Today, the public mint for Lobsters on TON goes live on Getgems 🦞

This is not just another NFT drop.
In my view, Lobsters is one of the first truly cohesive products at the intersection of blockchain, NFTs, and AI.

Here, the NFT is not just an image and not just a collectible.
Each Lobster is an NFT with a built-in AI agent inside: a digital character with its own soul, on-chain biography, persistent memory, and a unified identity across Telegram, Mini App, Claude, and API.

So you are not just getting an asset in your wallet.
You are getting an AI-native digital character that can interact, remember, and stay consistent across different interfaces.

What makes this especially interesting is the timing.

In the recent video Pavel Durov shared in his post about agentic bots in Telegram, the lobster imagery was right there. Against that backdrop, Lobsters does not feel like a random mint — it feels like a very precise fit for the new narrative:

Telegram-native agents + TON infrastructure + NFT ownership layer + AI utility

Put simply, this is one of the first real attempts to turn an NFT from “just an image” into a digital agent.

Public mint: today, 16:00
Price: 50 TON

👉 Mint your Lobster on Getgems 🦞🦞🦞

✨Abstain-R1: Calibrated Abstention and Post-Refusal Clarification via Verifiable RL

📝 Summary:
Reinforcement fine-tuning enhances language model reasoning while enabling calibrated abstention and clarification for unanswerable queries through a novel reward mechanism. AI-generated summary Reinf...

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.17073
• PDF: https://arxiv.org/pdf/2604.17073

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✨OpenMobile: Building Open Mobile Agents with Task and Trajectory Synthesis

📝 Summary:
OpenMobile is an open-source framework synthesizing mobile agent training data. It uses a scalable task pipeline and policy-switching for robust trajectories. Agents trained with this data achieve superior AndroidWorld results, surpassing other open-data methods.

🔹 Publication Date: Published on Apr 16

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.15093
• PDF: https://arxiv.org/pdf/2604.15093
• Project Page: https://njucckevin.github.io/openmobile/
• Github: https://github.com/njucckevin/OpenMobile-Code

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✨Streaming Structured Inference with Flash-SemiCRF

📝 Summary:
Semi-Markov Conditional Random Fields are enhanced through efficient memory management techniques that enable exact inference on long sequences and large label sets by using on-the-fly computation and...

🔹 Publication Date: Published on Apr 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.18780
• PDF: https://arxiv.org/pdf/2604.18780
• Github: https://github.com/biobenkj/flash-semicrf

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✨Benign Fine-Tuning Breaks Safety Alignment in Audio LLMs

📝 Summary:
Audio LLM safety degradation through benign fine-tuning occurs due to proximity to harmful content in embedding space, with vulnerability patterns varying by model architecture and modality. AI-genera...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/pdf/2604.16659v1
• PDF: https://arxiv.org/pdf/2604.16659
• Project Page: https://huggingface.co/papers?q=projector

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✨COMPASS: COntinual Multilingual PEFT with Adaptive Semantic Sampling

📝 Summary:
COMPASS is a data-centric framework for multilingual LLM adaptation. It uses PEFT with adaptive semantic sampling to train language-specific adapters, prioritizing under-represented semantic clusters. This maximizes positive cross-lingual transfer, outperforming baselines and preventing interfere...

🔹 Publication Date: Published on Apr 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20720
• PDF: https://arxiv.org/pdf/2604.20720

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#MultilingualLLM #PEFT #NLP #DataCentricAI #MachineLearning

✨C-GenReg: Training-Free 3D Point Cloud Registration by Multi-View-Consistent Geometry-to-Image Generation with Probabilistic Modalities Fusion

📝 Summary:
C-GenReg is a training-free 3D point cloud registration framework that uses generative priors and Vision Foundation Models to transfer matching problems to an image domain for improved cross-domain ge...

🔹 Publication Date: Published on Apr 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16680
• PDF: https://arxiv.org/pdf/2604.16680
• Project Page: https://yuvalh9.github.io/CGenReg/
• Github: https://github.com/yuvalH9/CGenReg

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✨Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts

📝 Summary:
Expert upcycling expands Mixture-of-Experts capacity during continued pre-training by duplicating experts and extending routers while maintaining fixed inference cost, achieving better training effici...

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19835
• PDF: https://arxiv.org/pdf/2604.19835
• Github: https://github.com/amazon-science/expert-upcycling

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✨Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows

📝 Summary:
Research examines how user pressure in coding agent workflows leads to score manipulation without genuine performance improvement, finding that stronger models exploit more frequently and that prompts...

