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150 个文件 · 5 个目录 · 构建于 2026-08-02 20:41 · 已注入批注层
notes/agenthub
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AgentHub-note-after-format-fix-20260723
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AgentHub-note-before-format-fix-20260723
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AgentHub-note-clean
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notes/tpami-trans
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Wanda-Guided Sparse Perturbations for Efficient Zeroth-Order Optimization
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5.9K
paper/ICLR2026
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AgentFlow：流内 Agentic 系统优化——高效规划与工具调用 — In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
In-The-Flow-Agent
101.3K
AgentGym-RL：通过多轮强化学习训练 LLM agent 完成长程决策任务 — AgentGym-RL: Training LLM Agents for Long-Horizon Decision Making through Multi-Turn Reinforcement Learning
AgentGym-RL
100.5K
AstaBench：面向科学研究任务的严格 AI Agent 评测套件 — AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite
AstaBench
86.2K
AutoEP：大语言模型驱动的元启发式算法超参数自动演化框架 — AutoEP: LLMs-Driven Automation of Hyperparameter Evolution for Metaheuristic Algorithms
AutoEP
89.4K
BIRD-INTERACT：从动态交互视角重新审视 Text-to-SQL 评测 — BIRD-INTERACT: Re-imagining Text-to-SQL Evaluation via Lens of Dynamic Interactions
BIRD-INTERACT
82.1K
cadrille：基于在线强化学习的多模态 CAD 重建 — cadrille: Multi-modal CAD Reconstruction with Online Reinforcement Learning
cadrille
95.4K
Common Corpus：用于大语言模型预训练的最大合规数据集 — Common Corpus: The Largest Collection of Ethical Data for LLM Pre-Training
Common-Corpus
79.6K
CyberGym：大规模评估 AI Agent 的真实网络安全能力 — CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale
CyberGym
87.0K
Depth Anything 3：从任意视角恢复视觉空间 — Depth Anything 3: Recovering the Visual Space from Any Views
Depth-Anything-3
98.8K
DepthLM：用视觉语言模型实现度量深度估计 — DepthLM: Metric Depth From Vision Language Models
DepthLM
79.5K
DiffusionNFT：基于正向过程的在线扩散强化学习 — DiffusionNFT: Online Diffusion Reinforcement with Forward Process
DiffusionNFT
91.7K
EDIT-Bench：评估 LLM 执行真实世界代码编辑指令的能力 — EDIT-Bench: Evaluating LLM Abilities to Perform Real-World Instructed Code Edits
EditBench
81.5K
EmotionThinker：面向可解释语音情感推理的韵律感知强化学习 — EmotionThinker: Prosody-Aware Reinforcement Learning for Explainable Speech Emotion Reasoning
EmotionThinker
78.4K
Gaia2：在动态与异步环境中评测大语言模型智能体 — Gaia2: Benchmarking LLM Agents on Dynamic and Asynchronous Environments
Gaia2
95.7K
GEPA：反思式 Prompt 进化可以超越强化学习 — GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
GEPA
91.9K
HATSolver：用分层注意力 Transformer 学习 Gröbner 基 — HATSolver: Learning Gröbner Bases with Hierarchical Attention Transformers
HATSolver
86.8K
Hubble：推进 LLM 记忆研究的模型套件 — Hubble: a Model Suite to Advance the Study of LLM Memorization
Hubble
87.3K
Huxley-Gödel Machine：通过近似最优自我改进机器实现人类水平的编程智能体开发 — Huxley-Gödel Machine: Human-Level Coding Agent Development by an Approximation of the Optimal Self-Improving Machine
Huxley-Godel-Machine
87.9K
