StateAct: Program State, before Pixels, for Long-Horizon Computer-Use Agents
Paper • 2607.22798 • Published • 34
AI & ML interests
Formerly, MDEL, we have renamed ourselves after the model we deployed, Aurora-M. Visit us here: https://huggingface.co/aurora-m
Recent Activity
View all activity
Ziyang
submitted a
paper to Daily Papers about 4 hours ago
ajibawa-2023
posted an update 11 days ago
Post
3521
Technical-Architectures-Large
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
Dataset: ajibawa-2023/Technical-Architectures-Large
This dataset provides over 210,000 distinct enterprise software architectures generated using two open source models: GPT-OSS-120B and Qwen3-Coder-Next-FP8.
These architectures model realistic enterprise systems complete with client layers, edge security, API gateways, service meshes, compliance boundaries, and multi-cloud infrastructure topologies.
ajibawa-2023
posted an update about 1 month ago
Post
6932
Shell-Code-Large
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
Dataset: ajibawa-2023/Shell-Code-Large
Shell-Code-Large is a large-scale corpus of Shell scripting source code comprising approximately 640,000 code samples stored in JSON Lines (.jsonl) format. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, DevOps automation, cloud infrastructure engineering, system administration, and software engineering automation.
By providing a high-volume, language-specific corpus focused exclusively on Shell scripting, Shell-Code-Large enables systematic experimentation in automation workflows, deployment pipelines, infrastructure management, and command-line tooling. These domains remain foundational to Linux systems, cloud-native platforms, CI/CD environments, and modern DevOps practices.
Shell-Code-Large addresses the need for a dedicated Shell-focused dataset at substantial scale, enabling targeted research into scripting patterns, command composition, workflow orchestration, infrastructure automation, and operational engineering practices
georgosgeorgos
authored 4
papers about 2 months ago
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR
Paper • 2606.08854 • Published
Intrinsic Selection and Particle Resampling for Inference-Time Scaling Beyond Domain Verifiability
Paper • 2606.08850 • Published
GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedback
Paper • 2603.27448 • Published
SCHA-VAE: Hierarchical Context Aggregation for Few-Shot Generation
Paper • 2110.12279 • Published
WojciechKusa
authored 2
papers about 2 months ago
ajibawa-2023
posted an update 3 months ago
Post
2232
Stitched-Reasoning-Trajectories-7M
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
Dataset: ajibawa-2023/Stitched-Reasoning-Trajectories-7M
Stitched-Reasoning-Trajectories-7M is a massive-scale, synthetic multi-hop reasoning dataset. It was built by algorithmically "stitching" together discrete reasoning traces from the original glaiveai/reasoning-v1-20m dataset into continuous, coherent, and logically structured multi-agent trajectories.
By extracting internal sub-questions from <think> blocks and mapping high-information keyword overlaps, this dataset transforms single-turn Q&A pairs into deep, multi-step research plans. To ensure high quality and eliminate "topic drift," every trajectory has been verified using a dense semantic embedding model (BAAI/bge-large-en-v1.5).
The resulting dataset consists of 709 .jsonl files containing over 7.2 million entirely deduplicated, highly coherent reasoning chains.
huu-ontocord
authored a
paper 3 months ago
ajibawa-2023
posted an update 3 months ago
Post
1348
Ruby-Code-Large
Dataset : ajibawa-2023/Ruby-Code-Large
Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.
By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.
Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.
Dataset : ajibawa-2023/Ruby-Code-Large
Ruby-Code-Large is a large-scale corpus of Ruby programming language source code comprising 331,743 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, web application development, and software engineering automation within the Ruby ecosystem.
By offering a substantial, language-focused dataset, Ruby-Code-Large enables targeted experimentation in dynamic programming, object-oriented design, and rapid application development—areas where Ruby is widely used, particularly in web frameworks and scripting.