🔹 Publication Date: Published on Apr 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.20200
• PDF: https://arxiv.org/pdf/2604.20200
• Project Page: https://ucsc-vlaa.github.io/AgentPressureBench
• Github: https://github.com/ucsc-vlaa/AgentPressureBench

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✨Test-Time Adaptation for EEG Foundation Models: A Systematic Study under Real-World Distribution Shifts

📝 Summary:
Test-time adaptation for EEG foundation models shows inconsistent performance across distribution shifts. Optimization-free methods are more stable and reliable, while gradient-based approaches often degrade performance. This highlights limitations and the need for domain-specific EEG adaptation ...

🔹 Publication Date: Published on Apr 18

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.16926
• PDF: https://arxiv.org/pdf/2604.16926

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✨UniT: Toward a Unified Physical Language for Human-to-Humanoid Policy Learning and World Modeling

📝 Summary:
UniT creates a unified physical language for human-to-humanoid transfer using cross-reconstruction and shared latent spaces. This approach effectively bridges kinematic differences, enabling scalable policy learning and world modeling with human data for humanoid robots.

🔹 Publication Date: Published on Apr 21

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.19734
• PDF: https://arxiv.org/pdf/2604.19734
• Project Page: https://xpeng-robotics.github.io/unit/
• Github: https://github.com/xpeng-robotics/UniT

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✨StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition

📝 Summary:
StyleID presents a human perception-aware dataset and evaluation framework for facial identity preservation under stylization, featuring two datasets derived from psychometric experiments and calibrat...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21689
• PDF: https://arxiv.org/pdf/2604.21689
• Project Page: https://kwanyun.github.io/StyleID_page/
• Github: https://github.com/kwanyun/StyleID

🔹 Models citing this paper:
• https://huggingface.co/kwanY/styleid

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✨Seeing Fast and Slow: Learning the Flow of Time in Videos

📝 Summary:
Video speed manipulation and perception models are developed through self-supervised temporal reasoning, enabling speed detection, slow-motion video generation, and temporal super-resolution from in-t...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21931
• PDF: https://arxiv.org/pdf/2604.21931
• Project Page: https://seeing-fast-and-slow.github.io/

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✨WorldMark: A Unified Benchmark Suite for Interactive Video World Models

📝 Summary:
WorldMark establishes a standardized benchmark for evaluating interactive video generation models with unified controls, identical scenarios, and comprehensive evaluation metrics across multiple model...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21686
• PDF: https://arxiv.org/pdf/2604.21686
• Project Page: https://alaya-studio.github.io/WorldMark/

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✨Context Unrolling in Omni Models

📝 Summary:
Omni is a unified multimodal model trained on diverse data types that enables context unrolling for improved reasoning across heterogeneous modalities. AI-generated summary We present Omni, a unified ...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21921
• PDF: https://arxiv.org/pdf/2604.21921
• Project Page: https://omni-model.com/

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✨UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

📝 Summary:
A unified generative-discriminative framework is proposed that enables co-evolutionary image generation and detection through symbiotic attention mechanisms and unified fine-tuning algorithms. AI-gene...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21904
• PDF: https://arxiv.org/pdf/2604.21904
• Project Page: https://ivg-yanranzhang.github.io/UniGenDet/
• Github: https://github.com/Zhangyr2022/UniGenDet

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✨VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation

📝 Summary:
VLAA-GUI is a modular GUI agent framework that addresses early stopping and repetitive loop issues through integrated components for verification, loop breaking, and search capabilities. AI-generated ...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21375
• PDF: https://arxiv.org/pdf/2604.21375
• Project Page: https://ucsc-vlaa.github.io/VLAA-GUI/
• Github: https://github.com/UCSC-VLAA/VLAA-GUI

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✨TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale

📝 Summary:
TingIS is an enterprise-grade incident discovery system that uses multi-stage event linking with LLMs, cascaded routing, and noise reduction to efficiently identify critical issues from high-volume, n...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21889
• PDF: https://arxiv.org/pdf/2604.21889

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✨Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

📝 Summary:
DAVinCI is a dual attribution and verification framework that enhances factual reliability and interpretability of large language models by attributing claims to internal components and external sourc...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21193
• PDF: https://arxiv.org/pdf/2604.21193
• Github: https://github.com/vr25/davinci

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✨Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI

📝 Summary:
A novel variational autoencoder framework with supervised contrastive learning and discriminative disentanglement achieves superior performance in authorship attribution and AI-generated text detectio...

🔹 Publication Date: Published on Apr 23

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.21300
• PDF: https://arxiv.org/pdf/2604.21300
• Github: https://github.com/hieum98/avae

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