🔥 GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?

💡 The paper introduces GameCraft-Bench, a benchmark for evaluating the ability of coding agents to generate playable games end-to-end in a real game engine. The problem of end-to-end game generation is challenging as it requires coding agents to create complete playable games from natural language descriptions while meeting specific evaluation criteria. The authors formalize this problem and propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging.

The framework is instantiated as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. The benchmark evaluates coding agents based on three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. The authors evaluate frontier coding agents using GameCraft-Bench and find that end-to-end game generation remains highly challenging, with the strongest agent achieving only 41.46 percent and most agents scoring below 40 percent.

Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. The paper contributes to the field of game generation by providing a comprehensive evaluation framework and a benchmark for assessing the capabilities of coding agents in generating playable games. The results highlight the difficulties of end-to-end game generation and provide insights for future research in this area. The GameCraft-Bench benchmark, code, and data are made available for further research and development.

📅 Published on Jun 16

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.17861
• PDF: https://arxiv.org/pdf/2606.17861
• Project Page: https://tongxuluo.github.io/gamecraft-bench-website/

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📢 By: https://xn--r1a.website/PaperNexus

#GameDevelopmentAI #EndToEndGameGeneration #GameEngineBenchmarking #AIForGameDesign #ProceduralContentGeneration

🔥 From RAG to Memory: Non-Parametric Continual Learning for Large Language Models

💡 The paper discusses the challenges of continual learning in large language models and how current methods such as retrieval-augmented generation have limitations in mimicking human long-term memory. The authors propose a new framework called HippoRAG 2 which builds upon previous work and enhances it with deeper passage integration and more effective online use of a large language model. This approach improves performance across factual, sense-making, and associative memory tasks, addressing the deterioration in performance seen in previous methods that tried to augment vector embeddings with structures like knowledge graphs. The results show that HippoRAG 2 outperforms standard retrieval-augmented generation comprehensively, achieving a 7 percent improvement in associative memory tasks over the state-of-the-art embedding model, while also exhibiting superior factual knowledge and sense-making memory capabilities. The work contributes to non-parametric continual learning for large language models, paving the way for more effective and human-like memory capabilities in artificial intelligence systems.

📅 Published on Feb 20, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2502.14802
• PDF: https://arxiv.org/pdf/2502.14802

🤖 Models citing this paper:
• https://huggingface.co/muthuk1/graphrag-inference-hackathon

📊 Datasets citing this paper:
• https://huggingface.co/datasets/osunlp/HippoRAG_2
• https://huggingface.co/datasets/g7haha/HippoRAG_2

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📢 By: https://xn--r1a.website/PaperNexus

#ContinualLearning #LargeLanguageModels #NonParametricLearning #RetrievalAugmentedGeneration #LongTermMemory

🔥 ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?

💡 The paper proposes ImageWAM, a new framework for world action models that replaces video generation with pretrained image editing models for robot control. Traditional world action models rely on video generation, which has three major limitations: high computational costs due to dense multi-frame future tokens, wasted capacity on action-irrelevant details, and potential errors in long-horizon future imagination. The authors question the need for video generation in world action models and propose using image editing instead. ImageWAM uses pretrained image editing models to predict robot actions by focusing on action-relevant visual differences and localized visual changes. The model does not decode the target frame at inference time, but rather uses the output of the image editing model as a compact world-action context. The results show that ImageWAM outperforms standard baselines and competitive world action models without requiring additional policy pretraining, and it reduces computational costs to one sixth and latency to one quarter of video-based models. The authors also provide attention analysis that supports the effectiveness of image editing as an alternative to video-based world-action modeling. Overall, the paper demonstrates that image editing can be a more efficient and effective approach to world action modeling, achieving better performance with reduced computational costs.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19531
• PDF: https://arxiv.org/pdf/2606.19531
• Project Page: https://zhangwenyao1.github.io/ImageWAM/

🤖 Models citing this paper:
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-4B-RoboTwin
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-4B-LIBERO
• https://huggingface.co/yuyangalin/ImageWAM-FLUX.2-9B-LIBERO

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📢 By: https://xn--r1a.website/PaperNexus

#ImageEditingForRobotControl #WorldActionModels #VideoGenerationAlternatives #PretrainedImageModels #RobotControlWithImageEditing

🔥 S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence

💡 The paper introduces S-Agent, a spatial reasoning framework that enhances visual language models to enable continuous 3D world understanding from multi-view imagery. The problem addressed is that existing visual language models and tool-augmented agents are limited to static and stateless inference from isolated visual observations, which is insufficient for real-world spatial intelligence.

