Recent advances in large language models (LLMs) have enabled software engineering agents to solve complex code modification tasks. However, most existing approaches rely on execution feedback from Docker-based environments, which require dependency-complete setup, program execution, and test validation, making agent training costly and difficult to scale. We propose SWE-World, a Docker-free framework that replaces containerized execution during downstream agent optimization with a learned surrogate environment. Trained on real agent-environment interaction traces, SWE-World predicts both step-level execution feedback and final test outcomes, preserving the standard agent-environment loop without repeatedly constructing or running Docker environments. This surrogate also enables test-time scaling (TTS) by scoring multiple candidate trajectories through simulated evaluation. Experiments on SWE-bench Verified show that SWE-World improves Qwen2.5-Coder-32B-Instruct from 6.2% to 52.0% with Docker-free SFT, 55.0% with Docker-free RL, and 68.2% with TTS. The code is available at https://anonymous.4open.science/r/SWE-World-2D1E.

Code reasoning is essential for building AI systems that can understand, generate, and verify complex software. Yet most open-source code corpora focus on isolated snippets or single functions, failing to capture the long-range dependencies and cross-file interactions that define real-world repositories. We introduce MegaCode, a 300B repository-level corpus designed to support structured code reasoning and long-context modeling. MegaCode preserves full project structure—including build scripts, configuration files, and inter-file links—and is constructed through a multi-stage curation pipeline combining deduplication, rule-based filtering, and model-based quality assessment. Models pretrained on MegaCode demonstrate consistent gains on code reasoning and long-context benchmarks compared with those trained on flattened code datasets. Also, we validate the effectiveness of adding repo relevent infomation such as LSP, repo structure and repo summary. These results highlight the importance of maintaining repository structure for next-generation code intelligence. We release the dataset, curation pipeline, and evaluation suite to facilitate research on large-scale code pretraining and repository-level understanding.

Retrieval-augmented generation (RAG) systems have advanced large language models (LLMs) in complex deep search scenarios requiring multi-step reasoning and iterative information retrieval. However, existing approaches face critical limitations that lack high-quality training trajectories or suffer from the distributional mismatches in simulated environments and prohibitive computational costs for real-world deployment. This paper introduces SimpleDeepSearcher, a lightweight yet effective framework that bridges this gap through strategic data engineering rather than complex training paradigms. Our approach synthesizes high-quality training data by simulating realistic user interactions in live web search environments, coupled with a multi-criteria curation strategy that optimizes the diversity and quality of input and output side. Experiments on five benchmarks across diverse domains demonstrate that SFT on only 871 curated samples yields significant improvements over RL-based baselines. Our work establishes SFT as a viable pathway by systematically addressing the data-scarce bottleneck, offering practical insights for efficient deep search systems. Our anonymous code is available at https://github.com/RUCAIBox/SimpleDeepSearcher

Stickers, while widely recognized for enhancing empathetic communication in online interactions, remain underexplored in current empathetic dialogue research, notably due to the challenge of a lack of comprehensive datasets. In this paper, we introduce the Agent for STICKERCONV (Agent4SC), which uses collaborative agent interactions to realistically simulate human behavior with sticker usage, thereby enhancing multimodal empathetic communication. Building on this foundation, we develop a multimodal empathetic dialogue dataset, STICKERCONV, comprising 12.9K dialogue sessions, 5.8K unique stickers, and 2K diverse conversational scenarios. This dataset serves as a benchmark for multimodal empathetic generation. To advance further, we propose PErceive and Generate Stickers (PEGS), a multimodal empathetic response generation framework, complemented by a comprehensive set of empathy evaluation metrics based on LLM. Our experiments demonstrate PEGS’s effectiveness in generating contextually relevant and emotionally resonant multimodal empathetic responses, contributing to the advancement of more nuanced and engaging empathetic dialogue systems.