Search: controllability - ai.jp.net

Research Paper #Audio Generation, Video Processing, AI 🔬 ResearchAnalyzed: Jan 3, 2026 08:45

EchoFoley: Event-Centric Sound Generation for Videos

Published:Dec 31, 2025 08:58

•

1 min read

•

ArXiv

Analysis

This paper addresses limitations in video-to-audio generation by introducing a new task, EchoFoley, focused on fine-grained control over sound effects in videos. It proposes a novel framework, EchoVidia, and a new dataset, EchoFoley-6k, to improve controllability and perceptual quality compared to existing methods. The focus on event-level control and hierarchical semantics is a significant contribution to the field.

Key Takeaways

Reference

“EchoVidia surpasses recent VT2A models by 40.7% in controllability and 12.5% in perceptual quality.”

Permalink ArXiv

Research Paper #Quantum Computing, Quantum Control 🔬 ResearchAnalyzed: Jan 3, 2026 09:30

Time-Optimal Dicke-State Generation with Limited Control

Published:Dec 30, 2025 18:38

•

1 min read

•

ArXiv

Analysis

This paper investigates the generation of Dicke states, crucial for quantum computing, in qubit arrays. It focuses on a realistic scenario with limited control (single local control) and explores time-optimal state preparation. The use of the dCRAB algorithm for optimal control and the demonstration of robustness are significant contributions. The quadratic scaling of preparation time with qubit number is an important practical consideration.

Key Takeaways

•Demonstrates time-optimal generation of Dicke states with limited control.
•Employs the dCRAB algorithm for optimal control.
•Shows quadratic scaling of preparation time with the number of qubits.
•Highlights robustness of the control scheme.

Reference

“The shortest possible state-preparation times scale quadratically with N.”

Permalink ArXiv

Paper #llm 🔬 ResearchAnalyzed: Jan 3, 2026 16:46

DiffThinker: Generative Multimodal Reasoning with Diffusion Models

Published:Dec 30, 2025 11:51

•

1 min read

•

ArXiv

Analysis

This paper introduces DiffThinker, a novel diffusion-based framework for multimodal reasoning, particularly excelling in vision-centric tasks. It shifts the paradigm from text-centric reasoning to a generative image-to-image approach, offering advantages in logical consistency and spatial precision. The paper's significance lies in its exploration of a new reasoning paradigm and its demonstration of superior performance compared to leading closed-source models like GPT-5 and Gemini-3-Flash in vision-centric tasks.

Key Takeaways

•Introduces DiffThinker, a diffusion-based framework for generative multimodal reasoning.
•Reformulates multimodal reasoning as a generative image-to-image task.
•Demonstrates superior performance in vision-centric tasks compared to leading MLLMs.
•Highlights four core properties: efficiency, controllability, native parallelism, and collaboration.

Reference

“DiffThinker significantly outperforms leading closed source models including GPT-5 (+314.2%) and Gemini-3-Flash (+111.6%), as well as the fine-tuned Qwen3-VL-32B baseline (+39.0%), highlighting generative multimodal reasoning as a promising approach for vision-centric reasoning.”

Permalink ArXiv

Research Paper #Control Theory, Partial Differential Equations 🔬 ResearchAnalyzed: Jan 3, 2026 18:58

Small-time Global Controllability of Fourth-Order Parabolic Equations

Published:Dec 29, 2025 09:50

•

1 min read

•

ArXiv

Analysis

This paper explores the controllability of a specific type of fourth-order nonlinear parabolic equation. The research focuses on how to control the system's behavior using time-dependent controls acting through spatial profiles. The key findings are the establishment of small-time global approximate controllability using three controls and small-time global exact controllability to non-zero constant states. This work contributes to the understanding of control theory in higher-order partial differential equations.

Key Takeaways

Reference

“The paper establishes the small-time global approximate controllability of the system using three scalar controls, and then studies the small-time global exact controllability to non-zero constant states.”