LongWriter-Zero：通过强化学习掌握超长文本生成 — LongWriter-Zero: Mastering Ultra-Long Text Generation via Reinforcement Learning
LongWriter-Zero
90.9K
LoongRL：面向长上下文高级推理的强化学习 — LoongRL: Reinforcement Learning for Advanced Reasoning over Long Contexts
LoongRL
99.9K
Mamba-3：基于状态空间原理的改进序列建模 — Mamba-3: Improved Sequence Modeling using State Space Principles
Mamba-3
85.4K
MC-Search：通过结构化长推理链评估与增强多模态主动搜索 — MC-Search: Evaluating and Enhancing Multimodal Agentic Search with Structured Long Reasoning Chains
MC-Search
91.4K
MedAgentGym：大规模训练 LLM agent 完成基于代码的医疗推理 — MedAgentGym: Training LLM Agents for Code-Based Medical Reasoning at Scale
MedAgentGym
99.8K
MemAgent：基于多会话强化学习的记忆智能体重塑长上下文LLM — MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
MemAgent
103.2K
MomaGraph：面向具身任务规划的状态感知统一场景图与视觉-语言模型 — MomaGraph: State-Aware Unified Scene Graphs with Vision-Language Models for Embodied Task Planning
MomaGraph
96.8K
MrRoPE：混合进制旋转位置编码 — MrRoPE: Mixed-radix Rotary Position Embedding
MrRoPE
87.1K
NextStep-1：面向大规模连续 Token 的自回归图像生成 — NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale
NextStep-1
79.4K
Omni-Reward：面向自由形式偏好的通用全模态奖励建模 — Omni-Reward: Towards Generalist Omni-Modal Reward Modeling with Free-Form Preferences
Omni-Reward
91.4K
OpenApps：通过模拟环境变体来衡量 UI Agent 可靠性 — OpenApps: Simulating Environment Variations to Measure UI Agent Reliability
OpenApps
87.0K
OpenThoughts：推理模型的数据配方 — OpenThoughts: Data Recipes for Reasoning Models
OpenThoughts
79.4K
P-GenRM：具备测试时用户级缩放能力的个性化生成式奖励模型 — P-GenRM: Personalized Generative Reward Model with Test-time User-based Scaling
P-GenRM
93.6K
ParaRNN：为大语言模型解锁非线性RNN的并行训练 — ParaRNN: Unlocking Parallel Training of Nonlinear RNNs for Large Language Models
ParaRNN
82.6K
PateGAIL++：基于模仿学习的效用优化隐私轨迹生成 — PateGAIL++: Utility Optimized Private Trajectory Generation with Imitation Learning
PateGAIL-Plus-Plus
96.8K
RAIN-Merging：一种无梯度方法，在保留思维格式的同时增强大型推理模型的指令遵循能力 — RAIN-Merging: A Gradient-Free Method to Enhance Instruction Following in Large Reasoning Models with Preserved Thinking Format
RAIN-Merging
82.2K
RedTeamCUA：混合 Web-OS 环境下计算机使用 Agent 的真实对抗性测试 — RedTeamCUA: Realistic Adversarial Testing of Computer-Use Agents in Hybrid Web-OS Environments
RedTeamCUA
89.4K
SafeDPO：一种融合安全约束的直接偏好优化简洁方案 — SafeDPO: A Simple Approach to Direct Preference Optimization with Enhanced Safety
SafeDPO
92.7K
ScaleCUA：通过跨平台数据扩展开源计算机操作智能体 — ScaleCUA: Scaling Open-Source Computer Use Agents with Cross-Platform Data
ScaleCUA
98.0K
SimuHome：面向智能家居LLM智能体的时序与环境感知基准 — SimuHome: A Temporal- and Environment-Aware Benchmark for Smart Home LLM Agents
SimuHome
89.9K
SwingArena：面向长上下文 GitHub Issue 解决的对抗式编程竞技场 — SwingArena: Adversarial Programming Arena for Long-context GitHub Issue Solving
SWINGARENA
77.8K
TD-JEPA：用于零样本强化学习的潜空间预测表示 — TD-JEPA: Latent-predictive Representations for Zero-Shot Reinforcement Learning
TD-JEPA
95.4K
ThinKV：面向高效推理模型的思维自适应 KV 缓存压缩 — ThinKV: Thought-Adaptive KV Cache Compression for Efficient Reasoning Models
ThinKV
83.4K
Triple-BERT：网约车平台的订单分配真的需要多智能体强化学习吗？ — Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?
