NTT Research, NTT, Inc. and NTT DATA showcase foundational theory, system-level advances and enterprise-grade innovations across five major themes at premier AI conference

News Highlights:

• NTT Research, NTT, Inc. and NTT DATA deliver fifteen papers at NeurIPS 2025, reflecting their respective attention to foundational research, R&D and solutions.
• The papers explore understanding model behavior and representations, making models efficient and scalable, applying ML to physical sensing and advanced technologies, and securing AI through robust watermarking solutions.
• Takeaways include an activation probe that can reduce compute by six orders of magnitude over standard monitoring; exposure of security vulnerabilities; a proposal to improve standard Sparse Autoencoders (SAEs); and more.

NTT Research, Inc. and NTT R&D, divisions of NTT (TYO:9432), and NTT DATA, Inc. announced that NTT scientists and researchers have contributed to fifteen presentations at this year’s Conference on Neural Information Processing Systems (NeurIPS), a leading machine-learning (ML) and computational neuroscience conference. The eight papers associated with NTT Research mostly address foundational issues. The six papers generated by scientists in various NTT Inc. laboratories focus on system-level and applied-science themes. The paper from NTT DATA highlights the importance of trustworthy AI, directly relevant to enterprise adoption.

One of the three primary annual conferences in ML and AI research, NeurIPS 2025 is taking place Dec. 2-7 at the San Diego Convention Center.

“AI is becoming ubiquitous, but how these computational engines actually work remains—to a surprising degree—a mystery, which is why our scientists keep probing with fundamental questions,” NTT Research Physics of Artificial Intelligence (PAI) Group Director Hidenori Tanaka said. “At the same time, researchers must keep pace with operational challenges. Work in these domains is well represented at NeurIPS by our colleagues at NTT Inc., NTT DATA and collaborating institutions.”

The largest group of NTT-affiliated papers concern understanding and shaping model behavior, focus areas for the PAI Group. At a high level, these five papers address the question of how models think:

• “Kindness or Sycophancy? Understanding and Shaping Model Personality via Synthetic Games.” NTT Research PAI Group and PHI Lab. CogInterp Workshop. Explores how LLM “personalities” such as kindness or sycophancy emerge and evolve when models are trained on synthetic game-like interactions. Introduces controlled environments for probing and shaping personality traits.

• “In-Context Learning Strategies Emerge Rationally.” PAI Group, NTT-Harvard Center for Brain Science (CBS), Princeton. Poster Session. Provides a predictive framework for when models generalize vs. memorize in context. Understanding training dynamics may enable engineering reliable ICL behavior.

• “Inference-time alignment of language models by importance sampling on pre-logit space,” NTT Computer and Data Science Labs. Probabilistic Inference Workshop. Introduces an inference-time alignment method that uses importance sampling over pre-logit activations, allowing models to adopt aligned behaviors without retraining.

• “When Reasoning Meets Its Laws.” NTT Research, UI Urbana-Champlain, U Penn, NYU. Efficient Reasoning Workshop. Provides new theoretical results on how reasoning performance scales with model size and constraints, offering guiding principles for building efficient reasoning systems.

• “Detecting High-Stakes Interactions with Activation Probes.” Poster Session. NTT-CBS, LASR Labs, University College, MILA, Goodfire, University of Cambridge. Explains how lightweight activation probes can cheaply and effectively (with six orders of magnitude less compute compared to standard monitors) detect risky model behavior during deployment.

Also at a foundational level, four other papers explored advances in interpretability (understanding the internal mechanisms of complex models) and representation learning (automatic discovery of useful internal features or representations of data.) The papers include:

• “Neural Thermodynamics: Entropic Forces in Deep and Universal Representation Learning.” NTT Research, EPFL, MIT. Poster Session. Offers a thermodynamic framework for understanding how entropy drives feature formation in deep networks, unifying diverse phenomena in representation learning.

• “From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit.” NTT Research, Harvard, EPFL, U of Alberta. Poster session. Proposes a new Matching Pursuit (MP) Sparse Autoencoder (SAE) to capture complex, hierarchical, nonlinear and multimodal features that standard SAEs miss.

• “Projecting Assumptions: The Duality Between Sparse Autoencoders and Concept Geometry.” Poster session 4. NTT-CBS, Harvard. Because SAE architectures embed implicit geometric assumptions, SAEs not only reveal concepts inside models; they also shape what concepts can be discovered.

