Of the 12 papers this week, industrial deployments dominate — 8 come from first-tier platforms like YouTube, TikTok, Kuaishou, Tencent, and Walmart, all with online A/B experiment metrics. Research clusters around three overlapping directions: generative recommendation with LLM augmentation, GPU acceleration for large-scale retrieval, and industrial system architecture and attribution optimization. Generative recommendation moves from "generating item IDs" to "generating physical items": Kuaishou's RaG unifies generative recommendation with video generation, achieving +1.87% ad revenue on a 400M DAU platform. YouTube's TokenMinds extends Semantic ID from the item side to the user side, producing both discrete user tokens and dense embeddings, covering full user traffic. Both routes point to the same judgment — generative recommendation is moving from offline consistency verification to online revenue realization. User modeling accelerates its shift from dense vectors to discrete semantic IDs: Kuaishou and YouTube published SID-based frameworks almost simultaneously. This isn't just a change in representation form — it means that the underlying token space of recommendation systems is beginning to align with that of the LLM world, substantially lowering the cost of cross-scenario unification (short-form video / long-form video, recommendation / advertising). Industrial attribution and scaling methodology move toward precision: TikTok's Attribution Correction Framework aligns causal experiments with daily production attribution, reducing measured cannibalization by roughly 15 percentage points. Tencent's NOVA uses an agent to automate architecture evolution, achieving +2.02% GMV on L3 tasks online. Kuaishou's UniFormer proposes a model-centric scaling framework that explicitly decomposes the modeling space into feature and task dimensions. Together, these three reveal a pattern: as model architectures converge, engineering automation and measurement accuracy become th

AI infrastructure hit new milestones today: Microsoft's $7.3B Fairwater campus links hundreds of thousands of Blackwell GPUs into a single supercomputer via 800G Ethernet. DeepSeek V4's DSpark framework slashes inference latency by 80% with full-stack open source, while SubQ's dynamic sparse attenti

A massive day in AI: OpenAI previewed GPT-5.6 Sol with a new architecture and 1M context, but the release was held back by the Commerce Department requiring per-customer approval — a regulatory first that could reshape how frontier models ship. Meanwhile, GLM-5.2 became the first open-weight model t

Agent infrastructure funding hit new highs today: Sail raised $80M for long-running agent inference, and PimDeWitte closed $320M at a $2.3B valuation for world model data. SWE-bench Pro replaced the compromised SWE-bench Verified, while OpenAI's economic report revealed Codex consumes 99.8% of its o

AI infrastructure is heating up fast. OpenAI and Broadcom released Jalapeño, their first custom LLM inference chip, claiming 4x throughput and 5x energy efficiency over GPUs. Cursor is training a 1.5 trillion-parameter model from scratch on xAI's Colossus cluster — an app-layer company going full-st

AI hit multiple milestones today. OpenAI's GPT-5 cracked a three-year immunology mystery, while GPT-5.5-Cyber launched a "Patch the Planet" initiative to fix open-source bugs. Anthropic released Claude Tag, turning the assistant into a persistent Slack team member — Andrej Karpathy called it the thi

AI security took center stage today. OpenAI released GPT-5.5-Cyber with SOTA performance on CyberGym, while the Five Eyes intelligence alliance issued a rare joint warning that AI models could launch devastating cyberattacks within months. Cursor announced a partnership with SpaceX to train new AI m

AI infrastructure security took center stage today as researchers revealed AgentJacking — a novel supply-chain attack that exploits public Sentry keys to hijack coding agents like Claude Code and Cursor. Meanwhile, Tesla quietly filed a "MEGAPOD" trademark hinting at turning its Supercharger network

Google DeepMind dropped a bombshell with a 57-page ASI roadmap, formally defining Superhuman AI as output exceeding tens of thousands of top experts working for a decade. Meta AI released SAGE-OPD, a selective distillation framework that boosts agent task success rates by 13.3% — a practical fix for