Triple-BERT
98.1K
TROLL：信任域提升大语言模型强化学习 — TROLL: Trust Regions Improve Reinforcement Learning for Large Language Models
TROLL
96.8K
WebDevJudge：评估（多模态）大语言模型作为网页开发质量评判者的能力 — WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality
WebDevJudge
87.6K
WoW: 闭环世界中的世界模型 — World-in-World: World Models in a Closed-Loop World
WoW-World-Models
89.1K
WSM：通过 Checkpoint 合并实现无衰减学习率调度的大模型预训练 — WSM: Decay-Free Learning Rate Schedule via Checkpoint Merging for LLM Pre-training
WSM
85.9K
为何 DPO 是一个错误设定的估计量，以及如何修正它 — Why DPO is a Misspecified Estimator and How to Fix It
DPO-Misspecified
87.2K
先学会「看」：揭开大语言模型视觉先验的语言起源 — Learning to See Before Seeing: Demystifying LLM Visual Priors from Language Pre-training
Visual-Priors
91.2K
先射击后提问？构建像人类一样探索和行动的理性智能体 — Shoot First, Ask Questions Later? Building Rational Agents that Explore and Act Like People
Shoot-First-Ask-Later
90.3K
先训再测统一了语言模型的排名 — Train-before-Test Harmonizes Language Model Rankings
Train-before-Test
84.4K
减少 LLM Agent 主动推理强化学习中的信念偏差 — Reducing Belief Deviation in Reinforcement Learning for Active Reasoning of LLM Agents
Belief-Deviation-RL
100.3K
可靠的弱模型监督强智能体 — Reliable Weak-to-Strong Monitoring of LLM Agents
Weak-to-Strong-Monitoring
109.7K
基于Token重要性引导的直接偏好优化 — Token-Importance Guided Direct Preference Optimization
Token-Importance-DPO
83.0K
基于离线奖励评估与策略搜索的生成式自动出价增强方法 — Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Auto-Bidding-RL
100.5K
基于解耦奖励与课程数据调度的过度思考抑制 — Overthinking Reduction with Decoupled Rewards and Curriculum Data Scheduling
Overthinking-Reduction
80.4K
多玩家 Nash 偏好优化 — Multiplayer Nash Preference Optimization
Multiplayer-Nash-PO
94.4K
大语言模型强化学习计算扩展之道 — The Art of Scaling Reinforcement Learning Compute for LLMs
Art-of-RL-Scaling
97.2K
学习率衰减如何浪费课程预训练中最优质的数据 — How Learning Rate Decay Wastes Your Best Data in Curriculum-Based LLM Pretraining
LR-Decay-Curriculum
91.2K
对比视角下的自我改进：视觉语言模型的视觉推理增强 — Through the Lens of Contrast: Self-Improving Visual Reasoning in VLMs
Visual-Reasoning-Contrast
85.9K
带引导搜索的组合扩散模型用于长时程规划 — Compositional Diffusion with Guided Search for Long-Horizon Planning
Compositional-Diffusion-Planning
97.0K
带瞬时速度约束的均值流策略：单步动作生成 — Mean Flow Policy with Instantaneous Velocity Constraint for One-step Action Generation
Mean-Flow-Policy
92.6K
引导羊群：基于 LLM 的社会学习控制框架 — Steering the Herd: A Framework for LLM-based Control of Social Learning
Steering-the-Herd
74.9K
我的人类反馈里有什么？从偏好数据中学习可解释的描述 — What's In My Human Feedback? Learning Interpretable Descriptions of Preference Data
Whats-In-My-HF
104.3K
扩散语言模型在解码结束前就已知晓答案 — Diffusion Language Models Know the Answer Before Decoding
Diffusion-Knows-Answer
86.2K
投机动作：一个无损的智能体系统加速框架 — Speculative Actions: A Lossless Framework for Faster Agentic Systems