• “Gaussian Processes for Shuffled Regression.” NTT Human Informatics Lab. Exhibit Hall Poster. Develops a Gaussian Process method for regression tasks where input-output correspondences are unknown or shuffled, estimating both structure and predictions jointly.

The next five papers reflect NTT Inc.’s focus on applied science. The first two focus on efficient and distributed AI systems, and the next three on sensing, imaging and applied systems:

• “LLM capable of 1-GPU inference: tsuzumi.” NTT Human Informatics Laboratories. NeurIPS talk. Presents a lightweight, high-efficiency LLM that maintains competitive performance while running on a single GPU, enabling broader accessibility and deployment.

• “Revisiting 1-peer exponential graph for enhancing decentralized learning efficiency.” Exhibit Hall Poster. NTT Communications Science Laboratories. Analyzes decentralized optimization using exponential graph structures and shows how 1-peer communication patterns can significantly improve convergence efficiency.

• “Learning Pairwise Potentials via Differentiable Recurrent Dynamics.” ML & Physical Sciences Workshop. NTT Communications Science Labs., Ga Tech. Presents a differentiable recurrent method to learn pairwise potentials in dynamical systems. It bridges physics-based modeling with modern ML optimization for better modeling of physical interactions.

• “Transformer Enabled Dual-Comb Ghost Imaging for Optical Fiber Sensing.” NTT Research PHI Lab, UC Irvine, Apple/Cal Tech, Korea University. Learning to Sense Workshop. Demonstrates how transformers can dramatically improve ghost-imaging reconstructions for fiber-optic sensing systems, enhancing spatial resolution and signal robustness. (More findings are presented in a separate NeurIPS poster.)

• “Enhancing Visual Prompting through Expanded Transformation Space and Overfitting Mitigation.” Exhibit Hall Poster. NTT Software Innovation Program. Proposes an expanded set of geometric and photometric transformations for visual prompting and introduces methods to reduce overfitting, improving robustness and accuracy.

Finally, there is the contribution from NTT DATA and other collaborators, which falls under a heading of security, provenance and trustworthiness, topics of keen concern to the enterprise customers of this division of NTT:

• “Breaking Distortion-free Watermarks in Large Language Models.” NTT DATA, J.P. Morgan, UCLA. Lock-LLM Workshop. By adversarial probing of current watermark approaches, one can recover secret keys and token permutations and generate text that passes watermark detection. This paper is in effect a wake-up call, revealing existing vulnerabilities.

“With the EU AI Act mandating watermarking for all AI-generated content, the topic has become increasingly urgent,” said Shayleen Reynolds, NTT DATA director and AI lead. “This work shows that even state-of-the-art watermarking schemes can be reverse engineered, revealing significant risks for enterprises that depend on watermarking for content provenance, plagiarism detection, copyright protection, and output authenticity. The findings expose a foundational vulnerability in today’s AI trust and traceability mechanisms.”

NeurIPS 2025 marks the 39th year of this event. The multi-track interdisciplinary annual meeting includes invited talks, demonstrations, symposia, and oral and poster presentations of refereed papers. Accompanying the conference is a professional exposition focusing on machine learning in practice, tutorials and topical workshops that provide a less formal setting for the exchange of ideas.

About NTT Research

NTT Research opened its offices in July 2019 in Silicon Valley to conduct basic research and advance technologies as a foundational model for developing high-impact innovation across NTT Group’s global business. Currently, four groups are housed at NTT Research facilities in Sunnyvale: the Physics and Informatics (PHI) Lab, the Cryptography and Information Security (CIS) Lab, the Medical and Health Informatics (MEI) Lab, and the Physics of Artificial Intelligence (PAI) Group. The organization aims to advance science in four areas: 1) quantum information, neuroscience and photonics; 2) cryptographic and information security; 3) medical and health informatics; and 4) artificial intelligence. NTT Research is part of NTT, a global technology and business solutions provider with an annual R&D investment of thirty percent of its profits.

The names NTT and NTT Research, as well as the NTT and NTT Research logos, are trademarks and service marks of NTT, Inc. or NTT Research, Inc., and/or their affiliates. All other referenced product names are trademarks of their respective owners. © 2025 NTT Research, Inc.

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