The clearest narrative in 2026-W25: open-source model frontiers have shifted from catching up to running alongside closed-source models — and in some dimensions, surpassing them. Four models launched this week: GLM-5.2, DeepSeek-V4, Nemotron 3 Ultra, and Ling-2.6. Parameter counts range from 284B to 1.6T, all support 1M token context windows, and all are open-source. Community benchmarks and independent analysis report that these models now match GPT-5.5 and Opus 4.8 on knowledge work, coding, and scientific reasoning — and are cheaper. The second theme: Agent infrastructure is moving from scattered tools to platforms. Amazon Bedrock AgentCore Harness went GA — two API calls to deploy a production-grade Agent. Cursor launched Origin, a Git replacement designed for Agent workloads. Meanwhile, Agent evaluation methodology is shifting from aggregate leaderboards to predictive validity — an IBM paper directly challenges whether static leaderboards transfer to deployment scenarios. The third theme: micro-innovations in inference efficiency are accelerating. Pine AI proposes an editable/composable KV cache paradigm, reducing p90 TTFT by 53–398x. LMSYS used SGLang-JAX to optimize a 1T-parameter MoE model on TPUs, cutting prefill by 53%. Jeff Dean published the evolution of TPUs from v2 to Ironwood — 30x energy efficiency gains. The combination of hardware and algorithm innovations is making 1M token inference economically viable. Additionally, regulatory tensions escalated sharply this week — Anthropic restricted use of the Fable model, then the US Commerce Department imposed export license requirements on Fable and Mythos. Andrew Ng argues this will accelerate the AI sovereignty movement. Healthcare also saw multiple product-level advances, from rare disease diagnosis to full-body ultrasound CT.

This week's recommendation systems research clusters around three themes: full lifecycle co-design for large-scale graph retrieval, Transformer-based sequence modeling deployed across platforms, and a shift from DNN to Transformer-native architectures for multi-task ranking. Meta, Airbnb, Alibaba, Shopee, and NetEase Cloud Music all published online deployment work with specific AB metrics. Thread 1 (End-to-end design of large-scale graph systems): Meta's RankGraph-2 (Meta) couples graph construction, representation learning, and online serving into a joint optimization. On a billion-node graph, it reduces compute cost by 83%, achieves 3.8x the recall of GAT+Deep Graph Infomax, and lifts online CTR by +0.96% and CVR by +2.75%. Along the same line, HighLevel's ScoreGate (HighLevel) uses a statistical fusion of two scores to adaptively control the number of retrieved chunks in RAG. In production, it cuts tokens by 34.8% while maintaining recall between 97.77% and 99.34%. Thread 2 (Generative recommendation moves from theory to production): Airbnb's JourneyFormer (Airbnb) deploys a Transformer-based sequence model in search ranking to handle long, sparse user behavior. Alibaba's OneBar (Alibaba) uses an end-to-end generative framework for video e-commerce query recommendation, achieving a 21.67% GMV lift. Both point to the same direction: generative recommendation needs engineering trade-offs under real constraints (cold start, latency, sparse labels) rather than chasing offline metrics alone. Thread 3 (Transformer-native paradigm for multi-task ranking): Shopee's OneRank (Shopee) eliminates the encoder-predictor separation, embedding task-private channels and gradient isolation inside the Transformer. Online CTR is up +1.2%, CVR +0.8%. NetEase Cloud Music's PIANO (NetEase Cloud Music) uses a learnable [CLS] token for list-level multi-objective re-ranking, lifting CTR by +0.62% and CVR by +4.45%. Both demonstrate that internalizing multi-objective reasoning into the Tr

AI hit a major inflection point today. DeepSeek dropped DeepSeek-V4, a 1.6T MoE model that slashes long-context costs by 3.7x and beats GPT-5.4 — all open-source. Meanwhile, Subquadratic claims to have cracked the O(n²) attention bottleneck, and GLM-5.2 is now the first open model that independent d