Advances in Enterprise Large Language Models

Exciting breakthrough in LLM Research: A comprehensive survey reveals that Large Language Models (LLMs) are proving to be highly effective embedding models, marking a significant shift from traditional encoder-only models like BERT to decoder-only architectures. The research, led by scholars from Beihang University, University of Technology Sydney, and other prestigious institutions, demonstrates two primary approaches for deriving embeddings from LLMs: >> Direct Prompting Strategy • Leverages LLMs' instruction-following capabilities to generate topic-specific embeddings • Utilizes contextual representations for enhanced semantic understanding • Implements prompt engineering techniques for optimal embedding generation >> Data-Centric Tuning Approach • Employs supervised contrastive learning with carefully curated datasets • Incorporates multi-task learning frameworks for improved generalization • Utilizes knowledge distillation from cross-encoder models for enhanced performance >> Advanced Implementation Details The research reveals sophisticated techniques including: • Bidirectional contextualization for enhanced semantic capture • Low-rank adaptation for efficient parameter tuning • Integration of both dense and sparse embedding approaches • Implementation of innovative pooling strategies for token aggregation >> Performance Insights The study demonstrates remarkable improvements over traditional models: • Superior performance in classification, clustering, and retrieval tasks • Enhanced capability in handling long-context dependencies • Improved cross-lingual representation capabilities • Better scalability with model size and training data This groundbreaking research opens new possibilities for applications in information retrieval, natural language processing, and recommendation systems.

𝗧𝗟;𝗗𝗥 NeurIPS 2025 marks the definitive shift from "Chat" to "Autonomy." The research signals a split reality for the enterprise: generic models are converging into a commoditized "Artificial Hivemind," leaving proprietary data as your only real moat. However, the upside is massive. New "Gated Attention" architectures are redefining inference efficiency, while breakthroughs in 1,000-layer Deep RL are finally unlocking agents capable of navigating complex, long-horizon enterprise workflows without getting stuck. NeurIPS is around the corner and wanted to highlight some trends based on the best papers (https://lnkd.in/ejp6vEjD) 𝟯 𝗣𝗮𝗽𝗲𝗿𝘀 (𝗮𝗻𝗱 𝘁𝗵𝗲𝗺𝗲𝘀) 𝗬𝗼𝘂 𝗡𝗲𝗲𝗱 𝘁𝗼 𝗞𝗻𝗼𝘄 𝟭. 𝗧𝗵𝗲 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗶𝗮𝘁𝗶𝗼𝗻 𝗖𝗿𝗶𝘀𝗶𝘀  • 𝗣𝗮𝗽𝗲𝗿: 𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗛𝗶𝘃𝗲𝗺𝗶𝗻𝗱: The Open-Ended Homogeneity of Language Models  • 𝗧𝗵𝗲 𝗦𝗶𝗴𝗻𝗮𝗹: Models trained on synthetic data and each other’s outputs are suffering from "inter-model homogeneity." They are converging on the same "average" answers.  • 𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: If you rely on a vanilla wrapper around GPT, Claude and Gemini your business logic is becoming a commodity. 𝟮. 𝗧𝗵𝗲 𝗡𝗲𝘄 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱  • 𝗣𝗮𝗽𝗲𝗿: Gated Attention for Large Language Models (Qwen Team)  • 𝗧𝗵𝗲 𝗦𝗶𝗴𝗻𝗮𝗹: By adding a simple "gate" to attention heads, we can stabilize training at massive scales and prevent "attention sinks."  • 𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: This is the update for your self-hosted inference. Models using Gated Attention (like Qwen3-Next) can offer significantly better performance-per-dollar. 𝟯. 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗨𝗻𝗹𝗼𝗰𝗸 𝗣𝗮𝗽𝗲𝗿: 1000 Layer Networks for Self-Supervised RL 𝗧𝗵𝗲 𝗦𝗶𝗴𝗻𝗮𝗹: We used to think RL couldn't scale in depth like LLMs. This paper proves we can train 1,000-layer RL networks using self-supervised contrastive learning. 𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: This enables L5 Autonomous Agents - agents that can navigate complex ERP/CRM workflows without getting stuck in loops. 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗖𝗧𝗢𝘀 𝗮𝗻𝗱 𝗖𝗔𝗜𝗢𝘀  𝟭. 𝗣𝗶𝘃𝗼𝘁 𝘁𝗼 "𝗗𝗮𝘁𝗮 𝗜𝗻𝗷𝗲𝗰𝘁𝗶𝗼𝗻": Go beyond prompt engineering with context and data engeineering. Focus even more on RAG and Fine-Tuning pipelines that inject your proprietary data to break the "Hivemind" average.  𝟮. 𝗔𝗱𝗼𝗽𝘁𝗶𝗻𝗴 𝗚𝗮𝘁𝗲𝗱 𝗠𝗼𝗱𝗲𝗹𝘀: When evaluating open-weights models for 2026, mandate "Gated Attention" architectures to lower your long-term inference TCO.  𝟯. 𝗣𝗶𝗹𝗼𝘁 𝗗𝗲𝗲𝗽 𝗥𝗟: Move your "Agent" pilots beyond simple tool use. Start testing self-supervised RL on internal workflows to build agents that learn from your experts' corrections.

