Simin Karvigh, PhD

Longfeng Wu is an applied scientist intern at Amazon in Sunnyvale, CA, where she has been focusing on developing large-scale generative recommendation models based on semantic identifiers (IDs) aimed at fundamentally transforming how recommendations are generated and delivered. A Ph.D. student in computer science, Longfeng is advised by Dawei Zhou at the Sanghani Center for Artificial Intelligence and Data Analytics.

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Anyscale has contributed Ray to the PyTorch Foundation, joining PyTorch, vLLM, and DeepSpeed under open governance to advance scalable, distributed AI infrastructure. Janakiram MSV (Forbes) explains how this governance model reduces fragmentation and supports transparent collaboration across the AI ecosystem in this article: 🔗 https://lnkd.in/dwy4HpTC #PyTorchFoundation #Ray #OpenSourceAI #AIInfrastructure #DistributedAI

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📢 LLMs can reason. But can they act, adapt, and collaborate over time? Researchers from UIUC, with collaborators from Meta, Amazon, and Google DeepMind, just released a deep survey on Agentic Reasoning for Large Language Models — and it reframes how we should think about “reasoning” altogether. The core shift: This work argues that strong benchmarks ≠ real intelligence. Instead of one-shot reasoning, agentic reasoning treats LLMs as interactive agents that: - Plan over long horizons - Use tools and external systems - Learn from feedback and memory - Coordinate with other agents They organize agentic reasoning into three layers: - Foundational → planning, tool use, search - Self-evolving → reflection, memory, continual improvement - Collective → multi-agent collaboration and role coordination Why this matters right now: - If you’re building or working with agents, this explains why: - Prompt tricks alone don’t scale to real-world tasks - Memory and feedback loops are becoming first-class design primitives - Multi-agent systems are less about “more agents” and more about reasoning structure It also clearly separates: - In-context reasoning (orchestration at inference time) vs Post-training reasoning (RL and fine-tuning to internalize behaviors) That distinction alone changes how you design agent systems. Feels like a roadmap for where agentic AI is actually headed — not just what demos look cool on Twitter. #AgenticAI #LLMResearch #AIEngineering #Meta #Amazon #Google

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Research Paper Highlights: "When Retrieval Succeeds and Fails: Rethinking Retrieval-Augmented Generation for LLMs", By Yongjie Wang Large Language Models (LLMs) have transformed language understanding and generation, enabling numerous real-world applications. Yet because they are trained on static datasets, LLMs struggle with rapidly changing information and niche domain-specific tasks. Retrieval-Augmented Generation (RAG) emerged to bridge this gap by supplying LLMs with fresh, relevant external knowledge. As LLMs grow more capable, this paper reevaluates the importance, relevance, and future direction of RAG systems. Challenges in RAG: - Contextually irrelevant retrieval can mislead LLMs and undermine answer quality. - Dependency on external retrieval systems adds architectural complexity and latency. - Difficulty in aligning retrieved content with LLM reasoning, leading to hallucinations or mismatched answers. - Retrieval bottlenecks, especially when queries are ambiguous or poorly formed. Key Takeaways: - RAG remains highly valuable in scenarios where LLMs alone fail, especially in domains requiring up-to-date, factual, or specialized knowledge. - The field needs re-examination and innovation, as conventional RAG designs may not keep pace with rapidly evolving LLM capabilities. - A deeper understanding of RAG objectives, architecture, and limitations is essential for building the next generation of knowledge-augmented systems. - Effective RAG can significantly boost reliability, factual grounding, and domain expertise when combined with powerful LLMs. This work highlights the need to rethink the role of RAG in an era where LLMs are rapidly advancing. It stresses that while LLMs are improving, RAG remains crucial for dynamic, specialized, and fact-sensitive tasks. Further reading-https://lnkd.in/da5_QtP6

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We’re excited to share our #NeurIPS2025 work: LayerIF: Estimating Layer Quality in Large Language Models Using Influence Functions! 📌 Our key idea: LLMs vary widely in how well different layers are trained. Instead of relying on heuristics or weight-based signals, LayerIF uses training-data influence scores to measure how each layer contributes to validation performance. This provides a data-centric, model-agnostic assessment of layer quality. 📈 We find: • Improved expert allocation for LoRA-MoE architectures • Data-driven layer sparsity allocation for pruning • A simple, generalizable pipeline for layer-wise diagnostics • Strong alignment between our influence-based signals and downstream performance If you’re working on LLM training, pruning, interpretability, or MoE systems, we’d love to chat. 📍 Come meet us at NeurIPS San Diego tomorrow at 4:30 PM during the poster session. 🔗 Paper link: https://lnkd.in/gVR284ws

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By building a taxonomy for compound AI systems, researchers are showing that when agents are optimized using structured evaluation frameworks, downstream task accuracy improves. Better evaluation → better agents. Featuring Melissa Pan and Mert Cemri of University of California, Berkeley / CSGE Berkeley Interviewed by Jessica Dai and SAIL Media Thank you to ‪Lambda‬ for making these interviews possible! 👉 Subscribe to our calendar for priority invites to upcoming events: lu.ma/twill #AI #ArtificialIntelligence #MultiAgentSystems #LLM #LLMAgents #CompoundAI #MachineLearning #AIResearch #BerkeleyAI #CSGEBerkeley #TechEvents #StartupCommunity #SiliconValley #FutureOfAI #AI #ArtificialIntelligence #MultiAgentSystems #LLM #LLMAgents #CompoundAI #MachineLearning #AIResearch #BerkeleyAI #CSGEBerkeley #TechEvents #StartupCommunity

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Compressing LLMs but worried about accuracy? 🎯 EoRA uses eigenspace low-rank approximation to compensate for errors—no retraining needed. A promising direction for scalable, task-adaptive LLMs. New paper from #NVIDIAResearch.

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Based on years of experience building LLM applications, Shuai Guo shares detailed insights for practitioners interested in optimizing their process before, during, and after their project launch.

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😎 New blog post up summarizing our research team's work on PyGim, a highly performant, open-source library for efficient graph neural network execution. Thanks to lead author Christina Giannoula and co-authors Peiming Yang, Ivan Fernandez, Jiacheng Yang, Sankeerth Durvasula, Yu Xin Li, Mohammad Sadrosadati, Juan Gomez Luna, Onur Mutlu, and Gennady Pekhimenko. Read it now: https://lnkd.in/gUP7-68j

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As AI becomes increasingly capable, scientists may increasingly play the role of selector and evaluator Join us tomorrow as Chenhao Tan , Associate Professor of Computer Science and Data Science at the University of Chicago (and Faculty Co-Director for the Novel Intelligence Research Initiative), shares recent advances in AI-enabled hypothesis generation and research evaluation, as well as an introduction of a dedicated benchmark for hypothesis generation, HypoBench Part of the Schmidt AI in Science Speaker Series https://lnkd.in/gCbFUZcN

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