AIMING Lab
@aiming-lab • AIMING Lab @ UNC-Chapel Hill. AIMING stands for Adaptive Intelligence through Alignment, Interaction and Learning.. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
86%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
9 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (23), Unknown (4), HTML (3)
Average size
817
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-05-15(75d ago)GitHub API fallback30 repositories
86%
of the visible star count comes from this organization's top three repositories.
817
stars per repository in this same snapshot.
Python
is the most common language here, with 9 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 57% of its visible star count.
Flagship share 57%Breakout repo: AutoResearchClaw
Organization pages work best when you separate portfolio breadth from flagship concentration. In AIMING Lab's case, the visible top three repositories account for about 86% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Python (23), Unknown (4), HTML (3). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars | 🍴 Forks | Updated |
|---|---|---|---|---|---|
| 1 | aiming-lab/AutoResearchClaw Fully autonomous & self-evolving research from idea to paper. Chat an Idea. Get a Paper. 🦞 | Python | 13.9K | 1.6K | 2 weeks ago |
| 2 | aiming-lab/SimpleMem SimpleMem: Efficient Lifelong Memory for LLM Agents — Text & Multimodal | Python | 3.7K | 384 | 5 days ago |
| 3 | aiming-lab/MetaClaw 🦞 Just talk to your agent — it learns and EVOLVES 🧬. | Python | 3.5K | 454 | 1 months ago |
| 4 | aiming-lab/Agent0 [COLM'26 & ICML'26] Agent0 Series: Self-Evolving Agents from Zero Data | Python | 1.2K | 145 | 2 weeks ago |
| 5 | aiming-lab/SkillRL SkillRL: Evolving Agents via Recursive Skill-Augmented Reinforcement Learning | Python | 914 | 71 | 2 months ago |
| 6 | aiming-lab/AutoHarness AutoHarness: Automated Harness Engineering for AI Agents | Python | 359 | 28 | 3 months ago |
| 7 | aiming-lab/MDocAgent MDocAgent: A Multi-Modal Multi-Agent Framework for Document Understanding | Python | 353 | 35 | 11 months ago |
| 8 | aiming-lab/GRAPE GRAPE: Guided-Reinforced Vision-Language-Action Preference Optimization | Python | 161 | 8 | 1 years ago |
| 9 | aiming-lab/MMedPO [ICML'25] MMedPO: Aligning Medical Vision-Language Models with Clinical-Aware Multimodal Preference Optimization | Python | 74 | 7 | 1 years ago |
| 10 | aiming-lab/ClawArena | Python | 58 | 5 | 4 weeks ago |
| 11 | aiming-lab/ReAgent-V [NeurIPS'25] ReAgent-V: A Reward-Driven Multi-Agent Framework for Video Understanding | Python | 51 | 4 | 10 months ago |
| 12 | aiming-lab/SynthAgent SynthAgent: Adapting Web Agents with Synthetic Supervision, ACL 2026 | Python | 33 | 1 | 3 months ago |
| 13 | aiming-lab/MIRA When Visualizing is the First Step to Reasoning: MIRA, a Benchmark for Visual Chain-of-Thought | Python | 32 | 3 | 5 months ago |
| 14 | aiming-lab/EduVisAgent [ICLR'26] EduVisAgent: A Benchmark and Multi-Agent Framework for Pedagogical Visualization | Python | 30 | 2 | 11 months ago |
| 15 | aiming-lab/WebHarbor | HTML | 27 | 35 | 1 weeks ago |
| 16 | aiming-lab/MJ-Video [NeurIPS'25 Spotlight] MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation | Python | 20 | 2 | 1 years ago |
| 17 | aiming-lab/CITER [COLM'25] CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing | Python | 19 | 5 | 1 years ago |
| 18 | aiming-lab/WebXSkill WebXSkill: Skill Learning for Autonomous Web Agents | 10 | 1 | 3 months ago | |
| 19 | aiming-lab/ATP Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails | Python | 10 | 1 | 9 months ago |
| 20 | aiming-lab/SimpleOCR [ACL'26 Findings] SimpleOCR: Rendering Visualized Questions to Teach MLLMs to Read | Python | 9 | 1 | 4 months ago |
| 21 | aiming-lab/ClawForge | Python | 8 | 0 | 2 months ago |
| 22 | aiming-lab/MedVerse [ACL'26] Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution | Python | 8 | 0 | 3 months ago |
| 23 | aiming-lab/GLIMPSE [EMNLP'25 Oral] GLIMPSE: Do Large Vision-Language Models Truly Think With Videos or Just Glimpse at Them? | Python | 8 | 0 | 11 months ago |
| 24 | aiming-lab/EduVisBench | Python | 8 | 1 | 1 years ago |
| 25 | aiming-lab/webharbor.github.io | HTML | 0 | 0 | 2 months ago |
| 26 | aiming-lab/SimpleMem-Page | Python | 0 | 0 | 6 months ago |
| 27 | aiming-lab/Anyprefer | 0 | 0 | 1 years ago | |
| 28 | aiming-lab/MJ-VIDEO.github.io | HTML | 0 | 0 | 1 years ago |
| 29 | aiming-lab/MMIE [ICLR'25 Oral] MMIE: Massive Multimodal Interleaved Comprehension Benchmark for Large Vision-Language Models | 0 | 0 | 1 years ago | |
| 30 | aiming-lab/CSR [NeurIPS 2024] Calibrated Self-Rewarding Vision Language Models | 0 | 0 | 2 years ago |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
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