Permalink ArXiv

Research Paper #Motion Generation, AI, Deep Learning 🔬 ResearchAnalyzed: Jan 3, 2026 16:28

Pose-Guided Residual Refinement for Text-to-Motion Generation

Published:Dec 27, 2025 04:45

•

1 min read

•

ArXiv

Analysis

This paper addresses the limitations of existing text-to-motion generation methods, particularly those based on pose codes, by introducing a hybrid representation that combines interpretable pose codes with residual codes. This approach aims to improve both the fidelity and controllability of generated motions, making it easier to edit and refine them based on text descriptions. The use of residual vector quantization and residual dropout are key innovations to achieve this.

Key Takeaways

•Proposes PGR$^2$M, a novel approach for text-to-motion generation and editing.
•Combines pose codes and residual codes for improved fidelity and controllability.
•Employs residual vector quantization and residual dropout.
•Demonstrates improved performance compared to existing methods on benchmark datasets.
•Enables intuitive and structure-preserving motion edits.

Reference

“PGR$^2$M improves Fréchet inception distance and reconstruction metrics for both generation and editing compared with CoMo and recent diffusion- and tokenization-based baselines, while user studies confirm that it enables intuitive, structure-preserving motion edits.”

Permalink ArXiv

Research #Simulator 🔬 ResearchAnalyzed: Jan 10, 2026 07:21

SymDrive: Advancing Realistic Driving Simulation with Symmetric Auto-regressive Online Restoration

Published:Dec 25, 2025 10:28

•

1 min read

•

ArXiv

Analysis

The ArXiv article introduces SymDrive, a novel driving simulator promising realistic and controllable performance. The core innovation lies in its use of symmetric auto-regressive online restoration for generating driving scenarios.

Key Takeaways

•SymDrive aims to create more realistic driving simulations.
•The core methodology involves symmetric auto-regressive online restoration.
•The simulator promises enhanced controllability in driving scenarios.

Reference

“The article is sourced from ArXiv.”

Permalink ArXiv

Research #Diffusion 🔬 ResearchAnalyzed: Jan 10, 2026 07:22

Integrating Latent Priors with Diffusion Models: Residual Prior Diffusion Framework

Published:Dec 25, 2025 09:19

•

1 min read

•

ArXiv

Analysis

This research explores a novel framework, Residual Prior Diffusion, to improve diffusion models by incorporating coarse latent priors. The integration of such priors could lead to more efficient and controllable generative models.

Key Takeaways

•Proposes a new probabilistic framework called Residual Prior Diffusion.
•Aims to enhance diffusion models by incorporating latent priors.
•Potentially improves the efficiency and controllability of generative models.

Reference

“Residual Prior Diffusion is a probabilistic framework integrating coarse latent priors with Diffusion Models.”

Permalink ArXiv

Research #Video Diffusion 🔬 ResearchAnalyzed: Jan 10, 2026 07:35

ACD: New Method for Directing Video Diffusion Models

Published:Dec 24, 2025 16:24

•

1 min read

•

ArXiv

Analysis

This ArXiv article likely introduces a novel approach for controlling video generation using diffusion models, focusing on attention mechanisms. The method, ACD, suggests improvements in the controllability of video content creation.

Key Takeaways

•Presents a new method (ACD) for controlling video generation.
•Utilizes attention mechanisms for improved controllability.
•Published on ArXiv, suggesting it's early research.

Reference

“The paper likely focuses on 'Direct Conditional Control for Video Diffusion Models via Attention Supervision' based on the title.”

Permalink ArXiv

Research #Control Systems 🔬 ResearchAnalyzed: Jan 10, 2026 08:02

Controllability Analysis of Elastic Networks

Published:Dec 23, 2025 15:56

•

1 min read

•

ArXiv

Analysis

This ArXiv paper explores the controllability of complex mechanical systems, specifically networks of elastic elements. The research likely contributes to understanding and controlling the behavior of structures in various engineering applications.

Key Takeaways

•Focuses on the controllability of networks with elastic components.
•Explores the concept of asymmetric exact controllability.
•Published on ArXiv, indicating potential early-stage research.

Reference

“The paper focuses on asymmetric exact controllability.”