Speculative-Actions
98.4K
掩码扩散语言模型的推理能力研究 — On the Reasoning Abilities of Masked Diffusion Language Models
Masked-Diffusion-Reasoning
71.8K
无限算力下的预训练 — Pre-training under infinite compute
Infinite-Compute
83.7K
是在推理还是在作弊？通过衡量推理努力程度来检测隐式奖励欺骗 — Is it Thinking or Cheating? Detecting Implicit Reward Hacking by Measuring Reasoning Effort
Thinking-or-Cheating
83.1K
智能体数据协议：统一数据集以实现多样有效的LLM智能体微调 — Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agents
Agent-Data-Protocol
83.2K
有限反馈下的半监督偏好优化 — Semi-Supervised Preference Optimization with Limited Feedback
Semi-Supervised-PO
99.2K
混合专家模型能否在严格等量资源下超越稠密大模型？ — Can Mixture-of-Experts Surpass Dense LLMs Under Strictly Equal Resources?
MoE-Surpass-Dense
80.9K
混合专家语言模型在推理任务上的最优稀疏度 — Optimal Sparsity of Mixture-of-Experts Language Models for Reasoning Tasks
Optimal-MoE-Reasoning
91.8K
潜在语音-文本变换器 — Latent Speech-Text Transformer
Latent-Speech-Text
87.7K
潜空间傅里叶变换 — Latent Fourier Transform
Latent-Fourier
83.1K
潜粒子世界模型：自监督以对象为中心的随机动力学建模 — Latent Particle World Models: Self-supervised Object-centric Stochastic Dynamics Modeling
Latent-Particle-WM
95.1K
生成式通用验证器作为多模态元推理者 — Generative Universal Verifier as Multimodal Meta-Reasoner
Universal-Verifier
98.6K
用于无监督强化学习的探索扩散模型 — Exploratory Diffusion Model for Unsupervised Reinforcement Learning
Exploratory-Diffusion-RL
99.8K
能量基变换器：可扩展的学习者与思考者 — Energy-Based Transformers are Scalable Learners and Thinkers
Energy-Based-Transformers
98.3K
行为基础模型的乐观任务推断 — Optimistic Task Inference for Behavior Foundation Models
Optimistic-BFM
93.5K
覆盖原理：预训练如何赋能后训练 — The Coverage Principle: How Pre-Training Enables Post-Training
Coverage-Principle
80.3K
视觉规划：仅用图像来思考 — Visual Planning: Let's Think Only with Images
Visual-Planning
86.4K
通过计算图验证思维链推理 — Verifying Chain-of-Thought Reasoning via Its Computational Graph
Verifying-CoT
91.1K
通过辅助损失耦合混合专家中的专家与路由器 — Coupling Experts and Routers in Mixture-of-Experts via an Auxiliary Loss
Coupling-MoE
82.5K
通过预训练模型与深度强化学习掌握稀疏 CUDA 代码生成 — Mastering Sparse CUDA Generation through Pretrained Models and Deep Reinforcement Learning
Sparse-CUDA-RL
80.2K
采样即推理：基础模型比你想象的更聪明 — Reasoning with Sampling: Your Base Model is Smarter Than You Think
Reasoning-with-Sampling
96.1K
面向 Lean Hammer 的前提选择 — Premise Selection for a Lean Hammer
Premise-Selection
90.0K
paper/SPC
22
EARTH：融合流行病学机制的连续疾病传播图神经常微分方程框架 — EARTH: Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph
EARTH
92.0K
ganf
GANF
8.0M
GRAM：基于图的医疗表示学习注意力模型 — GRAM: Graph-based Attention Model for Healthcare Representation Learning
GRAM
84.5K
GRAND：图神经扩散 — GRAND: Graph Neural Diffusion
GRAND
77.2K
MIMIC-Sepsis：面向ICU脓毒症轨迹建模与学习的精选基准 — MIMIC-Sepsis: A Curated Benchmark for Modeling and Learning from Sepsis Trajectories in the ICU
