AI & ML Papers

🔥 Efficient Reasoning with Balanced Thinking

💡 The paper Efficient Reasoning with Balanced Thinking proposes a training-free framework called ReBalance to address the issues of overthinking and underthinking in large reasoning models. Overthinking occurs when models expend redundant computational steps on simple problems, while underthinking happens when models fail to explore sufficient reasoning paths despite their inherent capabilities. These issues lead to inefficiencies and potential inaccuracies, limiting practical deployment in resource-constrained settings. The ReBalance framework leverages confidence as a continuous indicator of reasoning dynamics to identify overthinking and underthinking behaviors. It computes a steering vector to guide the models reasoning trajectories by aggregating hidden states from a small-scale dataset into reasoning mode prototypes. A dynamic control function modulates the steering vectors strength and direction based on real-time confidence, pruning redundancy during overthinking and promoting exploration during underthinking. The authors conducted extensive experiments on four models ranging from 0.5B to 32B and across nine benchmarks in math reasoning, general question answering, and coding tasks. The results demonstrate that ReBalance effectively reduces output redundancy while improving accuracy, offering a general, training-free, and plug-and-play strategy for efficient and robust large reasoning model deployment. The framework achieves efficient reasoning with balanced thinking, making it a valuable contribution to the field of artificial intelligence and natural language processing.

📅 Published on Mar 12 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2603.12372 • PDF: https://arxiv.org/pdf/2603.12372 • Project Page: https://rebalance-ai.github.io 🤖 Models citing this paper: • https://huggingface.co/Yulin-Li/ReBalance • https://huggingface.co/openpangu/openPangu-Embedded-7B-V1.1 ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #EfficientReasoning #BalancedThinking #OverthinkingInAI #UnderthinkingInAI #ReBalanceFramework

🔥 MediaPipe: A Framework for Building Perception Pipelines

💡 The paper introduces MediaPipe, a framework designed to simplify the development of perception applications. Building such applications is challenging due to the need to select and develop machine learning algorithms and models, create prototypes and demos, balance resource consumption with solution quality, and identify and mitigate problematic cases. MediaPipe addresses these challenges by providing tools for combining existing perception components, prototyping, and measuring performance across different platforms. The framework allows developers to build prototypes by combining components, advance them to polished cross-platform applications, and measure system performance and resource consumption on target platforms. This enables developers to focus on algorithm or model development and use MediaPipe as an environment for iteratively improving their application, with results that are reproducible across different devices and platforms. The key contribution of MediaPipe is that it facilitates the development of perception applications by providing a framework for combining components, prototyping, and measuring performance, thereby simplifying the development process and enabling developers to focus on core aspects of their applications. The framework will be made available as an open-source resource, allowing developers to access and utilize it for their projects. Overall, MediaPipe has the potential to streamline the development of perception applications and improve the efficiency of the development process.

📅 Published on Jun 14, 2019 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/1906.08172 • PDF: https://arxiv.org/pdf/1906.08172 🚀 Spaces citing this paper: • https://huggingface.co/spaces/Jha-Pranav/PixelCare ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #MachineLearningFrameworks #PerceptionPipelines #CrossPlatformDevelopment #ComputerVisionApplications #MediaPipeFramework

This channels is for Programmers, Coders, Software Engineers. 0️⃣ Python 1️⃣ Data Science 2️⃣ Machine Learning 3️⃣ Data Visualization 4️⃣ Artificial Intelligence 5️⃣ Data Analysis 6️⃣ Statistics 7️⃣ Deep Learning 8️⃣ programming Languages ✅ https://t.me/addlist/8_rRW2scgfRhOTc0 ✅ https://t.me/Codeprogrammer

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🔥 Native and Compact Structured Latents for 3D Generation

💡 This paper addresses the challenge of 3D generative modeling where existing representations struggle to capture complex topologies and detailed appearance of 3D assets. To overcome this, the authors introduce a new sparse voxel representation called O-Voxel, which encodes both geometry and appearance of 3D objects. O-Voxel can robustly model arbitrary topology, including open, non-manifold, and fully-enclosed surfaces, and captures comprehensive surface attributes. The authors design a Sparse Compression VAE based on O-Voxel, which provides a high spatial compression rate and a compact latent space. They train large-scale models with 4B parameters on diverse public 3D asset datasets and achieve highly efficient inference. The results show that the generated assets have significantly better geometry and material quality compared to existing models. The approach offers a significant advancement in 3D generative modeling by enabling high-quality generation with efficient inference and robust topology handling.

