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 ...
Cache-to-Cache(C2C): Direct Semantic Communication Between Large Language Models via KV-Cache Fusion
Can large language models collaborate without sending a single token of text? a team of researchers from Tsinghua University, Infinigence AI, The Chinese University of Hong Kong, Shanghai AI ...
AI companies use model specifications to define target behaviors during training and evaluation. Do current specs state the intended behaviors with enough precision, and do frontier models exhibit ...
The landscape of AI is expanding. Today, many of the most powerful LLMs (large language models) reside primarily in the cloud, offering incredible capabilities but also concerns about privacy and ...
Shobha is a data analyst with a proven track record of developing innovative machine-learning solutions that drive business value.
Vibe Coding is redefining the software landscape by harnessing artificial intelligence to make code creation faster, more intuitive, and accessible to virtually anyone. In 2025, this trend has moved ...
Canary-1b-v2: Multilingual ASR + Translation (En ↔ 24 Languages) Canary-1b-v2 is a billion-parameter Encoder-Decoder model trained on Granary, delivering high-quality transcription and translation ...
The Model Context Protocol (MCP) team has released the preview version of the MCP Registry, a system that could be the final puzzle piece for making enterprise AI truly production-ready. More than ...
Xiaomi’s MiMo team released MiMo-Audio, a 7-billion-parameter audio-language model that runs a single next-token objective over interleaved text and discretized speech, scaling pretraining beyond 100 ...
Agentic RAG combines the strengths of traditional RAG—where large language models (LLMs) retrieve and ground outputs in external context—with agentic decision-making and tool use. Unlike static ...
At the center of this release is the evolution of RAG architectures. Traditional RAG pipelines typically involve static queries to vector stores followed by synthesis via large language models.
What is catastrophic forgetting in foundation models? Foundation models excel in diverse domains but are largely static once deployed. Fine-tuning on new tasks often introduces catastrophic forgetting ...
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