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3yr ROI

10-20x

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Talent Scout

T

Takyoung Kim

University of Illinois Urbana-Champaign

J

Jinseok Nam

Amazon

C

Chandrayee Basu

Amazon

X

Xing Fan

Amazon

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References (53)

[1]
User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
2025Yuhan Liu, Michael J.Q. Zhang et al.
[2]
Self-Challenging Language Model Agents
2025Yifei Zhou, Sergey Levine et al.
[3]
LLMs Get Lost In Multi-Turn Conversation
2025Philippe Laban, Hiroaki Hayashi et al.
[4]
LongFuncEval: Measuring the effectiveness of long context models for function calling
2025Kiran Kate, Tejaswini Pedapati et al.
[5]
Evaluating LLM-based Agents for Multi-Turn Conversations: A Survey
2025Shengyue Guan, Jindong Wang et al.
[6]
ChatBench: From Static Benchmarks to Human-AI Evaluation
2025Serina Chang, Ashton Anderson et al.
[7]
Survey on Evaluation of LLM-based Agents
2025Asaf Yehudai, Lilach Eden et al.
[8]
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
2025Qiusi Zhan, Richard Fang et al.
[9]
Multi-turn Evaluation of Anthropomorphic Behaviours in Large Language Models
2025Lujain Ibrahim, Canfer Akbulut et al.
[10]
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
2025Adam Suma, Samuel Dauncey
[11]
"Stupid robot, I want to speak to a human!" User Frustration Detection in Task-Oriented Dialog Systems
2024Mireia Hernandez Caralt, Ivan Sekuli'c et al.
[12]
Defense Against Prompt Injection Attack by Leveraging Attack Techniques
2024Yulin Chen, Haoran Li et al.
[13]
τ-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
2024Shunyu Yao, Noah Shinn et al.
[14]
Red Teaming Language Models for Processing Contradictory Dialogues
2024Xiaofei Wen, Bangzheng Li et al.
[15]
The Instruction Hierarchy: Training LLMs to Prioritize Privileged Instructions
2024Eric Wallace, Kai Xiao et al.
[16]
Generating Hard-Negative Out-of-Scope Data with ChatGPT for Intent Classification
2024Zhijian Li, Stefan Larson et al.
[17]
InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents
2024Qiusi Zhan, Zhixiang Liang et al.
[18]
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
2024Jianhao Yan, Yun Luo et al.
[19]
Inconsistent dialogue responses and how to recover from them
2024Mian Zhang, Lifeng Jin et al.
[20]
The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance
2024A. Salinas, Fred Morstatter

Showing 20 of 53 references

Founder's Pitch

"REIN offers a tool for conversational agents to recover from errors in real-time by injecting reasoning steps without altering model parameters."

Conversational AIScore: 7View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

As conversational agents become widely used, the ability to handle unexpected errors and ambiguous user interactions efficiently is crucial for maintaining engagement and effectiveness.

Product Angle

Turn REIN into a SaaS product that offers real-time conversational error recovery as a service, which development teams can license to improve their AI-based systems.

Disruption

REIN could replace existing expensive and time-consuming solutions like extensive retraining or frequent system prompt adjustments, saving operational costs and speeding up deployment.

Product Opportunity

The increasing deployment of conversational AI across industries, such as customer service and virtual assistants, creates a large market opportunity for tools that enhance dialogue quality and reduce failure rates.

Use Case Idea

Develop an API product that allows developers to integrate REIN as a plug-and-play error recovery tool into existing conversational AI systems, enhancing their robustness against user-induced errors.

Science

The paper proposes 'Reasoning Inception' (REIN), a method allowing conversational agents to recover from errors in real-time by injecting external reasoning steps. This is achieved by using an inception module that detects dialogue errors and provides corrective reasoning without altering the core model parameters or prompts.

Method & Eval

The method was evaluated by simulating common conversational errors and testing if the REIN approach could correctly recover the dialogue, demonstrating improved task success over baseline methods.

Caveats

The approach heavily relies on correctly defining error types and recovery plans. Inaccurate error diagnosis could lead to inappropriate interventions or no intervention at all.

Author Intelligence

Takyoung Kim

University of Illinois Urbana-Champaign
tk30@illinois.edu

Jinseok Nam

Amazon

Chandrayee Basu

Amazon

Xing Fan

Amazon

Chengyuan Ma

Amazon

Heng Ji

University of Illinois Urbana-Champaign

Gokhan Tur

University of Illinois Urbana-Champaign

Dilek Hakkani-Tür

University of Illinois Urbana-Champaign