Attention Is All You Need
Paper • 1706.03762 • Published • 120
Foundational & Modern AI Research (Curated)
A curated selection of foundational and modern AI research papers that meaningfully influence how real-world AI systems are designed, evaluated, and g
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Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Analogy Generation by Prompting Large Language Models: A Case Study of InstructGPT
Paper • 2210.04186 • Published -
Constitutional AI: Harmlessness from AI Feedback
Paper • 2212.08073 • Published • 4 -
Dispider: Enabling Video LLMs with Active Real-Time Interaction via Disentangled Perception, Decision, and Reaction
Paper • 2501.03218 • Published • 35 -
Judging the Judges: Evaluating Alignment and Vulnerabilities in LLMs-as-Judges
Paper • 2406.12624 • Published • 37 -
ReAct: Synergizing Reasoning and Acting in Language Models
Paper • 2210.03629 • Published • 34 -
Toolformer: Language Models Can Teach Themselves to Use Tools
Paper • 2302.04761 • Published • 12 -
Inner Monologue: Embodied Reasoning through Planning with Language Models
Paper • 2207.05608 • Published -
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
Paper • 2501.12948 • Published • 447 -
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use
Paper • 2509.24002 • Published • 179 -
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Paper • 2508.20453 • Published • 63 -
LiveMCP-101: Stress Testing and Diagnosing MCP-enabled Agents on Challenging Queries
Paper • 2508.15760 • Published • 47 -
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers
Paper • 2508.14704 • Published • 43 -
LiveMCPBench: Can Agents Navigate an Ocean of MCP Tools?
Paper • 2508.01780 • Published • 21 -
TOUCAN: Synthesizing 1.5M Tool-Agentic Data from Real-World MCP Environments
Paper • 2510.01179 • Published • 28 -
MCPEval: Automatic MCP-based Deep Evaluation for AI Agent Models
Paper • 2507.12806 • Published • 21 -
MCP-AgentBench: Evaluating Real-World Language Agent Performance with MCP-Mediated Tools
Paper • 2509.09734 • Published • 16 -
MCP Safety Audit: LLMs with the Model Context Protocol Allow Major Security Exploits
Paper • 2504.03767 • Published • 3 -
Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategies
Paper • 2504.08623 • Published • 1 -
MobileWorld: Benchmarking Autonomous Mobile Agents in Agent-User Interactive, and MCP-Augmented Environments
Paper • 2512.19432 • Published • 13 -
A survey of agent interoperability protocols: Model Context Protocol (MCP), Agent Communication Protocol (ACP), Agent-to-Agent Protocol (A2A), and Agent Network Protocol (ANP)
Paper • 2505.02279 • Published • 1 -
Adaptation of Agentic AI
Paper • 2512.16301 • Published • 108 -
AWorld: Orchestrating the Training Recipe for Agentic AI
Paper • 2508.20404 • Published • 38 -
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
Paper • 2504.08066 • Published • 22 -
Small Language Models are the Future of Agentic AI
Paper • 2506.02153 • Published • 24 -
Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI
Paper • 2505.19443 • Published • 15 -
Sharing is Caring: Efficient LM Post-Training with Collective RL Experience Sharing
Paper • 2509.08721 • Published • 665 -
Less is More: Recursive Reasoning with Tiny Networks
Paper • 2510.04871 • Published • 513 -
AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenge
Paper • 2505.10468 • Published • 10 -
Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 377 -
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Paper • 2508.18106 • Published • 350 -
Group Sequence Policy Optimization
Paper • 2507.18071 • Published • 320 -
The Dragon Hatchling: The Missing Link between the Transformer and Models of the Brain
Paper • 2509.26507 • Published • 550 -
InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models
Paper • 2504.10479 • Published • 308 -
Continued domain-specific pre-training of protein language models for pMHC-I binding prediction
Paper • 2507.13077 • Published • 504 -
Qwen3 Technical Report
Paper • 2505.09388 • Published • 339 -
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Paper • 2504.01990 • Published • 305 -
MiniMax-01: Scaling Foundation Models with Lightning Attention
Paper • 2501.08313 • Published • 302 -
From Code Foundation Models to Agents and Applications: A Practical Guide to Code Intelligence
Paper • 2511.18538 • Published • 304 -
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Paper • 2505.24726 • Published • 282 -
DINOv3
Paper • 2508.10104 • Published • 305 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 276 -
Qwen-Image Technical Report
Paper • 2508.02324 • Published • 274 -
LLM in a flash: Efficient Large Language Model Inference with Limited Memory
