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 ...
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 ...
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.
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 ...
Orchestration Host routes across many servers/tools App-local chaining Agent/toolkit routes intents → operations ...
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 ...
Meta has released MobileLLM-R1, a family of lightweight edge reasoning models now available on Hugging Face. The release includes models ranging from 140M to 950M parameters, with a focus on efficient ...
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 ...
The research introduced a two-phase training process. First, they used supervised fine-tuning (SFT) on high-quality trajectories sampled from Claude-4 Sonnet using rejection sampling, effectively ...
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 ...
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 ...
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