlled, compliant, and purposeful AI conversations — Star Parlant on GitHub! 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Text-to-audio generation has emerged as a transformative approach for synthesizing sound directly from textual prompts, offering practical use in music production, gaming, and virtual experiences. Under the hood, these models typically employ Gaussian flow-based techniques such as diffusion or rectified flows. These methods model the incremental steps that transition from random noise to structured audio. While highly effective in producing high-quality soundscapes, the slow inference speeds have posed a barrier to real-time interactivity. It is particularly limiting when creative users expect an instrument-like responsiveness from these tools. Latency is the primary issue with these systems. Current text-to-audio models can take several seconds or even minutes to generate a few seconds of audio. The core bottleneck lies in their step-based inference architecture, requiring between 50 and 100 iterations per output. Previous acceleration strategies focus on distillation methods where smaller models are trained under the supervision of larger teacher models to replicate multi-step inference in fewer steps. However, these distillation methods are computationally expensive. They demand large-scale storage for intermediate training outputs or require simultaneous operation of several models in memory, which hinders their adoption, especially on mobile or edge devices. Also, such methods often sacrifice output diversity and introduce over-saturation artifacts. While a few adversarial post-training methods have been attempted to bypass the cost of distillation, their success has been limited. Most existing implementations rely on partial distillation for initialization or do not scale well to complex audio synthesis. Also, audio applications have seen fewer fully adversarial solutions. Tools like Presto integrate adversarial objectives but still depend on teacher models and CFG-based training for prompt adherence, which restricts their generative diversity. Researchers from UC San Diego, Stability AI, and Arm introduced Adversarial Relativistic-Contrastive (ARC) post-training. This approach sidesteps the need for teacher models, distillation, or classifier-free guidance. Instead, ARC enhances an existing pre-trained rectified flow generator by integrating two novel training objectives: a relativistic adversarial loss and a contrastive discriminator loss. These help the generator produce high-fidelity audio in fewer steps while maintaining strong alignment with text prompts. When paired with the Stable Audio Open (SAO) framework, the result was a system capable of generating 12 seconds of 44.1 kHz stereo audio in only 75 milliseconds on an H100 GPU and around 7 seconds on mobile devices. With ARC methodology, they introducedStable Audio Open Small, a compact and efficient version of SAO tailored for resource-constrained environments. This model contains 497 million parameters and uses an architecture built on a latent diffusion transformer. It consists of three main components: a waveform-compressing autoencoder, a T5-based text embedding system for semantic conditioning, and a DiT (Diffusion Transformer) that operates within the latent space of the autoencoder. Stable Audio Open Small can generate stereo audio up to 11 seconds long at 44.1 kHz. It is designed to be deployed using the ‘stable-audio-tools’ library and supports ping-pong sampling, enabling efficient few-step generation. The model demonstrated exceptional inference efficiency, achieving generation speeds of under 7 seconds on a Vivo X200 Pro phone after applying dynamic Int8 quantization, which also cut RAM usage from 6.5GB to 3.6 GB. This makes it especially viable for on-device creative applications like mobile audio tools and embedded systems. The ARC training approach involves replacing the traditional L2 loss with an adversarial formulation where generated and real samples, paired with identical prompts, are evaluated by a discriminator trained to distinguish between them. A contrastive objective teaches the discriminator to rank accurate audio-text pairs higher than mismatched ones to improve prompt relevance. These paired objectives eliminate the need for CFG while achieving better prompt adherence. Also, ARC adopts ping-pong sampling to refine the audio output through alternating denoising and re-noising cycles, reducing inference steps without compromising quality. ARC’s performance was evaluated extensively. In objective tests, it achieved an FDopenl3 score of 84.43, a KLpasst score of 2.24, and a CLAP score of 0.27, indicating balanced quality and semantic precision. Diversity was notably strong, with a CLAP Conditional Diversity Score (CCDS) of 0.41. Real-Time Factor reached 156.42, reflecting outstanding generation speed, while GPU memory usage remained at a practical 