The S-Agent method involves formulating spatial reasoning as spatio-temporal evidence accumulation, rather than isolated frame-level prediction. This is achieved by casting the visual language model as a semantic planner that decides what evidence is needed, while a hierarchy of spatial tools and experts grounds objects in 2D, lifts them into 3D geometric evidence, and aggregates this evidence into high-level spatial knowledge. The framework also includes a temporal memory mechanism, comprising scene memory and agent memory, which enables evidence integration across frames and reasoning steps.

The results show that S-Agent consistently improves both open-source and closed-source visual language models in a training-free manner. Additionally, supervised fine-tuning on S-Agent-generated spatial trajectories yields S-Agent-8B, a compact spatial agent that significantly surpasses similar-scale baselines and performs comparably to advanced closed-source models. The comprehensive experiments on multi-view and video spatial reasoning benchmarks demonstrate the effectiveness of the S-Agent framework in enhancing spatial intelligence. Overall, the paper contributes a novel spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos, which has the potential to improve real-world spatial intelligence applications.

📅 Published on Jun 18

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.20515
• PDF: https://arxiv.org/pdf/2606.20515
• Project Page: https://ropedia.github.io/S-Agent

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📢 By: https://xn--r1a.website/PaperNexus

#SpatialReasoning #VisualLanguageModels #3DWorldUnderstanding #SpatioTemporalEvidence #ToolUseInAI

🔥 IndexTTS: An Industrial-Level Controllable and Efficient Zero-Shot Text-To-Speech System

💡 The paper introduces IndexTTS, an enhanced text-to-speech system that combines the XTTS and Tortoise models to achieve improved naturalness, voice cloning, and controllable usage. The system addresses the limitations of existing text-to-speech systems, particularly in Chinese scenarios where polyphonic characters and long-tail characters can be challenging to pronounce. To overcome this, the authors propose a hybrid character-pinyin modeling approach that allows for more controllable pronunciations.

The authors also compare Vector Quantization with Finite-Scalar Quantization for codebook utilization of acoustic speech tokens, and introduce a conformer-based speech conditional encoder and BigVGAN2 to enhance voice cloning. The results show that IndexTTS achieves significant improvements in naturalness, content consistency, and zero-shot voice cloning compared to the XTTS model.

In comparison to other popular open-source text-to-speech systems, IndexTTS has a relatively simple training process, more controllable usage, and faster inference speed, while also surpassing their performance. The system is designed to be efficient and controllable, making it suitable for industrial-level applications. The authors provide demos of the system, which are available for evaluation. Overall, the paper presents a novel approach to text-to-speech synthesis that achieves state-of-the-art results and has the potential for practical applications.

📅 Published on Feb 8, 2025

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2502.05512
• PDF: https://arxiv.org/pdf/2502.05512
• Project Page: https://index-tts.github.io

🤖 Models citing this paper:
• https://huggingface.co/IndexTeam/IndexTTS-2
• https://huggingface.co/IndexTeam/Index-TTS
• https://huggingface.co/taraskurtizan/IndexTTS-2

📊 Datasets citing this paper:
• https://huggingface.co/datasets/echodict/index-tts

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/IndexTeam/IndexTTS
• https://huggingface.co/spaces/Pendrokar/TTS-Spaces-Arena
• https://huggingface.co/spaces/alexnasa/OutofLipSync

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📢 By: https://xn--r1a.website/PaperNexus

#TextToSpeechSystems #ZeroShotLearning #VoiceCloningTechnology #ControllableSpeechSynthesis #SpeechRecognitionModels

🔥 QUEST: Training Frontier Deep Research Agents with Fully Synthetic Tasks

💡 The paper introduces QUEST, a family of open deep research agents that can perform well across diverse long horizon search tasks. The problem addressed is that existing open agents often generalize poorly across different task types, while frontier systems remain proprietary. To solve this, the authors propose a training recipe that combines mid training, supervised fine tuning, and reinforcement learning. A key component of this recipe is a curated data synthesis pipeline that applies to different task types and enables synthesizing training data with verifiable rewards without human annotation. The pipeline uses unified rubric trees to generate tasks. The authors also incorporate a built in context management mechanism that enables effective long horizon reasoning and knowledge synthesis. The results show that QUEST approaches or surpasses frontier closed source agents across eight deep research benchmarks using only 8K synthesized tasks. The models, data, and training scripts are released, making it possible for others to use and build upon the work. The contributions of the paper are the proposed training recipe, the data synthesis pipeline, and the release of the QUEST models, which provide a general purpose deep research agent that can handle a wide range of tasks, including fact seeking, citation grounding, and report synthesis.

