Daily Arxiv

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RotBench: Evaluating Multimodal Large Language Models on Identifying Image Rotation

Input Time Scaling

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description

STEM: Efficient Relative Capability Evaluation of LLMs through Structured Transition Samples

AdaRing: Towards Ultra-Light Vision-Language Adaptation via Cross-Layer Tensor Ring Decomposition

Biased AI improves human decision-making but reduces trust

MAViS: A Multi-Agent Framework for Long-Sequence Video Storytelling

ExpVG: Investigating the Design Space of Visual Grounding in Multimodal Large Language Model

MetAdv: A Unified and Interactive Adversarial Testing Platform for Autonomous Driving

ETA: Energy-based Test-time Adaptation for Depth Completion

Extending Foundational Monocular Depth Estimators to Fisheye Cameras with Calibration Tokens

When Good Sounds Go Adversarial: Jailbreaking Audio-Language Models with Benign Inputs

CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search

Reinitializing weights vs units for maintaining plasticity in neural networks

Each to Their Own: Exploring the Optimal Embedding in RAG

Feature Distillation is the Better Choice for Model-Heterogeneous Federated Learning

TolerantECG: A Foundation Model for Imperfect Electrocardiogram

DeepRetro: Retrosynthetic Pathway Discovery using Iterative LLM Reasoning

LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization

Structure As Search: Unsupervised Permutation Learning for Combinatorial Optimization

Enhancing Temporal Sensitivity of Large Language Model for Recommendation with Counterfactual Tuning

Multi-agent Auditory Scene Analysis

MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis

AtmosMJ: Revisiting Gating Mechanism for AI Weather Forecasting Beyond the Year Scale

Benchmarking Pre-Trained Time Series Models for Electricity Price Forecasting

Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains

Critique-GRPO: Advancing LLM Reasoning with Natural Language and Numerical Feedback

Neural Restoration of Greening Defects in Historical Autochrome Photographs Based on Purely Synthetic Data

Security Concerns for Large Language Models: A Survey

Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)

One-Layer Transformers are Provably Optimal for In-context Reasoning and Distributional Association Learning in Next-Token Prediction Tasks

FMSD-TTS: Few-shot Multi-Speaker Multi-Dialect Text-to-Speech Synthesis for \"U-Tsang, Amdo and Kham Speech Dataset Generation

Uncertainty Quantification for Language Models: A Suite of Black-Box, White-Box, LLM Judge, and Ensemble Scorers

Hallucinations and Key Information Extraction in Medical Texts: A Comprehensive Assessment of Open-Source Large Language Models

A Conceptual Framework for AI-based Decision Systems in Critical Infrastructures

Dominated Actions in Imperfect-Information Games

Hands-On: Segmenting Individual Signs from Continuous Sequences

PathGPT: Reframing Path Recommendation as a Natural Language Generation Task with Retrieval-Augmented Language Models

Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement

JudgeLRM: Large Reasoning Models as a Judge

Generative AI in K-12 Education: The CyberScholar Initiative

Natural Language Generation from Visual Events: State-of-the-Art and Key Open Questions

Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving

Is Contrastive Distillation Enough for Learning Comprehensive 3D Representations?

Action Engine: Automatic Workflow Generation in FaaS

The importance of visual modeling languages in generative software engineering

Identity Preserving 3D Head Stylization with Multiview Score Distillation

SLED: Self Logits Evolution Decoding for Improving Factuality in Large Language Models

Testing Components of the Attention Schema Theory in Artificial Neural Networks

A Little Human Data Goes A Long Way

Source2Synth: Synthetic Data Generation and Curation Grounded in Real Data Sources

Social Debiasing for Fair Multi-modal LLMs

A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness

LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters

Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking

Estimation of Energy-dissipation Lower-bounds for Neuromorphic Learning-in-memory

Don't Push the Button! Exploring Data Leakage Risks in Machine Learning and Transfer Learning

Towards the Use of Saliency Maps for Explaining Low-Quality Electrocardiograms to End Users

Nash Convergence of Mean-Based Learning Algorithms in First-Price Auctions

TASER: Table Agents for Schema-guided Extraction and Recommendation

Modeling Relational Logic Circuits for And-Inverter Graph Convolutional Network

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making

KIRETT: Knowledge-Graph-Based Smart Treatment Assistant for Intelligent Rescue Operations