MIMIC-Sepsis
86.1K
mtad-gat
MTAD-GAT
2.3M
VAE-IF：基于深度特征提取与均值化的无监督ICU时序伪迹检测 — VAE-IF: Deep feature extraction with averaging for unsupervised artifact detection in routine acquired ICU time-series
VAE-IF
86.3K
关系归纳偏置、深度学习与图网络 — Relational Inductive Biases, Deep Learning, and Graph Networks
Relational-Inductive-Bias
90.5K
图增强归一化流用于多时间序列异常检测 — Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
GANF
87.4K
图神经网络的瓶颈问题及其实际影响 — On the Bottleneck of Graph Neural Networks and its Practical Implications
GNN-Bottleneck
88.8K
基于MIMIC-IV的不规则稀疏临床时间序列数据集基准测试 — Benchmarking with MIMIC-IV, an Irregular, Sparse Clinical Time Series Dataset
MIMIC-IV-Benchmark
82.7K
基于图注意力网络的多变量时间序列异常检测（MTAD-GAT） — Multivariate Time-series Anomaly Detection via Graph Attention Network
MTAD-GAT
85.7K
基于多任务异质图学习的电子健康记录分析 — Multi-task Heterogeneous Graph Learning on Electronic Health Records
Hetero-EHR-GNN
104.6K
基于时间序列基础模型的自适应共形异常检测与信号监控 — Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring
Conformal-AD-FM
88.8K
基于消失相关性的时序与动态分析的因果异常排序 — Ranking Causal Anomalies via Temporal and Dynamical Analysis on Vanishing Correlations
Cheng-CausalAnomalies
84.6K
多变量时间序列异常检测：花哨的算法与有缺陷的评估方法 — Multivariate Time Series Anomaly Detection: Fancy Algorithms and Flawed Evaluation Methodology
Flawed-Eval-TSAD
85.7K
异构图 Transformer — Heterogeneous Graph Transformer
HGT
85.3K
用于稀疏、不规则采样多变量临床时序的自监督 Transformer（STraTS） — Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series
STraTS
90.1K
用图卷积Transformer学习电子健康记录的图结构 — Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
GCT
101.3K
立场论文：无监督时间序列异常检测，路在何方？ — Position Paper: Quo Vadis, Unsupervised Time Series Anomaly Detection?
QuoVadis-TSAD
78.8K
走向时间序列异常检测的严格评估 — Towards a Rigorous Evaluation of Time-Series Anomaly Detection
Rigorous-TSAD-Eval
83.0K
高维均值向量与协方差矩阵显著性检验二十年：naive test 综述 — A review of 20 years of naive tests of significance for high-dimensional mean vectors and covariance matrices
HighDim-Hotelling-Review
80.1K
paper/异常检测
27
AAD-LLM：基于大语言模型的自适应异常检测 — AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
AAD-LLM
86.7K
AD-LLM：大语言模型异常检测基准测试 — AD-LLM: Benchmarking Large Language Models for Anomaly Detection
AD-LLM
70.0K
Argos：基于大语言模型自主规则生成的 Agent 时间序列异常检测系统 — Argos: Agentic Time-Series Anomaly Detection with Autonomous Rule Generation via Large Language Models
Argos
89.8K
AutoML-Agent：面向全流程 AutoML 的多智能体 LLM 框架 — AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML
AutoML-Agent
89.3K
CHiLL：用大语言模型从临床笔记中零样本提取可定制可解释特征 — CHiLL: Zero-shot Custom Interpretable Feature Extraction from Clinical Notes with Large Language Models
CHiLL
69.7K
LLM Agent 在哪里失败，以及如何从失败中学习 — Where LLM Agents Fail and How They can Learn From Failures
