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Talent Scout

S

Soumya Suvra Ghosal

University of Maryland, College Park

S

Souradip Chakraborty

University of Maryland, College Park

V

Vaibhav Singh

IIT Bombay

F

Furong Huang

University of Maryland, College Park

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References (69)

[1]
Qwen3Guard Technical Report
2025Hai Zhao, Chenhan Yuan et al.
[2]
Bounded Rationality for LLMs: Satisficing Alignment at Inference-Time
2025M. Chehade, Soumya Suvra Ghosal et al.
[3]
SAFEPATH: Preventing Harmful Reasoning in Chain-of-Thought via Early Alignment
2025Wonje Jeung, Sangyeon Yoon et al.
[4]
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model
2025Xinyue Lou, You Li et al.
[5]
SFT or RL? An Early Investigation into Training R1-Like Reasoning Large Vision-Language Models
2025Guiming Hardy Chen, Haoqin Tu et al.
[6]
Kimi-VL Technical Report
2025Kimi Team Angang Du, Bohong Yin et al.
[7]
SafeMLRM: Demystifying Safety in Multi-modal Large Reasoning Models
2025Junfeng Fang, Yukai Wang et al.
[8]
R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization
2025Yi Yang, Xiaoxuan He et al.
[9]
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RL
2025Yi Peng, Gongrui Zhang et al.
[10]
Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models
2025Wenxuan Huang, Bohan Jia et al.
[11]
Safety Tax: Safety Alignment Makes Your Large Reasoning Models Less Reasonable
2025Tiansheng Huang, Sihao Hu et al.
[12]
The Hidden Risks of Large Reasoning Models: A Safety Assessment of R1
2025KAI-QING Zhou, Chengzhi Liu et al.
[13]
SafeChain: Safety of Language Models with Long Chain-of-Thought Reasoning Capabilities
2025Fengqing Jiang, Zhangchen Xu et al.
[14]
Safety Reasoning with Guidelines
2025Haoyu Wang, Zeyu Qin et al.
[15]
Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies
2025Manojkumar Parmar, Yuvaraj Govindarajulu
[16]
LlamaV-o1: Rethinking Step-by-step Visual Reasoning in LLMs
2025Omkar Thawakar, Dinura Dissanayake et al.
[17]
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2025Adam Suma, Samuel Dauncey
[18]
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[19]
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[20]
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Showing 20 of 69 references

Founder's Pitch

"SafeThink provides a lightweight, inference-time defense for reasoning models, reducing safety risks without sacrificing performance."

AI SafetyScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

3/4 signals

7.5

Quick Build

4/4 signals

10

Series A Potential

3/4 signals

7.5

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Why It Matters

This research is crucial as it addresses the safety risks posed by advanced reasoning models, particularly when they are susceptible to jailbreak attacks, by offering a non-invasive solution that doesn't compromise their reasoning abilities.

Product Angle

Productize as a plugin for AI developers to enhance the safety of AI reasoning models under adversarial conditions, offering a competitive edge in safety-conscious markets.

Disruption

The solution could replace existing, more computationally and resource-intensive methods of ensuring safety in AI models by offering a simpler, more effective alternative.

Product Opportunity

There is a growing need for AI safety solutions, especially in sectors like customer service and content generation, where the implications of unsafe output can be significant. Companies developing AI chatbots or assistants are potential customers.

Use Case Idea

An API or tool for AI application developers to integrate into chatbots or virtual assistants to monitor and ensure the safety of generated content during interactions.

Science

The paper introduces SafeThink, a method that uses a safety reward model to monitor reasoning steps in models. When a safety breach is detected, an optimized prefix or steering token is injected into the reason chain to redirect it, allowing for the preservation of both safety and reasoning efficacy.

Method & Eval

SafeThink was tested on six multimodal large reasoning models and evaluated using four jailbreak benchmarks. It successfully reduced attack success rates significantly while maintaining baseline reasoning performance.

Caveats

The approach may not fully account for unforeseen types of adversarial attacks or scenarios that were not included in the evaluation benchmarks. Scalability across various models and contexts might also present challenges.

Author Intelligence

Soumya Suvra Ghosal

University of Maryland, College Park

Souradip Chakraborty

University of Maryland, College Park

Vaibhav Singh

IIT Bombay

Furong Huang

University of Maryland, College Park

Dinesh Manocha

University of Maryland, College Park

Amrit Singh Bedi

University of Central Florida