Day 19/30 of SLMs/LLMs: Mixture-of-Experts, Efficient Transformers, and Sparse Models As language models grow larger, two challenges dominate: cost and efficiency. Bigger models bring higher accuracy but also higher latency, energy use, and deployment complexity. The next phase of progress is about making models faster, lighter, and more intelligent per parameter. A leading direction is the Mixture-of-Experts (MoE) architecture. Instead of activating every parameter for each input, MoE models route tokens through a few specialized “experts.” Google’s Switch Transformer and DeepMind’s GLaM demonstrated that activating only 5 to 10 percent of weights can achieve the same accuracy as dense models at a fraction of the compute. Open models like Mixtral 8x7B extend this idea by using eight experts per layer but activating only two for each forward pass. The result is performance similar to a 70B model while operating at roughly 12B compute cost. Another active area of innovation is Efficient Transformers. Traditional attention scales quadratically with sequence length, which limits how much context a model can process. New variants such as FlashAttention, Longformer, Performer, and Mamba improve memory efficiency and speed. FlashAttention in particular accelerates attention calculations by performing them directly in GPU memory, achieving two to four times faster throughput on long sequences. Sparse Models also contribute to efficiency by reducing the number of active parameters during training or inference. Structured sparsity, combined with quantization and pruning, allows models to run on smaller devices without a major loss in quality. Advances in sparsity-aware optimizers now make it possible to deploy billion-parameter models on standard hardware with near state-of-the-art accuracy. These techniques share a single goal: scaling intelligence without scaling cost. The focus is shifting from building larger networks to building smarter ones. A 7B model that uses retrieval, sparse activation, and efficient attention can outperform a much larger dense model in both speed and reliability.

Large Language Models (LLMs) are powerful, but how we 𝗮𝘂𝗴𝗺𝗲𝗻𝘁, 𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗲 them truly defines their impact. Here's a simple yet powerful breakdown of how AI systems are evolving: 𝟭. 𝗟𝗟𝗠 (𝗕𝗮𝘀𝗶𝗰 𝗣𝗿𝗼𝗺𝗽𝘁 → 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲)   ↳ This is where it all started. You give a prompt, and the model predicts the next tokens. It's useful — but limited. No memory. No tools. Just raw prediction. 𝟮. 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹-𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻)   ↳ A significant leap forward. Instead of relying only on the LLM’s training, we 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗲 𝗿𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗳𝗿𝗼𝗺 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝘀𝗼𝘂𝗿𝗰𝗲𝘀 (like vector databases). The model then crafts a much more relevant, grounded response.   This is the backbone of many current AI search and chatbot applications. 𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗟𝗟𝗠𝘀 (𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗥𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 + 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲)   ↳ Now we’re entering a new era. Agent-based systems don’t just answer — they think, plan, retrieve, loop, and act.   They: - Use 𝘁𝗼𝗼𝗹𝘀 (APIs, search, code) - Access 𝗺𝗲𝗺𝗼𝗿𝘆 - Apply 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗰𝗵𝗮𝗶𝗻𝘀 - And most importantly, 𝗱𝗲𝗰𝗶𝗱𝗲 𝘄𝗵𝗮𝘁 𝘁𝗼 𝗱𝗼 𝗻𝗲𝘅𝘁 These architectures are foundational for building 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀, 𝗰𝗼𝗽𝗶𝗹𝗼𝘁𝘀, 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀. The future is not just about 𝘸𝘩𝘢𝘵 the model knows, but 𝘩𝘰𝘸 it operates. If you're building in this space — RAG and Agent architectures are where the real innovation is happening.

