Email authentication protocols like SPF, DKIM, and DMARC play a critical role in protecting domains from spoofing and phishing. However, when SEGs are introduced into the email path, the interaction with these protocols becomes more complex. Security gateways(SEGs) are a core part of many organizations’ email infrastructure. They act as intermediaries between the public internet and internal mail systems, inspecting, filtering, and routing messages. This blog examines how security gateways handle SPF, DKIM, and DMARC, with real-world examples from popular gateways such as Proofpoint, Mimecast, and Avanan. We’ll also cover best practices for maintaining authentication integrity and avoiding misconfigurations that can compromise email authentication or lead to false DMARC failures. Security gateways often sit at the boundary between your organization and the internet, managing both inbound and outbound email traffic. Their role affects how email authentication protocols behave. An inbound SEG examines emails coming into your organization. It checks SPF, DKIM, and DMARC to determine if the message is authentic and safe before passing it to your internal mail servers. An outbound SEG handles emails sent from your domain. It may modify headers, rewrite envelope addresses, or even apply DKIM signing. All of these can impact SPF,  DKIM, or DMARC validation on the recipient’s side. Understanding how SEGs influence these flows is crucial to maintaining proper authentication and avoiding unexpected DMARC failures. Inbound Handling of SPF, DKIM, and DMARC by Common Security Gateways When an email comes into your organization, your security gateway is the first to inspect it. It checks whether the message is real, trustworthy, and properly authenticated. Let’s look at how different SEGs handle these checks. Avanan (by Check Point) SPF: Avanan verifies whether the sending server is authorized to send emails for the domain by checking the SPF record. DKIM: It verifies if the message was signed by the sending domain and if that signature is valid. DMARC: It uses the results of the SPF and DKIM check to evaluate DMARC. However, final enforcement usually depends on how DMARC is handled by Microsoft 365 or Gmail, as Avanan integrates directly with them. Avanan offers two methods of integration:1. API integration: Avanan connects via APIs, no change in MX, usually Monitor or Detect modes.2. Inline integration: Avanan is placed inline in the mail flow (MX records changed), actively blocking or remediating threats. Proofpoint Email Protection SPF: Proofpoint checks SPF to confirm the sender’s IP is authorized to send on behalf of the domain. You can set custom rules (e.g. treat “softfail” as “fail”). DKIM: It verifies DKIM signatures and shows clear pass/fail results in logs. DMARC: It fully evaluates DMARC by combining SPF and DKIM results with alignment checks. Administrators can configure how to handle messages that fail DMARC, such as rejecting, quarantining, or delivering them. Additionally, Proofpoint allows whitelisting specific senders you trust, even if their emails fail authentication checks. Integration Methods Inline Mode: In this traditional deployment, Proofpoint is positioned directly in the email flow by modifying MX records. Emails are routed through Proofpoint’s infrastructure, allowing it to inspect and filter messages before they reach the recipient’s inbox. This mode provides pre-delivery protection and is commonly used in on-premises or hybrid environments. API-Based (Integrated Cloud Email Security – ICES) Mode: Proofpoint offers API-based integration, particularly with cloud email platforms like Microsoft 365 and Google Workspace. In this mode, Proofpoint connects to the email platform via APIs, enabling it to monitor and remediate threats post-delivery without altering the email flow. This approach allows for rapid deployment and seamless integration with existing cloud email services. Mimecast SPF: Mimecast performs SPF checks to verify whether the sending server is authorized by the domain’s SPF record. Administrators can configure actions for SPF failures, including block, quarantine, permit, or tag with a warning. This gives flexibility in balancing security with business needs. DKIM: It validates DKIM signatures by checking that the message was correctly signed by the sending domain and that the content hasn’t been tampered with. If the signature fails, Mimecast can take actions based on your configured policies. DMARC: It fully evaluates DMARC by combining the results of SPF and DKIM with domain alignment checks. You can choose to honor the sending domain’s DMARC policy (none, quarantine, reject) or apply custom rules, for example, quarantining or tagging messages that fail DMARC regardless of the published policy. This allows more granular control for businesses that want to override external domain policies based on specific contexts. Integration Methods Inline Deployment: Mimecast is typically deployed as a cloud-based secure email gateway. Organizations update their domain’s MX records to point to Mimecast, so all inbound (and optionally outbound) emails pass through it first. This allows Mimecast to inspect, filter, and process emails before delivery, providing robust protection. API Integrations: Mimecast also offers API-based services through its Mimecast API platform, primarily for management, archival, continuity, and threat intelligence purposes. However, API-only email protection is not Mimecast’s core model. Instead, the APIs are used to enhance the inline deployment, not replace it. Barracuda Email Security Gateway SPF: Barracuda checks the sender’s IP against the domain’s published SPF record. If the check fails, you can configure the system to block, quarantine, tag, or allow the message, depending on your policy preferences. DKIM: It validates whether the incoming message includes a valid DKIM signature. The outcome is logged and used to inform further policy decisions or DMARC evaluations. DMARC: It combines SPF and DKIM results, checks for domain alignment, and applies the DMARC policy defined by the sender. Administrators can also choose to override the DMARC policy, allowing messages to pass or be treated differently based on organizational needs (e.g., trusted senders or internal exceptions). Integration Methods Inline mode (more common and straightforward): Barracuda Email Security Gateway is commonly deployed inline by updating your domain’s MX records to point to Barracuda’s cloud or on-premises gateway. This ensures that all inbound emails pass through Barracuda first for filtering and SPF, DKIM, and DMARC validation before being delivered to your mail