Papers40

#Reinforcement Learning

MindWatcher: Toward Smarter Multimodal Tool-Integrated Reasoning

Jiawei Chen, Xintian Shen et al.Dec 29arXiv

MindWatcher is a smart AI agent that can think step by step and decide when to use tools like web search, image zooming, and a code calculator to solve tough, multi-step problems.

#Tool-Integrated Reasoning#Interleaved Thinking#Multimodal Chain-of-Thought

NVIDIA Nemotron 3: Efficient and Open Intelligence

Intermediate

NVIDIA, : et al.Dec 24arXiv

Nemotron 3 is a new family of open AI models (Nano, Super, Ultra) built to think better while running faster and cheaper.

#Nemotron 3#Mixture-of-Experts#LatentMoE

Nemotron 3 Nano: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning

Intermediate

NVIDIA, : et al.Dec 23arXiv

Nemotron 3 Nano is a new open-source language model that mixes two brain styles (Mamba and Transformer) and adds a team of special experts (MoE) so it thinks better while running much faster.

#Mixture-of-Experts#Mamba-2#Transformer

SpatialTree: How Spatial Abilities Branch Out in MLLMs

Intermediate

Yuxi Xiao, Longfei Li et al.Dec 23arXiv

SpatialTree is a new, four-level "ability tree" that tests how multimodal AI models (that see and read) handle space: from basic seeing to acting in the world.

#Spatial Intelligence#Multimodal Large Language Models#Hierarchical Benchmark

Step-DeepResearch Technical Report

Intermediate

Chen Hu, Haikuo Du et al.Dec 23arXiv

Search is not the same as research; real research needs planning, checking many sources, fixing mistakes, and writing a clear report.

#Deep Research#Atomic Capabilities#ReAct Agent

DiRL: An Efficient Post-Training Framework for Diffusion Language Models

Intermediate

Ying Zhu, Jiaxin Wan et al.Dec 23arXiv

This paper builds DiRL, a fast and careful way to finish training diffusion language models so they reason better.

#Diffusion Language Model#Blockwise dLLM#Post-Training

Multi-hop Reasoning via Early Knowledge Alignment

Intermediate

Yuxin Wang, Shicheng Fang et al.Dec 23arXiv

This paper adds a tiny but powerful step called Early Knowledge Alignment (EKA) to multi-step retrieval systems so the model takes a quick, smart look at relevant information before it starts planning.

#Retrieval-Augmented Generation#Iterative RAG#Multi-hop Reasoning

Reinforcement Learning for Self-Improving Agent with Skill Library

Intermediate

Jiongxiao Wang, Qiaojing Yan et al.Dec 18arXiv

This paper teaches AI agents to learn new reusable skills and get better over time by using reinforcement learning, not just prompts.

#Reinforcement Learning#Skill Library#Sequential Rollout

JustRL: Scaling a 1.5B LLM with a Simple RL Recipe

Intermediate

Bingxiang He, Zekai Qu et al.Dec 18arXiv

JustRL shows that a tiny, steady recipe for reinforcement learning (RL) can make a 1.5B-parameter language model much better at math without fancy tricks.

#Reinforcement Learning#GRPO#Policy Entropy

Zoom-Zero: Reinforced Coarse-to-Fine Video Understanding via Temporal Zoom-in

Intermediate

Xiaoqian Shen, Min-Hung Chen et al.Dec 16arXiv

Zoom-Zero helps AI answer questions about videos by first finding the right moment and then zooming in to double-check tiny details.

#Grounded Video Question Answering#Temporal Grounding#Coarse-to-Fine

Differentiable Evolutionary Reinforcement Learning

Intermediate

Sitao Cheng, Tianle Li et al.Dec 15arXiv

This paper introduces DERL, a two-level learning system that automatically builds better reward functions for reinforcement learning agents.

#Differentiable Evolutionary Reinforcement Learning#Meta-Optimizer#Meta-Reward

Toward Ambulatory Vision: Learning Visually-Grounded Active View Selection

Intermediate

Juil Koo, Daehyeon Choi et al.Dec 15arXiv

This paper teaches robots to move their camera to a better spot before answering a question about what they see.

#Active Perception#Embodied AI#Vision-Language Models

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