The challenge of integrating multiple large language models (LLMs) in enterprise AI isn’t just about picking the best model, it’s about choosing the right mix for each specific scenario. When I was tasked with leveraging Azure AI Foundry alongside Microsoft 365 Copilot, Copilot Studio, Claude Sonnet 4, and Opus 4.1 to enhance workflows, the advice I heard was to double down on a single, well‑tuned model for simplicity. In our environment, that approach started to break down at scale. Model pluralism turned out to be the unexpected solution, using multiple LLMs in parallel, each optimised for different tasks. The complexity was daunting at first, from integration overhead to security and governance concerns. But this approach let us tighten data grounding and security in ways a single model couldn’t. For example, routing the most sensitive tasks to Opus 4.1 helped us measurably reduce security exposure in our internal monitoring, while Claude Sonnet 4 noticeably improved the speed and quality of customer‑facing interactions. In practice, the chain looked like this: we integrated multiple LLMs, mapped each one to the tasks it handled best, and saw faster execution on specialised workloads, fewer security and compliance issues, and a clear uplift in overall workflow effectiveness. Just as importantly, the architecture became more robust, if one model degraded or failed, the others could pick up the slack, which matters in a high‑stakes enterprise environment. The lesson? The “obvious” choice, standardising on a single model for simplicity, can overlook critical realities like security, governance, and scalability. Model pluralism gave us the flexibility and resilience we needed once we moved beyond small pilots into real enterprise scale. For those leading enterprise AI initiatives, how are you balancing the trade‑off between operational simplicity and a pluralistic, multi‑model architecture? What does your current model mix look like?

Large language models (LLMs) can improve their performance not just by retraining but by continuously evolving their understanding through context, as shown by the Agentic Context Engineering (ACE) framework. Consider a procurement team using an AI assistant to manage supplier evaluations. Instead of repeatedly inputting the same guidelines or losing specific insights, ACE helps the AI remember and refine past supplier performance metrics, negotiation strategies, and risk factors over time. This evolving “context playbook” allows the AI to provide more accurate supplier recommendations, anticipate potential disruptions, and adapt procurement strategies dynamically. In supply chain planning, ACE enables the AI to accumulate domain-specific rules about inventory policies, lead times, and demand patterns, improving forecast accuracy and decision-making as new data and insights become available. This approach results in up to 17% higher accuracy in agent tasks and reduces adaptation costs and time by more than 80%. It also supports self-improvement through feedback like execution outcomes or supply chain KPIs, without requiring labeled data. By modularizing the process—generating suggestions, reflecting on results, and curating updates—ACE builds robust, scalable AI tools that continuously learn and adapt to complex business environments. #AI #SupplyChain #Procurement #LLM #ContextEngineering #BusinessIntelligence

Large Language Diffusion Models (LLaDA) Proposes a diffusion-based approach that can match or beat leading autoregressive LLMs in many tasks. If true, this could open a new path for large-scale language modeling beyond autoregression. More on the paper: Questioning autoregressive dominance While almost all large language models (LLMs) use the next-token prediction paradigm, the authors propose that key capabilities (scalability, in-context learning, instruction-following) actually derive from general generative principles rather than strictly from autoregressive modeling. Masked diffusion + Transformers LLaDA is built on a masked diffusion framework that learns by progressively masking tokens and training a Transformer to recover the original text. This yields a non-autoregressive generative model—potentially addressing left-to-right constraints in standard LLMs. Strong scalability Trained on 2.3T tokens (8B parameters), LLaDA performs competitively with top LLaMA-based LLMs across math (GSM8K, MATH), code (HumanEval), and general benchmarks (MMLU). It demonstrates that the diffusion paradigm scales similarly well to autoregressive baselines. Breaks the “reversal curse” LLaDA shows balanced forward/backward reasoning, outperforming GPT-4 and other AR models on reversal tasks (e.g. reversing a poem line). Because diffusion does not enforce left-to-right generation, it is robust at backward completions. Multi-turn dialogue and instruction-following After supervised fine-tuning, LLaDA can carry on multi-turn conversations. It exhibits strong instruction adherence and fluency similar to chat-based AR LLMs—further evidence that advanced LLM traits do not necessarily rely on autoregression. https://lnkd.in/eYp9Hi5y

