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Jiaqi-hkust 
posted an update 18 days ago
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We open-sourced Game-Creation-Agents — an agentic system that turns a one-line game idea into a playable browser game. 🎮

Why it matters:
- It lowers the barrier: you don’t need a full studio or deep engine skills to start.
- It compresses prototyping from weeks to hours.
- It lets creators test more ideas, faster, and focus on what’s actually fun.
- It brings game creation closer to a conversation — and makes it accessible to more people.

This isn’t just about generating code. It’s about giving more people the power to bring their ideas to life.

Try it:
https://github.com/Tencent/Game-Creation-Agents

#AI #GameDev #OpenSource
Jiaqi-hkust 
posted an update 3 months ago
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2,425
🧠 Remember-R1: Our fix for MLLMs forgetting the image during long reasoning

We noticed a frustrating problem: when multimodal models reason over long chains, they gradually stop looking at the image—and start hallucinating based on their own text.

So we built Remember‑R1, a simple RL framework that directly supervises visual attention on the original reasoning trajectory—no inference overhead, no proxy tasks.

We use three complementary rewards: coverage, persistence, and focus. They encourage the model to keep attending to relevant visual evidence even in later reasoning steps.

Results across 7 benchmarks and 2 model sizes: better reasoning, and—more importantly—visual attention decays much more slowly during generation.

No extra cost at inference, just cleaner supervision where it counts.

📄 Paper: https://arxiv.org/abs/2608.01314
💻 Code: https://github.com/Ch921-cell/Remember-R1

Happy to answer any questions and receive feedback!

#MultimodalAI #RL #MLLM #CoT #VisualReasoning
Jiaqi-hkust 
posted an update 3 months ago
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3,107
IQA-T1: Evidence‑Based Image Quality Assessment with MLLMs

Most MLLMs are blind to low‑level degradations—noise, blur, compression artifacts look the same as clean images in their internal representations. That leads to quality scores based on semantic “gut feeling” rather than real perceptual evidence.

IQA-T1 changes that. We equip the model with a toolbox of 15 perceptual tools (noise residual maps, Fourier spectra, gradient maps, etc.) that generate structured visual evidence on demand. The model learns how to use tools via supervised fine‑tuning on our Q‑Tool dataset (11k evidence‑grounded reasoning chains), and when to call them via GRPO reinforcement learning that balances accuracy, tool count, and redundancy.

The result: SOTA performance across 7 benchmarks (avg PLCC 0.795), using only 2.34 tools per image on average. Every predicted score is now interpretable and backed by hard visual evidence.

All code, weights, dataset, and demo are open. Check them out and give it a spin!

📄 arxiv.org/abs/2607.12375v1
💻 github.com/zibuyu-02/IQA-T1
🤗 model/data: huggingface.co/zibuyu-02/IQA-T1
🎮 demo: huggingface.co/spaces/Jiaqi-hkust/IQA-T1
Jiaqi-hkust 
authored 2 papers 5 months ago
Jiaqi-hkust 
posted an update 5 months ago
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3,995
🚀 Introducing Robust-U1: Teaching MLLMs to Self-Recover Corrupted Visual Content

Multimodal Large Language Models (MLLMs) have achieved impressive visual understanding, yet they remain highly brittle under real-world corruptions—noise, blur, compression artifacts, adverse weather.

Standard MLLMs suffer dramatic performance drops, and existing robustness solutions come with fundamental limits: black‑box feature alignment lacks interpretability, while white‑box text reasoning cannot restore the lost pixel‑level visual details. This raises a crucial question:

🧐 Can MLLMs recover corrupted visual content by themselves?

If the answer is yes, we can move beyond merely “compensating” for corruption and instead build a more intrinsic, generalizable form of resilience. Robust-U1 is our answer to that question.

