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Apr 26, 2026 · Industry Insights

Multimodal Perception and AI Fusion: Highlights from Tsinghua’s 9th Big Data Intelligent Lecture

The 9th Tsinghua Big Data Intelligent Lecture gathered leading scholars and industry experts to showcase cutting‑edge research on multimodal perception, embodied intelligence, spatial AI, large‑model multimodal systems, and industrial time‑series databases, emphasizing their technical depth and real‑world impact.

GLM5V TurboSenseNovaartificial intelligence

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Multimodal Perception and AI Fusion: Highlights from Tsinghua’s 9th Big Data Intelligent Lecture

Machine Heart

Apr 25, 2026 · Artificial Intelligence

ICLR 2026 Award Winners: Two Outstanding Papers and Alec Radford’s Classic Work Honored with Test‑of‑Time Award

The ICLR 2026 conference announced its award winners, highlighting two Outstanding Papers—"Transformers are Inherently Succinct" and "LLMs Get Lost In Multi‑Turn Conversation"—a Honorable Mention, and two Test‑of‑Time awards for the seminal DCGAN and DDPG papers, after receiving about 19,000 submissions with a 28% acceptance rate.

Generative Adversarial NetworksICLR 2026Test of Time

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ICLR 2026 Award Winners: Two Outstanding Papers and Alec Radford’s Classic Work Honored with Test‑of‑Time Award

Bighead's Algorithm Notes

Mar 5, 2026 · Artificial Intelligence

AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

The paper introduces AB‑SSM, an adaptive bidirectional state‑space model that incorporates a time‑varying linear structure and a bidirectional layer to capture market non‑stationarity and asset correlations, and demonstrates through extensive US, China, and crypto experiments that it outperforms traditional, deep‑learning, and DRL baselines in profit‑risk trade‑offs, efficiency, and scalability.

Financial AIadaptive linear time-varyingbidirectional SSM

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AB‑SSM: Adaptive Bidirectional State‑Space Model for High‑Frequency Portfolio Management

Bighead's Algorithm Notes

Feb 6, 2026 · Artificial Intelligence

Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)

This article summarizes recent quantitative‑finance research, presenting abstracts and key findings of three papers—BPASGM for machine‑learning‑driven portfolio construction, PIKAN‑enhanced deep reinforcement learning with physics‑informed regularization, and GAPNet’s dynamic graph‑based stock relation learning—along with links to numerous related studies.

deep reinforcement learninggraph neural networksmachine learning

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Weekly Quantitative Finance Paper Summary (Jan 31–Feb 6 2026)

Network Intelligence Research Center (NIRC)

Feb 3, 2026 · Artificial Intelligence

INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework

The article presents INCS, a novel framework that combines graph neural networks and deep reinforcement learning to achieve protocol‑agnostic, millisecond‑level, globally optimized network configuration synthesis, addressing scalability, protocol dependence, and lack of optimization in traditional SMT‑based methods, and demonstrates its superior performance on large‑scale topologies.

DDPGGraph Neural NetworkNetwork Synthesis

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INCS: A DRL‑Based Intent‑Driven Network‑Wide Configuration Synthesis Framework

Bighead's Algorithm Notes

Dec 21, 2025 · Artificial Intelligence

Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Logic-Q introduces a program‑sketch paradigm that injects lightweight, plug‑and‑play market‑trend logic into deep reinforcement learning agents, dramatically improving trend detection, reducing drawdowns, and outperforming state‑of‑the‑art DRL strategies on multiple quantitative‑trading benchmarks.

Bayesian OptimizationLogic-QMarket Trend Detection

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Logic-Q: Program Sketch Optimization Boosts Deep Reinforcement Learning for Quantitative Trading

Bighead's Algorithm Notes

Nov 22, 2025 · Artificial Intelligence

Quantitative Finance Paper Roundup (Nov 15‑21, 2025)

This roundup presents six recent arXiv papers covering crypto portfolio optimization, Sharpe‑driven stock selection with liquidity constraints, ensemble deep reinforcement learning for stock trading, dynamic machine‑learning‑based stock recommendation, a risk‑sensitive trading framework, and a generative AI model for limit order book messages, each with reported empirical results.

Quantitative Financecryptocurrencydeep reinforcement learning

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Quantitative Finance Paper Roundup (Nov 15‑21, 2025)

SF Technology Team

Nov 10, 2025 · Artificial Intelligence

Deep RL Powers Multi‑Population Evolution for Better Many‑Objective Optimization

This study introduces DQNMaOEA, a deep reinforcement learning‑guided multi‑population coevolutionary algorithm that adaptively selects sub‑populations and allocates computational resources, achieving significantly higher solution quality and up to 25% faster runtimes on benchmark and large‑scale logistics many‑objective problems compared with state‑of‑the‑art methods.

