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PACE: Perceived-Latency-Aware Cascading Service Routing and Filler Control for QoE-Efficient Retrieval-Augmented Dialogue Serving
PACE cuts perceived latency in retrieval-augmented dialogue serving via cascading routing and filler control, reaching 0.41s P95 under load.
PACE is a serving framework for retrieval-augmented dialogue that optimizes Perceived Time-to-First-Response (PTFR) under quality and cost constraints. It combines a load-adaptive cascading router, a joint path-filler controller, and volatility-aware cache admission, deployed on a humanoid-robot sales service. On 75k CarQA requests, the cascade halved pure-LLM P95 PTFR (0.29s vs 0.53s) and the adaptive controller reached 0.41s P95, 2.4x better than RAG at high load; filler calls dropped 94% and stale answers fell from 86% to 0%.
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HyQuant: Hybrid-Precision Quantization for LLM Attention
HyQuant keeps most LLM attention states low-bit while preserving vertical-line tokens and local windows in high precision, maintaining near-lossless accuracy.
HyQuant is a hybrid-precision quantization framework for LLM attention that quantizes most attention states to low bits while keeping accuracy-critical vertical-line tokens and local-window states in full precision, selected via lightweight attention-pattern signals. In the prefill stage it uses a hybrid-precision attention operator, and in the decode stage it applies the same principle to KV-cache compression with fused dequantization and attention computation. Across diverse tasks, models, and datasets it maintains nearly lossless accuracy; code is available on GitHub.
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Tines develops Cases to optimize automation and improve operational efficiency across the enterprise
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Rapidly scaling online storage to serve over 1 billion ChatGPT users
OpenAI's Habitat online storage platform now handles over 70 million requests per second and 500 PB of data for 1 billion users.
OpenAI details the evolution of Habitat, its online storage platform backing ChatGPT and other products, which began in mid-2024 as a Python client-side library over Azure Cosmos DB. Habitat now processes more than 70 million requests per second, serves over 500 petabytes of data across nearly 40 geographic regions, and supports over 1 billion users weekly. By mid-2025 the client library approach became brittle, so OpenAI moved Habitat into a standalone service to centralize deployments, observability, and multi-tenancy reliability. This is part one of a two-part series; a future post will cover read optimization and scaling the Azure Cosmos DB partnership.
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Introducing agentic video understanding with Gemini
Google DeepMind launches agentic video understanding for Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite, cutting video-analysis tokens up to 88%.
Google DeepMind launched agentic video understanding across Gemini 3.7 Flash, 3.6 Flash, and 3.5 Flash-Lite via the Gemini API in Google AI Studio and the Gemini Enterprise Agent Platform. The feature replaces static fixed-FPS ingestion with an agentic loop that dynamically searches frames, audio, and transcripts, cutting token consumption by up to 88%, reducing costs by up to 66%, and improving accuracy by up to 7%. Gemini 3.7 Flash with the feature sits at the accuracy-to-cost Pareto frontier on tested video benchmarks, and the capability will later power YouTube's Ask YouTube feature.
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A10 Networks introduces AI Gateway to secure and manage enterprise AI
A10 Networks launches AI Gateway, a control plane for routing, cost management, and governance of enterprise AI agents and LLMs.
A10 Networks announced general availability of the A10 AI Gateway, a centralized control plane providing identity-based AI access policies, smart routing that matches request complexity to model capability, and per-request dollar cost tracking with per-team token budgets. The product enforces business-layer TPM/RPM rate limiting and integrates with A10's TrojAI and ThreatX AI security portfolio across the AI lifecycle. It runs entirely in the customer environment—on-premises, private cloud, or air-gapped—for full data sovereignty.
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