📅 Published on Dec 16, 2025 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2512.14692 • PDF: https://arxiv.org/pdf/2512.14692 • Project Page: https://microsoft.github.io/TRELLIS.2/ 🤖 Models citing this paper: • https://huggingface.co/microsoft/TRELLIS.2-4B • https://huggingface.co/mancub/TRELLIS.2-4B • https://huggingface.co/Jinstudio/TRELLIS.2-4B 📊 Datasets citing this paper: • https://huggingface.co/datasets/serpentine-b/t2 🚀 Spaces citing this paper: • https://huggingface.co/spaces/microsoft/TRELLIS.2 • https://huggingface.co/spaces/TencentARC/Pixal3D • https://huggingface.co/spaces/broyang/3dai ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #3DGenerativeModeling #SparseVoxelRepresentation #CompactLatentSpace #3DAssetGeneration #GeometricDeepLearning

🔥 Color Pass-Through via Camera-Display Coupling

💡 The paper Color Pass-Through via Camera-Display Coupling addresses the issue of color discrepancy between the original scene and its displayed image on a smartphone screen. Despite advances in camera and display technology, the displayed image often differs noticeably from the original scene in terms of color, brightness, and contrast. This is because most pipelines separate the high-dimensional capture-to-display process into two stages, calibrating the camera and display separately and then connecting them through low-dimensional color transforms, which leads to information bottlenecks and error accumulation. To overcome this challenge, the authors propose Color Pass-Through, an end-to-end learned framework that operates directly on captured images. The key insight is to treat the camera and display as a coupled system rather than calibrating them in isolation. By coupling the camera and display, the authors achieve two practical advantages: it brings the entire real-world scene to the display via end-to-end optimization, and it allows for efficient one-step calibration for each distinct observer via the complete capture-to-display path. The authors validate Color Pass-Through using both digital and human observers. Compared to representative baselines, their method achieves an average gain of 2.0 points on a 5-point user study and more than 2x improvement on quantitative metrics, demonstrating improved reproduction of the perceived color of the original scene. The results show that the proposed approach can effectively reduce the color discrepancy between the original scene and its displayed image, leading to a more accurate and faithful representation of the scene.

📅 Published on Jul 14 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2607.12746 • PDF: https://arxiv.org/pdf/2607.12746 • Project Page: https://lyricccco.github.io/color-pass-through/ ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #ColorPassThrough #CameraDisplayCoupling #ColorDiscrepancyCorrection #DisplayColorCalibration #CaptureToDisplayProcessing

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🔥 Beyond Relevance-Centric Retrieval: Rubric-Oriented Document Set Selection and Ranking

💡 The paper addresses the issue of document set selection and ranking, which is crucial for large language models and AI agents that rely on search results. Existing evaluation systems score documents independently and aggregate them using metrics like DCG, ignoring interactions between documents such as redundancy, conflict, and complementarity. This limitation makes it difficult to determine what makes one document set better than another. To address this issue, the authors propose a comprehensive evaluate-diagnose-optimize framework. They design Setwise Eval Kit, a three-level, nine-dimension document set evaluation benchmark that covers both short-form and long-form scenarios, comprising approximately 28,000 high-quality evaluation rubrics. The authors systematically evaluate 12 rerankers and find that even the best method achieves no more than 45 percent coverage, and cross-document coordination dimensions are universally weak. No single method maintains top performance across both settings. Building on this, the authors propose Rubric4Setwise, a training-free method that converts rubric-based evaluation criteria into document set selection signals. This method achieves the best downstream generation performance with fewer documents and search rounds. It is the only method that maintains state-of-the-art results across both scenarios, validating the effectiveness of closing the loop from evaluation to optimization. The paper's contributions include a comprehensive evaluation framework, a new benchmark for document set evaluation, and a novel method for document set selection and ranking that outperforms existing methods. The results demonstrate the importance of considering cross-document interactions and using rubric-based evaluation criteria to improve document set selection and ranking.