servers. Deployment Behind the Corporate Firewall: Alternatively, Barracuda can be deployed in transparent or bridge mode without modifying MX records. In this setup, the gateway is placed inline at the network level, such as behind a firewall, and intercepts mail traffic transparently. This method is typically used in complex on-premises environments where changing DNS records is not feasible. Cisco Secure Email (formerly IronPort) Cisco Secure Email acts as an inline gateway for inbound email, usually requiring your domain’s MX records to point to the Cisco Email Security Appliance or cloud service. SPF: Cisco Secure Email verifies whether the sending server is authorized in the sender domain’s SPF record. Administrators can set detailed policies on how to handle SPF failures. DKIM: It validates the DKIM signature on incoming emails and logs whether the signature is valid or has failed. DMARC: It evaluates DMARC by combining SPF and DKIM results along with domain alignment checks. Admins can configure specific actions, such as quarantine, reject, or tag, based on different failure scenarios or trusted sender exceptions. Integration methods On-premises Email Security Appliance (ESA): You deploy Cisco’s hardware or virtual appliance inline, updating MX records to route mail through it for filtering. Cisco Cloud Email Security: Cisco offers a cloud-based email security service where MX records are pointed to Cisco’s cloud infrastructure, which filters and processes inbound mail. Cisco Secure Email also offers advanced, rule-based filtering capabilities and integrates with Cisco’s broader threat protection ecosystem, enabling comprehensive inbound email security. Outbound Handling of SPF, DKIM, and DMARC by Common Security Gateways When your organization sends emails, security gateways can play an active role in processing and authenticating those messages. Depending on the configuration, a gateway might rewrite headers, re-sign messages, or route them through different IPs – all actions that can help or hurt the authentication process. Let’s look at how major SEGs handle outbound email flow. Avanan – Outbound Handling and Integration Methods Outbound Logic Avanan analyzes outbound emails primarily to detect data loss, malware, and policy violations. In API-based integration, emails are sent directly by the original mail server (e.g., Microsoft 365 or Google Workspace), so SPF and DKIM signatures remain intact. Avanan does not alter the message or reroute traffic, which helps maintain full DMARC alignment and domain reputation. Integration Methods 1. API Integration: Connects to Microsoft 365 or Google Workspace via API. No MX changes are needed. Emails are scanned after they are sent, with no modification to SPF, DKIM, or the delivery path.  How it works: Microsoft Graph API or Google Workspace APIs are used to monitor and intervene in outbound emails. Protection level: Despite no MX changes, it can offer inline-like protection, meaning it can block, quarantine, or encrypt emails before they are delivered externally. SPF/DKIM/DMARC impact: Preserves original headers and signatures since mail is sent directly from Microsoft/Google servers. 2. Inline Integration: Requires changing MX records to route email through Avanan. In this mode, Avanan can intercept and inspect outbound emails before delivery. Depending on the configuration, this may affect SPF or DKIM if not properly handled. How it works: Requires adding Avanan’s Protection level: Traditional inline security with full visibility and control, including encryption, DLP, policy enforcement, and advanced threat protection. SPF/DKIM/DMARC impact: SPF configuration is needed by adding Avanan’s include mechanism to the sending domain’s SPF record. The DKIM record of the original sending source is preserved. For configurations, you can refer to the steps in this blog. Proofpoint – Outbound Handling and Integration Methods Outbound Logic Proofpoint analyzes outbound emails to detect and prevent data loss (DLP), to identify advanced threats (malware, phishing, BEC) originating from compromised internal accounts, and to ensure compliance. Their API integration provides crucial visibility and powerful remediation capabilities, while their traditional gateway (MX record) deployment delivers true inline, pre-delivery blocking for outbound traffic. Integration methods 1. API Integration: No MX record changes are required for this deployment method. Integration is done with Microsoft 365 or Google Workspace. How it works: Through its API integration, Proofpoint gains deep visibility into outbound emails and provides layered security and response features, including: Detect and alert: Identifies sensitive content (Data Loss Prevention violations), malicious attachments, or suspicious links in outbound emails. Post-delivery remediation (TRAP): A key capability of the API model is Threat Response Auto-Pull (TRAP), which enables Proofpoint to automatically recall, quarantine, or delete emails after delivery. This is particularly useful for internally sent messages or those forwarded to other users. Enhanced visibility: Aggregates message metadata and logs into Proofpoint’s threat intelligence platform, giving security teams a centralized view of outbound risks and user behavior. Protection level: API-based integration provides strong post-delivery detection and response, as well as visibility into DLP incidents and suspicious behavior.  SPF/DKIM/DMARC impact: Proofpoint does not alter SPF, DKIM, or DMARC because emails are sent directly through Microsoft or Google servers. Since Proofpoint’s servers are not involved in the actual sending process, the original authentication headers remain intact. 