Papers784

All Beginner Intermediate Advanced

All Sources arXiv

MetricAnything: Scaling Metric Depth Pretraining with Noisy Heterogeneous Sources

Intermediate

Baorui Ma, Jiahui Yang et al.Jan 29arXiv

Metric Anything is a new way to teach AI real, ruler-like distances (metric depth) from very mixed and noisy 3D data.

#metric depth estimation#sparse metric prompt#monocular depth

PLANING: A Loosely Coupled Triangle-Gaussian Framework for Streaming 3D Reconstruction

Intermediate

Changjian Jiang, Kerui Ren et al.Jan 29arXiv

PLANING is a new way to build 3D worlds from a moving single camera by combining two kinds of pieces: sharp triangles for shape and soft Gaussians for looks.

#Streaming 3D Reconstruction#Triangle Primitives#Neural Gaussians

CAR-bench: Evaluating the Consistency and Limit-Awareness of LLM Agents under Real-World Uncertainty

Intermediate

Johannes Kirmayr, Lukas Stappen et al.Jan 29arXiv

CAR-bench is a new 'driving test' for AI assistants that checks if they can stay careful, honest, and consistent during real back-and-forth conversations in a car.

#LLM agents#benchmarking#consistency

Causal World Modeling for Robot Control

Intermediate

Lin Li, Qihang Zhang et al.Jan 29arXiv

Robots used to copy actions from videos without truly understanding how the world changes, so they often messed up long, multi-step jobs.

#robot world model#autoregressive diffusion#causal masking

Retrieval-Infused Reasoning Sandbox: A Benchmark for Decoupling Retrieval and Reasoning Capabilities

Intermediate

Shuangshuang Ying, Zheyu Wang et al.Jan 29arXiv

This paper builds a safe science “playground” called DeR that fairly tests how AI finds facts (retrieval) and how it thinks with those facts (reasoning) without mixing them up.

#retrieval-augmented generation#document-grounded reasoning#deep research benchmark

MMFineReason: Closing the Multimodal Reasoning Gap via Open Data-Centric Methods

Intermediate

Honglin Lin, Zheng Liu et al.Jan 29arXiv

MMFineReason is a huge, open dataset (1.8 million examples, 5.1 billion solution tokens) that teaches AIs to think step by step about pictures and text together.

#multimodal reasoning#vision-language models#chain-of-thought

Language-based Trial and Error Falls Behind in the Era of Experience

Intermediate

Haoyu Wang, Guozheng Ma et al.Jan 29arXiv

Big language models are great at words but waste lots of time and energy when they try random actions in non-language games like Sudoku, Sokoban, 2048, FrozenLake, and Rubik’s Cube.

#SCOUT#Reinforcement Learning#Supervised Fine-Tuning

DreamActor-M2: Universal Character Image Animation via Spatiotemporal In-Context Learning

Intermediate

Mingshuang Luo, Shuang Liang et al.Jan 29arXiv

DreamActor-M2 is a new way to make a still picture move by copying motion from a video while keeping the character’s look the same.

#character image animation#spatiotemporal in-context learning#video diffusion

OCRVerse: Towards Holistic OCR in End-to-End Vision-Language Models

Intermediate

Yufeng Zhong, Lei Chen et al.Jan 29arXiv

OCRVerse is a new AI model that can read both plain text in documents and the visual structures in charts, webpages, and science plots, all in one system.

#Holistic OCR#Vision-Language Model#Supervised Fine-Tuning

Beyond Imitation: Reinforcement Learning for Active Latent Planning

Intermediate

Zhi Zheng, Wee Sun LeeJan 29arXiv

The paper shows how to make AI think faster and smarter by planning in a hidden space instead of writing long step-by-step sentences.

#latent reasoning#chain-of-thought#variational autoencoder

Scalable Power Sampling: Unlocking Efficient, Training-Free Reasoning for LLMs via Distribution Sharpening

Intermediate

Xiaotong Ji, Rasul Tutunov et al.Jan 29arXiv

The paper shows a fast, training-free way to boost an LLM’s step-by-step reasoning by smartly reusing the model’s own probabilities.

#power distribution sampling#distribution sharpening#low-temperature sampling

KromHC: Manifold-Constrained Hyper-Connections with Kronecker-Product Residual Matrices

Intermediate

Wuyang Zhou, Yuxuan Gu et al.Jan 29arXiv

Hyper-Connections (HC) make the usual single shortcut in neural networks wider by creating several parallel streams and letting the model mix them, but this can become unstable when stacked deep.

#Hyper-Connections#Manifold-Constrained Hyper-Connections#Doubly Stochastic Matrix

12 13 14 15 16