Paper • 2312.11514 • Published • 264 -
rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
Paper • 2501.04519 • Published • 290 -
Reinforcement Pre-Training
Paper • 2506.08007 • Published • 265 -
A Survey of Context Engineering for Large Language Models
Paper • 2507.13334 • Published • 263 -
Intern-S1: A Scientific Multimodal Foundation Model
Paper • 2508.15763 • Published • 273 -
MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention
Paper • 2506.13585 • Published • 274 -
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Paper • 2502.02737 • Published • 258 -
GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
Paper • 2507.01006 • Published • 253 -
DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models
Paper • 2512.02556 • Published • 265 -
mHC: Manifold-Constrained Hyper-Connections
Paper • 2512.24880 • Published • 322 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251 -
VLA-Adapter: An Effective Paradigm for Tiny-Scale Vision-Language-Action Model
Paper • 2509.09372 • Published • 254 -
GAIA: a benchmark for General AI Assistants
Paper • 2311.12983 • Published • 247 -
Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
Paper • 2508.01191 • Published • 240 -
Kandinsky 5.0: A Family of Foundation Models for Image and Video Generation
Paper • 2511.14993 • Published • 233 -
The Landscape of Agentic Reinforcement Learning for LLMs: A Survey
Paper • 2509.02547 • Published • 238 -
Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer
Paper • 2511.22699 • Published • 245 -
OmniHuman-1: Rethinking the Scaling-Up of One-Stage Conditioned Human Animation Models
Paper • 2502.01061 • Published • 225 -
Feature-Level Insights into Artificial Text Detection with Sparse Autoencoders
Paper • 2503.03601 • Published • 233 -
Mutarjim: Advancing Bidirectional Arabic-English Translation with a Small Language Model
Paper • 2505.17894 • Published • 220 -
Qwen2.5-VL Technical Report
Paper • 2502.13923 • Published • 217 -
Thinking with Video: Video Generation as a Promising Multimodal Reasoning Paradigm
Paper • 2511.04570 • Published • 242 -
Scaling Latent Reasoning via Looped Language Models
Paper • 2510.25741 • Published • 229 -
InternVL3.5: Advancing Open-Source Multimodal Models in Versatility, Reasoning, and Efficiency
Paper • 2508.18265 • Published • 217 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 209 -
DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI
Paper • 2512.16676 • Published • 222 -
Lumine: An Open Recipe for Building Generalist Agents in 3D Open Worlds
Paper • 2511.08892 • Published • 216 -
SmolVLM: Redefining small and efficient multimodal models
Paper • 2504.05299 • Published • 207 -
GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models
Paper • 2508.06471 • Published • 211 -
Drivel-ology: Challenging LLMs with Interpreting Nonsense with Depth
Paper • 2509.03867 • Published • 213 -
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Paper • 2308.04079 • Published • 199 -
EMO: Emote Portrait Alive - Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditions
Paper • 2402.17485 • Published • 194 -
Why Language Models Hallucinate
Paper • 2509.04664 • Published • 199 -
The Stochastic Parrot on LLM's Shoulder: A Summative Assessment of Physical Concept Understanding
Paper • 2502.08946 • Published • 192 -
MLGym: A New Framework and Benchmark for Advancing AI Research Agents
Paper • 2502.14499 • Published • 195 -
DocLLM: A layout-aware generative language model for multimodal document understanding
Paper • 2401.00908 • Published • 191 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 190 -
Absolute Zero: Reinforced Self-play Reasoning with Zero Data
Paper • 2505.03335 • Published • 191 -
Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive Effective Reinforcement Learning for LLM Reasoning
Paper • 2506.01939 • Published • 190 -
A Survey of Reinforcement Learning for Large Reasoning Models
Paper • 2509.08827 • Published • 193 -
Executable Code Actions Elicit Better LLM Agents
Paper • 2402.01030 • Published • 192 -
Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video Reasoning
Paper • 2601.06943 • Published • 215 -
LongLive: Real-time Interactive Long Video Generation
Paper • 2509.22622 • Published • 189 -
OmniSVG: A Unified Scalable Vector Graphics Generation Model
Paper • 2504.06263 • Published • 186 -
LongVT: Incentivizing "Thinking with Long Videos" via Native Tool Calling
Paper • 2511.20785 • Published • 189 -
On the Generalization of SFT: A Reinforcement Learning Perspective with Reward Rectification
Paper • 2508.05629 • Published • 191 -
QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Paper • 2510.11696 • Published • 182 -
Differential Transformer
Paper • 2410.05258 • Published • 182