2. Gateway Integration (MX Record/Smart Host): This method requires updating MX records or routing outbound mail through Proofpoint via a smart host. How it works: Proofpoint acts as an inline gateway, inspecting emails before delivery. Inbound mail is filtered via MX changes; outbound mail is relayed through Proofpoint’s servers. Threat and DLP filtering: Scans outbound messages for sensitive content, malware, and policy violations. Real-time enforcement: Blocks, encrypts, or quarantines emails before they’re delivered. Policy controls: Applies rules based on content, recipient, or behavior. Protection level: Provides strong, real-time protection for outbound traffic with pre-delivery enforcement, DLP, and encryption. SPF/DKIM/DMARC impact: Proofpoint becomes the sending server: SPF: You need to configure ProofPoint’s SPF. DKIM: Can sign messages; requires DKIM setup. DMARC: DMARC passes if SPF and DKIM are set up properly. Please refer to this article to configure SPF and DKIM for ProofPoint. Mimecast – Outbound Handling and Integration Methods Outbound Logic Mimecast inspects outbound emails to prevent data loss (DLP), detect internal threats such as malware and impersonation, and ensure regulatory compliance. It primarily functions as a Secure Email Gateway (SEG), meaning it sits directly in the outbound email flow. While Mimecast offers APIs, its core outbound protection is built around this inline gateway model. Integration Methods 1. Gateway Integration (MX Record change required) This is Mimecast’s primary method for outbound email protection. Organizations route their outbound traffic through Mimecast by configuring their email server (e.g., Microsoft 365, Google Workspace, etc.) to use Mimecast as a smart host. This enables Mimecast to inspect and enforce policies on all outgoing emails in real time. How it works: Updating outbound routing in your email system (smart host settings), or Using Mimecast SMTP relay to direct messages through their infrastructure. Mimecast then scans, filters, and applies policies before the email reaches the final recipient. Protection level: Advanced DLP: Identifies and prevents sensitive data leaks. Impersonation and Threat Protection: Blocks malware, phishing, and abuse from compromised internal accounts. Email Encryption and Secure Messaging: Applies encryption policies or routes messages via secure portals. Regulatory Compliance: Enforces outbound compliance rules based on content, recipient, or metadata. SPF/DKIM/DMARC impact: SPF: Your SPF record must include Mimecast’s SPF mechanism based on your region to avoid SPF failures. DKIM: A new DKIM record should be configured to make sure your emails are DKIM signed when routing through Mimecast. DMARC: With correct SPF and DKIM setup, Mimecast ensures DMARC alignment, maintaining your domain’s sending reputation. Please refer to the steps in this detailed article to set up SPF and DKIM for Mimecast. 2. API Integration (Complementary to Gateway) Mimecast’s APIs complement the main gateway by providing automation, reporting, and management tools rather than handling live outbound mail flow. They allow you to manage policies, export logs, search archived emails, and sync users. APIs enhance visibility and operational tasks but do not provide real-time filtering or blocking of outbound messages. Since APIs don’t process live mail, they have no direct effect on SPF, DKIM, or DMARC; those depend on your gateway (smart host) setup. Barracuda – Outbound Handling and Integration Methods Outbound Logic Barracuda analyzes outbound emails to prevent data loss (DLP), block malware, stop phishing/impersonation attempts from compromised internal accounts, and ensure compliance. Barracuda offers flexible deployment options, including both traditional gateway (MX record) and API-based integrations. While both contribute to outbound security, their roles are distinct. Integration Methods 1. Gateway Integration (MX Record / Smart Host) — Primary Inline Security How it works: All outbound emails pass through Barracuda’s security stack for real-time inspection, threat blocking, and policy enforcement before delivery. Protection level: Comprehensive DLP (blocking, encrypting, or quarantining sensitive content)  Outbound spam and virus filtering  Enforcement of compliance and content policies This approach offers a high level of control and immediate threat mitigation on outbound mail flow. SPF/DKIM/DMARC impact: SPF: Update SPF records to include Barracuda’s sending IPs or SPF include mechanism. DKIM: Currently, no explicit setup is needed; DKIM of the main sending source is preserved. Refer to this article for more comprehensive guidance on Barracuda SEG configuration. 2. API Integration (Complementary & Advanced Threat Focus) How it works: The API accesses cloud email environments to analyze historical and real-time data, learning normal communication patterns to detect anomalies in outbound emails. It also supports post-delivery remediation, enabling the removal of malicious emails from internal mailboxes after sending. Protection level: Advanced AI-driven detection and near real-time blocking of outbound threats, plus strong post-delivery cleanup capabilities. SPF/DKIM/DMARC impact: Since mail is sent directly by the original mail server (e.g., Microsoft 365), SPF and DKIM signatures remain intact, preserving DMARC alignment and domain reputation. Cisco Secure Email (formerly IronPort) – Outbound Handling and Integration Methods Outbound Logic Cisco Secure Email protects outbound email by preventing data loss (DLP), blocking spam and malware from internal accounts, stopping business email compromise (BEC) and impersonation attacks, and ensuring compliance. Cisco provides both traditional gateway appliances/cloud gateways and modern API-based solutions for layered outbound security. Integration Methods 1. Gateway Integration (MX Record / Smart Host) – Cisco Secure Email Gateway (ESA) How it works: Organizations update MX records to route mail through the Cisco Secure Email Gateway or configure their mail server (e.g., Microsoft 365, Exchange) to smart host outbound email via the gateway. All outbound mail is inspected and policies enforced before delivery. Protection level: Granular DLP (blocking, encrypting, quarantining sensitive content) Outbound spam and malware filtering to protect IP reputation Email encryption for sensitive outbound messages Comprehensive content and attachment policy enforcement SPF: Check this article for comprehensive guidance on Cisco SPF settings. DKIM: Refer to this article for detailed guidance on Cisco DKIM settings. 2. API Integration – Cisco Secure Email Threat Defense How it works: Integrates directly via API with Microsoft 365 (and potentially Google Workspace), continuously monitoring email metadata, content, and user behavior across inbound, outbound, and internal messages. Leverages Cisco’s threat intelligence and AI to detect anomalous outbound activity linked to BEC, account takeover, and phishing. Post-Delivery Remediation: Automates the removal or quarantine of malicious or policy-violating emails from mailboxes even after sending. Protection level: Advanced, AI-driven detection of sophisticated outbound threats with real-time monitoring and automated remediation. Complements gateway filtering by adding cloud-native visibility and swift post-send action. SPF/DKIM/DMARC impact: Since emails are sent directly by the original mail server, SPF and DKIM signatures remain intact, preserving DMARC alignment and domain reputation. If you have any questions or need assistance, feel free to reach out to EasyDMARC technical support.

