数据来源：HuggingFace Papers Latest Papers1. Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational ModelingMulti-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage tha ...

数据来源：HuggingFace Papers Latest Papers1. Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational ModelingMulti-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage tha ...

数据来源：HuggingFace Papers Latest Papers1. Improving Multi-step RAG with Hypergraph-based Memory for Long-Context Complex Relational ModelingMulti-step retrieval-augmented generation (RAG) has become a widely adopted strategy for enhancing large language models (LLMs) on tasks that demand global comprehension and intensive reasoning. Many RAG systems incorporate a working memory module to consolidate retrieved information. However, existing memory designs function primarily as passive storage tha ...

数据来源：HuggingFace Papers Latest Papers1. NeoVerse: Enhancing 4D World Model with in-the-wild Monocular VideosIn this paper, we propose NeoVerse, a versatile 4D world model that is capable of 4D reconstruction, novel-trajectory video generation, and rich downstream applications. We first identify a common limitation of scalability in current 4D world modeling methods, caused either by expensive and specialized multi-view 4D data or by cumbersome training pre-processing. In contrast, our NeoVerse ...

数据来源：HuggingFace Papers Latest Papers1. Can LLMs Predict Their Own Failures? Self-Awareness via Internal CircuitsLarge language models (LLMs) generate fluent and complex outputs but often fail to recognize their own mistakes and hallucinations. Existing approaches typically rely on external judges, multi-sample consistency, or text-based self-critique, which incur additional compute or correlate weakly with true correctness. We ask: can LLMs predict their own failures by inspecting internal st ...

数据来源：HuggingFace Papers Latest Papers1. InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit FieldsExisting depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decod ...

数据来源：HuggingFace Papers Latest Papers1. Entropy-Adaptive Fine-Tuning: Resolving Confident Conflicts to Mitigate ForgettingSupervised Fine-Tuning (SFT) is the standard paradigm for domain adaptation, yet it frequently incurs the cost of catastrophic forgetting. In sharp contrast, on-policy Reinforcement Learning (RL) effectively preserves general capabilities. We investigate this discrepancy and identify a fundamental distributional gap: while RL aligns with the model’s internal belief, SFT for ...

数据来源：HuggingFace Papers Latest Papers1. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL OptimizationAs language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each capturing a distinct preference, to guide models toward these desi ...

数据来源：HuggingFace Papers Latest Papers1. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL OptimizationAs language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each capturing a distinct preference, to guide models toward these desi ...

数据来源：HuggingFace Papers Latest Papers1. GDPO: Group reward-Decoupled Normalization Policy Optimization for Multi-reward RL OptimizationAs language models become increasingly capable, users expect them to provide not only accurate responses but also behaviors aligned with diverse human preferences across a variety of scenarios. To achieve this, Reinforcement learning (RL) pipelines have begun incorporating multiple rewards, each capturing a distinct preference, to guide models toward these desi ...

数据来源：HuggingFace Papers Latest Papers1. Thinking with Map: Reinforced Parallel Map-Augmented Agent for GeolocalizationThe image geolocalization task aims to predict the location where an image was taken anywhere on Earth using visual clues. Existing large vision-language model (LVLM) approaches leverage world knowledge, chain-of-thought reasoning, and agentic capabilities, but overlook a common strategy used by humans — using maps. In this work, we first equip the model \textit{Thinking with M ...

数据来源：HuggingFace Papers Latest Papers1. Watching, Reasoning, and Searching: A Video Deep Research Benchmark on Open Web for Agentic Video ReasoningIn real-world video question answering scenarios, videos often provide only localized visual cues, while verifiable answers are distributed across the open web; models therefore need to jointly perform cross-frame clue extraction, iterative retrieval, and multi-hop reasoning-based verification. To bridge this gap, we construct the first video deep r ...

数据来源：HuggingFace Papers Latest Papers1. MemGovern: Enhancing Code Agents through Learning from Governed Human ExperiencesWhile autonomous software engineering (SWE) agents are reshaping programming paradigms, they currently suffer from a “closed-world” limitation: they attempt to fix bugs from scratch or solely using local context, ignoring the immense historical human experience available on platforms like GitHub. Accessing this open-world experience is hindered by the unstructured and fragme ...

数据来源：HuggingFace Papers Latest Papers1. Controlled Self-Evolution for Algorithmic Code OptimizationSelf-evolution methods enhance code generation through iterative “generate-verify-refine” cycles, yet existing approaches suffer from low exploration efficiency, failing to discover solutions with superior complexity within limited budgets. This inefficiency stems from initialization bias trapping evolution in poor solution regions, uncontrolled stochastic operations lacking feedback guidance, an ...

数据来源：HuggingFace Papers Latest Papers1. Urban Socio-Semantic Segmentation with Vision-Language ReasoningAs hubs of human activity, urban surfaces consist of a wealth of semantic entities. Segmenting these various entities from satellite imagery is crucial for a range of downstream applications. Current advanced segmentation models can reliably segment entities defined by physical attributes (e.g., buildings, water bodies) but still struggle with socially defined categories (e.g., schools, park ...