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Cut2Next: Generating Next Shot via In-Context Tuning

DIVER: A Multi-Stage Approach for Reasoning-intensive Information Retrieval

Omni-Effects: Unified and Spatially-Controllable Visual Effects Generation

Chimera: Harnessing Multi-Agent LLMs for Automatic Insider Threat Simulation

Klear-Reasoner: Advancing Reasoning Capability via Gradient-Preserving Clipping Policy Optimization

TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree

AMFT: Aligning LLM Reasoners by Meta-Learning the Optimal Imitation-Exploration Balance

LSDTs: LLM - Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning

Do Biased Models Have Biased Thoughts?

Early Detection of Pancreatic Cancer Using Multimodal Learning on Electronic Health Record

LLM Unlearning Without an Expert Curated Dataset

Multi-Faceted Large Embedding Tables for Pinterest Ads Ranking

Echo: Decoupling Inference and Training for Large-Scale RL Alignment on Heterogeneous Swarms

Situated Epitemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge

RCR-Router: Efficient Role-Aware Context Routing for Multi-Agent LLM Systems with Structured Memory

Position: The Current AI Conference Model is Unsustainable! Diagnosing the Crisis of Centralized AI Conference

GTPO and GRPO-S: Token and Sequence-Level Reward Shaping with Policy Entropy

A Few Words Can Distort Graphs: Knowledge Poisoning Attacks on Graph-based Retrieval-Augmented Generation of Large Language Models

Explaining Time Series Classifiers with PHAR: Rule Extraction and Fusion from Post-hoc Attributions

Role-Aware Language Models for Secure and Contextualized Access Control in Organizations

DynaSwarm: Dynamically Graph Structure Selection for LLM-based Multi-agent System

Post-Completion Learning for Language Models

Alternates, Assemble! Selecting Optimal Alternates for Citizens' Assemblies

Argus Inspection: Do Multimodal Large Language Models Possess the Eye of Panoptes?

RAGtifier: Evaluating RAG Generation Approaches of State-of-the-Art RAG Systems for the SIGIR LiveRAG Competition

Unsupervised Document and Template Clustering using Multimodal Embeddings

Saturation Self-Organizing Map

CulturalFrames: Assessing Cultural Expectation Alignment in Text-to-Image Models and Evaluation Metrics

To Judge or not to Judge: Using LLM Judgments for Advertiser Keyphrase Relevance at eBay

Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey

Mj\"olnir: A Deep Learning Parametrization Framework for Global Lightning Flash Density

Federated Learning: A Survey on Privacy-Preserving Collaborative Intelligence

Democracy of AI Numerical Weather Models: An Example of Global Forecasting with FourCastNetv2 Made by a University Research Lab Using GPU

Retrieval-Augmented Generation with Conflicting Evidence

SPIE: Semantic and Structural Post-Training of Image Editing Diffusion Models with AI feedback

Evaluating Trust in AI, Human, and Co-produced Feedback Among Undergraduate Students

ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

ChatBench: From Static Benchmarks to Human-AI Evaluation

Adaptive Computation Pruning for the Forgetting Transformer

AI-induced sexual harassment: Investigating Contextual Characteristics and User Reactions of Sexual Harassment by a Companion Chatbot

CrossWordBench: Evaluating the Reasoning Capabilities of LLMs and LVLMs with Controllable Puzzle Generation

Opioid Named Entity Recognition (ONER-2025) from Reddit

OSMa-Bench: Evaluating Open Semantic Mapping Under Varying Lighting Conditions

TIDE: Temporal-Aware Sparse Autoencoders for Interpretable Diffusion Transformers in Image Generation

Flexible Prefrontal Control over Hippocampal Episodic Memory for Goal-Directed Generalization

EvoP: Robust LLM Inference via Evolutionary Pruning

Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions

Zero-shot Emotion Annotation in Facial Images Using Large Multimodal Models: Benchmarking and Prospects for Multi-Class, Multi-Frame Approaches

PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN

Forget the Data and Fine-Tuning! Just Fold the Network to Compress

FBFL: A Field-Based Coordination Approach for Data Heterogeneity in Federated Learning

Decoding-based Regression

AdEval: Alignment-based Dynamic Evaluation to Mitigate Data Contamination in Large Language Models

Chemist-aligned retrosynthesis by ensembling diverse inductive bias models

Adaptive Informed Deep Neural Networks for Power Flow Analysis

A Risk Taxonomy and Reflection Tool for Large Language Model Adoption in Public Health

Learning Marmoset Vocal Patterns with a Masked Autoencoder for Robust Call Segmentation, Classification, and Caller Identification

Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning

Zero-Shot Generalization of Vision-Based RL Without Data Augmentation

Hypergraph-based Motion Generation with Multi-modal Interaction Relational Reasoning

