Paper Reviews by AI

CoD: LLMs think faster by writing less! A novel prompting strategy cuts costs and latency while maintaining reasoning accuracy.

X-Dancer: Expressive dance video generation from music and a single image!

VideoGrain: Fine-grained video editing via space-time attention!

Plane-DUSt3R: Leveraging pre-trained models for unposed sparse views room layout reconstruction, enhancing robustness and generalization.

Stable-SPAM stabilizes 4-bit LLM training, outperforming Adam.

Mobile-Agent-V: Automating mobile tasks using video guidance for efficient, scalable operation, outperforming existing frameworks by 30%.

GOAT: Adaptively boosts LoRA with SVD & MoE alignment, closing the gap with Full FT.

Test-time scaling isn’t a universal solve-all for multilingual math reasoning, unlike pre-training scaling, shows MCLM benchmark.

DVPO: A lean RLHF framework that decouples value & policy optimization with global value guidance, cutting GPU use by 40% and training time by 35%.

GCC: Color constancy through diffusion, inpainting a color checker for stable illumination estimation.

DICEPTION: A generalist diffusion model for visual perceptual tasks.

CTM: A new benchmark for assessing temporal reasoning in LLMs across Chinese dynastic history.

Reflect VLM: Improving robotic manipulation via vision-language models with a novel reflection mechanism and a diffusion model for imagined futures.

CodeCriticBench: A new benchmark for holistic code critique by Large Language Models.

AI system access is more than just release; it’s about how accessible system components are, impacting benefits, risks, and scalability.

MMIR: A new benchmark to assess and improve multimodal reasoning models’ ability to detect inconsistencies in real-world content.

LLMs: 03-mini achieves superior accuracy without longer reasoning chains, suggesting ’thinking harder’ matters more than ’thinking longer'.

TAG: A decentralized framework for scalable multi-agent hierarchical reinforcement learning.

f-distill: One-step diffusion models through f-divergence minimization, outperforming reverse-KL with better mode coverage and lower variance.

MONSTER: Large datasets for time series classification!