- OmniGAIA: Towards Native Omni-Modal AI Agents
Xiaoxi Li, Wenxiang Jiao, Jiarui Jin, Shijian Wang, Guanting Dong · Feb 26, 2026 · Citations: 0
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Human intelligence naturally intertwines omni-modal perception -- spanning vision, audio, and language -- with complex reasoning and tool usage to interact with the world.
- Confidence-Driven Multi-Scale Model Selection for Cost-Efficient Inference
Bo-Wei Chen, Chung-Chi Chen, An-Zi Yen · Feb 25, 2026 · Citations: 0
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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\%.
- PyVision-RL: Forging Open Agentic Vision Models via RL
Shitian Zhao, Shaoheng Lin, Ming Li, Haoquan Zhang, Wenshuo Peng · Feb 24, 2026 · Citations: 0
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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.
- 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
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The Model Context Protocol (MCP) standardizes tool use for LLM-based agents and enable third-party servers.
- RoPE-LIME: RoPE-Space Locality + Sparse-K Sampling for Efficient LLM Attribution
Isaac Picov, Ritesh Goru · Feb 6, 2026 · Citations: 0
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Explaining closed-source Large Language Model (LLM) outputs is challenging because API access prevents gradient-based attribution, while perturbation methods are costly and noisy when they depend on regenerated text.
- OmniRAG-Agent: Agentic Omnimodal Reasoning for Low-Resource Long Audio-Video Question Answering
Yifan Zhu, Xinyu Mu, Tao Feng, Zhonghong Ou, Yuning Gong · Feb 3, 2026 · Citations: 0
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To address these issues, we propose OmniRAG-Agent, an agentic omnimodal QA method for budgeted long audio-video reasoning.
- STAR: Similarity-guided Teacher-Assisted Refinement for Super-Tiny Function Calling Models
Jiliang Ni, Jiachen Pu, Zhongyi Yang, Jingfeng Luo, Conggang Hu · Feb 3, 2026 · Citations: 0
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The proliferation of Large Language Models (LLMs) in function calling is pivotal for creating advanced AI agents, yet their large scale hinders widespread adoption, necessitating transferring their capabilities into smaller ones.
- What Matters For Safety Alignment?
Xing Li, Hui-Ling Zhen, Lihao Yin, Xianzhi Yu, Zhenhua Dong · Jan 7, 2026 · Citations: 0
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This paper presents a comprehensive empirical study on the safety alignment capabilities.
- Measuring AI Ability to Complete Long Software Tasks
Thomas Kwa, Ben West, Joel Becker, Amy Deng, Katharyn Garcia · Mar 18, 2025 · Citations: 0
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Despite rapid progress on AI benchmarks, the real-world meaning of benchmark performance remains unclear.
- Should You Use Your Large Language Model to Explore or Exploit?
Keegan Harris, Aleksandrs Slivkins · Jan 31, 2025 · Citations: 0
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We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff.