Evolutionary AlgorithmsLogisticsdeep reinforcement learning

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Deep RL Powers Multi‑Population Evolution for Better Many‑Objective Optimization

Network Intelligence Research Center (NIRC)

Mar 18, 2024 · Artificial Intelligence

Deep Reinforcement Learning for Online Resource Allocation in Network Slicing

This article presents a dynamic RAN slicing model and an online PW‑DRL approach that combines deep learning, reinforcement learning, and Lyapunov optimization to allocate resources adaptively, detailing a four‑step decision process, LSTM/CNN predictions, and experimental results showing improved transmission rates and acceptance ratios across DTT, DS, and TO slices.

Lyapunov optimizationNetwork SlicingOFDMA

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Deep Reinforcement Learning for Online Resource Allocation in Network Slicing

Network Intelligence Research Center (NIRC)

Nov 27, 2023 · Artificial Intelligence

How Deep Reinforcement Learning Shapes 15‑Minute City Community Planning

This article explains how a deep reinforcement learning model, built on a graph‑based representation of urban elements and trained with PPO, can automate land‑use and road planning to achieve Service, Ecology, and Traffic objectives for 15‑minute city neighborhoods.

15-minute cityGraph Neural NetworkPPO

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How Deep Reinforcement Learning Shapes 15‑Minute City Community Planning

DataFunSummit

Aug 5, 2023 · Operations

Intelligent Decision-Making in Supply Chains: Conference Overview and Speaker Sessions

The forum gathers leading industry experts and scholars to discuss intelligent decision technologies across manufacturing, retail, energy, and pharmaceutical supply chains, presenting case studies, deep reinforcement learning applications, and optimization methods that drive agile, data‑driven supply chain transformation.

artificial intelligencedeep reinforcement learningindustrial engineering

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Intelligent Decision-Making in Supply Chains: Conference Overview and Speaker Sessions

DataFunTalk

Apr 17, 2023 · Artificial Intelligence

Speculation: GPT-5 May Adopt Model‑Based Deep Reinforcement Learning for Unlimited Self‑Improvement

The article argues that the next generation GPT is likely to employ model‑based deep reinforcement learning, turning the model into both a policy and a world model, which could enable rapid, data‑efficient self‑enhancement but also raise serious safety and societal risks.

AI SafetyGPT-5deep reinforcement learning

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Speculation: GPT-5 May Adopt Model‑Based Deep Reinforcement Learning for Unlimited Self‑Improvement

DaTaobao Tech

Aug 18, 2022 · Artificial Intelligence

Introduction to Deep Reinforcement Learning: Theory, Algorithms, and Applications

This article introduces deep reinforcement learning by explaining its Markov decision process foundations, then categorizes the main algorithm families—value‑based methods like DQN, policy‑based approaches such as PG/DPG/DDPG, and actor‑critic techniques including A3C, PPO, and DDPG—detailing their architectures, training procedures, and key advantages.

DQNMDPactor-critic

0 likes · 14 min read

Introduction to Deep Reinforcement Learning: Theory, Algorithms, and Applications

Tencent Cloud Developer

Apr 8, 2022 · Databases

Tencent Cloud Native Database AI Autonomy: SIGMOD Research and Intelligent Tuning System

Tencent Cloud’s native database team achieved a SIGMOD breakthrough by embedding AI into MySQL, creating an autonomous “database brain” that uses deep‑reinforcement learning, genetic pre‑heating and a closed‑loop learner/actor architecture to automatically observe, analyze, and tune diverse workloads, delivering rapid performance gains, anomaly detection, and self‑optimizing features while addressing adaptability, stability, and interpretability challenges.

AI OptimizationCDBTuneDatabase Autonomy

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Tencent Cloud Native Database AI Autonomy: SIGMOD Research and Intelligent Tuning System

Tencent Tech

Dec 31, 2020 · Artificial Intelligence

How Tencent’s WeKick AI Dominated the Google Football Kaggle Competition

Tencent AI Lab’s WeKick AI clinched the inaugural Google Football Kaggle championship with a score of 1785.8, outpacing over 1,100 research teams by leveraging a migrated full‑stack architecture, custom reinforcement‑learning frameworks, GAIL imitation learning, and a multi‑style League training pipeline that together showcase the power and generality of deep reinforcement learning for complex multi‑agent tasks.

