- Step 3.5 Flash: Open Frontier-Level Intelligence with 11B Active Parameters
Ailin Huang, Ang Li, Aobo Kong, Bin Wang, Binxing Jiao · Feb 11, 2026 · Citations: 0
Pairwise Preference Tool Use
We introduce Step 3.5 Flash, a sparse Mixture-of-Experts (MoE) model that bridges frontier-level agentic intelligence and computational efficiency.
- Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0
Automatic Metrics Tool Use
Experiments on the Massive Multitask Language Understanding (MMLU) benchmark show that our approach achieves accuracy comparable to the largest model while reducing computational costs by 20\% to 40\%.
- Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
Lai Wei, Liangbo He, Jun Lan, Lingzhong Dong, Yutong Cai · Feb 12, 2026 · Citations: 0
Automatic Metrics Tool Use
To address this, we propose Region-to-Image Distillation, which transforms zooming from an inference-time tool into a training-time primitive, thereby internalizing the benefits of agentic zooming into a single forward pass of an MLLM.
- REDSearcher: A Scalable and Cost-Efficient Framework for Long-Horizon Search Agents
Zheng Chu, Xiao Wang, Jack Hong, Huiming Fan, Yuqi Huang · Feb 15, 2026 · Citations: 0
Automatic Metrics Tool Use
To address these challenges, we propose REDSearcher, a unified framework that codesigns complex task synthesis, midtraining, and posttraining for scalable searchagent optimization.
- A Benchmark for Deep Information Synthesis
Debjit Paul, Daniel Murphy, Milan Gritta, Ronald Cardenas, Victor Prokhorov · Feb 24, 2026 · Citations: 0
Automatic Metrics Tool Use
To address this, we introduce DEEPSYNTH, a novel benchmark designed to evaluate agents on realistic, time-consuming problems that combine information gathering, synthesis, and structured reasoning to produce insights.
- PyVision-RL: Forging Open Agentic Vision Models via RL
Shitian Zhao, Shaoheng Lin, Ming Li, Haoquan Zhang, Wenshuo Peng · Feb 24, 2026 · Citations: 0
Automatic Metrics Tool Use
Reinforcement learning for agentic multimodal models often suffers from interaction collapse, where models learn to reduce tool usage and multi-turn reasoning, limiting the benefits of agentic behavior.
- OmniGAIA: Towards Native Omni-Modal AI Agents
Xiaoxi Li, Wenxiang Jiao, Jiarui Jin, Shijian Wang, Guanting Dong · Feb 26, 2026 · Citations: 0
Tool Use
To bridge this gap, we introduce OmniGAIA, a comprehensive benchmark designed to evaluate omni-modal agents on tasks necessitating deep reasoning and multi-turn tool execution across video, audio, and image modalities.
- SoK: Agentic Skills -- Beyond Tool Use in LLM Agents
Yanna Jiang, Delong Li, Haiyu Deng, Baihe Ma, Xu Wang · Feb 24, 2026 · Citations: 0
Tool Use
Agentic systems increasingly rely on reusable procedural capabilities, a.k.a., agentic skills, to execute long-horizon workflows reliably.
- OpaqueToolsBench: Learning Nuances of Tool Behavior Through Interaction
Skyler Hallinan, Thejas Venkatesh, Xiang Ren, Sai Praneeth Karimireddy, Ashwin Paranjape · Feb 16, 2026 · Citations: 0
Tool Use
Tool-calling is essential for Large Language Model (LLM) agents to complete real-world tasks.
- MCPShield: A Security Cognition Layer for Adaptive Trust Calibration in Model Context Protocol Agents
Zhenhong Zhou, Yuanhe Zhang, Hongwei Cai, Moayad Aloqaily, Ouns Bouachir · Feb 15, 2026 · Citations: 0
Tool Use
The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers.
