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Talent Scout

H

Haozhen Zhang

Nanyang Technological University

H

Haodong Yue

Tsinghua University

T

Tao Feng

University of Illinois Urbana-Champaign

Q

Quanyu Long

Nanyang Technological University

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References (42)

[1]
OpenAI GPT-5 System Card
2025Aaditya K. Singh, Adam Fry et al.
[2]
Memory in the Age of AI Agents
2025Yuyang Hu, Shichun Liu et al.
[3]
General Agentic Memory Via Deep Research
2025B. Y. Yan, Chaofan Li et al.
[4]
Controlling Performance and Budget of a Centralized Multi-agent LLM System with Reinforcement Learning
2025Bowen Jin, TJ Collins et al.
[5]
LightMem: Lightweight and Efficient Memory-Augmented Generation
2025Jizhan Fang, Xinle Deng et al.
[6]
Memory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning
2025Sikuan Yan, Xiufeng Yang et al.
[7]
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
2025Hongli Yu, Tinghong Chen et al.
[8]
Router-R1: Teaching LLMs Multi-Round Routing and Aggregation via Reinforcement Learning
2025Haozhen Zhang, Tao Feng et al.
[9]
Memory OS of AI Agent
2025Jiazheng Kang, Mingming Ji et al.
[10]
Qwen3 Technical Report
2025An Yang, Anfeng Li et al.
[11]
Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory
2025P. Chhikara, Dev Khant et al.
[12]
A-MEM: Agentic Memory for LLM Agents
2025Wujiang Xu, Zujie Liang et al.
[13]
LongMemEval: Benchmarking Chat Assistants on Long-Term Interactive Memory
2024Di Wu, Hongwei Wang et al.
[14]
GraphRouter: A Graph-based Router for LLM Selections
2024Tao Feng, Yanzhen Shen et al.
[15]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[16]
SnapKV: LLM Knows What You are Looking for Before Generation
2024Yuhong Li, Yingbing Huang et al.
[17]
Evaluating Very Long-Term Conversational Memory of LLM Agents
2024Adyasha Maharana, Dong-Ho Lee et al.
[18]
A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts
2024Kuang-Huei Lee, Xinyun Chen et al.
[19]
Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
2024Yichao Fu, Peter Bailis et al.
[20]
EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty
2024Yuhui Li, Fangyun Wei et al.

Showing 20 of 42 references

Founder's Pitch

"BudgetMem provides a runtime memory framework for LLMs with query-aware budget-tier routing to optimize performance-cost trade-offs."

AI InfrastructureScore: 8View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

4/4 signals

10

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

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Why It Matters

As memory extraction is a costly process, this research allows more efficient LLM operations by offering fine-tuned performance-cost controls, crucial for real-time applications.

Product Angle

Commercialize BudgetMem as a SaaS platform offering performance-cost optimized memory solutions for LLM applications, focusing initially on customer support and virtual assistant markets.

Disruption

This technology could displace existing monolithic and inefficient LLM memory management solutions, offering tailored and dynamic memory usage strategies instead.

Product Opportunity

The demand for scalable LLMs in various industries is growing, particularly in sectors like customer service and knowledge management where cost efficiency and performance are critical.

Use Case Idea

Integrate BudgetMem into enterprise LLM services that require real-time memory optimization in processing conversations, improving both response time and accuracy.

Science

BudgetMem reimagines agent memory processing into modular pipelines with budget-tiers. Each memory module can operate at different budget levels (LOW/MID/HIGH), and a lightweight neural router decides on the most cost-effective module execution route using reinforcement learning.

Method & Eval

The approach uses a modular memory pipeline and reinforcement learning-based routing to manage budget cost. It surpasses existing models on performance-cost betterment in studies across datasets like LoCoMo and HotpotQA.

Caveats

Benefits depend on precise budget-tier strategy alignment with specific application needs; misalignment could lead to inefficient operations.

Author Intelligence

Haozhen Zhang

Nanyang Technological University
haozhen001@e.ntu.edu.sg

Haodong Yue

Tsinghua University

Tao Feng

University of Illinois Urbana-Champaign

Quanyu Long

Nanyang Technological University

Jianzhu Bao

Nanyang Technological University

Bowen Jin

University of Illinois Urbana-Champaign

Weizhi Zhang

University of Illinois Chicago

Xiao Li

Sun Yat-sen University

Jiaxuan You

University of Illinois Urbana-Champaign

Chengwei Qin

The Hong Kong University of Science and Technology (Guangzhou)
chengweiqin@hkust-gz.edu.cn

Wenya Wang

Nanyang Technological University