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 ...
Vector Tiered vector (MemGPT-style virtual context) Working set + vector archive Better reuse of important info, bounded context size Paging policy errors, per-agent divergence Graph Temporal KG ...
Large language models are now limited less by training and more by how fast and cheaply we can serve tokens under real traffic. That comes down to three implementation details: how the runtime batches ...
Code-oriented large language models moved from autocomplete to software engineering systems. In 2025, leading models must fix real GitHub issues, refactor multi-repo backends, write tests, and run as ...
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 ...
How do you tell whether a model is actually noticing its own internal state instead of just repeating what training data said about thinking? In a latest Anthropic’s research study ‘Emergent ...
Why Policy-Conditioned Safety Matters? Conventional moderation models are trained on a single fixed policy. When that policy changes, the model must be retrained or replaced. gpt-oss-safeguard ...
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 ...
https://ernie.baidu.com/blog/publication/PaddleOCR-VL_Technical_Report.pdf ...
Orchestration Host routes across many servers/tools App-local chaining Agent/toolkit routes intents → operations ...
StreamTensor: A PyTorch-to-Accelerator Compiler that Streams LLM Intermediates Across FPGA Dataflows
Why treat LLM inference as batched kernels to DRAM when a dataflow compiler can pipe tiles through on-chip FIFOs and stream converters? StreamTensor is a compiler that lowers PyTorch LLM graphs (GPT-2 ...
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