AIGAILGoogle Football

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How Tencent’s WeKick AI Dominated the Google Football Kaggle Competition

Alibaba Cloud Developer

Jun 4, 2020 · Artificial Intelligence

Can Deep Reinforcement Learning Revolutionize Time-Series Data Compression?

This article reviews the challenges of compressing massive time‑series data, surveys existing methods, and introduces a novel two‑stage deep reinforcement learning framework (AMMMO) that adaptively selects compression modes, demonstrating significant compression ratio improvements and high throughput on large‑scale IoT and server workloads.

adaptive algorithmsdata storagedeep reinforcement learning

0 likes · 18 min read

Can Deep Reinforcement Learning Revolutionize Time-Series Data Compression?

Tencent Database Technology

Jul 8, 2019 · Databases

SIGMOD 2019 Highlights: Blockchain Keynote, CDBTune Paper on Cloud Database Tuning, and Emerging Database Research

The report summarizes SIGMOD/PODS 2019 in Amsterdam, covering a blockchain-focused keynote, the CDBTune paper on end‑to‑end automatic cloud database tuning using deep reinforcement learning, modern hardware research, award‑winning systems, and related industry activities, providing a comprehensive academic overview.

Database TuningResearch HighlightsSIGMOD

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SIGMOD 2019 Highlights: Blockchain Keynote, CDBTune Paper on Cloud Database Tuning, and Emerging Database Research

Alibaba Cloud Developer

Aug 29, 2017 · Artificial Intelligence

Can Deep Reinforcement Learning Shrink Packing Costs? A New 3D Bin Packing Study

This paper introduces a novel three‑dimensional bin‑packing problem where the objective is to minimize the surface area of a single flexible container, proves its NP‑hardness, and demonstrates that a deep reinforcement learning approach using a Pointer Network improves packing efficiency by roughly five percent over traditional heuristics on real‑world data.

3D bin packingcombinatorial optimizationdeep reinforcement learning

0 likes · 16 min read

Can Deep Reinforcement Learning Shrink Packing Costs? A New 3D Bin Packing Study

Dada Group Technology

Jun 9, 2017 · Artificial Intelligence

Deep Reinforcement Learning: Concepts, Black‑Box Optimization, and the Cross‑Entropy Method

This article introduces reinforcement learning and deep reinforcement learning, explains key algorithms such as DQN and policy gradients, discusses black‑box optimization and the cross‑entropy method, and provides resources and code examples for further study.

Black-Box OptimizationDQNartificial intelligence

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Deep Reinforcement Learning: Concepts, Black‑Box Optimization, and the Cross‑Entropy Method

Alibaba Cloud Developer

May 26, 2017 · Artificial Intelligence

How StarCraft Became a Testbed for Advanced AI and Multi‑Agent Learning

This article explains why Alibaba's Cognitive Computing Lab uses StarCraft as a research platform, outlines the game's unique challenges for AI, and details their deep reinforcement learning and multi‑agent approaches, including the BiCNet architecture and experimental results.

AI researchBiCNetMulti-Agent

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How StarCraft Became a Testbed for Advanced AI and Multi‑Agent Learning

Alibaba Cloud Developer

May 11, 2017 · Artificial Intelligence

How Alibaba’s ‘Ali Xiaomi’ Chatbot Merges NLU, Knowledge Graphs, and Deep RL

Alibaba’s ‘Ali Xiaomi’ chatbot leverages a layered architecture that integrates intent recognition, multi‑type matching, knowledge‑graph‑based entity management, deep reinforcement learning, and hybrid retrieval‑generation models to deliver high‑accuracy, scalable conversational services across e‑commerce, customer support, and intelligent recommendation scenarios.

ChatbotKnowledge Graphdeep reinforcement learning

0 likes · 18 min read

How Alibaba’s ‘Ali Xiaomi’ Chatbot Merges NLU, Knowledge Graphs, and Deep RL

Alibaba Cloud Developer

Nov 14, 2016 · Artificial Intelligence

How Alibaba Boosted Double 11 Sales with Deep Reinforcement Learning

Alibaba’s Double 11 event shattered sales records by leveraging deep reinforcement learning and adaptive online learning in its search and recommendation systems, which increased click‑through rates by 10‑20% and dramatically improved the real‑time shopping experience for hundreds of millions of users.

AI in e-commerceAlibabaadaptive online learning

0 likes · 4 min read

How Alibaba Boosted Double 11 Sales with Deep Reinforcement Learning