Current AI-driven game development has made substantial progress in asset generation, gameplay design, and web-based game coding, yet project-level code engineering on professional game engines remains largely unexplored due to the absence of large-scale datasets and deterministic evaluation methods. We present JamSet and JamBench, the first project-level game code framework dataset and benchmark built on a professional game engine. Our key insight is that Game Jam competitions, community events where developers build complete games under tight time constraints, yield thousands of open-source projects suitable for this purpose. Building on the Godot engine’s text-based format and headless execution mode, we design a deterministic verification pipeline from file integrity to runtime behavior collection, distilling 8,133 verified projects from over 240,000 repositories. Of these, 300 manually verified projects form JamBench; the rest constitute JamSet. JamBench defines theme-driven generation and code completion tasks, evaluated through a pipeline combining compilation pass rates, Structural Completeness Score (SCS), and Behavioral Alignment Score (BAS). Evaluation of 9 frontier models reveals a capability cliff as project scale increases, with runtime pass rates dropping from 80.4% on small projects to 5.7% on large ones(Task2a). Code Agents improve compilation rates yet yield no gains in runtime behavioral quality, indicating that the bottleneck lies in architectural design rather than syntactic correctness. Experiments validate JamSet as effective training data. All data and code are publicly available.

Chain-of-thought (CoT) reasoning has become a default paradigm across reasoning-intensive tasks and is widely assumed to be, at worst, harmless. Recent efficient-reasoning work treats unnecessary CoT purely as a cost to be reduced. We show that in boundary-dense classification tasks such as content moderation and toxicity detection, CoT is instead actively harmful. We identify the overthinking effect: extended reasoning amplifies weak surface signals into false evidence of violation, inflating false positive rates on ambiguous non-violating samples while its gains on ambiguous violating samples mask the harm in aggregate metrics. To address this, we propose Oreki, an adaptive CoT framework that uses K-fold cross-probing to label per-sample reasoning necessity and fine-tunes on a mixture of full-reasoning and reasoning-skipped formats. Across four base models on video moderation and multi-label toxicity detection, Oreki preserves CoT’s recall gains while suppressing its false positive inflation, outperforming both Always-CoT and Never-CoT. On toxicity detection, it cuts low-agreement false positive rate from 0.467 to 0.257 while raising overall F1 from 0.792 to 0.851.

Online video content moderation is critical for protecting minors. Violations are increasingly implicit and context-dependent: a swimwear is innocuous in a vlog but high-risk with suggestive posing. Yet, traditional binary systems fundamentally fail to capture this continuous severity spectrum, directly leading to both excessive false positives for context-heavy samples and missing deeply implicit violations. We introduce KUROKO, a content moderation system that acts as a behind-the-scenes assistant, driving a paradigm shift in content moderation from binary classification to severity-aware ranking. Our contributions are threefold: (1) Human-Centric Severity Ranking: KUROKO shifts from rigid binary divides to a continuous risk spectrum, producing nuanced, expert-level rationales closely aligning with human judgment. (2) Verifiable Alignment and Interpretability: By integrating a severity-aware data engine and a verifiable reinforcement learning approach, we replace black-box decisions with transparent, actionable diagnostics. (3) Cost-Efficient Deployment: We engineer a coarse-to-fine inference architecture equipped with a lightweight recall classifier, enabling compact models to rival proprietary VLMs at a fraction of the computational cost. Extensive offline and online A/B tests demonstrate KUROKO’s advantages over traditional binary baselines and its competitiveness with leading commercial APIs at a fraction of their scale. Online, KUROKO yields more human-confirmed enforcement actions under an equal human-review budget, while lowering violative exposure and user negative feedback.

Large Vision-Language Models (VLMs) have already been applied to the understanding of Graphical User Interfaces (GUIs) and have achieved notable results. However, existing VLMs often overly rely on internal text-based knowledge while neglecting visual inputs. This imbalance may lead models to produce answers that do not align with the visual content in GUI comprehension tasks. Such inaccuracies are termed as ‘hallucinations’ where models generate incorrect or illogical responses upon visual verification against GUI elements. These errors result in misinterpretations and diminish the model’s practical utility in applied settings. To address these issues, we introduce VGA, a fine-tuned model designed for comprehensive GUI understanding. Our model aims to balance attention image and text to enhance interpretation and reduce hallucinations. We construct a Vision Question Answering (VQA) dataset of 63.8k high-quality examples with our propose Referent Method, focusing on response with visual content of images. We then design a two-stage fine-tuning method to enhance both the model’s accuracy to extract information from image content and alignment with human intent. Experiments show that our approach enhances the model’s ability to extract information from images and achieves state-of-the-art results in GUI understanding tasks. https://github.com/Linziyang1999/VGA-visual-GUI-assistant