Qwen3.5-27B

Headlines · 5

1. 6d
   EC²-Bench Evaluates Edge-Cloud Collaboration for Long-Horizon AI Agents

   Northeastern University, Tsinghua University and China Mobile researchers released EC²-Bench, an open benchmark for edge-cloud collaboration in long-horizon AI agent tasks. It contains 128 executable digital-workspace tasks across six scenarios, with 100 tasks carrying fine-grained privacy annotations covering 3,385 sensitive information units, and evaluates task performance, resource costs and privacy exposure. The study compares four collaboration strategies; with Qwen3.5-9B, the best strategy raised the completion score from 39.12 under edge-only execution to 60.46 while using cloud resources costing 15% of the cloud-only setup.

   DoNews · 🔥 0
2. Oct 24
   DynaFlow learns free-form multi-agent orchestration

   A research paper by Ruhao Liu, Qi Li, Shizun Wang, and Xinchao Wang introduces DynaFlow, which adds executable workflow synthesis to a main agent’s action space. The agent can decide when to collaborate and generate task-specific programs for sequential or parallel sub-agent execution, structured information exchange, and verification; the paper reports 88.08% overall accuracy in Forced mode across six benchmarks using a Qwen3.5-27B backbone.

   Hugging Face · Papers · 🔥 0
3. Oct 21
   Baseten releases mammoth-1 and elephant-1 decision models

   Baseten released mammoth-1 and elephant-1, open-weight models from Base Labs that return probability distributions for boolean, multiple-choice, and ordered-score questions instead of generating text. The 27B mammoth-1 and 9B elephant-1 were post-trained from Qwen3.5 models on 6.21 million examples; Baseten reports mammoth-1 first among 208 models on Jev Decision Index 0.4, while Artificial Analysis scored elephant-1 65.2, tying d1-ultra. A r/LocalLLaMA report says elephant-1 uses an MIT license and costs about $8 per million tasks, compared with roughly $31 for d1-ultra, while both models are available through Baseten Model APIs with zero data retention during the reported trial.

   Baseten Blog · 🔥 0
4. 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
5. Oct 19
   EVO-WAM lets world action models self-train on verified imagined trajectories

   Researchers behind EVO-WAM report a self-training loop in which a world action model generates complete imagined video-action trajectories, a vision-language model checks whether the task was completed, and an inverse dynamics model checks that the generated actions match the imagery before verified segments are used for fine-tuning. The team reports that Cosmos3's average success rate rose from 26.9% to 68.0% on seven unseen RoboTwin 2.0 simulation tasks and from 20.0% to 76.7% on three unseen long-horizon real-robot tasks with 10 trials each, while DreamZero improved from 28.5% to 46.4%. An ablation found 43.7% fourth-round success with vision-only filtering versus 68.0% with the action-consistency check, and the authors note gains were not monotonic, with a dip in round three.

   36氪 人工智能 · 🔥 0

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