If you’re deploying LLMs at scale, here’s what you need to consider. Balancing inference speed, resource efficiency, and ease of integration is the core challenge in deploying multimodal and large language models. Let’s break down what the top open-source inference servers bring to the table AND where they fall short: vLLM → Great throughput & GPU memory efficiency ✅ But: Deployment gets tricky in multi-model or multi-framework environments ❌ Ollama → Super simple for local/dev use ✅ But: Not built for enterprise scale ❌ HuggingFace TGI → Clean integration & easy to use ✅ But: Can stumble on large-scale, multi-GPU setups ❌ NVIDIA Triton → Enterprise-ready orchestration & multi-framework support ✅ But: Requires deep expertise to configure properly ❌ The solution is to adopt a hybrid architecture: → Use vLLM or TGI when you need high-throughput, HuggingFace-compatible generation. → Use Ollama for local prototyping or privacy-first environments. → Use Triton to power enterprise-grade systems with ensemble models and mixed frameworks. → Or best yet: Integrate vLLM into Triton to combine efficiency with orchestration power. This layered approach helps you go from prototype to production without sacrificing performance or flexibility. That’s how you get production-ready multimodal RAG systems!

Our new Preprint on Distributed (Multimodal) Large Language Models in available online: https://lnkd.in/g6Rnd__s Last year, I started thinking about how we can leverage our ongoing work on federated learning in the emerging research areas relevant to (multimodal) large language models. The simple answer was decentralizing the (M)LLM pipeline! However, when we started reading more recent studies, it became clear that there is a need to a more comprehensive survey on existing body of work. With contributions from our team at solid lab and our collaborators, we have drafted the first version of this evolving work. We are now conducting research on multiple identified research directions that are outlined in this survey paper. We are also sharing all source files of the schematics that are designed for the survey for public use (https://lnkd.in/g6Rnd__s). The main reason is to ensure other researchers, specifically students who are new to this domain, can conveniently benefit from this survey, adapt/modify/use the figures as it finds their research, and help us push this interesting direction forward. Any feedback from community is encouraged, including related works that we might have missed given the fast pace of research progress in this domain. 📘 “Distributed LLMs and Multimodal Large Language Models: A Survey on Advances, Challenges, and Future Directions”, in collaboration with - Md Jueal mia, Yasaman Saadati, Ahmed Imteaj, Ph.D., Sina Nabavi, Urmish Thakker, Md Zarif Hossain, Awal Ahmed Fime, and S S Iyengar, explores various solutions for decentralizing LLMs and MLLMs. 🔍 We categorize 100+ papers and uncover emerging directions across six core areas: ✅Distributed Training ✅Distributed Inference and Optimization ✅Computing Infrastructure ✅Federated Fine-tuning ✅Edge Intelligence ✅Communication Efficiency 📌Resources -Link to the paper PDF: https://lnkd.in/g3AdSQTG -Link to the Latex source file of paper: https://lnkd.in/gUTNsQ8w -Link to access source file of figures: https://lnkd.in/g92CmpnP - Full survey and GitHub with updates: https://lnkd.in/g6Rnd__s #llms #multimodalllms #largelanguagemodel #distributedcomputing #decentralizedAI #edgeAI

Large language models (#LLMs) like Open AI's Chat #GPT are great for some things. Write me a poem about the future of retail in the style of Shakespeare. But ask for last month's sales by product category, and it will either hallucinate or shrug its shoulders. Imagine if you knew everything that's lurking in your own data. What do our most loyal customers love about our product this month? Or how should we allocate $1M in spend to acquire high-value customers based on past performance? This gap is the distinction between the public large language models and enterprise IP inclusive of customer data, product performance, and marketing data. It's what we call a Large Knowledge Model (LKM), combining the conversational nature inherent in LLM yet powered by business data, enriched by our proprietary data assets, in a way that's safe, compliant, and, most critically, actionable. We're putting these GenCX models into place for our clients and already beginning to unlock uncommon insights, and putting new operating frameworks in place to unleash this intelligence across the org. While #GenCX is new, it builds on more than a decade of AI, automation, and customer identity innovations at #merkle . It may not be in the voice of Shakespeare, but I am excited for GenCX to meaningfully improve customer experiences and drive growth for our clients. And, as of this week, it's available to use with #salesforce Einstein GPT along with our recent expansion of generative AI tools via our recent deal with Microsoft's #Azure OpenAI platform. We try to bring some humanity and humility to the table by linking everything we explore to a problem to solve, starting typically a workshop that's equal parts education and inspiration along with a few prototypical use cases to see and experience. Learn more here: