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T01. LLM agent orchestration and harness design
External research on how multiple language-model agents are planned, routed, and evaluated.
Show this topic only- T01-13Similar problemSince 2025
Why Do Multi-Agent LLM Systems Fail?
- T01-14Candidate approachSince 2025
Multi-Agent Collaboration via Evolving Orchestration
- T01-19Similar problemSince 2025
tau^2-Bench: Evaluating Conversational Agents in a Dual-Control Environment
- T01-12Similar problemSince 2025
tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains
- T01-1Structural parallel
ReAct: Synergizing Reasoning and Acting in Language Models
- T01-2Structural parallel
Toolformer: Language Models Can Teach Themselves to Use Tools
- T01-3Structural parallel
HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face
- T01-4Candidate approach
Generative Agents: Interactive Simulacra of Human Behavior
- T01-5Candidate approach
Reflexion: Language Agents with Verbal Reinforcement Learning
- T01-6Candidate approach
CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society
- T01-7Similar problem
WebArena: A Realistic Web Environment for Building Autonomous Agents
- T01-8Similar problem
SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- T01-9Structural parallel
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversations
- T01-10Structural parallel
MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
- T01-11Structural parallel
SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
- T01-15Structural parallel
Tree of Thoughts: Deliberate Problem Solving with Large Language Models
- T01-16Similar problem
AgentBench: Evaluating LLMs as Agents
- T01-17Similar problem
GAIA: a benchmark for General AI Assistants
- T01-18Similar problem
OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments
T02. Recursive language models and long-context handling
Research on reading documents that do not fit in one context window.
Show this topic only- T02-1Structural parallelSince 2025
Recursive Language Models
- T02-2Structural parallel
RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
- T02-3Similar problem
Walking Down the Memory Maze: Beyond Context Limit through Interactive Reading (MemWalker)
- T02-4Candidate approach
MemGPT: Towards LLMs as Operating Systems
- T02-5Structural parallel
Chain of Agents: Large Language Models Collaborating on Long-Context Tasks
- T02-6Similar problem
Lost in the Middle: How Language Models Use Long Contexts
- T02-7Similar problem
RULER: What's the Real Context Size of Your Long-Context Language Models?
- T02-8Candidate approach
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
- T02-9Structural parallel
Recursively Summarizing Books with Human Feedback
- T02-10Candidate approach
Efficient Streaming Language Models with Attention Sinks (StreamingLLM)
- T02-11Similar problem
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding
- T02-12Candidate approach
Ring Attention with Blockwise Transformers for Near-Infinite Context
- T02-13Candidate approach
Extending Context Window of Large Language Models via Positional Interpolation
- T02-14Candidate approach
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
- T02-15Candidate approach
H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
T03. Tool use, function calling, and the Model Context Protocol
External research and specifications for letting a model call outside tools.
Show this topic only- T03-13Similar problemSince 2025
Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions
- T03-12Similar problemSince 2025
The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models
- T03-9Similar problemSince 2025
Tool Learning with Foundation Models
- T03-1Structural parallel
WebGPT: Browser-assisted question-answering with human feedback
- T03-2Structural parallel
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
- T03-6Similar problem
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
- T03-7Structural parallel
Gorilla: Large Language Model Connected with Massive APIs
- T03-8Candidate approach
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs
T04. Planning, reflection, judging, and self-improving agents
Research on step-by-step reasoning, self-critique, and model-as-judge evaluation.
Show this topic only- T04-1Structural parallel
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
- T04-4Structural parallel
Self-Refine: Iterative Refinement with Self-Feedback
- T04-7Structural parallel
Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena
- T04-8Candidate approach
G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
- T04-9Similar problem
Let's Verify Step by Step
- T04-10Candidate approach
Agent-as-a-Judge: Evaluate Agents with Agents
- T04-11Similar problem
Large Language Models Cannot Self-Correct Reasoning Yet
- T04-12Candidate approach
Self-Rewarding Language Models
- T04-13Candidate approach
Voyager: An Open-Ended Embodied Agent with Large Language Models
- T04-14Candidate approach
STaR: Bootstrapping Reasoning With Reasoning
- T04-15Structural parallel
Constitutional AI: Harmlessness from AI Feedback
- T04-16Similar problem
CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing
T05. Human review and approval workflows
Human-in-the-loop and approval research, including automation-induced complacency.
Show this topic only- T05-22Candidate approachSince 2025
What You Approve Is What Executes: Consent Integrity for Black-Box LLM Agents
- T05-23Candidate approachSince 2025
Oversight Has a Capacity: Calibrating Agent Guards to a Subjective, Fatiguing Human
- T05-21Similar problemSince 2025
Human-In-the-Loop Software Development Agents (HULA)
- T05-1Similar problem
Ironies of Automation
- T05-2Similar problem
The Out-of-the-Loop Performance Problem and Level of Control in Automation
- T05-3Similar problem
Humans and Automation: Use, Misuse, Disuse, Abuse
- T05-4Structural parallel
A Model for Types and Levels of Human Interaction with Automation
- T05-5Similar problem
Complacency and Bias in Human Use of Automation: An Attentional Integration
- T05-6Structural parallel
On Optimum Recognition Error and Reject Tradeoff
- T05-7Structural parallel
Learning with Rejection
- T05-8Structural parallel
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
- T05-9Structural parallel
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
- T05-10Structural parallel
Consistent Estimators for Learning to Defer to an Expert
- T05-11Structural parallel
Learning to Complement Humans
- T05-12Structural parallel
Machine Learning with a Reject Option: A survey
- T05-13Similar problem
Trust in Automation: Designing for Appropriate Reliance
- T05-14Similar problem
Guidelines for Human-AI Interaction
- T05-15Similar problem
Effect of Confidence and Explanation on Accuracy and Trust Calibration in AI-Assisted Decision Making
- T05-16Similar problem
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
- T05-17Similar problem
To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-making
- T05-18Similar problem
The flaws of policies requiring human oversight of government algorithms
- T05-19Structural parallel
Identifying the Risks of LM Agents with an LM-Emulated Sandbox (ToolEmu)
- T05-20Structural parallel
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
- T05-24Candidate approach
Deep reinforcement learning from human preferences
- T05-25Candidate approach
Human-in-the-loop machine learning: a state of the art
T06. RAG, knowledge graphs, ontologies, and provenance
Retrieval-augmented generation and structured knowledge with traceable sources.
Show this topic only- T06-14Similar problemSince 2025
Enhancing retrieval-augmented generation for interoperable industrial knowledge representation and inference toward cognitive digital twins
- T06-1Structural parallel
Dense Passage Retrieval for Open-Domain Question Answering
- T06-2Similar problem
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
- T06-3Structural parallel
Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering
- T06-4Similar problem
Retrieval-Augmented Generation for Large Language Models: A Survey
- T06-5Structural parallel
From Local to Global: A Graph RAG Approach to Query-Focused Summarization
- T06-6Candidate approach
HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models
- T06-7Structural parallel
Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection
- T06-8Structural parallel
Enabling Large Language Models to Generate Text with Citations
- T06-9Similar problem
Measuring Attribution in Natural Language Generation Models
- T06-10Structural parallel
Provenance Semirings
- T06-11Structural parallel
Provenance in Databases: Why, How, and Where
- T06-12Similar problem
Knowledge Graphs
- T06-13Similar problem
A benchmark dataset with Knowledge Graph generation for Industry 4.0 production lines
T07. Structured output, schema validation, and document understanding
Research on forcing machine-checkable output and reading business documents.
Show this topic only- T07-10Structural parallelSince 2025
XGrammar: Flexible and Efficient Structured Generation Engine for Large Language Models
- T07-11Similar problemSince 2025
JSONSchemaBench: A Rigorous Benchmark of Structured Outputs for Language Models
- T07-1Structural parallel
LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking
- T07-2Structural parallel
OCR-free Document Understanding Transformer
- T07-3Similar problem
TableFormer: Table Structure Understanding with Transformers
- T07-4Candidate approach
Nougat: Neural Optical Understanding for Academic Documents
- T07-5Similar problem
FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
- T07-6Similar problem
DocVQA: A Dataset for VQA on Document Images
- T07-7Structural parallel
PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models
- T07-8Structural parallel
Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning
- T07-9Structural parallel
Efficient Guided Generation for Large Language Models
- T07-12Similar problem
Let Me Speak Freely? A Study On The Impact Of Format Restrictions On Large Language Model Performance
- T07-13Similar problem
Grammar-Aligned Decoding
T08. OCR, vision-language models, and industrial display reading
Research on reading meters, indicators, and shop-floor displays from images.
Show this topic only- T08-1Similar problemSince 2025
Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench
- T08-2Structural parallelSince 2025
DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models
- T08-14Candidate approachSince 2025
Qwen2.5-VL Technical Report
- T08-3Similar problem
Convolutional Neural Networks for Automatic Meter Reading
- T08-4Similar problem
Utilizing Smartphone-Based Machine Learning in Medical Monitor Data Collection: Seven Segment Digit Recognition
- T08-5Structural parallel
An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition
- T08-6Structural parallel
Real-time Scene Text Detection with Differentiable Binarization
- T08-7Candidate approach
TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models
- T08-9Structural parallel
General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Model
- T08-11Candidate approach
OCRBench: On the Hidden Mystery of OCR in Large Multimodal Models
- T08-12Structural parallel
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
- T08-13Candidate approach
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
T09. Industrial time series, anomaly detection, and predictive maintenance
Sensor-driven fault detection, remaining useful life, and condition monitoring research.
Show this topic only- T09-1Similar problem
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of Progress
- T09-2Similar problem
Towards a Rigorous Evaluation of Time-series Anomaly Detection
- T09-3Similar problem
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection Benchmark
- T09-4Similar problem
Volume Under the Surface: A New Accuracy Evaluation Measure for Time-Series Anomaly Detection
- T09-5Similar problem
A Review on Outlier/Anomaly Detection in Time Series Data
- T09-6Structural parallel
Anomaly Detection in Time Series: A Comprehensive Evaluation
- T09-7Similar problem
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding
- T09-8Structural parallel
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural Network (OmniAnomaly)
- T09-9Similar problem
A Dataset to Support Research in the Design of Secure Water Treatment Systems (SWaT)
- T09-10Structural parallel
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy
- T09-11Structural parallel
TranAD: Deep Transformer Networks for Anomaly Detection in Multivariate Time Series Data
- T09-12Structural parallel
Time-Series Anomaly Detection Service at Microsoft
- T09-13Similar problem
Damage Propagation Modeling for Aircraft Engine Run-to-Failure Simulation
- T09-14Similar problem
A review on machinery diagnostics and prognostics implementing condition-based maintenance
- T09-15Similar problem
Machinery health prognostics: A systematic review from data acquisition to RUL prediction
- T09-16Candidate approach
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
T10. AutoML, model selection, evaluation design, and experiment tracking
Research on choosing and validating models instead of shipping a single fitted model.
Show this topic only- T10-1Similar problem
Random Search for Hyper-Parameter Optimization
- T10-2Structural parallel
Auto-WEKA: Combined Selection and Hyperparameter Optimization of Classification Algorithms
- T10-3Structural parallel
Efficient and Robust Automated Machine Learning (auto-sklearn)
- T10-4Candidate approach
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
- T10-5Candidate approach
BOHB: Robust and Efficient Hyperparameter Optimization at Scale
- T10-6Candidate approach
Optuna: A Next-generation Hyperparameter Optimization Framework
- T10-7Structural parallel
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
- T10-8Structural parallel
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning
- T10-9Similar problem
AMLB: an AutoML Benchmark
- T10-10Similar problem
Statistical Comparisons of Classifiers over Multiple Data Sets
- T10-11Similar problem
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
- T10-12Similar problem
Evaluating time series forecasting models: An empirical study on performance estimation methods
- T10-13Structural parallel
OpenML: Networked Science in Machine Learning
- T10-14Structural parallel
Developments in MLflow: A System to Accelerate the Machine Learning Lifecycle
- T10-15Candidate approach
Model Cards for Model Reporting
- T10-16Candidate approach
Datasheets for Datasets
T11. Object detection, multi-object tracking, and video understanding
Detection and tracking backbones behind camera-based safety and inspection work.
