Papers129

Distribution Matching Variational AutoEncoder

This paper shows a new way to teach an autoencoder to shape its hidden space (the 'latent space') to look like any distribution we want, not just a simple bell curve.

#Distribution Matching VAE#Latent Space#Self-Supervised Learning

DeepCode: Open Agentic Coding

Beginner

Zongwei Li, Zhonghang Li et al.Dec 8arXiv

DeepCode is an AI coding system that turns long, complicated papers into full, working code repositories.

#agentic coding#document-to-code#information-flow management

Native Parallel Reasoner: Reasoning in Parallelism via Self-Distilled Reinforcement Learning

Beginner

Tong Wu, Yang Liu et al.Dec 8arXiv

This paper teaches a language model to think along several paths at the same time instead of one step after another.

#parallel reasoning#reinforcement learning for LLMs#self-distillation

DoVer: Intervention-Driven Auto Debugging for LLM Multi-Agent Systems

Beginner

Ming Ma, Jue Zhang et al.Dec 7arXiv

LLM multi-agent systems often fail quietly (no crash) and leave long, twisty logs that are hard to debug by hand.

#DoVer#intervention-driven debugging#LLM multi-agent systems

Active Video Perception: Iterative Evidence Seeking for Agentic Long Video Understanding

Beginner

Ziyang Wang, Honglu Zhou et al.Dec 5arXiv

Long Video Understanding (LVU) is hard because the important clues are tiny, far apart in time, and buried in hours of mostly unimportant footage.

#Active Video Perception#Long Video Understanding#Plan-Observe-Reflect

AI & Human Co-Improvement for Safer Co-Superintelligence

Beginner

Jason Weston, Jakob FoersterDec 5arXiv

This paper argues that the fastest and safest path to super-smart AI is for humans and AIs to improve together, not for AI to improve alone.

#Co-improvement#Human-AI collaboration#Co-superintelligence

COOPER: A Unified Model for Cooperative Perception and Reasoning in Spatial Intelligence

Beginner

Zefeng Zhang, Xiangzhao Hao et al.Dec 4arXiv

COOPER is a single AI model that both “looks better” (perceives depth and object boundaries) and “thinks smarter” (reasons step by step) to answer spatial questions about images.

#COOPER#multimodal large language model#unified model

VideoSSM: Autoregressive Long Video Generation with Hybrid State-Space Memory

Beginner

Yifei Yu, Xiaoshan Wu et al.Dec 4arXiv

VideoSSM is a new way to make long, stable, and lively videos by giving the model two kinds of memory: a short-term window and a long-term state-space memory.

#autoregressive video diffusion#state-space model#hybrid memory

Fast-Decoding Diffusion Language Models via Progress-Aware Confidence Schedules

Beginner

Amr Mohamed, Yang Zhang et al.Dec 2arXiv

Diffusion language models (dLLMs) can write all parts of an answer in parallel, but they usually take many tiny cleanup steps, which makes them slow.

#diffusion language models#early exit decoding#progress-aware threshold

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