Permalink ArXiv

Research #Style Transfer 🔬 ResearchAnalyzed: Jan 10, 2026 08:52

LouvreSAE: Advancing Style Transfer with Sparse Autoencoders

Published:Dec 22, 2025 00:36

•

1 min read

•

ArXiv

Analysis

The article's focus on interpretable and controllable style transfer using sparse autoencoders is a significant advancement in the field. This approach has the potential to provide artists and designers with more nuanced control over the stylistic transformation process.

Key Takeaways

•Leverages sparse autoencoders for style transfer.
•Aims for interpretability and control over the style transfer process.
•Potentially benefits artists and designers.

Reference

“The article's source is ArXiv.”

Permalink ArXiv

Research #3D Reconstruction 🔬 ResearchAnalyzed: Jan 10, 2026 08:59

EcoSplat: Novel Approach to Controllable 3D Gaussian Splatting from Images

Published:Dec 21, 2025 11:12

•

1 min read

•

ArXiv

Analysis

The article likely introduces a new method for 3D reconstruction using Gaussian splatting, with a focus on efficiency and controllability. The research appears to optimize the process of creating 3D representations from multiple images, potentially improving speed and quality.

Key Takeaways

•Focuses on efficiency within the 3D Gaussian Splatting framework.
•Utilizes a feed-forward approach, potentially improving processing speed.
•Employs multi-view images as input for 3D reconstruction.

Reference

“The research originates from ArXiv, suggesting a focus on academic contribution and novel methodologies.”

Permalink ArXiv

Research #Image Generation 🔬 ResearchAnalyzed: Jan 10, 2026 09:23

Improving Image Generation: A Dual Approach to Encoder Optimization

Published:Dec 19, 2025 18:59

•

1 min read

•

ArXiv

Analysis

This research focuses on enhancing representation encoders for text-to-image tasks, which is a crucial area for improving the quality and controllability of generated images. The study likely explores methods to optimize encoders for both semantic understanding and image reconstruction, potentially improving image generation and editing capabilities.

Key Takeaways

•Focuses on improving encoders for text-to-image tasks.
•Emphasizes the importance of both semantics and reconstruction.
•Potentially improves image generation and editing quality.

Reference

“The research aims to improve representation encoders for text-to-image generation and editing.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 08:20

Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs

Published:Dec 19, 2025 03:32

•

1 min read

•

ArXiv

Analysis

The article introduces a method called "Reasoning Palette" for controlling and exploring the reasoning capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs). The core idea is to modulate reasoning by using latent contextualization. This suggests a focus on improving the controllability and interpretability of these models' reasoning processes. The use of "latent contextualization" implies a sophisticated approach to influencing the internal representations and decision-making of the models.

Key Takeaways

•Focuses on improving the controllability and interpretability of LLM/VLM reasoning.
•Employs "latent contextualization" for reasoning modulation.
•The research is likely aimed at enhancing the exploration capabilities of LLMs and VLMs.

Reference

“”

Permalink ArXiv

Research #Video Gen 🔬 ResearchAnalyzed: Jan 10, 2026 09:50

Robust Camera Control for Video Generation Using Infinite-Homography

Published:Dec 18, 2025 20:03

•

1 min read

•

ArXiv

Analysis

This ArXiv paper explores a novel approach to camera-controlled video generation, aiming for improved robustness. The use of infinite-homography is a promising technique that could enhance the fidelity and control of generated videos.

Key Takeaways

•The research focuses on enhancing camera control within video generation models.
•Infinite-homography is the core technical innovation being investigated.
•The paper is likely targeting improvements in realism and controllability of generated video.

Reference

“The source of the article is ArXiv.”

Permalink ArXiv

Research #Vocoder 🔬 ResearchAnalyzed: Jan 10, 2026 10:02

Pseudo-Cepstrum: Advancing Pitch Modification in Neural Vocoders

Published:Dec 18, 2025 13:31

•

1 min read

•

ArXiv

Analysis

This ArXiv paper explores a novel method for pitch modification within the context of Mel-based neural vocoders, a critical area for speech synthesis and audio manipulation. The research likely contributes to more natural and controllable speech generation.