Show this topic only- T11-6.3Candidate approachSince 2025
SAM 2: Segment Anything in Images and Videos
- T11-1.1Structural parallel
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale (ViT)
- T11-2.1Structural parallel
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- T11-2.2Structural parallel
You Only Look Once: Unified, Real-Time Object Detection (YOLO)
- T11-2.3Structural parallel
Focal Loss for Dense Object Detection (RetinaNet)
- T11-2.4Structural parallel
End-to-End Object Detection with Transformers (DETR)
- T11-2.5Structural parallel
Deformable DETR: Deformable Transformers for End-to-End Object Detection
- T11-2.6Structural parallel
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
- T11-2.7Candidate approach
DETRs Beat YOLOs on Real-time Object Detection (RT-DETR)
- T11-2.8Candidate approach
Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
- T11-2.9Candidate approach
YOLOv10: Real-Time End-to-End Object Detection
- T11-3.1Structural parallel
Simple Online and Realtime Tracking (SORT)
- T11-3.2Similar problem
Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT)
- T11-3.3Structural parallel
ByteTrack: Multi-Object Tracking by Associating Every Detection Box
- T11-3.4Candidate approach
Observation-Centric SORT: Rethinking SORT for Robust Multi-Object Tracking (OC-SORT)
- T11-4.1Structural parallel
Evaluating Multiple Object Tracking Performance: The CLEAR MOT Metrics (MOTA, MOTP)
- T11-4.2Structural parallel
Performance Measures and a Data Set for Multi-Target, Multi-Camera Tracking (IDF1)
- T11-4.3Structural parallel
HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking
- T11-4.4Structural parallel
MOT16: A Benchmark for Multi-Object Tracking
- T11-5.1Structural parallel
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset (I3D)
- T11-5.2Similar problem
SlowFast Networks for Video Recognition
- T11-5.3Candidate approach
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
- T11-6.1Structural parallel
Learning Transferable Visual Models From Natural Language Supervision (CLIP)
- T11-6.2Structural parallel
Segment Anything (SAM)
T12. Video understanding and evidence selection with vision-language models
Research on explaining what a camera saw and pointing back to the evidence frame.
Show this topic only- T12-2Structural parallelSince 2025
Adaptive Keyframe Sampling for Long Video Understanding
- T12-1Structural parallelSince 2025
Frame-Voyager: Learning to Query Frames for Video Large Language Models
- T12-13Similar problemSince 2025
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
- T12-3Structural parallel
Self-Chained Image-Language Model for Video Localization and Question Answering (SeViLA)
- T12-4Structural parallel
VideoAgent: Long-form Video Understanding with Large Language Model as Agent
- T12-5Candidate approach
A Simple LLM Framework for Long-Range Video Question-Answering (LLoVi)
- T12-6Similar problem
Can I Trust Your Answer? Visually Grounded Video Question Answering (NExT-GQA)
- T12-7Structural parallel
TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding
- T12-8Structural parallel
VTimeLLM: Empower LLM to Grasp Video Moments
- T12-9Similar problem
QVHighlights: Detecting Moments and Highlights in Videos via Natural Language Queries (Moment-DETR)
- T12-11Structural parallel
BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models
- T12-12Similar problem
EgoSchema: A Diagnostic Benchmark for Very Long-form Video Language Understanding
- T12-14Similar problem
Evaluating Object Hallucination in Large Vision-Language Models (POPE)
- T12-15Similar problem
VideoHallucer: Evaluating Intrinsic and Extrinsic Hallucinations in Large Video-Language Models
T13. Edge AI, streaming inference, and resource scheduling
Running models near the machine under limited compute and latency budgets.