LLMs have shown impressive capabilities across various programming tasks, yet their potential for program optimization has not been fully explored. While some recent efforts have used LLMs to enhance performance in languages like C++ and Python, the broader application of LLMs to optimize code, especially in low-level programming contexts, remains limited. Existing LLM benchmarks largely focus on code generation from natural language or solving GitHub issues, as seen in HumanEval, MBPP, APPS, SWE-bench, and SWE-agent. Moreover, models such as Codex, AlphaCode, and Code Llama primarily aim to improve code generation quality rather than performance. However, select research has begun addressing optimization, including parallelization and code efficiency improvements, though many of these approaches are constrained by the need for formal verification, limiting scalability. In contrast, some newer methods embrace test-based validation, allowing optimization of more complex programs with loops. Learning-based strategies in compiler optimization—like AutoPhase, which uses reinforcement learning for pass sequencing, and Coreset, which applies graph neural networks—have shown promise in improving performance. Superoptimization techniques aim to find the most efficient version of a program but are typically restricted to small-scale problems. Additionally, frameworks like AutoTVM and Ansor have focused on optimizing GPU kernel code through statistical modeling and search. Recently, LLM-driven optimization has gained attention, with reinforcement learning approaches guiding LLMs using feedback from test cases. Techniques like CodeRL and PPOCoder leverage policy optimization methods to fine-tune models for better performance, even across resource-constrained programming languages like Verilog.  Stanford, UIUC, CMU, and Visa Research researchers explore using LLMs to optimize assembly code performance—an area traditionally handled by compilers like GCC. They introduce a reinforcement learning framework using Proximal Policy Optimization (PPO), guided by a reward balancing correctness and speedup over the gcc -O3 baseline. Using a dataset of 8,072 real-world programs, their model, Qwen2.5-Coder-7B-PPO, achieves a 96.0% test pass rate and a 1.47× average speedup, outperforming 20 other models, including Claude-3.7-sonnet. Their results show that with RL training, LLMs can effectively outperform conventional compiler optimizations.  The methodology involves optimizing compiled C programs for performance using an RL approach. Given a C program C, it is compiled to assembly P using gcc -O3. The goal is to generate a new assembly program P’ that is functionally equivalent but faster. Correctness is verified using a test set, and speedup is measured by execution time improvement. Using CodeNet as the dataset, the authors apply PPO to train a language model that generates improved code. Two reward functions—Correctness-Guided Speedup and Speedup-Only—are used to guide training based on program validity, correctness, and performance gains.  The study evaluates various language models on optimizing assembly code, revealing that most models struggle with low test pass rates and minimal speedups. However, Qwen2.5-Coder-7B-PPO, trained with reinforcement learning, significantly outperforms others, achieving 96% accuracy and a 1.47× average speedup. Ablation studies show that using gcc -O3 as a reference aids performance, while removing it leads to sharp declines. Notably, models like Claude-3.7-sonnet can surpass compilers by identifying hardware-specific optimizations, such as replacing loops with a single popcnt instruction, demonstrating their ability to perform semantic-level code transformations beyond traditional compiler capabilities.  In conclusion, the study explores using LLMs to optimize assembly code, a domain where traditional compilers struggle due to the complexity of low-level performance tuning. The authors fine-tune Qwen2.5-Coder-7B using PPO, rewarding both correctness (via test cases) and speedup over gcc -O3. They introduce a benchmark of 8,072 real-world C programs to evaluate performance. The model achieves a 96.0% test pass rate and a 1.47× average speedup, outperforming 20 other models, including Claude-3.7-sonnet. While effective, limitations include a lack of formal correctness guarantees and variability in hardware performance across systems.  Check out the 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 and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Evaluating Enterprise-Grade AI Assistants: A Benchmark for Complex, Voice-Driven WorkflowsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Beyond Aha Moments: Structuring Reasoning in Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/RXTX: A Machine Learning-Guided Algorithm for Efficient Structured Matrix MultiplicationSana Hassanhttps://www.marktechpost.com/author/sana-hassan/From Protocol to Production: How Model Context Protocol (MCP) Gateways Enable Secure, Scalable, and Seamless AI Integrations Across Enterprises

As businesses increasingly integrate AI assistants, assessing how effectively these systems perform real-world tasks, particularly through voice-based interactions, is essential. Existing evaluation methods concentrate on broad conversational skills or limited, task-specific tool usage. However, these benchmarks fall short when measuring an AI agent’s ability to manage complex, specialized workflows across various domains. This gap highlights the need for more comprehensive evaluation frameworks that reflect the challenges AI assistants face in practical enterprise settings, ensuring they can truly support intricate, voice-driven operations in real-world environments.  To address the limitations of existing benchmarks, Salesforce AI Research & Engineering developed a robust evaluation system tailored to assess AI agents in complex enterprise tasks across both text and voice interfaces. This internal tool supports the development of products like Agentforce. It offers a standardized framework to evaluate AI assistant performance in four key business areas: managing healthcare appointments, handling financial transactions, processing inbound sales, and fulfilling e-commerce orders. Using carefully curated, human-verified test cases, the benchmark requires agents to complete multi-step operations, use domain-specific tools, and adhere to strict security protocols across both communication modes.  