Every time you prompt an LLM, it doesn’t generate a complete answer all at once — it builds the response one word (or token) at a time. At each step, the model predicts the probability of what the ...
How can we build AI systems that keep learning new information over time without forgetting what they learned before or retraining from scratch? Google Researchers has introduced Nested Learning, a ...
Tabular data is still where many important models run in production. Finance, healthcare, energy and industry teams work with tables of rows and columns, not images or long text. Prior Labs now ...
How do you build reliable AI agents that plug into your existing Go services without bolting on a separate language stack? Google has just released Agent Development Kit for Go. Go developers can now ...
Existing data science agents often rely on Text to SQL over relational databases. This constraint limits them to structured tables and simple schema, which does not match many enterprise environments ...
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 ...
In this tutorial, we explore how exploration strategies shape intelligent decision-making through agent-based problem solving. We build and train three agents, Q-Learning with epsilon-greedy ...
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 ...
In the traditional cascade modeling approach, automatic speech recognition (ASR) first produces a single text string, which is then passed to retrieval. Small transcription errors can change query ...
ACE positions “context engineering” as a first-class alternative to parameter updates. Instead of compressing instructions into short prompts, ACE accumulates and organizes domain-specific tactics ...
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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