Tag
Proposes Demo2Reward, a test-time prompt optimization technique for VLM reward models using a few expert demonstrations, significantly reducing false positives and improving policy learning in robotics without additional model training.
This paper presents a hybrid framework that combines structured clinical data with LLM-generated narratives for coronary artery disease prediction, achieving high fidelity in variable extraction and comparing ML models with LLM-based zero-shot and few-shot classification.
GraphARC is a new benchmark for abstract reasoning on graph-structured data, extending the ARC paradigm to graphs. Evaluations of state-of-the-art language models reveal a comprehension-execution gap and performance degradation on larger instances, highlighting scaling challenges.
This paper introduces ACIL, an automatic Chain-of-Thought framework to enhance In-Context Learning by generating and pruning reasoning chains, improving LLM performance on complex tasks.
This paper explores using few-shot prompted LLMs for actionable triage categorization of online patient inquiries into self-care, schedule-visit, urgent-clinician-review, or emergency-referral. The best model (Claude Haiku 4.5 with 12-shot prompting) achieves macro-F1 of 0.475, surpassing supervised baselines, but the authors conclude that LLMs can support triage prioritization and selective human review, not autonomous deployment.
FFAvatar proposes a feed-forward framework for reconstructing high-quality, animatable 3D Gaussian head avatars from few unposed images in seconds, achieving a 5.5 PSNR improvement over state-of-the-art on the NeRSemble benchmark.
FEST is a few-shot demonstration-guided reinforcement learning algorithm that achieves strong performance with minimal supervised fine-tuning data by combining supervised signals, on-policy learning, and weighted training to prevent overfitting.
Independent study shows 227M-parameter hypernetwork adds zero gain over well-crafted few-shot prompts for tool-use in 3B Llama, achieving 79.7% of GPT-5 performance at 10× lower latency.
FSPO proposes a few-shot preference optimization algorithm for LLM personalization that reframes reward modeling as meta-learning, enabling models to quickly infer personalized reward functions from limited user preferences. The method achieves 87% personalization performance on synthetic users and 70% on real users through careful synthetic preference dataset construction.