Key Takeaways

•Investigates pitch modification techniques for neural vocoders.
•Applies to Mel-based vocoders, a popular architecture.
•Potentially improves the naturalness and controllability of synthesized speech.

Reference

“The research focuses on pitch modification for Mel-Based Neural Vocoders.”

Permalink ArXiv

Research #Video Gen 🔬 ResearchAnalyzed: Jan 10, 2026 10:06

Decoupling Video Generation: Advancing Text-to-Video Diffusion Models

Published:Dec 18, 2025 10:10

•

1 min read

•

ArXiv

Analysis

This research explores a novel approach to text-to-video generation by separating scene construction and temporal synthesis, potentially improving video quality and consistency. The decoupling strategy could lead to more efficient and controllable video creation processes.

Key Takeaways

•The research focuses on enhancing text-to-video generation.
•The core idea is to decouple scene construction and temporal synthesis.
•This approach aims to improve video quality and controllability.

Reference

“Factorized Video Generation: Decoupling Scene Construction and Temporal Synthesis in Text-to-Video Diffusion Models”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:59

Step-Tagging: Controlling Language Reasoning Models

Published:Dec 16, 2025 12:01

•

1 min read

•

ArXiv

Analysis

The article likely discusses a novel approach to improve the controllability and interpretability of Language Reasoning Models (LRMs). The core idea revolves around 'step monitoring' and 'step-tagging,' suggesting a method to track and potentially influence the reasoning steps taken by the model during generation. This could lead to more reliable and explainable AI systems. The source being ArXiv indicates this is a research paper, likely detailing the methodology, experiments, and results of this new technique.

Key Takeaways

•Focuses on controlling the generation process of Language Reasoning Models.
•Employs 'step monitoring' and 'step-tagging' techniques.
•Aims to improve the reliability and explainability of AI systems.
•Likely a research paper from ArXiv.

Reference

“”

Permalink ArXiv

Research #RL 🔬 ResearchAnalyzed: Jan 10, 2026 11:00

Enhancing AI Alignment: Explainable RL from Human Feedback

Published:Dec 15, 2025 19:18

•

1 min read

•

ArXiv

Analysis

This research explores a crucial area of AI development, focusing on how explainability can improve the alignment of reinforcement learning models with human preferences. The paper's contribution potentially lies in making AI behavior more transparent and controllable.

Key Takeaways

•Focuses on improving AI alignment through explainable reinforcement learning.
•Utilizes human feedback to guide and refine AI behavior.
•Aims to enhance the transparency and controllability of AI systems.

Reference

“Explainable reinforcement learning from human feedback to improve alignment”

Permalink ArXiv

Research #Video Gen 🔬 ResearchAnalyzed: Jan 10, 2026 11:36

AutoMV: Automated Multi-Agent System for Music Video Creation

Published:Dec 13, 2025 05:53

•

1 min read

•

ArXiv

Analysis

The research paper on AutoMV presents a novel approach to automated music video generation using a multi-agent system. This work potentially streamlines creative workflows but its practical impact depends on the quality and controllability of the generated videos.

Key Takeaways

•AutoMV utilizes a multi-agent system, suggesting a distributed approach to video generation.
•The system aims to automate the process of creating music videos.
•The source is a research paper on ArXiv, implying an early-stage exploration.

Reference

“AutoMV is an automatic multi-agent system for music video generation.”

Permalink ArXiv

Research #Generative Models 🔬 ResearchAnalyzed: Jan 10, 2026 11:59

Causal Minimality Offers Greater Control over Generative Models

Published:Dec 11, 2025 14:59

•

1 min read

•

ArXiv

Analysis

This ArXiv paper explores the use of causal minimality to improve the interpretability and controllability of generative models, a critical area in AI safety and robustness. The research potentially offers a path toward understanding and managing the 'black box' nature of these complex systems.

Key Takeaways

•Causal minimality aims to enhance the understanding of how generative models make decisions.
•The research potentially allows for greater control over the outputs generated by these models.
•This work contributes to the ongoing effort to make AI more transparent and reliable.

Reference

“The paper focuses on using Causal Minimality.”