💡 Paper: https://arxiv.org/abs/2606.08063
🔗 Code: github.com/jqtangust/Robust-U1
🌍 Demo: Jiaqi-hkust/Robust-U1

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Jiaqi-hkust 
submitted a paper to Daily Papers 5 months ago
Jiaqi-hkust 
posted an update 5 months ago
Jiaqi-hkust 
posted an update 7 months ago
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5,871
🛰️ Introducing Awesome-Remote-Sensing-Agents: The Largest Curated Collection of Intelligent Remote Sensing Agents

We are excited to share our new repository Awesome-Remote-Sensing-Agents – a comprehensive, community-driven collection of 100+ papers at the intersection of remote sensing and intelligent agents (LLMs, VLM, multi‑agent systems, etc.).

🔗 GitHub Repository: https://github.com/PolyX-Research/Awesome-Remote-Sensing-Agents

Our repository organizes this rapidly growing field into a structured, easy‑to‑navigate resource for researchers, practitioners, and enthusiasts.

📚 What’s Inside?
We’ve carefully curated papers across 6 key application domains:
🌿 Ecological Monitoring – forest fires, biodiversity, climate science
🚨 Emergency Response – flood mapping, wildfire tracking, disaster geolocalization
⛏️ Geological Exploration – mineral mapping, lithological recognition, geologic reasoning
🌊 Marine Supervision – ocean science, autonomous surface vehicles
🌾 Precision Agriculture – crop disease detection, land use simulation
🏙️ Urban Governance – change detection, urban planning, embodied navigation

🤝 Join the Community!
We warmly welcome contributions to keep this list up‑to‑date:
📝 Add missing papers via Pull Request
🏷️ Propose new or refined categories
🔗 Report broken links or outdated entries
💬 Discuss via GitHub Issues or contact the authors
Jiaqi-hkust 
authored a paper 10 months ago
Jiaqi-hkust 
submitted a paper to Daily Papers 10 months ago
Jiaqi-hkust 
posted an update 10 months ago
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We have open-sourced Robust-R1 (AAAI 2026 Oral), a new paradigm in the field of anti-degradation and robustness enhancement for multimodal large models.

Multimodal Large Language Models struggle to maintain reliable performance under extreme real-world visual degradations, which impede their practical robustness. Existing robust MLLMs predominantly rely on implicit training/adaptation that focuses solely on visual encoder generalization, suffering from limited interpretability and isolated optimization. To overcome these limitations, we propose Robust-R1, a novel framework that explicitly models visual degradations through structured reasoning chains. Our approach integrates: (i) supervised fine-tuning for degradation-aware reasoning foundations, (ii) reward-driven alignment for accurately perceiving degradation parameters, and (iii) dynamic reasoning depth scaling adapted to degradation intensity. To facilitate this approach, we introduce a specialized 11K dataset featuring realistic degradations synthesized across four critical real-world visual processing stages, each annotated with structured chains connecting degradation parameters, perceptual influence, pristine semantic reasoning chain, and conclusion. Comprehensive evaluations demonstrate state-of-the-art robustness: Robust-R1 outperforms all general and robust baselines on the real-world degradation benchmark R-Bench, while maintaining superior anti-degradation performance under multi-intensity adversarial degradations on MMMB, MMStar, and RealWorldQA.

We have made all of our papers, codes, data, model weights and demos fully open-source:
Paper: Robust-R1: Degradation-Aware Reasoning for Robust Visual Understanding (2512.17532) (help us to upvote)
GitHub code: https://github.com/jqtangust/Robust-R1 (help us to star)
HF model: https://huggingface.co/Jiaqi-hkust/Robust-R1
HF data: Jiaqi-hkust/Robust-R1
HF Space: Jiaqi-hkust/Robust-R1

We sincerely invite everyone to give it a try.

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Jiaqi-hkust 
authored a paper 10 months ago
Jiaqi-hkust 
submitted a paper to Daily Papers 10 months ago
Jiaqi-hkust 
authored 6 papers 11 months ago