Guy Lebanon

A 12-month professional, voluntary collaboration has finally borne fruit. This morning, I was honored to see the University of Washington News (https://lnkd.in/gRRpDqjA) and the UW Milgard School of Business (https://lnkd.in/gKJ_pwbm) highlight our Responsible GenAI Framework. Why do we care about responsible generative AI? Because GenAI systems are no longer experimental curiosities. They shape decisions, influence behavior, generate content at scale, and increasingly affect trust, fairness, privacy, and accountability in organizations and society. Without intentional design and governance, these systems can amplify bias, create opaque risks, and erode confidence rather than enable innovation. This framework is about making GenAI useful, ethical, and sustainable at the same time. It emphasizes responsibility not as a constraint, but as an enabler of long-term value, credibility, and impact. I am grateful to the collaborators, reviewers, and institutions who supported this work, and excited to see responsible GenAI move from aspiration to practice. To learn more: - Read the RGAF Framework (PDF): RGAF Framework https://lnkd.in/gTaxnndV #GenAI #ResponsibleGenAI International Society of Service Innovation Professionals (ISSIP) Milgard CBA

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Here is a clip on the value of Agent Bricks from my interview with Matei Zaharia (Databricks CTO & Cofounder). Full interview pragmatically covering where AI is today: https://lnkd.in/eVnM9jVX

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Deep Research Agents are going to be a core part of enterprise workflows, but we’ve had almost no way to evaluate real multi-step reasoning. With ResearchRubrics, we’re finally setting a bar for research agents, and early results show even frontier models have a long way to go.

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Tom Aarsen recently came across two interesting Sentence Transformer models - one finetuned from ModernBERT-base, the other from Pinecone ConstBERT, which surprised me a bit since ConstBERT is typically used as a multi-vector model. Even more surprising? On a simple yet telling benchmark (TripletEvaluator: % of times sim(query, positive) > sim(query, negative)), the ConstBERT-based model slightly edged out the ModernBERT one: 🔹 ModernBERT: 98.8% 🔹 ConstBERT: 99.1% It's a small margin, but fascinating that a model designed for late interaction is holding its own—even excelling—in single-vector settings. Makes you wonder about architectural flexibility and how far you can stretch these models with the right fine-tuning. Curious if others have seen similar results or explored hybrid use cases this way—happy to chat! #NLP #InformationRetrieval #SentenceTransformers #BERT #IR #ML

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Agentic AI breaks static governance This new survey paper on Agentic Reasoning for Large Language Models brings together researchers from UIUC, Meta Facebook, Amazon, Google DeepMind, UC San Diego, and Yale. The core shift is simple and consequential: We are moving from models that reason to systems that plan, act, adapt, remember, and collaborate over time. The paper frames agentic AI across three layers: • Foundational agents that plan, search, and use tools • Self-evolving agents that adapt via feedback and memory • Multi-agent systems that coordinate roles and objectives Why this matters for AI governance: Once reasoning is coupled with action and memory, static Responsible AI controls stop working. Pre-deployment reviews, model cards, and one-off risk assessments cannot govern systems that change behaviour at inference time. The authors explicitly flag governance of agentic reasoning as an open problem. That is the real takeaway. If governance does not operate at deployment, inference, and autonomy thresholds, it is not governing the system in motion. I recommend reading the paper in full. It is one of the most comprehensive mappings of agentic systems to date. Paper: https://lnkd.in/gtZsfQYB Question How are you thinking about control and accountability once reasoning, memory, and action are coupled in live agentic systems? #AIGovernance #ResponsibleAI #AgenticAI #AISafety #EnterpriseAI #AIControl

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I'm excited to share three research papers from our talented team at Together AI that are advancing the frontiers of efficient and effective AI systems: ## 🚀 Accelerating Large Model Inference Our researchers have developed "Ladder-residual," a novel parallelism-aware architecture that significantly accelerates large model inference through communication overlapping. This innovation addresses one of the key bottlenecks in deploying large language models at scale, making AI systems faster and more cost-effective. 📄 Read more: [Ladder-residual: parallelism-aware architecture for accelerating large model inference with communication overlapping](https://lnkd.in/g825uRRz) ## 🤝 Enhancing Model Alignment Through Collective Intelligence By leveraging the collective intelligence of open-source models, we've developed new methods to improve AI alignment without the computational expense of traditional alignment techniques. This research demonstrates how cooperation between diverse models can lead to superior performance and better alignment with human values. 📄 Read more: [Improving Model Alignment Through Collective Intelligence of Open-Source Models](https://lnkd.in/g9FZ83P8) ## 📱 Optimizing On-device and Cloud Collaboration Our collaboration with researchers from Stanford introduces cost-efficient methods for on-device and cloud language model cooperation. This work enables more powerful AI capabilities on edge devices while intelligently leveraging cloud resources, resulting in better user experiences and reduced costs. 📄 Read more: [Cost-efficient Collaboration between On-device and Cloud Language Models](https://lnkd.in/ggM6v3R3) These papers represent our commitment to developing AI that is more efficient, aligned, and accessible. I'd love to hear your thoughts on these research directions or discuss potential collaborations! #TogetherAI #MachineLearning #AI #ResearchAndDevelopment #LLM #EdgeComputing #AIAlignment

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Among other things, my team has been focusing on AI research for agents. We just blogged about a recent project: the Test-Time Diffusion Deep Researcher (TTD-DR). This framework uses a Deep Research agent to draft and revise its own drafts using high-quality retrieved information. This approach achieves new state-of-the-art results in writing long-form research reports and completing complex reasoning tasks. The work achieves better results than any other deep research agent we compared to. It also shows that self-evaluation and self-improvement are must-have components of a successful agent. https://lnkd.in/gBZpVMQy

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