Permalink ArXiv

Research #Diffusion 🔬 ResearchAnalyzed: Jan 10, 2026 12:06

New Method for Improving Diffusion Steering in Generative AI Models

Published:Dec 11, 2025 06:44

•

1 min read

•

ArXiv

Analysis

This ArXiv paper addresses a key issue in diffusion models, proposing a novel criterion and correction method to enhance the stability and effectiveness of steering these models. The research potentially improves the controllability of generative models, leading to more reliable and predictable outputs.

Key Takeaways

•Addresses the 'collapse of generative paths' in diffusion models.
•Proposes a new criterion for assessing path stability.
•Introduces a correction method to improve diffusion steering.

Reference

“The paper focuses on diffusion steering.”

Permalink ArXiv

Research #llm 🔬 ResearchAnalyzed: Jan 4, 2026 07:25

DMP-TTS: Disentangled multi-modal Prompting for Controllable Text-to-Speech with Chained Guidance

Published:Dec 10, 2025 10:28

•

1 min read

•

ArXiv

Analysis

The article introduces DMP-TTS, a new approach for text-to-speech (TTS) that emphasizes control and flexibility. The use of disentangled multi-modal prompting and chained guidance suggests an attempt to improve the controllability of generated speech, potentially allowing for more nuanced and expressive outputs. The focus on 'disentangled' prompting implies an effort to isolate and control different aspects of speech generation (e.g., prosody, emotion, speaker identity).

Key Takeaways

•DMP-TTS is a new TTS approach.
•It uses disentangled multi-modal prompting.
•It incorporates chained guidance for control.

Reference

“”

Permalink ArXiv

Research #Image Generation 🔬 ResearchAnalyzed: Jan 10, 2026 12:39

OpenSubject: Advancing Subject-Driven Image Generation with Video-Derived Priors

Published:Dec 9, 2025 06:49

•

1 min read

•

ArXiv

Analysis

The research paper explores a novel approach to subject-driven image generation by leveraging video-derived identity and diversity priors. This method could significantly improve the realism and controllability of image manipulation tasks by enhancing understanding of the subject's visual characteristics.

Key Takeaways

•Leverages video data to derive identity and diversity priors.
•Aims to improve subject-driven image generation and manipulation.
•Potentially enhances realism and controllability in image editing.

Reference

“The research focuses on using video data to inform image generation and manipulation.”

Permalink ArXiv

Research #Summarization 🔬 ResearchAnalyzed: Jan 10, 2026 13:23

PERCS: Persona-Guided Controllable Biomedical Summarization Dataset

Published:Dec 3, 2025 01:13

•

1 min read

•

ArXiv

Analysis

The paper introduces PERCS, a novel dataset designed to improve the controllability of biomedical summarization, which is a significant contribution to the field of AI and natural language processing. The focus on persona-guided summarization addresses a crucial need for generating summaries tailored to different audiences and purposes.

Key Takeaways

•PERCS is a dataset for persona-guided biomedical summarization.
•The dataset likely enables generation of summaries tailored to specific audiences.
•This work contributes to controllable AI and NLP.

Reference

“The dataset is related to biomedical summarization.”

Permalink ArXiv

Research #LLM Alignment 🔬 ResearchAnalyzed: Jan 10, 2026 14:47

W2S-AlignTree: Enhancing LLM Alignment with Monte Carlo Tree Search at Inference Time

Published:Nov 14, 2025 17:42

•

1 min read

•

ArXiv

Analysis

The research introduces W2S-AlignTree, a novel method for improving the alignment of Large Language Models (LLMs) during inference. This approach leverages Monte Carlo Tree Search to guide the alignment process, potentially leading to more reliable and controllable LLM outputs.

Key Takeaways

•W2S-AlignTree aims to improve LLM alignment during the inference phase.
•The method employs Monte Carlo Tree Search for guiding the alignment process.
•This approach seeks to enhance the reliability and controllability of LLM outputs.

Reference

“W2S-AlignTree uses Monte Carlo Tree Search for inference-time alignment.”

