Semantic Search At LinkedIn

Semantic search with large language models (LLMs) enables retrieval by meaning rather than keyword overlap, but scaling it requires major inference efficiency advances. We present LinkedIn's LLM-based semantic search framework for AI Job Search and AI People Search, combining an LLM relevance judge, embedding-based retrieval, and a compact Small Language Model trained via multi-teacher distillation to jointly optimize relevance and engagement. A prefill-oriented inference architecture co-designed with model pruning, context compression, and text-embedding hybrid interactions boosts ranking throughput by over 75x under a fixed latency constraint while preserving near-teacher-level NDCG, enabling one of the first production LLM-based ranking systems with efficiency comparable to traditional approaches and delivering significant gains in quality and user engagement.

LFD: Layer Fused Decoding to Exploit External Knowledge in Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) incorporates external knowledge into large language models (LLMs), improving their adaptability to downstream tasks and enabling information updates. Surprisingly, recent empirical evidence demonstrates that injecting noise into retrieved relevant documents paradoxically facilitates exploitation of external knowledge and improves generation quality. Although counterintuitive and challenging to apply in practice, this phenomenon enables granular control and rigorous analysis of how LLMs integrate external knowledge. Therefore, in this paper, we intervene on noise injection and establish a layer-specific functional demarcation within the LLM: shallow layers specialize in local context modeling, intermediate layers focus on integrating long-range external factual knowledge, and deeper layers primarily rely on parametric internal knowledge. Building on this insight, we propose Layer Fused Decoding (LFD), a simple decoding strategy that directly combines representations from an intermediate layer with final-layer decoding outputs to fully exploit the external factual knowledge. To identify the optimal intermediate layer, we introduce an internal knowledge score (IKS) criterion that selects the layer with the lowest IKS value in the latter half of layers. Experimental results across multiple benchmarks demonstrate that LFD helps RAG systems more effectively surface retrieved context knowledge with minimal cost.

Flow Matching based Sequential Recommender Model

Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model that employs a straight flow trajectory and a modified loss tailored for the recommendation task. Additionally, from the diffusion-model perspective, we integrate a reconstruction loss to improve robustness against noise perturbations, thereby retaining user preferences during the forward process. In the reverse process, we employ a deterministic reverse sampler, specifically an ODE-based updating function, to eliminate unnecessary randomness, thereby ensuring that the generated recommendations closely align with user needs. Extensive evaluations on four benchmark datasets reveal that FMRec achieves an average improvement of 6.53% over state-of-the-art methods. The replication code is available at https://github.com/FengLiu-1/FMRec.

A Taxonomy of AgentOps for Enabling Observability of Foundation Model based Agents

The ever-improving quality of LLMs has fueled the growth of a diverse range of downstream tasks, leading to an increased demand for AI automation and a burgeoning interest in developing foundation model (FM)-based autonomous agents. As AI agent systems tackle more complex tasks and evolve, they involve a wider range of stakeholders, including agent users, agentic system developers and deployers, and AI model developers. These systems also integrate multiple components such as AI agent workflows, RAG pipelines, prompt management, agent capabilities, and observability features. In this case, obtaining reliable outputs and answers from these agents remains challenging, necessitating a dependable execution process and end-to-end observability solutions. To build reliable AI agents and LLM applications, it is essential to shift towards designing AgentOps platforms that ensure observability and traceability across the entire development-to-production life-cycle. To this end, we conducted a rapid review and identified relevant AgentOps tools from the agentic ecosystem. Based on this review, we provide an overview of the essential features of AgentOps and propose a comprehensive overview of observability data/traceable artifacts across the agent production life-cycle. Our findings provide a systematic overview of the current AgentOps landscape, emphasizing the critical role of observability/traceability in enhancing the reliability of autonomous agent systems.