📅 Published on May 22

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2605.24218
• PDF: https://arxiv.org/pdf/2605.24218
• Project Page: https://osu-nlp-group.github.io/QUEST/

🤖 Models citing this paper:
• https://huggingface.co/osunlp/QUEST-35B-RL
• https://huggingface.co/osunlp/QUEST-35B-MT-Plus-SFT
• https://huggingface.co/osunlp/QUEST-9B

📊 Datasets citing this paper:
• https://huggingface.co/datasets/osunlp/QUEST-RL-Data
• https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective
• https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Open-ended

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/osunlp/QUEST

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📢 By: https://xn--r1a.website/PaperNexus

#DeepResearchAgents #LongHorizonSearchTasks #SyntheticTaskGeneration #ReinforcementLearningMethods #OpenAgentArchitectures

🔥 PaperFlow: Profiling, Recommending, and Adapting Across Daily Paper Streams

💡 The paper introduces PaperFlow, a framework for scientific paper recommendation that addresses the dynamic nature of scientific reading, where user interests shift and feedback accumulates over time. The traditional approach to scientific paper recommendation evaluates static ranking over a fixed candidate set, which does not reflect real-world reading behavior. PaperFlow organizes the recommendation process into three stages: Profiling, Recommending, and Adapting.

In the Profiling stage, a structured and inspectable scholarly profile is constructed and maintained from heterogeneous evidence, even in cases of cold start where limited information is available. The Recommending stage ranks each daily paper stream through multi-signal aggregation, considering a fixed display budget. The Adapting stage updates the user state from semantically distinct feedback signals and models interest drift across days.

To evaluate PaperFlow, the authors define a longitudinal user-day benchmark that consists of 24 simulated research users, 50 daily paper streams, and 1200 user-day episodes. This benchmark also includes 20727 unique papers and 497448 episode-paper records. The authors also specify a blind human-evaluation protocol to validate the alignment between automatic metrics and expert judgments.

The results show that PaperFlow outperforms five scientific recommendation baselines in terms of oracle-based ranking, behavioral alignment with simulated reading selections, and blind human-evaluation score. This indicates that PaperFlow is effective in capturing user interests and adapting to changes in their reading behavior over time. Overall, the paper contributes to the development of a more dynamic and personalized scientific paper recommendation system that reflects real-world reading behavior.

📅 Published on Jun 5

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.07454
• PDF: https://arxiv.org/pdf/2606.07454
• Project Page: https://openraiser.github.io/PaperFlow

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📢 By: https://xn--r1a.website/PaperNexus

#ScientificPaperRecommendation #DynamicUserModeling #PersonalizedRecommendationSystems #ScholarlyProfileConstruction #AdaptiveInformationRetrieval

🔥 Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models

💡 The paper addresses the challenge of financial sentiment analysis, which is crucial for investment decision-making. Traditional natural language processing models are limited by their size and training data, resulting in poor generalization and effectiveness. Large Language Models, despite their superior performance in various NLP tasks, also face challenges in financial sentiment analysis due to the discrepancy between their pre-training objective and the task of predicting sentiment labels. Additionally, the concise nature of financial news often lacks sufficient context, which can compromise the reliability of Large Language Models' sentiment analysis.

To overcome these challenges, the authors propose a retrieval-augmented Large Language Model framework. This framework consists of two modules: an instruction-tuned Large Language Model module that ensures the model behaves as a predictor of sentiment labels, and a retrieval-augmentation module that retrieves additional context from reliable external sources. This approach enables the model to leverage external context to improve its sentiment analysis capabilities.

The authors evaluate their framework against traditional models and other Large Language Models, such as ChatGPT and LLaMA. The results show that their approach achieves a significant performance gain, with improvements in accuracy and F1 score ranging from 15% to 48%. This demonstrates the effectiveness of the proposed retrieval-augmented Large Language Model framework in enhancing financial sentiment analysis. Overall, the paper contributes to the development of more accurate and reliable financial sentiment analysis models, which can inform better investment decisions.

