How can developers reliably generate, control, and inspect large volumes of realistic dialogue data without building a custom simulation stack every time? Meet SDialog, an open sourced Python toolkit ...
How far can we push large language model speed by reusing “free” GPU compute, without giving up autoregressive level output quality? NVIDIA researchers propose TiDAR, a sequence level hybrid language ...
Optical character recognition has moved from plain text extraction to document intelligence. Modern systems must read scanned and digital PDFs in one pass, preserve layout, detect tables, extract key ...
In this article we will analyze how Google, OpenAI, and Anthropic are productizing ‘agentic’ capabilities across computer-use control, tool/function calling, orchestration, governance, and enterprise ...
OpenZL formalizes compression as a computational graph: nodes are codecs/graphs, edges are typed message streams, and the finalized graph is serialized with the payload. Any frame produced by any ...
Orchestration Host routes across many servers/tools App-local chaining Agent/toolkit routes intents → operations ...
TUMIX runs a group of heterogeneous agents—text-only Chain-of-Thought, code-executing, web-searching, and guided variants—in parallel, then iterates a small number of refinement rounds where each ...
Neuphonic frames the model for on-device privacy (no audio/text leaves the machine without user’s approval) and notes that all generated audio includes a Perth (Perceptual Threshold) watermarker to ...
decoding MLPerf Inference v5.1 2025 results, scenarios, TTFT/TPOT, power metrics for GPUs, CPUs, accelerators, datacenter, edge ...
Google AI Proposes ReasoningBank: A Strategy-Level AI Agent Memory Framework that Makes LLM Agents Self-Evolve at Test Time ...
An open-source framework that couples LLM-driven program mutations with evolutionary search to automate algorithm discovery and optimization. Code and report are public. 2) How does it achieve higher ...
OpenAI introduced GDPval, a new evaluation suite designed to measure how AI models perform on real-world, economically valuable tasks across 44 occupations in nine GDP-dominant U.S. sectors. Unlike ...
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