Show this topic only- T13-1Structural parallel
Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge
- T13-2Similar problem
Live Video Analytics at Scale with Approximation and Delay-Tolerance
- T13-3Similar problem
AWStream: Adaptive Wide-Area Streaming Analytics
- T13-4Similar problem
Chameleon: Scalable Adaptation of Video Analytics
- T13-5Similar problem
Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics
- T13-6Structural parallel
Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers
- T13-7Structural parallel
Clipper: A Low-Latency Online Prediction Serving System
- T13-8Structural parallel
Serving DNNs like Clockwork: Performance Predictability from the Bottom Up
- T13-9Candidate approach
INFaaS
- T13-10Candidate approach
Orca: A Distributed Serving System for Transformer-Based Generative Models
- T13-11Candidate approach
Efficient Memory Management for Large Language Model Serving with PagedAttention
- T13-12Candidate approach
Gandiva: Introspective Cluster Scheduling for Deep Learning
- T13-13Candidate approach
Tiresias: A GPU Cluster Manager for Distributed Deep Learning
- T13-14Candidate approach
AntMan: Dynamic Scaling on GPU Clusters for Deep Learning
- T13-15Candidate approach
Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads
- T13-16Candidate approach
Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning
- T13-17Structural parallel
MLPerf Inference Benchmark
- T13-18Similar problem
MillWheel: Fault-Tolerant Stream Processing at Internet Scale
- T13-19Similar problem
The Dataflow Model: A Practical Approach to Balancing Correctness, Latency, and Cost in Massive-Scale, Unbounded, Out-of-Order Data Processing
T14. Industrial protocol translation, code generation, and program synthesis
Research on generating and checking the code that talks to plant equipment.
Show this topic only- T14-5Structural parallelSince 2025
Training LLMs for Generating IEC 61131-3 Structured Text with Online Feedback
- T14-1Similar problem
Evaluating Large Language Models Trained on Code
- T14-2Similar problem
ChatGPT for PLC/DCS Control Logic Generation
- T14-3Structural parallel
LLM4PLC: Harnessing Large Language Models for Verifiable Programming of PLCs in Industrial Control Systems
- T14-4Candidate approach
Agents4PLC: Automating Closed-loop PLC Code Generation and Verification in Industrial Control Systems using LLM-based Agents
- T14-6Similar problem
Automated Control Logic Test Case Generation using Large Language Models
- T14-7Similar problem
Automated generation of OPC UA information models - A review and outlook
- T14-8Candidate approach
Discoverer: Automatic Protocol Reverse Engineering from Network Traces
- T14-9Candidate approach
NetPlier: Probabilistic Network Protocol Reverse Engineering from Message Traces
- T14-10Structural parallel
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
- T14-11Structural parallel
Syntax-Guided Synthesis
- T14-12Structural parallel
Automating string processing in spreadsheets using input-output examples
- T14-13Structural parallel
Combinatorial Sketching for Finite Programs
- T14-14Candidate approach
Polyglot: Automatic Extraction of Protocol Message Format using Dynamic Binary Analysis
- T14-15Similar problem
Program Synthesis with Large Language Models
T15. OPC UA, Asset Administration Shell, MQTT, and manufacturing interoperability
Specifications and research for describing equipment and moving its data.
Show this topic only- T15-2.10Structural parallelSince 2025
ESP32-Based Sparkplug B Gateway Framework for Brownfield PLC Integration into an IIoT Unified Namespace: A Data Foundation for Intelligent Industrial Systems
- T15-2.1Similar problem
The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0
- T15-2.2Similar problem
Insights into Mapping Solutions Based on OPC UA Information Model Applied to the Industry 4.0 Asset Administration Shell
- T15-2.3Structural parallel
Reusing OPC UA information models in the Asset Administration Shell
- T15-2.4Similar problem
OPC UA versus ROS, DDS, and MQTT: Performance Evaluation of Industry 4.0 Protocols
- T15-2.5Structural parallel
Evaluation and Extension of OPC UA Publish/Subscribe MQTT Binding
- T15-2.6Candidate approach
Open-Source Implementations of the Reactive Asset Administration Shell: A Survey
- T15-2.7Candidate approach
Generation of Asset Administration Shell With Large Language Model Agents: Toward Semantic Interoperability in Digital Twins in the Context of Industry 4.0
- T15-2.8Structural parallel
File- and API-based interoperability of digital twins by model transformation: An IIoT case study using asset administration shell
- T15-2.9Similar problem
Streaming Machine Generated Data via the MQTT Sparkplug B Protocol for Smart Factory Operations
- T15-2.11Structural parallel
A Cyber-Physical Machine Tools Platform using OPC UA and MTConnect
T16. Manufacturing knowledge graphs and semantic layers
Research on giving plant data a shared meaning across systems.
