GPT-5.4-mini

Headlines · 2

1. Oct 21
   UCSB and Microsoft's ScholarEvolve lets AI agents read papers to rewrite their own harness

   Researchers at UC Santa Barbara and Microsoft have proposed ScholarEvolve, a system that automates reading research papers, implementing their methods as code and evaluating the resulting changes to an AI agent's harness, the software layer that surrounds a model. It splits the harness into five modules—Tools, Context, Skills, Memory and Workflow—and uses a Research Agent and a Coding Agent to retrieve work from sources such as arXiv, group approaches by mechanism through topic modeling and test candidate combinations. In author-reported results, ScholarEvolve raised Qwen3.5-27B's completion rate on AppWorld Challenge from 49.6% to 63.6% without changing the model's parameters, and also reported gains for GPT-5.4-mini on AppWorld Challenge and τ²-Bench Telecom; the figures have not been independently verified.

   机器之心 · 🔥 0
2. Oct 18
   Zhejiang University proposes EMem-Bench for embodied agent memory

   Zhejiang University's OmniAI team introduced EmbodiedMemory-Bench (EMem-Bench), a benchmark with four task types—passive observation, dynamic tracking, interaction failure and experience generalization—and 2,554 executable samples testing whether embodied agents can use past observations, state changes and action feedback in later tasks. The team also proposed an external memory system, Embodied-Memorizer (EMem), and fine-tuned Qwen3-VL-8B into EMem-8B; in the authors' evaluation, EMem raised average success from 35.3% to 58.9% with a GPT-5.4-mini backbone. Under the paper's full-context setting, Gemini-3-Flash led at 64.2% average success and GPT-5.4 reached 56.8%. The report says the benchmark drew nearly 30,000 Hugging Face downloads in about three weeks, with results mainly from simulation.

   新智元 · 🔥 0

Experience and discussion from the community

Share my GPT-5.4-mini experienceAsk about GPT-5.4-mini