Traditional AI benchmarks often focus on general knowledge or basic instructions, but enterprise settings require more advanced capabilities. AI agents in these contexts must integrate with multiple tools and systems, follow strict security and compliance procedures, and understand specialized terms and workflows. Voice-based interactions add another layer of complexity due to potential speech recognition and synthesis errors, especially in multi-step tasks. Addressing these needs, the benchmark guides AI development toward more dependable and effective assistants tailored for enterprise use. Salesforce’s benchmark uses a modular framework with four key components: domain-specific environments, predefined tasks with clear goals, simulated interactions that reflect real-world conversations, and measurable performance metrics. It evaluates AI across four enterprise domains: healthcare appointment management, financial services, sales, and e-commerce. Tasks range from simple requests to complex operations involving conditional logic and multiple system calls. With human-verified test cases, the benchmark ensures realistic challenges that test an agent’s reasoning, precision, and tool handling in both text and voice interfaces.  The evaluation framework measures AI agent performance based on two main criteria: accuracy, how correctly the agent completes the task, and efficiency, which are evaluated through conversational length and token usage. Both text and voice interactions are assessed, with the option to add audio noise to test system resilience. Implemented in Python, the modular benchmark supports realistic client-agent dialogues, multiple AI model providers, and configurable voice processing using built-in speech-to-text and text-to-speech components. An open-source release is planned, enabling developers to extend the framework to new use cases and communication formats. Initial testing across top models like GPT-4 variants and Llama showed that financial tasks were the most error-prone due to strict verification requirements. Voice-based tasks also saw a 5–8% drop in performance compared to text. Accuracy declined further on multi-step tasks, especially those requiring conditional logic. These findings highlight ongoing challenges in tool-use chaining, protocol compliance, and speech processing. While robust, the benchmark lacks personalization, real-world user behavior diversity, and multilingual capabilities. Future work will address these gaps by expanding domains, introducing user modeling, and incorporating more subjective and cross-lingual evaluations.  Check out the Technical details. 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 and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Beyond Aha Moments: Structuring Reasoning in Large Language ModelsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/RXTX: A Machine Learning-Guided Algorithm for Efficient Structured Matrix MultiplicationSana Hassanhttps://www.marktechpost.com/author/sana-hassan/From Protocol to Production: How Model Context Protocol (MCP) Gateways Enable Secure, Scalable, and Seamless AI Integrations Across EnterprisesSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Researchers from Renmin University and Huawei Propose MemEngine: A Unified Modular AI Library for Customizing Memory in LLM-Based Agents

Scene at UKREiiF 2025 outside the Canary bar UKREiiF is getting bigger by the year, with more than 16,000 professionals attending the 2025 construction conference in Leeds this week during three days of sunny weather, networking, panel discussions and robust amounts of booze. It has grown so big over the past few years that it seems almost to have outgrown the city of Leeds itself. A running joke among attendees was the varying quality of accommodation people had managed to secure. All of the budget hotels in the city were fully booked months in advance of the conference, with many - including at least one member of Parliament - reduced to kipping in bed and breakfasts of a questionable nature. Many were forced to stay in nearby towns including York, Wakefield and Bradford and catch the train to the conference each morning. But these snags served as ice breakers for more important conversations at an event which has come at a key pivot point for the industry. With the government on the brink of launching its 10-year industrial strategy and its new towns programme, opportunity was in the air. Networking events between government departments and potential suppliers of all sectors were well attended, although many discussion panels focused on the question of how all of this work would be paid for. And hanging over the conference like a storm cloud were the mounting issues at the Building Safety Regulator which are continuing to cause expensive delays to high rise schemes across the country. While many attendees eyed a huge amount of potential work to fill up pipelines, it was clear the industry is still facing some systemic challenges which could threaten a much-needed recovery following a long period of turmoil. How will the issues at the Building Safety Regulator be fixed? You did not even have to go inside an event titled “Gateways and Growing Pains: Tackling the Building Safety Act” to see how much this issue is affecting construction at the moment. The packed out tent was overflowing into the space outside, with those inside stood like sardines to watch a panel discussion about what has been happening in the high rise residential sector over the past year.  