3DFacePolicy: Audio-Driven 3D Facial Animation Based on Action Control

Return Prediction for Mean-Variance Portfolio Selection: How Decision-Focused Learning Shapes Forecasting Models

OE3DIS: Open-Ended 3D Point Cloud Instance Segmentation

VisionUnite: A Vision-Language Foundation Model for Ophthalmology Enhanced with Clinical Knowledge

DreamStory: Open-Domain Story Visualization by LLM-Guided Multi-Subject Consistent Diffusion

MEReQ: Max-Ent Residual-Q Inverse RL for Sample-Efficient Alignment from Intervention

Multidimensional Adaptive Coefficient for Inference Trajectory Optimization in Flow and Diffusion

AIOS: LLM Agent Operating System

Keep Your Friends Close: Leveraging Affinity Groups to Accelerate AI Inference Workflows

From Lab to Field: Real-World Evaluation of an AI-Driven Smart Video Solution to Enhance Community Safety

BELLA: Black box model Explanations by Local Linear Approximations

Artificial Intelligence Software Structured to Simulate Human Working Memory, Mental Imagery, and Mental Continuity

Fitting Description Logic Ontologies to ABox and Query Examples

Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths

Designing a Feedback-Driven Decision Support System for Dynamic Student Intervention

Large Language Models Do Not Simulate Human Psychology

IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model

InfiAlign: A Scalable and Sample-Efficient Framework for Aligning LLMs to Enhance Reasoning Capabilities

SEAgent: Self-Evolving Computer Use Agent with Autonomous Learning from Experience

Trainable Dynamic Mask Sparse Attention

Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance

Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training

Probabilistic Active Goal Recognition

When Imitation Learning Outperforms Reinforcement Learning in Surgical Action Planning

Effort-aware Fairness: Incorporating a Philosophy-informed, Human-centered Notion of Effort into Algorithmic Fairness Metrics

UnrealZoo: Enriching Photo-realistic Virtual Worlds for Embodied AI

System~2 Reasoning for Human--AI Alignment: Generality and Adaptivity via ARC-AGI

Time Is a Feature: Exploiting Temporal Dynamics in Diffusion Language Models

Training-Free Text-Guided Color Editing with Multi-Modal Diffusion Transformer

Towards Universal Neural Inference

SPARC: Soft Probabilistic Adaptive multi-interest Retrieval Model via Codebooks for recommender system

Dynamic Uncertainty-aware Multimodal Fusion for Outdoor Health Monitoring

Can We Trust AI to Govern AI? Benchmarking LLM Performance on Privacy and AI Governance Exams

Spatial Traces: Enhancing VLA Models with Spatial-Temporal Understanding

E3-Rewrite: Learning to Rewrite SQL for Executability, Equivalence, and Efficiency

When Deepfakes Look Real: Detecting AI-Generated Faces with Unlabeled Data due to Annotation Challenges

Attacks and Defenses Against LLM Fingerprinting

LyS at SemEval 2025 Task 8: Zero-Shot Code Generation for Tabular QA

Retrospective Sparse Attention for Efficient Long-Context Generation

Rational Inverse Reasoning

Cut2Next: Generating Next Shot via In-Context Tuning

Created by

Haebom

Author

Jingwen He, Hongbo Liu, Jiajun Li, Ziqi Huang, Yu Qiao, Wanli Ouyang, Ziwei Liu

Outline

This paper emphasizes the importance of cinematic continuity and editing patterns in multi-shot generation and presents Cut2Next, a novel framework that overcomes the limitations of existing methods. Cut2Next generates the next shot using a hierarchical multi-prompting strategy based on the Diffusion Transformer (DiT). Hierarchical multi-prompting utilizes relational and individual prompts to specify the overall context, editing style between shots, and the content and cinematic properties of each shot. Structural innovations such as Context-Aware Condition Injection (CACI) and Hierarchical Attention Mask (HAM) integrate various cues without adding parameters. We build a large-scale RawCuts dataset and a refined CuratedCuts dataset, and present CutBench for evaluation. Experimental results demonstrate that Cut2Next performs well in visual consistency and text fidelity. Specifically, user studies have confirmed a strong preference for adherence to intended editing patterns and cinematic continuity, validating its ability to generate high-quality, narratively consistent next shots.

Takeaways, Limitations

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Takeaways:

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Presenting new possibilities for multi-shot creation that take cinematic continuity and editing patterns into account.

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Effective use of Diffusion Transformer and hierarchical multi-prompting strategies

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Laying the foundation for future research by presenting large-scale datasets and evaluation criteria.

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Ensuring the reliability of subjective quality assessments through user research.

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Limitations:

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Further review of the size and diversity of the presented dataset is needed.

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Need to verify generalization performance for various genres and styles of movies

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Consideration needs to be given to computational costs and processing times.

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Further research is needed on the applicability of this method in real-world film production environments.

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