📅 Published on Jul 22 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2607.19747 • PDF: https://arxiv.org/pdf/2607.19747 • Project Page: https://rubric4setwise.github.io/ ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #DocumentSetSelection #RubricOrientedRanking #InformationRetrieval #DocumentEvaluation #SetwiseOptimization

🔥 Self Gradient Forcing: Native Long Video Extrapolation

💡 The paper proposes a new method called Self Gradient Forcing for native long video extrapolation. Recent autoregressive video diffusion methods are built upon Self Forcing, where the student is trained on histories produced by its own rollout rather than ground-truth video contexts. However, this approach has a limitation known as the historical context-gradient gap, where future losses cannot supervise how earlier generated latents should be written into more useful keys and values for later video-latent generation. To address this issue, the authors propose a two-pass training strategy called Self Gradient Forcing. The first pass performs a no-gradient autoregressive rollout matching inference and records both the self-generated context and the noisy latents fed to the model at a sampled denoising exit step. The second pass performs parallel context-gradient reconstruction for the recorded exit step. The generated context is used as a stop-gradient clean-latent input, while the model recomputes the context KV representations and future-to-context causal attention. The proposed method provides the missing memory-writing supervision within the native autoregressive training objective, using losses on future video latents to train the model to encode context into more effective causal memory. The authors evaluate their method across extensive long-horizon frame-wise and chunk-wise experiments under different initializations and achieve stronger native long-video extrapolation than Self Forcing, especially in subject identity, background/layout consistency, and temporal stability. Notably, using only a 5-second training window, Self Gradient Forcing can extrapolate to videos lasting several minutes.

📅 Published on Jul 22 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2607.20368 • PDF: https://arxiv.org/pdf/2607.20368 • Project Page: https://zhuang2002.github.io/SelfGradientForcing/ 🤖 Models citing this paper: • https://huggingface.co/JunhaoZhuang/Self_Gradient_Forcing ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #VideoExtrapolation #AutoregressiveVideoDiffusion #SelfGradientForcing #LongVideoGeneration #VideoDiffusionMethods

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🔥 ABot-World-0: Infinite Interactive World Rollout on a Single Desktop GPU

💡 The paper presents ABot-World-0, a system for real-time, long-horizon, closed-loop interaction in a virtual world. The system is trained on a large dataset of videos, games, and simulation engines to learn controllable world dynamics. The authors propose a multi-source data infrastructure to collect and process data, and a unified pipeline to apply quality checks, assessment, and synchronization of actions and text annotations. The system uses a teacher-forcing approach to train an action-conditioned video world model, which is then distilled into a causal student model through a process of teacher forcing and ODE distillation. The authors also introduce Long Forcing, a method to align long student self-rollouts with an extended-horizon teacher, mitigating accumulated distribution shift and autoregressive drift. The system provides a unified control interface for scene roaming and third-person character interaction, and uses reference-character memory to provide persistent appearance cues for identity consistency during third-person rollouts. The authors also co-design a streaming inference stack with a lightweight VAE decoder, efficient attention, memory-aware scheduling, and low-bit DIT inference. The results show that ABot-World-0 can stream 720p video at up to 16 frames per second on a single NVIDIA RTX 5090 desktop GPU, with 1.2 seconds action-to-first-frame latency and approximately 19 GB peak VRAM. Experiments on World Roam Benchmark and extended interactive rollouts demonstrate competitive controllability and coherent long-horizon world evolution. Overall, the paper presents a novel approach to real-time, long-horizon, closed-loop interaction in virtual worlds, with potential applications in fields such as robotics, gaming, and simulation.

📅 Published on Jul 21 🔗 Links: • GitHub: https://github.com/huggingface • arXiv: https://arxiv.org/abs/2607.19191 • PDF: https://arxiv.org/pdf/2607.19191 • Project Page: https://abot-world.amap.com/ 🤖 Models citing this paper: • https://huggingface.co/acvlab/ABot-World-0-5B-LF 🚀 Spaces citing this paper: • https://huggingface.co/spaces/acvlab/abot-world-interactive ━━━━━━━━━━━━━━━━━━━━━━━━ 📢 By: https://t.me/PaperNexus #VirtualWorldSimulation #InteractiveWorldModels #RealTimeWorldDynamics #ClosedLoopInteraction #ArtificialIntelligenceForGames

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