Audience members shared their horror stories of schemes which have been waiting for the best part of a year to get gateway 2 approval from the regulator, which is needed to start construction. There was a palpable sense of anger in the crowd, one professional describing the hold-ups which had affected his scheme as a “disgrace”. Others highlighted the apparent inconsistency of the regulator’s work. One attendee told how two identical buildings had been submitted to the regulator in separate gateway 2 applications and assigned to two separate technical teams for approval. One application had received no follow up questions, while the other had been extensively interrogated. “The industry should hold its head in shame with regard to what happened at Grenfell, but post that, it’s just complete disarray,” he said. More than 16,000 professionals attended the 2025 event While many are currently focusing on delays at pre-construction, others raised the looming gateway 3 approvals which are needed before occupation. Pareto Projects director Kuli Bajwa said: “Gateway 2 is an issue, but when we get to gateway 3, we’re committed to this project, money’s been spent, debt’s been taken out and week on week it’s costing money. It just keeps wracking up, so we need to resolve that with the regulator asap.” >> See also: Homes England boss calls on government to fix ‘unacceptably slow’ gateway 2 approvals Caddick Construction managing director for Yorkshire and the North East Steve Ford added: “I think where it will probably get interesting and quite heated I guess is at the point where some of these schemes get rejected at gateway 3, and the finger pointing starts as to why it’s not got through gateway 3.” Simon Latson, head of living for the UK and Ireland at JLL, offered a potential solution. “We will be dealing with the regulator all the way through the construction process, and you would like to think that there is a collaborative process where you get early engagement and you can say ‘I’m 12 weeks out from completion, I’m going to start sending you all of my completion documents, my fire alarm certificate’, and say ‘thanks very much that’s the last thing on my list’. That’s probably wishful thinking but that’s got to be a practical solution, as early engagement as possible.” How is the government going to pay for its infrastructure strategy? Ministers are expected to outline the government’s ten-year infrastructure strategy next month, outlining ambitions not only for transport but social infrastructure including schools and healthcare. At an event titled “A Decade of National Renewal: What Will This Mean for our Regions, Towns and Cities?”, a panel of experts including London deputy mayor Jules Pipe highlighted how much of this new infrastructure is needed to enable the government to achieve its housing targets. But how will it be funded? Tom Wagner, cofounder of investment firm Knighthead Capital, which operates largely in the West Midlands with assets including Birmingham City FC, gave a frank assessment of the government’s policies on attracting private sector investment. “There have been a lot of policies in the UK that have forced capital allocators to go elsewhere,” he said, calling for lower taxes and less restrictions on private finance in order to stop investors fleeing to more amenable destinations overseas.  “What we’ve found in the UK is, as we’re seeking to tax those who can most afford it, that’s fine, but unless they’re chained here, they’ll just go somewhere else. That creates a bad dynamic because those people are the capital providers, and right now what we need is capital infusion to foster growth.” The main square at the centre of the conference Pipe offered a counterpoint, suggesting low taxes were not the only reason which determines where wealthy people live and highlighted the appeal of cities which had been made livable by good infrastructure. “There are people living in some very expensive cities but they live there because of the cosmopolitan culture and the parks and the general vibe, and that’s what we have to get right. And the key thing that leads to that is good transport, making it livable.” Pipe also criticised the penny-pinching tendencies of past governments on infrastructure investment, including on major transports schemes like Crossrail 2 which were mothballed due to a lack of funds and a perceived lack of value added. “All these things were fought in the trenches with the Treasury about ‘oh well there’s no cost benefit to this’. And where is the major transport like that where after ten years people are saying ‘no one’s using it, that was a really bad idea, it’s never opened up any new businesses or new homes’? It’s absolute nonsense. But that seems to be how we judge it,” he said. One solution could be funding through business rates, an approach used on the Northern Line Extension to Battersea Power Station. But the benefits of this have been largely overlooked, Pipe said. “One scheme every ten or twenty years is not good enough. We need to do this more frequently”. What is the latest on the government’s new towns programme? Where are the new towns going to be built? It was a question which everybody was asking during the conference, with rumours circulating around potential sites in Cambridge of Plymouth. The government is set to reveal the first 12 locations of 10,000 homes each in July, an announcement which will inevitably unleash an onslaught of NIMBY outcries from affected communities. A large crowd gathered for an “exclusive update” on the programme from Michael Lyons, chair of the New Towns Taskforce appointed by the government to recommend suitable sites, with many in attendance hoping for a big reveal on the first sites. They were disappointed, but Lyons did provide some interesting insights into the taskforce’s work. Despite a “rather hairbrained” timescale given to the team, which was only established last September, Lyons said it was at a “very advanced stage” in its deliberations after spending the past few months touring the country speaking to developers, landowners and residents in search of potential sites. >> See also: Don’t scrimp on quality standards for new towns, taskforce chair tells housebuilders “We stand at a crucial moment in the history of home building in this country,” he said. The government’s commitment to so many large-scale developments could herald a return to ambitious spatial planning, he said, with communities strategically located close to the most practical locations for the supply of new infrastructure needed for people to move in. A line of tents at the docks site, including the London Pavilion “Infrastructure constraints, whether it’s water or power, sewage or transport, must no longer be allowed to hold back growth, and we’ve been shocked as we looked around the country at the extent to which plans ready to be advanced are held back by those infrastructure problems,” he said. The first sites will be in places where much of this infrastructure is already in place, he said, allowing work to start immediately.  