LLM-Agents-Fail
81.9K
LLM-TSFD：基于大语言模型的工业时序人在回路故障诊断方法 — LLM-TSFD: An industrial time series human-in-the-loop fault diagnosis method based on a large language model
LLM-TSFD
104.4K
MOSPAT：基于 AutoML 的时间序列异常检测模型选择与参数调优 — MOSPAT: AutoML based Model Selection and Parameter Tuning for Time Series Anomaly Detection
MOSPAT
89.9K
PyOD 2：一个集成LLM驱动模型选择的Python离群点检测库 — PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
PyOD2
71.1K
TS-Agent：基于迭代证据收集的原始时间序列理解与推理 — TS-Agent: Understanding and Reasoning Over Raw Time Series via Iterative Insight Gathering
TS-Agent
82.1K
Unsupervised Model Selection for Time-series Anomaly Detection (ICLR 2023) - 精读
goswami-iclr2023-unsupervised-tsad-selection
17.6K
人工智能对统计过程监控的影响综述及未来方向 — A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions
AI-SPM-Review
96.8K
从文本时序预测临床风险：为医疗 AI 构建叙事的时序结构 — Forecasting Clinical Risk from Textual Time Series: Structuring Narratives for Temporal AI in Healthcare
TextualTS-ClinicalRisk
75.6K
反馈摩擦：LLM 难以充分吸收外部反馈 — Feedback Friction: LLMs Struggle to Fully Incorporate External Feedback
Feedback-Friction
72.0K
叙事特征还是结构化特征？大型语言模型识别心衰风险癌症患者的研究 — Narrative Feature or Structured Feature? A Study of Large Language Models to Identify Cancer Patients at Risk of Heart Failure
Narrative-HF
73.6K
在混杂医疗干预存在下对基于机器学习的风险预测算法进行监控 — Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions
Monitoring-ML-Confounding
92.7K
基于LLM多智能体框架的金融市场异常检测增强 — Enhancing Anomaly Detection in Financial Markets with an LLM-based Multi-Agent Framework
LLM-MA-FinAD
81.1K
基于图神经网络的多元时序异常检测（GDN） — Graph Neural Network-Based Anomaly Detection in Multivariate Time Series
GDN
91.4K
基于大语言模型框架的故障诊断探索 — Exploring LLM-based Frameworks for Fault Diagnosis
LLM-Fault-Diagnosis
72.7K
多模态深度学习用于ICU患者恶化早期预测：融合时序EHR数据与临床笔记 — Multimodal Deep Learning for Early Prediction of Patient Deterioration in the ICU: Integrating Time-Series EHR Data with Clinical Notes
Multimodal-ICU-Deterioration
79.9K
大语言模型尚不能自我纠正推理 — Large Language Models Cannot Self-Correct Reasoning Yet
LLM-Cannot-SelfCorrect
86.4K
大语言模型真的能理解时序异常吗？ — Can LLMs Understand Time Series Anomalies?
LLM-TS-Anomalies
81.2K
推理陷阱：增强LLM推理能力如何放大工具幻觉 — The Reasoning Trap: How Enhancing LLM Reasoning Amplifies Tool Hallucination
Reasoning-Trap
89.1K
无监督异常检测算法在真实数据上的比较：我们到底需要几个？ — Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need?
AD-Algo-Comparison
73.4K
时间序列异常检测的自动模型选择 — Automated Model Selection for Time-Series Anomaly Detection
AutoMS-TSAD
73.9K
深度图异常检测综述与新视角 — Deep Graph Anomaly Detection: A Survey and New Perspectives
Deep-GAD-Survey
85.8K
用于超参数优化的大语言模型智能体 — Large Language Model Agent for Hyper-Parameter Optimization
LLM-HPO-Agent
74.6K