Papers4

#in-context learning

AOrchestra: Automating Sub-Agent Creation for Agentic Orchestration

Jianhao Ruan, Zhihao Xu et al.Feb 3arXiv

AOrchestra is like a smart conductor that builds the right mini-helpers (sub-agents) on demand to solve big, multi-step tasks.

#agent orchestration#sub-agent-as-tools#four-tuple abstraction

Not triaged yet

CL-bench: A Benchmark for Context Learning

Beginner

Shihan Dou, Ming Zhang et al.Feb 3arXiv

CL-bench is a new test that checks whether AI can truly learn new things from the information you give it right now, not just from what it memorized before.

#context learning#benchmark#rubric-based evaluation

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Enhancing Sentiment Classification and Irony Detection in Large Language Models through Advanced Prompt Engineering Techniques

Beginner

Marvin Schmitt, Anne Schwerk et al.Jan 13arXiv

Giving large language models a few good examples and step-by-step instructions can make them much better at spotting feelings in text.

#prompt engineering#few-shot learning#chain-of-thought

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UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation

Beginner

Jiehui Huang, Yuechen Zhang et al.Dec 8arXiv

UnityVideo is a single, unified model that learns from many kinds of video information at once—like colors (RGB), depth, motion (optical flow), body pose, skeletons, and segmentation—to make smarter, more realistic videos.

#multimodal video generation#multi-task learning#dynamic noise scheduling

Not triaged yet