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Latest from Kuaishou Tech

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Kuaishou Tech

Apr 29, 2026 · Operations

Boosting Oncall Interception from 15% to 55%: KOncall’s AI‑Driven Evolution at Kuaishou

Kuaishou’s R&D efficiency team built the KOncall intelligent on‑call platform, integrating LLM‑based retrieval‑augmented generation, Redis Pub/Sub streaming, OCR multimodal parsing, FAQ knowledge ops, and custom reranking, which raised automated query interception from 15% to 55% and processed over 116 000 requests, turning on‑call from a bottleneck into a capability starter.

AI operationsIncident ManagementLLM

0 likes · 26 min read

Boosting Oncall Interception from 15% to 55%: KOncall’s AI‑Driven Evolution at Kuaishou

Kuaishou Tech

Apr 24, 2026 · Artificial Intelligence

ICLR 2026: Kuaishou Tech Team’s Cutting‑Edge AI Research Highlights

This article reviews eight Kuaishou‑authored papers accepted at ICLR 2026, summarizing their problem statements, novel methods such as front‑door causal attribution, visual table retrieval, denoising rerankers, difficulty‑adaptive reasoning, diffusion code infilling, generative ordinal regression, multimodal video retrieval, e‑commerce dialogue benchmarks, and a new LLM creativity evaluator, together with reported experimental gains.

ICLR 2026KuaishouMultimodal Learning

0 likes · 19 min read

Kuaishou Tech

Apr 16, 2026 · Artificial Intelligence

Hierarchical Semantic RL Tackles Dynamic Action Spaces in Recommendations

Researchers from Kuaishou, Fudan and Tianjin University introduce the Hierarchical Semantic Reinforcement Learning (HSRL) framework, which maps high‑dimensional, dynamic item spaces into a fixed‑size semantic action space via semantic IDs, employs a hierarchical policy network and multi‑level critics, and demonstrates 13‑18% gains on public datasets and an 18.4% ad spend lift in billion‑scale online tests.

Large-Scale Deploymenthierarchical policyindustrial experiments

0 likes · 11 min read

Hierarchical Semantic RL Tackles Dynamic Action Spaces in Recommendations

Kuaishou Tech

Mar 19, 2026 · Backend Development

How Kuaishou Boosted Build Performance with AutoFDO, ThinLTO, BOLT, and Propeller

This article details Kuaishou's systematic compiler and build‑system optimizations—including AutoFDO, ThinLTO, BOLT, and a newly improved Propeller—showing how they reduced compilation time from hours to seconds, cut CPU usage by 10%, and achieved up to 15% performance gains while solving profile‑staleness and integration challenges.

AutoFDOBoltCompiler

0 likes · 25 min read

How Kuaishou Boosted Build Performance with AutoFDO, ThinLTO, BOLT, and Propeller

Kuaishou Tech

Mar 4, 2026 · Artificial Intelligence

How LLMs Are Revolutionizing Reinforcement Learning for Recommendation Systems

This survey examines the emerging LLM‑RL collaborative recommendation paradigm, outlining its research background, five main collaboration patterns, standardized evaluation protocols, and the key challenges and future directions for building smarter, more robust recommender systems.

LLMartificial intelligencerecommendation systems

0 likes · 14 min read

How LLMs Are Revolutionizing Reinforcement Learning for Recommendation Systems

Kuaishou Tech

Jan 28, 2026 · Artificial Intelligence

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

The paper introduces BLM‑Guard, an explainable multimodal ad‑moderation framework that combines interleaved‑modal chain‑of‑thought reasoning with a policy‑aligned reinforcement‑learning reward to detect hidden cross‑modal violations in short‑video ads, and presents a new benchmark that demonstrates state‑of‑the‑art performance across multiple risk scenarios.

Policy Alignmentad risk detectionbenchmark

0 likes · 12 min read

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

Kuaishou Tech

Jan 19, 2026 · Artificial Intelligence

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

OneSug introduces an end‑to‑end generative framework that unifies recall, coarse‑ranking, and fine‑ranking for e‑commerce query suggestion, addressing the limitations of traditional multi‑stage cascades and dramatically improving relevance, efficiency, and business metrics in real‑world deployments.

E-commerceGenerative ModelsMachine Learning

0 likes · 10 min read

How OneSug Revolutionizes E‑commerce Query Suggestion with End‑to‑End Generative Modeling

Kuaishou Tech

Jan 8, 2026 · Artificial Intelligence

Top 12 Kuaishou Papers Accepted at AAAI 2026: Breakthroughs in Recommendation, Video Generation, and LLM Research

Kuaishou secured 12 papers at AAAI 2026, covering advances in search and recommendation systems, multi‑camera video generation, multimodal understanding, generative model fundamentals, video large language models, experimental design, and LLM latent‑space reasoning, with three papers highlighted as oral presentations.

AILLMdiffusion

0 likes · 22 min read

Top 12 Kuaishou Papers Accepted at AAAI 2026: Breakthroughs in Recommendation, Video Generation, and LLM Research

Kuaishou Tech

Dec 19, 2025 · Artificial Intelligence

Why Sampling Noise, Not Train‑Inference Gap, Drives RL Instability in MOE Models

The article reveals that sampling noise, rather than train‑inference inconsistency, is the primary cause of reward collapse during RL training of MOE models, and demonstrates that suppressing this noise stabilizes training and speeds convergence.

AI codingMoE modelsModel Stability

0 likes · 6 min read

Why Sampling Noise, Not Train‑Inference Gap, Drives RL Instability in MOE Models

Kuaishou Tech

Dec 18, 2025 · Artificial Intelligence

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem

The paper introduces SSR, a novel multimodal recommendation framework that leverages graph Fourier transforms, energy‑balanced frequency bands, structured regularization, and low‑rank tensor decomposition to replace black‑box fusion with explainable, adaptive reasoning, achieving state‑of‑the‑art results on Amazon datasets and strong cold‑start performance.

Contrastive Learningcold startfrequency domain

0 likes · 15 min read

How SSR Turns Multimodal Recommendation into an Interpretable Frequency‑Domain Reasoning Problem