An emphasis on “identity and legibility” is also part of the criteria for the initial locations, with the government’s design and construction partners to be required to put placemaking at the heart of their schemes. “ We need to be confident that these can be distinctive places, and that the title of new town, whether it’s an urban extension or whether it’s even a reshaping of an existing urban area or a genuine greenfield site, that it genuinely can be seen and will be seen by its residents as a distinct community.” How do you manage a working public-private partnership? Successful public partnerships between the public sector and private housebuilders will be essential for the government to achieve its target to build 1.5 million homes by the end of this parliament in 2029. At an event hosted by Muse, a panel discussed where past partnerships have gone wrong and what lessons have been learned. Mark Bradbury, Thurrock council’s chief officer for strategic growth partnerships and special projects, spoke of the series of events which led to L&Q pulling out of the 2,800-home Purfleet-on-Thames scheme in Essex and its replacement by housing association Swan. “I think it was partly the complex nature of the procurement process that led to market conditions being quite different at the end of the process to the start,” he said. “Some of the original partners pulled out halfway through because their business model changed. I think the early conversations at Purfleet on Thames around the masterplan devised by Will Alsop, the potential for L&Q to be one of the partners, the potential for a development manager, the potential for some overseas investment, ended up with L&Q deciding it wasn’t for their business model going forwards. The money from the far east never materialised, so we ended up with somebody who didn’t have the track record, and there was nobody who had working capital.  “By then it was clear that the former partnership wasn’t right, so trying to persuade someone to join a partnership which wasn’t working was really difficult. So you’ve got to be really clear at the outset that this is a partnership which is going to work, you know where the working capital is coming from, and everybody’s got a track record.” Muse development director for residential Duncan Cumberland outlined a three-part “accelerated procurement process” which the developer has been looking at in order to avoid some of the setbacks which can hit large public private partnerships on housing schemes. The first part is developing a masterplan vision which has the support of community stakeholders, the second is outlining a “realistic and honest” business plan which accommodates viability challenges, and the third is working closely with public sector officials on a strong business case. A good partnership is almost like being in a marriage, Avison Young’s London co-managing director Kat Hanna added. “It’s hard to just walk away. We’re in it now, so we need to make it work, and perhaps being in a partnership can often be more revealing in tough times.”

Large Reasoning Models (LRMs) like OpenAI’s o1 and o3, DeepSeek-R1, Grok 3.5, and Gemini 2.5 Pro have shown strong capabilities in long CoT reasoning, often displaying advanced behaviors such as self-correction, backtracking, and verification—collectively known as “aha moments.” These behaviors have been observed to emerge through outcome-driven RL without the need for supervised fine-tuning. Models like DeepSeek-R1 and its open-source replications (e.g., TinyZero and Logic-RL) have demonstrated that carefully designed RL pipelines—using rule-based rewards, curriculum learning, and structured training—can induce such reflective reasoning abilities. However, these emergent behaviors tend to be unpredictable and inconsistent, limiting their practical reliability and scalability. To address this, researchers have explored structured RL frameworks that target specific reasoning types, such as deduction, abduction, and induction. These approaches involve aligning specialist models, merging them in parameter space, and applying domain-specific continual RL. Tools like Logic-RL use rule-conditioned RL to solve logic puzzles, improving transferability to tasks like math reasoning. Meanwhile, other works propose mechanisms to enhance reasoning robustness, such as training models to reason both forwards and backwards, or iteratively self-critiquing their outputs. Studies analyzing “aha moments” suggest that these behaviors stem from internal shifts in uncertainty, latent representation, and self-assessment, offering new insights into engineering more reliable reasoning models.  Researchers from the National University of Singapore, Tsinghua University, and Salesforce AI Research address the limitations of relying on spontaneous “aha moments” in large language models by explicitly aligning them with three core reasoning abilities: deduction, induction, and abduction. They introduce a three-stage pipeline—individual meta-ability alignment, parameter-space merging, and domain-specific reinforcement learning—significantly enhancing model performance. Using a programmatically generated, self-verifiable task suite, their approach boosts accuracy over instruction-tuned baselines by over 10%, with further gains from domain-specific RL. This structured alignment framework offers a scalable, generalizable method for improving reasoning across math, coding, and science domains.  The researchers designed tasks aligned with deduction, induction, and abduction by using a structured “given two, infer the third” format based on hypothesis (H), rule (R), and observation (O). Deduction is framed as satisfiability checking, induction as masked-sequence prediction, and abduction as reverse rule-graph inference. These tasks are synthetically generated and automatically verified. The training pipeline includes three stages: (A) independently training models for each reasoning type using REINFORCE++ with structured rewards, (B) merging models through weighted parameter interpolation, and (C) fine-tuning the unified model on domain-specific data via reinforcement learning, isolating the benefit of meta-ability alignment.  The study evaluates models aligned with meta-abilities—deduction, induction, and abduction—using a curriculum learning setup across difficulty levels. Models trained on synthetic tasks strongly generalize to seven unseen math, code, and science benchmarks. At both 7B and 32B scales, meta-ability–aligned and merged models consistently outperform instruction-tuned baselines, with the merged model offering the highest gains. Continued domain-specific RL from these merged checkpoints (Domain-RL-Meta) leads to further improvements over standard RL finetuning (Domain-RL-Ins), especially in math benchmarks. Overall, the alignment strategy enhances reasoning abilities, and its benefits scale with model size, significantly boosting performance ceilings across tasks.  