4.06 GB. Subjectively, ARC scored 4.4 for diversity, 4.2 for quality, and 4.2 for prompt adherence in human evaluations involving 14 participants. Unlike distillation-based models like Presto, which scored higher on quality but dropped to 2.7 on diversity, ARC presented a more balanced and practical solution. Several Key Takeaways from the Research by Stability AI on Adversarial Relativistic-Contrastive (ARC) post-training and  Stable Audio Open Small include:  ARC post-training avoids distillation and CFG, relying on adversarial and contrastive losses. ARC generates 12s of 44.1 kHz stereo audio in 75ms on H100 and 7s on mobile CPUs. It achieves 0.41 CLAP Conditional Diversity Score, the highest among tested models. Subjective scores: 4.4 (diversity), 4.2 (quality), and 4.2 (prompt adherence). Ping-pong sampling enables few-step inference while refining output quality. Stable Audio Open Small offers 497M parameters, supports 8-step generation, and is compatible with mobile deployments. On Vivo X200 Pro, inference latency dropped from 15.3s to 6.6s with half the memory. ARC and SAO Small provide real-time solutions for music, games, and creative tools. In conclusion, the combination of ARC post-training and Stable Audio Open Small eliminates the reliance on resource-intensive distillation and classifier-free guidance, enabling researchers to deliver a streamlined adversarial framework that accelerates inference without compromising output quality or prompt adherence. ARC enables fast, diverse, and semantically rich audio synthesis in high-performance and mobile environments. With Stable Audio Open Small optimized for lightweight deployment, this research lays the groundwork for integrating responsive, generative audio tools into everyday creative workflows, from professional sound design to real-time applications on edge devices. Check out the Paper, GitHub Page and Model on Hugging Face. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. Mohammad AsjadAsjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.Mohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge DeploymentMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Enterprise AI Without GPU Burn: Salesforce’s xGen-small Optimizes for Context, Cost, and PrivacyMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/ServiceNow AI Released Apriel-Nemotron-15b-Thinker: A Compact Yet Powerful Reasoning Model Optimized for Enterprise-Scale Deployment and EfficiencyMohammad Asjadhttps://www.marktechpost.com/author/mohammad_asjad/Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That Recovers Atomic Attention Units Hidden in Transformer Superposition

As machine learning systems become integral to various applications, from recommendation engines to autonomous systems, there’s a growing need to address their environmental sustainability. These systems require extensive computational resources, often running on custom-designed hardware accelerators. Their energy demands are substantial during training and inference phases, contributing to operational carbon emissions. Also, the hardware that powers these models carries its environmental burden, called embodied carbon, from manufacturing, materials, and life-cycle operations. Addressing these dual carbon sources is essential for reducing the ecological impact of machine learning technologies, especially as global adoption continues to accelerate across industries and use cases. Despite increasing awareness, current strategies for mitigating the carbon impact of machine learning systems remain fragmented. Most methods focus on operational efficiency, reducing energy consumption during training and inference, or improving hardware utilization. However, few approaches consider both sides of the equation: the carbon emitted during hardware operation and that embedded in the hardware’s design and manufacturing process. This split perspective overlooks how decisions made at the model design stage influence hardware efficiency and vice versa. Multi-modal models, which integrate visual and textual data, exacerbate this issue due to their inherently complex and heterogeneous computing requirements. Several techniques currently employed to enhance AI model efficiency, including pruning and distillation, aim to maintain accuracy while decreasing inference time or energy use. Hardware-aware neural architecture search (NAS) methods further explore architectural variants to fine-tune performance, typically favoring latency or energy minimization. Despite their sophistication, these methods often fail to account for embodied carbon, the emissions tied to the physical hardware’s construction and lifetime. Frameworks such as ACT, IMEC.netzero, and LLMCarbon have recently started modeling embodied carbon independently, but they lack the integration necessary for holistic optimization. Similarly, adaptations of CLIP for edge use cases, including TinyCLIP and ViT-based