Show this topic only- T16-8Candidate approachSince 2025
Intent-Driven Smart Manufacturing Integrating Knowledge Graphs and Large Language Models
- T16-6Structural parallelSince 2025
Knowledge Graphs as the Missing Data Layer for LLM-Based Industrial Asset Operations
- T16-7Similar problemSince 2025
Fault Cause Identification across Manufacturing Lines through Ontology-Guided and Process-Aware FMEA Graph Learning with LLMs
- T16-1Structural parallel
The Industrial Ontologies Foundry (IOF) Core Ontology
- T16-2Structural parallel
Semantic Integration of Bosch Manufacturing Data Using Virtual Knowledge Graphs
- T16-3Similar problem
Knowledge Graphs in Manufacturing and Production: A Systematic Literature Review
- T16-4Candidate approach
Literal-Aware Knowledge Graph Embedding for Welding Quality Monitoring: A Bosch Case
- T16-5Structural parallel
Generation of Asset Administration Shell with Large Language Model Agents
- T16-9Structural parallel
Ontop: Answering SPARQL queries over relational databases
- T16-10Structural parallel
RML: A Generic Language for Integrated RDF Mappings of Heterogeneous Data
- T16-12Similar problem
The Industry 4.0 Standards Landscape from a Semantic Integration Perspective
T17. Natural language to SQL and grounded report generation
Research on turning a question into a checked query and a sourced report.
Show this topic only- T17-5Similar problemSince 2025
Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows
- T17-9Structural parallelSince 2025
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
- T17-1Similar problem
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
- T17-2Similar problem
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
- T17-3Structural parallel
Semantic Evaluation for Text-to-SQL with Distilled Test Suites
- T17-4Similar problem
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
- T17-6Structural parallel
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
- T17-7Structural parallel
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
- T17-8Structural parallel
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
- T17-10Structural parallel
CHESS: Contextual Harnessing for Efficient SQL Synthesis
- T17-13Structural parallel
Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models
- T17-14Structural parallel
RARR: Researching and Revising What Language Models Say, Using Language Models
- T17-16Structural parallel
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation
- T17-17Similar problem
ToTTo: A Controlled Table-To-Text Generation Dataset
- T17-18Similar problem
QTSumm: Query-Focused Summarization over Tabular Data
- T17-19Candidate approach
InfiAgent-DABench: Evaluating Agents on Data Analysis Tasks
T18. AI quality management, model monitoring, and audit trails
Research and standards for keeping a deployed model accountable over time.
Show this topic only- T18-12Structural parallelSince 2025
Provenance Tracking in Large-Scale Machine Learning Systems
- T18-11Candidate approachSince 2025
Time to Retrain? Detecting Concept Drifts in Machine Learning Systems
- T18-1Similar problem
Hidden Technical Debt in Machine Learning Systems
- T18-2Structural parallel
The ML Test Score: A Rubric for ML Production Readiness and Technical Debt Reduction
- T18-3Structural parallel
Data Validation for Machine Learning
- T18-6Structural parallel
Closing the AI Accountability Gap: Defining an End-to-End Framework for Internal Algorithmic Auditing
- T18-7Similar problem
A Survey on Concept Drift Adaptation
- T18-8Similar problem
Learning under Concept Drift: A Review
- T18-9Structural parallel
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
- T18-10Similar problem
Operationalizing Machine Learning: An Interview Study
From research to product use
Operating capabilities, pilots, and technologies in development are identified separately.
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