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Talent Scout

H

Haritz Puerto

Technical University of Darmstadt

H

Haonan Li

Mohamed bin Zayed University of Artificial Intelligence

X

Xudong Han

Mohamed bin Zayed University of Artificial Intelligence

T

Timothy Baldwin

Mohamed bin Zayed University of Artificial Intelligence

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References (29)

[1]
Efficient Multi-Adapter LLM Serving via Cross-Model KV-Cache Reuse with Activated LoRA
2025Alice Li, Kristjan H. Greenewald et al.
[2]
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
2025Yongchan Kwon, Shang Zhu et al.
[3]
From Long to Short: LLMs Excel at Trimming Own Reasoning Chains
2025Wei Han, Geng Zhan et al.
[4]
Evaluating Language Model Reasoning about Confidential Information
2025Dylan Sam, Alexander Robey et al.
[5]
From "Aha Moments" to Controllable Thinking: Toward Meta-Cognitive Reasoning in Large Reasoning Models via Decoupled Reasoning and Control
2025Rui Ha, Chaozhuo Li et al.
[6]
Controlling What You Share: Assessing Language Model Adherence to Privacy Preferences
2025Guillem Ram'irez, Alexandra Birch et al.
[7]
Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers
2025Tommaso Green, Martin Gubri et al.
[8]
When Models Reason in Your Language: Controlling Thinking Language Comes at the Cost of Accuracy
2025Jirui Qi, Shan Chen et al.
[9]
Scaling Reasoning, Losing Control: Evaluating Instruction Following in Large Reasoning Models
2025Ting Fu, Jiawei Gu et al.
[10]
When Thinking Fails: The Pitfalls of Reasoning for Instruction-Following in LLMs
2025Xiaomin Li, Zhou Yu et al.
[11]
Reasoning Models Don't Always Say What They Think
2025Yanda Chen, Joe Benton et al.
[12]
Dynamic Early Exit in Reasoning Models
2025Chenxu Yang, Q. Si et al.
[13]
Effectively Controlling Reasoning Models through Thinking Intervention
2025Tong Wu, Chong Xiang et al.
[14]
Monitoring Reasoning Models for Misbehavior and the Risks of Promoting Obfuscation
2025Bowen Baker, Joost Huizinga et al.
[15]
AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents
2025Arman Zharmagambetov, Chuan Guo et al.
[16]
CoT-Valve: Length-Compressible Chain-of-Thought Tuning
2025Xinyin Ma, Guangnian Wan et al.
[17]
M-IFEval: Multilingual Instruction-Following Evaluation
2025Antoine Dussolle, Andrea Cardena D'iaz et al.
[18]
C3oT: Generating Shorter Chain-of-Thought without Compromising Effectiveness
2024Yu Kang, Xianghui Sun et al.
[19]
PrivacyLens: Evaluating Privacy Norm Awareness of Language Models in Action
2024Yijia Shao, Tianshi Li et al.
[20]
Benchmarking Complex Instruction-Following with Multiple Constraints Composition
2024Bosi Wen, Pei Ke et al.

Showing 20 of 29 references

Founder's Pitch

"Develop privacy-focused reasoning models to protect user data by following controllable instructions."

Privacy-Enhancing AIScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

This research addresses the critical issue of privacy leakage in AI reasoning models by proposing a method to control and limit the exposure of sensitive user information.

Product Angle

This can be productized as a middleware privacy layer for existing AI systems, enhancing their privacy features without compromising performance.

Disruption

It can replace existing AI systems that focus on utility over privacy, offering a competitive edge in privacy assurance.

Product Opportunity

With increasing privacy regulations like GDPR, companies in healthcare, finance, and tech sectors will invest in technology that protects user data. The market is vast as privacy and security remain top concerns globally.

Use Case Idea

A commercial application could be a privacy-compliant AI assistant for sensitive industries like healthcare and finance, ensuring user data is not inadvertently leaked.

Science

The paper presents a novel approach by fine-tuning reasoning models to follow instructions not just in the final output, but throughout the reasoning process. It introduces Staged Decoding, a methodology to separate reasoning and answering stages using LoRA adapters, improving instruction-following behavior and thus enhancing privacy.

Method & Eval

The researchers tested their models on two instruction-following and two privacy benchmarks, demonstrating significant improvements in privacy scores and instruction-following when compared to baseline models.

Caveats

The approach may reduce task utility, and there is a trade-off between increasing privacy and maintaining performance.

Author Intelligence

Haritz Puerto

Technical University of Darmstadt

Haonan Li

Mohamed bin Zayed University of Artificial Intelligence

Xudong Han

Mohamed bin Zayed University of Artificial Intelligence

Timothy Baldwin

Mohamed bin Zayed University of Artificial Intelligence

Iryna Gurevych

Technical University of Darmstadt