In conclusion, the study shows that large reasoning models can develop advanced problem-solving skills without depending on unpredictable “aha moments.” By aligning models with three core reasoning abilities—deduction, induction, and abduction—using self-verifiable tasks, the authors create specialist agents that can be effectively combined into a single model. This merged model outperforms instruction-tuned baselines by over 10% on diagnostic tasks and up to 2% on real-world benchmarks. When used as a starting point for domain-specific reinforcement learning, it raises performance by another 4%. This modular, systematic training approach offers a scalable and controllable foundation for building reliable, interpretable reasoning systems.  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 95k+ ML SubReddit and Subscribe to our Newsletter. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/RXTX: A Machine Learning-Guided Algorithm for Efficient Structured Matrix MultiplicationSana Hassanhttps://www.marktechpost.com/author/sana-hassan/From Protocol to Production: How Model Context Protocol (MCP) Gateways Enable Secure, Scalable, and Seamless AI Integrations Across EnterprisesSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Researchers from Renmin University and Huawei Propose MemEngine: A Unified Modular AI Library for Customizing Memory in LLM-Based AgentsSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Meta Introduces KernelLLM: An 8B LLM that Translates PyTorch Modules into Efficient Triton GPU Kernels

In an era dominated by artificial intelligence and smartphones, one of the most overlooked engines of economic growth sits quietly at the heart of every neighborhood: the public library.  Gone are the days when libraries were sanctuaries reserved for only reading and research. Today, they are being reimagined as dynamic hubs for workforce development, creative sector support, and cultural exchange. Across the country, these reservoirs of knowledge are evolving into digital and physical beacons of community resilience.  Local access, global reach: A case study in artist empowerment  In Huntsville, where I serve as the city’s first music officer, we’ve partnered with our public library system to develop a multifunctional creative hub—with music at its core. A primary pillar of our collaboration is Blast Music, a digital streaming platform designed to showcase local talent. It’s a model other cities can and should replicate.  Through the Blast program, artists are paid, promoted, and added to a curated library collection—offering not only exposure, but bona fide industry credentials. Over 100 local artists are currently featured on the platform, and we will welcome up to 50 additional artists into the program annually.  The ripple effect of Blast is real. The free service empowers local listeners to discover homegrown talent while giving musicians tools to grow their fan base and attract industry attention. Perhaps most importantly, Blast provides emerging artists with resume-worthy recognition—essential for building sustainable careers in a tough industry.  But Blast isn’t just about digital reach—it’s embedded in Huntsville’s cultural DNA. From artist showcases like the Ladies of Blast event at the Orion Amphitheater, to community events like Hear to Be Seen (a portrait exhibition of Blast musicians), to stages designated exclusively for Blast artist performances at Camp to Amp, PorchFest, and more, Blast is bringing music into public spaces and cultivating civic pride. That’s the kind of community infrastructure that libraries are uniquely equipped to deliver.  There’s no such thing as too much visibility, and even artists with international acclaim see value in the platform. Huntsville native Kim Tibbs, a vocalist, songwriter, Alabama Music Hall of Fame honoree and UK chart-topper, submitted her album The Science of Completion Volume I to Blast—not only for more exposure, but to mentor and support the next generation of artists in her hometown.   Libraries as talent incubators  Huntsville is part of a broader national trend. In cities like Chicago, Nashville, and Austin, libraries are integrating creative labs, media production studios, and music education into their core services—functioning as public-sector incubators for the creative economy.  As technology continues to reshape traditional jobs, libraries are well-positioned to bridge skill gaps and fuel the rise of creative economies, including the vital but often overlooked non-performance roles in the music industry.  Huntsville is doubling down on this approach. We’re investing millions into programs that bring interactive music technology workshops to teens at the local library—focusing on hands-on training in production, recording, and audio engineering. With professional equipment, studio spaces, and expert instruction, we’re preparing the next generation for careers both onstage and behind the scenes.  Local industry is stepping up too. Hear Technologies, a global leader in sound and AV production, has been designing cutting-edge audio devices for years. They’re now part of a dynamic team collaborating with city leaders to help develop the library’s music maker space, nurture new talent and accelerate our region’s creative growth.  This matters now, more than ever  Libraries have always been entry points for education, employment, and exploration. But today, they’re more than just information access points—they are gateways to opportunity and launchpads for industries that define the future. By utilizing public space and collaborating with local talent, libraries can become platforms for economic mobility and cultural innovation. This investment isn’t a feel-good gesture. It’s a smart, strategic move for any city building a future that works—for everyone.  The playlist is simple: Invest in creative ecosystems, embed them in trusted community institutions like public libraries, and treat music as critical infrastructure.   Matt Mandrella is music officer for the City of Huntsville, Alabama.