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Latest from Xiaohongshu Tech REDtech

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Xiaohongshu Tech REDtech

Aug 14, 2026 · Artificial Intelligence

dots3-note Preview: A First Step Toward Long‑Term Agents for Real‑World Service

The open‑source dots3-note Preview model, a 280B‑parameter multimodal agent with 512K context, introduces the TEMPO training scheme to improve long‑term reinforcement learning, achieves benchmark gains of up to 31.5% over baselines, and is evaluated on new VibeSearchBench and VibeLifeBench suites while acknowledging current limitations.

Agentic AIbenchmarklong-term planning

0 likes · 27 min read

dots3-note Preview: A First Step Toward Long‑Term Agents for Real‑World Service

Xiaohongshu Tech REDtech

Aug 13, 2026 · Artificial Intelligence

dots.tts: Open‑Source Continuous Autoregressive TTS Model for Sustainable, Scalable Voice Synthesis

dots.tts is a 2‑billion‑parameter, fully continuous, end‑to‑end autoregressive TTS model released by the Xiaohongshu dots team, offering state‑of‑the‑art zero‑shot voice cloning, low‑step inference, 1‑to‑1 audio‑text streaming, and an extensible training pipeline for research and deployment.

Speech SynthesisTTSaudio-text streaming

0 likes · 22 min read

dots.tts: Open‑Source Continuous Autoregressive TTS Model for Sustainable, Scalable Voice Synthesis

Xiaohongshu Tech REDtech

Aug 7, 2026 · Artificial Intelligence

Can AI Translation Miss Memes? CULTURE‑MT Benchmark at ICML 2026

The authors introduce CULTURE‑MT, the first Chinese‑English social‑media translation benchmark that evaluates cultural effectiveness, define a new metric, release the JUDGER automatic evaluator (86 % accuracy, κ = 0.72), and show that even top models like Gemini 3 pro achieve only 38 % perfect cultural translations.

AI translationbenchmarkcultural evaluation

0 likes · 10 min read

Can AI Translation Miss Memes? CULTURE‑MT Benchmark at ICML 2026

Xiaohongshu Tech REDtech

Aug 5, 2026 · Artificial Intelligence

Accelerating Xiaohongshu ‘Ask’ Inference: Slim Vision Tokens, Focused MoE

The article details how Xiaohongshu’s multimodal “Ask” service reduces visual token bloat and MoE expert compute by applying dynamic Vision Token compression (VisionZip) and similarity‑based expert re‑routing (SERE), achieving up to 13% lower first‑token latency and 16.5% faster end‑to‑end throughput while preserving answer quality.

MoE expert routingQwen modelsSERE

0 likes · 19 min read

Accelerating Xiaohongshu ‘Ask’ Inference: Slim Vision Tokens, Focused MoE

Xiaohongshu Tech REDtech

Jul 28, 2026 · Artificial Intelligence

Vision-OPD Enables Multimodal LLMs to See Fine Details in a Single Forward Pass

Vision-OPD introduces an online self‑distillation framework that lets a 9B multimodal LLM internalize fine‑grained visual evidence from whole images, achieving state‑of‑the‑art results on six detailed visual‑understanding benchmarks without extra inference tools.

Large Language ModelMultimodal LLMVision-OPD

0 likes · 13 min read

Vision-OPD Enables Multimodal LLMs to See Fine Details in a Single Forward Pass

Xiaohongshu Tech REDtech

Jul 24, 2026 · Artificial Intelligence

UniNote: Unifying Multimodal Representation and Ranking in a Single Embedding Model

UniNote introduces a unified multimodal embedding model that combines representation learning and ranking optimization in a single forward pass, using a two‑stage SFT‑then‑RL training paradigm and Matryoshka Representation Learning to achieve competitive Item‑to‑Item retrieval performance while reducing latency.

Item2ItemMatryoshka Representation LearningSFT

0 likes · 12 min read

UniNote: Unifying Multimodal Representation and Ranking in a Single Embedding Model

Xiaohongshu Tech REDtech

Jul 23, 2026 · Artificial Intelligence

HELMSMAN: Redefining Large-Scale Vector Retrieval on All‑Flash Servers (OSDI 2026)

HELMSMAN replaces DRAM‑heavy graph indexes with a clustering‑based, SSD‑first ANN system that uses a custom SPDK storage stack, adaptive LLSP pruning, and a GPU‑CPU construction pipeline, achieving 2‑16× throughput, up to 85% of in‑memory performance, and over 90% hardware cost reduction for billion‑scale search workloads.

Approximate Nearest NeighborClusteringGPU

0 likes · 13 min read

HELMSMAN: Redefining Large-Scale Vector Retrieval on All‑Flash Servers (OSDI 2026)

Xiaohongshu Tech REDtech

Jul 22, 2026 · Artificial Intelligence

BigMac: Breaking the Pareto Frontier of Compute‑Memory Trade‑offs in Multimodal LLM Training

BigMac introduces a dependency‑safe nested pipeline that preserves LLM compute efficiency while reducing encoder and generator activation memory to O(1), delivering 1.08‑1.9× speedup and stable memory usage for large‑scale multimodal model training.

Compute EfficiencyDistributed TrainingMemory Efficiency

0 likes · 15 min read

BigMac: Breaking the Pareto Frontier of Compute‑Memory Trade‑offs in Multimodal LLM Training

Xiaohongshu Tech REDtech

Jul 16, 2026 · Artificial Intelligence

Cut First‑Token Latency by 3.25×: Introducing HYPIC for Position‑Independent Caching in Hybrid‑Attention LLMs

HYPIC combines position‑independent caching with hybrid‑attention LLMs, reducing first‑token latency by 3.25× and sustaining QPS by 1.66× while keeping quality loss under 2 points, through a segment‑cumulative transition operator, a seam‑window fix for full‑attention layers, and segment‑parallel execution.

HYPICKV CacheLLM inference

0 likes · 13 min read

Cut First‑Token Latency by 3.25×: Introducing HYPIC for Position‑Independent Caching in Hybrid‑Attention LLMs

Xiaohongshu Tech REDtech

Jul 14, 2026 · Artificial Intelligence

How Xiaohongshu Built an Enterprise AI Personal Assistant from Zero to Full‑Staff Coverage

Xiaohongshu’s AI team describes a month‑long, three‑person effort that leveraged an AI‑Native project model, isolated Kubernetes clusters, a custom sandbox (NEX), token‑saving Self‑GC, cost‑aware routing, and a three‑layer memory architecture to roll out a secure, low‑cost AI personal assistant used by every employee.

AI agentEnterprise AIMemory Architecture

0 likes · 14 min read

How Xiaohongshu Built an Enterprise AI Personal Assistant from Zero to Full‑Staff Coverage