📅 Published on Oct 6, 2023

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2310.04027
• PDF: https://arxiv.org/pdf/2310.04027

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📢 By: https://xn--r1a.website/PaperNexus

#FinancialSentimentAnalysis #RetrievalAugmentedModels #LargeLanguageModels #NaturalLanguageProcessing #FinancialTextAnalysis

🔥 Moebius: 0.2B Lightweight Image Inpainting Framework with 10B-Level Performance

💡 The paper presents Moebius, a lightweight image inpainting framework that achieves high fidelity results with significantly reduced parameters and inference time. The problem addressed is that current industrial foundation models for image inpainting have high computational costs, making them impractical for deployment. To solve this, the authors propose a novel approach that reconstructs the diffusion backbone using local-global interaction blocks and adaptive distillation strategies.

The method involves introducing the Local-λ Mix Interaction block, which consists of Local-λ and Interactive-λ modules. This block summarizes spatial contexts and global semantic priors into fixed-size linear matrices, preserving complex latent interactions while reducing parameters. The authors also propose an adaptive multi-granularity distillation strategy that operates within the latent space to avoid expensive pixel-space decoding. This strategy dynamically balances multiple gradient-based losses to achieve high-fidelity alignment.

The results show that Moebius achieves high-fidelity image inpainting results that rival or surpass those of the 10B-level industrial generalist FLUX.1-Fill-Dev, while using less than 2% of the parameters and delivering a more than 15 times acceleration in total inference time. The Moebius framework has 0.22 billion parameters, compared to 11.9 billion parameters in the industrial generalist model, and achieves a new efficiency standard for high-fidelity inpainting. Overall, the paper presents a highly efficient and optimized image inpainting framework that can be practically deployed.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.19195
• PDF: https://arxiv.org/pdf/2606.19195
• Project Page: https://hustvl.github.io/Moebius

🤖 Models citing this paper:
• https://huggingface.co/hustvl/Moebius
• https://huggingface.co/simonw/Moebius-ONNX

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/multimodalart/Moebius
• https://huggingface.co/spaces/Mike0021/moebius
• https://huggingface.co/spaces/jonatei/MoebiusDemo

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📢 By: https://xn--r1a.website/PaperNexus

#ImageInpainting #DiffusionBackbone #LightweightDeepLearning #LocalGlobalInteraction #EfficientComputerVision

🔥 GateMem: Benchmarking Memory Governance in Multi-Principal Shared-Memory Agents

💡 The paper introduces GateMem, a benchmark for evaluating the performance of multi-principal shared-memory agents. The problem addressed is that current memory agents are not reliable in shared institutional settings, such as hospitals, workplaces, and households, where multiple users with different roles and authorization contexts access and update a common memory pool. The challenge is to balance utility, access control, and forgetting in these settings.

The GateMem benchmark is designed to evaluate memory agents in terms of their ability to provide utility for legitimate requests, control access to sensitive information, and forget information that is no longer needed or has been explicitly deleted. The benchmark consists of a set of tasks and evaluation metrics that span multiple domains, including medical, office, education, and household settings.

The results of the paper show that current memory agents are not able to achieve strong performance on all three aspects of utility, access control, and forgetting. The authors evaluated several baseline models and backbone architectures, and found that no method is able to simultaneously achieve high utility, robust access control, and reliable forgetting. The results also show that long-context prompting can achieve good governance scores, but at a high computational cost, while retrieval-based and external-memory methods can reduce the cost but may leak unauthorized or deleted information.

Overall, the paper highlights the need for more research on developing reliable multi-principal shared-memory agents that can balance utility, access control, and forgetting in shared institutional settings. The GateMem benchmark provides a useful tool for evaluating the performance of these agents and identifying areas for improvement.

📅 Published on Jun 17

🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2606.18829
• PDF: https://arxiv.org/pdf/2606.18829
• Project Page: https://rzhub.github.io/GateMem/project.html

📊 Datasets citing this paper:
• https://huggingface.co/datasets/Ray368/GateMem

🚀 Spaces citing this paper:
• https://huggingface.co/spaces/Ray368/GateMem-Submit

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📢 By: https://xn--r1a.website/PaperNexus

#MultiPrincipalSystems #SharedMemoryAgents #MemoryGovernance #AccessControlMechanisms #BenchmarkingArtificialIntelligence