In-Context Learning

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25 papers in the last 28 days · 0.4% of indexed attention

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Period ending 2026-09-21

9 new papers

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Period ending 2026-09-14

7 new papers

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Period ending 2026-09-07

10 new papers

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256 papers

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WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation
Nov 27, 2025Quanjian Song, Yiren Song, Kelly Peng +2Video World Models Egocentric Video
Predicting the Emergence of Induction Heads in Language Model Pretraining
Nov 21, 2025Tatsuya Aoyama, Ethan Gotlieb Wilcox, Nathan SchneiderLarge Language Model Pretraining In-Context Learning
BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
Nov 14, 2025Lan Li, Tao Hu, Da-Wei Zhou +3Domain Incremental Learning In-Context Learning
How Does the Pretraining Distribution Shape In-Context Learning? A Fundamental Trade-Off
Oct 1, 2025Waïss Azizian, Ali HasanIn-Context Learning Distributional Learning
How Can Mamba Learn In Context with Outliers and Generalize Provably?
Oct 1, 2025Hongkang Li, Songtao Lu, Xiaodong Cui +2Bidirectional Mamba In-Context Learning
Test time training enhances in-context learning of nonlinear functions
Sep 30, 2025Kento Kuwataka, Taiji SuzukiIn-Context Learning Transformer Layers
HERMAN: Hierarchical Representation Matching for CLIP-based Class-Incremental Learning
Sep 26, 2025Zhen-Hao Xie, Yan Wang, Lan Li +3Domain Incremental Learning In-Context Learning
Discovering New Theorems via LLMs with In-Context Proof Learning in Lean
Sep 16, 2025Kazumi Kasaura, Naoto Onda, Yuta Oriike +3Theorem Large Language Model Reasoning
ICR-RL: Deep Reinforcement Learning via In-Context Regression
Sep 14, 2025David Schiff, Ofir Lindenbaum, Yonathan EfroniDeep Reinforcement Learning Deepmind Control Suite
LLMs are Bayesian, In Expectation, Not in Realization
Jul 15, 2025Leon Chlon, Fatima Sheaib, Zein Khamis +3Posterior In-Context Learning
Context Tuning for In-Context Optimization
Jul 6, 2025Jack Lu, Ryan Teehan, Zhenbang Yang +1Few-Shot Learning Prompt Tuning
InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing
May 28, 2025Shuaiyi Li, Zhisong Zhang, Yang Deng +6Multimodal Knowledge Editing In-Context Learning
In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models
Mar 3, 2025David Ponce, Thierry EtchegoyhenInstruction Tuning In-Context Learning
From Markov to Laplace: How Mamba In-Context Learns Markov Chains
Feb 14, 2025Marco Bondaschi, Nived Rajaraman, Xiuying Wei +5Bidirectional Mamba In-Context Learning
From system models to class models: An in-context learning paradigm
Aug 25, 2023Marco Forgione, Filippo Pura, Dario PigaSystem Identification In-Context Learning
Transformers as In-Context Samplers: From Closed-Form Diffusion to Estimation-Free Sampling
Date pendingArman Adibi, Alireza Jafari, Mohammad Ghavamzadeh +1Samplers Transformer Architectures

Nov 27, 2025 · cs.CVJ/K move · Enter open · S save

WorldWander: Bridging Egocentric and Exocentric Worlds in Video Generation

Quanjian Song, Yiren Song, Kelly Peng, Yuan Gao+1

First Intelligence · Show Lab, National University of Singapore, Singapore

Recent advances in video world models enable interactive environments with free navigation, making translation between first-person (egocentric) and third-person (exocentric) perspectives increasingly important. However, existing studies focus on unidirectional exocentric-to-egocentric translation, overlooking reference-guided exocentric perspective synthesis. This capability is crucial for gaming and embodied AI applications. Motivated by this, we present WorldWander, an in-context learning framework tailored for translating between egocentric and exocentric worlds in video generation. Building upon advanced video diffusion transformers, WorldWander integrates (i) In-Context Perspective Alignment and (ii) Collaborative Position Encoding to model cross-view synchronization and character consistency. To support our task, we curate EgoExo-8K, a dynamic and scene-rich dataset containing synchronized egocentric-exocentric triplets from both synthetic and real-world scenarios. Experiments demonstrate that WorldWander achieves superior perspective synchronization, character consistency, and generalization, setting a new benchmark for egocentric-exocentric video translation.

Video World Models Egocentric Video Controllable Video Generation Egocentric Vision Novel View Synthesis In-Context Learning

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