←Search results

Search “Google”

By relevance · Total 1192 results Clear search

Wednesday

Tuesday

Sunday

Friday

Sunday

  1. Story1 reports · 1 facts

    Princeton and Google paper finds 4B model trained on agent states achieves higher task success rate

    Princeton University and Google published a paper studying the performance of a 4B model trained on agent states in agent tasks. The paper reports that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while the 4B model trained on agent states solved 91.5%. Its harness delegates tool selection at each step to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and trained AgentDecision-4B on the states the agent actually reached, including states after mistakes. On tasks similar to the training data, this type of 4B model reduced agent costs by 81.1% to 89.0% and increased speed by up to 2.5 times, with a success rate gap of within 3 percentage points; delegating all tool selections without a threshold lowered the success rate by 19.0 percentage points. The paper is available at arxiv.org/abs/2610.21412.

    Princeton University and Google published a paper studying the performance of a 4B model trained on agent states in agent tasks. The paper reports that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while the 4B model trained on agent states solved 91.5%. Its harness delegates tool selection at each step to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and trained AgentDecision-4B on th

    Matched fieldsTitle

  2. Story1 reports · 1 facts

    Google Research releases the Harness2 framework

    Google Research has released the Harness2 framework, which lets agents improve their own harness while the model remains frozen. According to the same report, Gemini 3.5 Flash improved by 8.4 points on WorkBuddy-Bench and 7.5 points on JobBench, and improved on all 3 benchmarks; Skills and scripts accounted for about 50% of the edits, and removing skills would cost 5.3 points. This news currently comes from a single-source report, with no subsequent independent developments yet.

    Matched fieldsTitle

Monday

  1. FactRelated story

    Princeton and Google paper finds 4B model trained on agent states achieves higher task success rate

    Princeton University and Google published a paper studying the performance of a 4B model trained on agent states in agent tasks. The paper reports that the most accurate decision model on isolated problems solved only 69.5% of agent tasks, while the 4B model trained on agent states solved 91.5%. Its harness delegates tool selection at each step to a fast decision model, adopting its answer only when confidence exceeds a threshold fitted on the agent's own runs, and trained AgentDecision-4B on the states the agent actually reached, including states after mistakes. On tasks similar to the training data, this type of 4B model reduced agent costs by 81.1% to 89.0% and increased speed by up to 2.5 times, with a success rate gap of within 3 percentage points; delegating all tool selections without a threshold lowered the success rate by 19.0 percentage points. The paper is available at arxiv.org/abs/2610.21412.

    Matched fieldsTitle

    View story:Princeton and Google paper finds 4B model trained on agent states achieves higher task success rate

  2. FactRelated story

    Google Research releases Harness² framework

    Google Research has released the Harness2 framework, which lets agents improve their own harness while the model remains frozen. According to the same report, Gemini 3.5 Flash improved by 8.4 points on WorkBuddy-Bench and 7.5 points on JobBench, and improved on all 3 benchmarks; Skills and scripts accounted for about 50% of the edits, and removing skills would cost 5.3 points. This news currently comes from a single-source report, with no subsequent independent developments yet.

    Matched fieldsTitle

    View story:Google Research releases the Harness2 framework