CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition
Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained kno...
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Real-world tasks often require models to learn from task-specific context rather than relying only on pre-trained kno...
Modern multi-agent knowledge systems increasingly accumulate knowledge through chains of autonomous transformations r...
Large Language Model (LLM) agents are increasingly adopted in real-world security operations with access to host arti...
Recent game world models can generate visually realistic and interactive environments conditioned on player actions. ...
Coding agents repeatedly search, navigate, and retain context from evolving repositories, but disconnected indexes, l...
Existing BraTS-GLI datasets provide a widely used benchmark for adult glioma MRI segmentation, but their task definit...
Thermal infrared (TIR) imaging is essential for UAV swarm operations in visually degraded environments. However, trac...
Domain Generalization (DG) aims to learn representations robust to distribution shifts. Recent geometric alignment me...
Modern coding agents are usually evaluated by whether they eventually produce a correct patch, but patch generation d...
Biomedical image analysis spans diverse modalities and tasks, yet real-world deployment is hindered by severe distrib...
RL for code correctness is now established: have the model generate a program, run it against hidden test cases, and ...
A language model p_θ(y mid x) trained on reasoning tasks learns to solve problems via multiple distinct strategies, y...