While a basic Large Language Model (LLM) agent—one that repeatedly calls external tools—is easy to create, these agents often struggle…
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Stanford, EPFL & UNC Researchers You can also read about how to introduce yourself. Weak-for-Strong Harnessing, W4S. A new Reinforcement…
Do your LLM benchmarks reject wrong-complexity and inter-protocol solutions, or do they pass under-specified units tests? Researchers from UCSD and…
This tutorial builds a small, compact framework to demonstrate how tool documentation can be converted into standard, callable APIs. We…
The agentic system is stochastic and context-dependent. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities…
The agentic system is stochastic and context-dependent. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities…
This tutorial will explore the basics of a video. IvyThe remarkable ability of to integrate machine learning across multiple frameworks.…
Sentient AI Has been released ROMA (Recursive Open Meta-Agent)The open-source Meta-Agent Framework is a powerful tool for creating high-performance, multi-agent…
How do you audit frontier LLMs for misaligned behavior in realistic multi-turn, tool-use settings—at scale and beyond coarse aggregate scores?…
What would be the compression ratio and rate of throughput if you trained a graph-compressor…