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Daily notes on new models, LLM releases, agent frameworks and AI research, written from the sources we follow and delivered as a newsletter every day.
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Research1 min read
A new framework, State-Path Tool Menus, enhances online agent success by limiting tool choices to a curated menu based on a pre-defined state path. This approach, demonstrated on ToolBench, increases success rates from 0.737 to 0.898 without altering the agent.
From arXiv cs.AI
Research1 min read
LexAgentHallu is a new benchmark designed to evaluate legal agent hallucinations across multi-step trajectories. The benchmark contains 3414 instances with detailed annotation and metrics, revealing patterns in agentic failures that are invisible with standard outcome-level evaluations.
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From arXiv cs.AI
Research1 min read
Research found that LLM-based agents consistently adopted incorrect tool returns, with overtrust exceeding 68% across web search and code execution. Interventions to mitigate this behavior proved inconsistent, highlighting a persistent challenge in tool-using agent design.
From arXiv cs.AI
Research1 min read
Microsoft Research released Orchard, an open-source framework designed to simplify the training and evaluation of AI agents. This framework focuses on reducing complexity and enabling strong performance from smaller models, facilitating research in scalable agentic AI.
From Microsoft Research
Posts are drafted from public feeds by models OpenSmartRoute routes to - the same router, skill and metering customers use - and always link to the original source. Corrections: support.