Permalink ArXiv

Technology #AI in Finance 📝 BlogAnalyzed: Dec 29, 2025 07:43

Scaling BERT and GPT for Financial Services with Jennifer Glore - #561

Published:Feb 28, 2022 16:55

•

1 min read

•

Practical AI

Analysis

This podcast episode from Practical AI features Jennifer Glore, VP of customer engineering at SambaNova Systems. The discussion centers on SambaNova's development of a GPT language model tailored for the financial services industry. The conversation covers the progress of financial institutions in adopting transformer models, highlighting successes and challenges. The episode also delves into SambaNova's experience replicating the GPT-3 paper, addressing issues like predictability, controllability, and governance. The focus is on the practical application of large language models (LLMs) in a specific industry and the hardware infrastructure that supports them.

Key Takeaways

•SambaNova is building hardware to support machine learning applications, specifically for the financial services industry.
•The episode discusses the challenges and successes of using transformer models in banking and finance.
•The conversation explores the practical aspects of replicating and deploying large language models like GPT-3.

Reference

“Jennifer shares her thoughts on the progress of industries like banking and finance, as well as other traditional organizations, in their attempts at using transformers and other models, and where they’ve begun to see success, as well as some of the hidden challenges that orgs run into that impede their progress.”

Permalink Practical AI

EchoFoley: Event-Centric Sound Generation for Videos

Analysis

Key Takeaways

Time-Optimal Dicke-State Generation with Limited Control

Analysis

Key Takeaways

DiffThinker: Generative Multimodal Reasoning with Diffusion Models

Analysis

Key Takeaways

Small-time Global Controllability of Fourth-Order Parabolic Equations

Analysis

Key Takeaways

Pose-Guided Residual Refinement for Text-to-Motion Generation

Analysis

Key Takeaways

SymDrive: Advancing Realistic Driving Simulation with Symmetric Auto-regressive Online Restoration

Analysis

Key Takeaways

Integrating Latent Priors with Diffusion Models: Residual Prior Diffusion Framework

Analysis

Key Takeaways

ACD: New Method for Directing Video Diffusion Models

Analysis

Key Takeaways

Controllability Analysis of Elastic Networks

Analysis

Key Takeaways

LouvreSAE: Advancing Style Transfer with Sparse Autoencoders

Analysis

Key Takeaways

EcoSplat: Novel Approach to Controllable 3D Gaussian Splatting from Images

Analysis

Key Takeaways

Improving Image Generation: A Dual Approach to Encoder Optimization

Analysis

Key Takeaways

Reasoning Palette: Modulating Reasoning via Latent Contextualization for Controllable Exploration for (V)LMs

Analysis

Key Takeaways

Robust Camera Control for Video Generation Using Infinite-Homography

Analysis

Key Takeaways

Pseudo-Cepstrum: Advancing Pitch Modification in Neural Vocoders

Analysis

Key Takeaways

Decoupling Video Generation: Advancing Text-to-Video Diffusion Models

Analysis

Key Takeaways

Step-Tagging: Controlling Language Reasoning Models

Analysis

Key Takeaways

Enhancing AI Alignment: Explainable RL from Human Feedback

Analysis

Key Takeaways

AutoMV: Automated Multi-Agent System for Music Video Creation

Analysis

Key Takeaways

Causal Minimality Offers Greater Control over Generative Models

Analysis

Key Takeaways

New Method for Improving Diffusion Steering in Generative AI Models

Analysis

Key Takeaways

DMP-TTS: Disentangled multi-modal Prompting for Controllable Text-to-Speech with Chained Guidance

Analysis

Key Takeaways

OpenSubject: Advancing Subject-Driven Image Generation with Video-Derived Priors

Analysis

Key Takeaways

PERCS: Persona-Guided Controllable Biomedical Summarization Dataset

Analysis

Key Takeaways

W2S-AlignTree: Enhancing LLM Alignment with Monte Carlo Tree Search at Inference Time

Analysis

Key Takeaways

Scaling BERT and GPT for Financial Services with Jennifer Glore - #561

Analysis

Key Takeaways

📬 Get AI News Delivered

Browse by Category