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Collections including paper arxiv:2604.27660

DataComp-VLM: Improved Open Datasets for Vision-Language Models

Paper • 2606.28551 • Published Jun 26 • 51
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use

Paper • 2607.01874 • Published Jul 2 • 21
PACE: A Proxy for Agentic Capability Evaluation

Paper • 2607.02032 • Published Jul 2 • 18
Measuring the Gap Between Human and LLM Research Ideas

Paper • 2607.01233 • Published Jul 1 • 18

Code as Agent Harness

Paper • 2605.18747 • Published May 18 • 224
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Paper • 2605.12500 • Published May 12 • 198
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
PhysBrain 1.0 Technical Report

Paper • 2605.15298 • Published May 14 • 61

GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)

Paper • 2604.17091 • Published Apr 18 • 25
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Paper • 2504.19413 • Published Apr 28, 2025 • 72
DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI

Paper • 2512.16676 • Published Dec 18, 2025 • 225
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

MultiWorld: Scalable Multi-Agent Multi-View Video World Models

Paper • 2604.18564 • Published Apr 20 • 49
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
MolmoAct2: Action Reasoning Models for Real-world Deployment

Paper • 2605.02881 • Published May 4 • 355

Natural-Language Agent Harnesses

Paper • 2603.25723 • Published Mar 26 • 23
From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

Paper • 2604.09459 • Published Apr 13 • 14
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

ssz1111/Ctx2Skill-Skills

Updated May 21 • 155
ssz1111/Ctx2Skill

Preview • Updated May 21 • 73 • 2
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Paper • 2605.12500 • Published May 12 • 198
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

Paper • 2605.03849 • Published May 5 • 128
ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration

Paper • 2605.03042 • Published May 4 • 152

From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling

Paper • 2603.25746 • Published Mar 26 • 43
TAPS: Task Aware Proposal Distributions for Speculative Sampling

Paper • 2603.27027 • Published Mar 27 • 145
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

Paper • 2603.25716 • Published Mar 26 • 75
LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Paper • 2603.27538 • Published Mar 29 • 148

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

Paper • 2602.08354 • Published Feb 9 • 138
Embarrassingly Simple Self-Distillation Improves Code Generation

Paper • 2604.01193 • Published Apr 1 • 55
ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement

Paper • 2604.01591 • Published Apr 2 • 39
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

DataComp-VLM: Improved Open Datasets for Vision-Language Models

Paper • 2606.28551 • Published Jun 26 • 51
SkillCoach: Self-Evolving Rubrics for Evaluating and Enhancing Agentic Skill-Use

Paper • 2607.01874 • Published Jul 2 • 21
PACE: A Proxy for Agentic Capability Evaluation

Paper • 2607.02032 • Published Jul 2 • 18
Measuring the Gap Between Human and LLM Research Ideas

Paper • 2607.01233 • Published Jul 1 • 18

ssz1111/Ctx2Skill-Skills

Updated May 21 • 155
ssz1111/Ctx2Skill

Preview • Updated May 21 • 73 • 2
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

Code as Agent Harness

Paper • 2605.18747 • Published May 18 • 224
SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Paper • 2605.12500 • Published May 12 • 198
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
PhysBrain 1.0 Technical Report

Paper • 2605.15298 • Published May 14 • 61

SenseNova-U1: Unifying Multimodal Understanding and Generation with NEO-unify Architecture

Paper • 2605.12500 • Published May 12 • 198
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation

Paper • 2605.03849 • Published May 5 • 128
ARIS: Autonomous Research via Adversarial Multi-Agent Collaboration

Paper • 2605.03042 • Published May 4 • 152

GenericAgent: A Token-Efficient Self-Evolving LLM Agent via Contextual Information Density Maximization (V1.0)

Paper • 2604.17091 • Published Apr 18 • 25
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

Paper • 2504.19413 • Published Apr 28, 2025 • 72
DataFlow: An LLM-Driven Framework for Unified Data Preparation and Workflow Automation in the Era of Data-Centric AI

Paper • 2512.16676 • Published Dec 18, 2025 • 225
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

MultiWorld: Scalable Multi-Agent Multi-View Video World Models

Paper • 2604.18564 • Published Apr 20 • 49
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73
MolmoAct2: Action Reasoning Models for Real-world Deployment

Paper • 2605.02881 • Published May 4 • 355

ShotStream: Streaming Multi-Shot Video Generation for Interactive Storytelling

Paper • 2603.25746 • Published Mar 26 • 43
TAPS: Task Aware Proposal Distributions for Speculative Sampling

Paper • 2603.27027 • Published Mar 27 • 145
Out of Sight but Not Out of Mind: Hybrid Memory for Dynamic Video World Models

Paper • 2603.25716 • Published Mar 26 • 75
LongCat-Next: Lexicalizing Modalities as Discrete Tokens

Paper • 2603.27538 • Published Mar 29 • 148

Natural-Language Agent Harnesses

Paper • 2603.25723 • Published Mar 26 • 23
From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models

Paper • 2604.09459 • Published Apr 13 • 14
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

Paper • 2602.08354 • Published Feb 9 • 138
Embarrassingly Simple Self-Distillation Improves Code Generation

Paper • 2604.01193 • Published Apr 1 • 55
ThinkTwice: Jointly Optimizing Large Language Models for Reasoning and Self-Refinement

Paper • 2604.01591 • Published Apr 2 • 39
From Context to Skills: Can Language Models Learn from Context Skillfully?

Paper • 2604.27660 • Published May 3 • 73

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