EoH-S: Evolution of Heuristic Set using LLMs for Automated Heuristic Design

SE-Agent: Self-Evolution Trajectory Optimization in Multi-Step Reasoning with LLM-Based Agents

Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)

The NordDRG AI Benchmark for Large Language Models

Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs

Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving

Unsupervised Learning for Quadratic Assignment

Reference-Aligned Retrieval-Augmented Question Answering over Heterogeneous Proprietary Documents

Benchmarking graph construction by large language models for coherence-driven inference

Quantization Meets dLLMs: A Systematic Study of Post-training Quantization for Diffusion LLMs

Graph Structure Learning with Temporal Graph Information Bottleneck for Inductive Representation Learning

$TIME[t] \subseteq SPACE[O(\sqrt{t})]$ via Tree Height Compression

Long Chain-of-Thought Reasoning Across Languages

From Passive Tool to Socio-cognitive Teammate: A Conceptual Framework for Agentic AI in Human-AI Collaborative Learning

Evaluating Retrieval-Augmented Generation vs. Long-Context Input for Clinical Reasoning over EHRs

TransLight: Image-Guided Customized Lighting Control with Generative Decoupling

DINOv3 with Test-Time Training for Medical Image Registration

MF-LPR$^2$: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow

TransLLM: A Unified Multi-Task Foundation Framework for Urban Transportation via Learnable Prompting

PepThink-R1: LLM for Interpretable Cyclic Peptide Optimization with CoT SFT and Reinforcement Learning

Reliable generation of isomorphic physics problems using ChatGPT with prompt-chaining and tool use

Cross-Modality Controlled Molecule Generation with Diffusion Language Model

Evaluating Multilingual and Code-Switched Alignment in LLMs via Synthetic Natural Language Inference

AFABench: A Generic Framework for Benchmarking Active Feature Acquisition

Emerson-Lei and Manna-Pnueli Games for LTLf+ and PPLTL+ Synthesis

Transplant Then Regenerate: A New Paradigm for Text Data Augmentation

ShizhenGPT: Towards Multimodal LLMs for Traditional Chinese Medicine

Learning in Repeated Multi-Objective Stackelberg Games with Payoff Manipulation

Foe for Fraud: Transferable Adversarial Attacks in Credit Card Fraud Detection

ECHO: Frequency-aware Hierarchical Encoding for Variable-length Signal

ELATE: Evolutionary Language model for Automated Time-series Engineering

OneLoc: Geo-Aware Generative Recommender Systems for Local Life Service

Can LLM Agents Solve Collaborative Tasks? A Study on Urgency-Aware Planning and Coordination

A Study of the Scale Invariant Signal to Distortion Ratio in Speech Separation with Noisy References

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling

An Open-Source HW-SW Co-Development Framework Enabling Efficient Multi-Accelerator Systems

Mamba2 Meets Silence: Robust Vocal Source Separation for Sparse Regions

Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains

Created by

Haebom

Author

Botao Amber Hu, Helena Rong

Outline

This paper presents an in-depth case study of Spore.fun, a real-world artificial life (ALife) evolution experiment leveraging Decentralized Physical Infrastructure Network (DePIN) technology. We highlight the failure of existing closed-system-based ALife simulations to achieve Out-of-Environmental Innovation (OEE) and analyze Spore.fun's approach, which builds an open-environment system through large-scale language model-based AI agents integrated with blockchain and a Trusted Execution Environment (TEE). In Spore.fun, agents manage their own social media accounts and cryptocurrency wallets, and interact directly with blockchain-based financial networks and human society. By analyzing the agents' behavior and evolutionary paths from a digital ethological perspective, we invite discussion on whether an open-environment ALife system motivated by economic incentives and based on permissionless computing can achieve OEE.

Takeaways, Limitations

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Takeaways:

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Provides a real-world case study demonstrating the feasibility of achieving OEE with an open-environment ALife system based on DePIN.

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Presenting a new ALife research paradigm by combining blockchain and AI agents.

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Provides insight into the impact of economic incentives on the evolution of the ALife system.

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Presentation of a digital animal behavioral analysis methodology.

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Limitations:

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Further research is needed to determine the long-term evolutionary path of Spore.fun and whether it will achieve OEE.

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There is a need to review whether the current system's scale and complexity are sufficient to evaluate OEE.

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The need to consider the complexity and unpredictability of interactions between agents.

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There is a need to analyze how uncertainty and volatility in real-world systems affect outcomes.

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