models, prioritize deployment feasibility and speed, overlooking total carbon output. These approaches provide partial solutions that are effective within their scope but insufficient for meaningful environmental mitigation. Researchers from FAIR at Meta and Georgia Institute of Technology developed CATransformers, a framework that introduces carbon as a primary design consideration. This innovation allows researchers to co-optimize model architectures and hardware accelerators by jointly evaluating their performance against carbon metrics. The solution targets devices for edge inference, where both embodied and operational emissions must be controlled due to hardware constraints. Unlike traditional methods, CATransformers enables early design space exploration using a multi-objective Bayesian optimization engine that evaluates trade-offs among latency, energy consumption, accuracy, and total carbon footprint. This dual consideration enables model configurations that reduce emissions without sacrificing the quality or responsiveness of the models, offering a meaningful step toward sustainable AI systems. The core functionality of CATransformers lies in its three-module architecture:  A multi-objective optimizer An ML model evaluator A hardware estimator The model evaluator generates model variants by pruning a large base CLIP model, altering dimensions such as the number of layers, feedforward network size, attention heads, and embedding width. These pruned versions are then passed to the hardware estimator, which uses profiling tools to estimate each configuration’s latency, energy usage, and total carbon emissions. The optimizer then selects the best-performing setups by balancing all metrics. This structure allows rapid evaluation of the interdependencies between model design and hardware deployment, offering precise insight into how architectural choices affect total emissions and performance outcomes. The practical output of CATransformers is the CarbonCLIP family of models, which delivers substantial gains over existing small-scale CLIP baselines. CarbonCLIP-S achieves the same accuracy as TinyCLIP-39M but reduces total carbon emissions by 17% and maintains latency under 15 milliseconds. CarbonCLIP-XS, a more compact version, offers 8% better accuracy than TinyCLIP-8M while reducing emissions by 3% and ensuring latency remains below 10 milliseconds. Notably, when comparing configurations optimized solely for latency, the hardware requirements often doubled, leading to significantly higher embodied carbon. In contrast, configurations optimized for carbon and latency achieved a 19-20% reduction in total emissions with minimal latency trade-offs. These findings underscore the importance of integrated carbon-aware design. Several Key Takeaways from the Research on CATransformers include: CATransformers introduces carbon-aware co-optimization for machine learning systems by evaluating operational and embodied carbon emissions. The framework applies multi-objective Bayesian optimization, integrating accuracy, latency, energy, and carbon footprint into the search process. A family of CLIP-based models, CarbonCLIP-S and CarbonCLIP-XS, was developed using this method. CarbonCLIP-S achieves a 17% reduction in emissions compared to TinyCLIP-39M, with similar accuracy and <15 ms latency. CarbonCLIP-XS offers 8% improved accuracy over TinyCLIP-8M while reducing carbon by 3% and achieving <10 ms latency. Designs optimized only for latency led to an increase of up to 2.4× in embodied carbon, showing the risk of ignoring sustainability. Combined optimization strategies provided 19-20% carbon reductions with minimal latency increases, demonstrating a practical trade-off path. The framework includes pruning strategies, hardware estimation, and architectural simulation based on real-world hardware templates. This research lays the groundwork for sustainable ML system design by embedding environmental metrics into the optimization pipeline. In conclusion, this research sheds light on a practical path toward building environmentally responsible AI systems. By aligning model design with hardware capabilities from the outset and factoring in carbon impact, the researchers demonstrate that it’s possible to make smarter choices that don’t just chase speed or energy savings but genuinely reduce emissions. The results highlight that conventional methods can unintentionally lead to higher carbon costs when optimized for narrow goals like latency. With CATransformers, developers have a tool to rethink how performance and sustainability can go hand in hand, especially as AI continues to scale across industries. Check out the Paper and GitHub Page. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. The post Meta AI Introduces CATransformers: A Carbon-Aware Machine Learning Framework to Co-Optimize AI Models and Hardware for Sustainable Edge Deployment appeared first on MarkTechPost.