Ruby-Code-Large addresses the lack of large, curated, Ruby-specific datasets, enabling focused research on expressive syntax, metaprogramming, and high-level abstractions.
ajibawa-2023
posted an update 3 months ago
Post
6121
Go-Code-Large
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
Dataset: ajibawa-2023/Go-Code-Large
Go-Code-Large is a large-scale corpus of Go (Golang) programming language source code, comprising 316,427 code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, cloud-native systems, and modern backend software engineering.
By offering a focused and curated dataset for Go, this corpus enables experimentation in concurrent programming, distributed systems, and performance-oriented backend services—domains where Go is widely adopted.
Go-Code-Large addresses the relative scarcity of large, language-specific datasets for Go, enabling targeted research into idiomatic Go patterns, concurrency primitives, and scalable system design.
- 2 replies
cabbage972
authored 4
papers 3 months ago
GitChameleon: Evaluating AI Code Generation Against Python Library Version Incompatibilities
Paper • 2507.12367 • Published • 7
MixtureVitae: Open Web-Scale Pretraining Dataset With High Quality Instruction and Reasoning Data Built from Permissive-First Text Sources
Paper • 2509.25531 • Published • 11
Agents Learn Their Runtime: Interpreter Persistence as Training-Time Semantics
Paper • 2603.01209 • Published • 1
FreshBrew: A Benchmark for Evaluating AI Agents on Java Code Migration
Paper • 2510.04852 • Published
JeanKaddour
submitted a
paper to Daily Papers 3 months ago
ajibawa-2023
posted an update 4 months ago
Post
2803
C-Code-Large
Dataset: ajibawa-2023/C-Code-Large
C-Code-Large is a large-scale corpus of C programming language source code comprising more than 4 million code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, and software engineering automation for the C ecosystem.
By offering a high-volume, language-focused dataset, C-Code-Large enables targeted experimentation in low-level programming, memory-constrained environments, and performance-critical systems, where C continues to be a dominant language.
C-Code-Large addresses the lack of large, curated, C-specific datasets, making it possible to conduct focused research on procedural programming paradigms, manual memory management, and system-level abstractions.
Dataset: ajibawa-2023/C-Code-Large
C-Code-Large is a large-scale corpus of C programming language source code comprising more than 4 million code samples stored in .jsonl format. The dataset is designed to support research and development in large language model (LLM) pretraining, static analysis, and software engineering automation for the C ecosystem.
By offering a high-volume, language-focused dataset, C-Code-Large enables targeted experimentation in low-level programming, memory-constrained environments, and performance-critical systems, where C continues to be a dominant language.
C-Code-Large addresses the lack of large, curated, C-specific datasets, making it possible to conduct focused research on procedural programming paradigms, manual memory management, and system-level abstractions.
ajibawa-2023
posted an update 5 months ago
Post
3874
Cpp-Code-Large
Dataset: ajibawa-2023/Cpp-Code-Large
Cpp-Code-Large is a large-scale corpus of C++ source code comprising more than 5 million lines of C++ code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the C++ ecosystem.
By providing a high-volume, language-specific corpus, Cpp-Code-Large enables systematic experimentation in C++-focused model training, domain adaptation, and downstream code understanding tasks.
Cpp-Code-Large addresses the need for a dedicated C++-only dataset at substantial scale, enabling focused research across systems programming, performance-critical applications, embedded systems, game engines, and large-scale native software projects.
Dataset: ajibawa-2023/Cpp-Code-Large
Cpp-Code-Large is a large-scale corpus of C++ source code comprising more than 5 million lines of C++ code. The dataset is designed to support research in large language model (LLM) pretraining, code intelligence, software engineering automation, and static program analysis for the C++ ecosystem.
By providing a high-volume, language-specific corpus, Cpp-Code-Large enables systematic experimentation in C++-focused model training, domain adaptation, and downstream code understanding tasks.
Cpp-Code-Large addresses the need for a dedicated C++-only dataset at substantial scale, enabling focused research across systems programming, performance-critical applications, embedded systems, game engines, and large-scale native software projects.
- 3 replies