Robin-Nico Kampa

Robin-Nico Kampa, Fabian Deuser, Anna Bößendörfer et al.

Autonomous AI coding agents are becoming a core tool for ML practitioners in industry and research alike. Despite this growing adoption, no standardized benchmark exists to evaluate their ability to design, implement, and train models from scratch across diverse domains. We introduce **1GC-7RC** (*Single Graphic Card: Seven Research Challenges*), a benchmark comprising seven ML tasks spanning language modeling, image classification, semantic segmentation, graph learning, tabular prediction, time-series forecasting, and text classification. Each task provides a locked data-preparation and evaluation script together with a baseline training script; the agent may only modify the training code, has no access to pretrained weights (with one controlled exception for semantic segmentation), no internet access, and must complete each task within a task-specific wall-clock budget (40-120 minutes) on a single GPU. We evaluate seven coding agents: five proprietary (Claude Code with Sonnet 4.6, Opus 4.6, and Opus 4.7; Codex CLI with GPT 5.5; and OpenCode with Qwen 3.6+) and two open-source (OpenCode with Kimi K2.5, Kimi K2.6). Across 5 runs per agent-task pair, we report substantial performance differences that reveal varying levels of implicit ML knowledge, planning ability, and time-budget management. The benchmark, harness, and all evaluation artifacts are publicly available on GitHub at https://github.com/Strolchii/1GC-7RC-Benchmark to facilitate reproducible comparison of future agents. Because our benchmark design is modular, the benchmark can be extended to new tasks and domains, adapted to different GPU budgets, and used to study multi-agent settings, making it a flexible platform for future research on autonomous research agents.

Tobias Hallmen, Robin-Nico Kampa, Fabian Deuser et al.

In this study, we present our methodology for two tasks: the Emotional Mimicry Intensity (EMI) Estimation Challenge and the Behavioural Ambivalence/Hesitancy (BAH) Recognition Challenge, both conducted as part of the 8th Workshop and Competition on Affective & Behavior Analysis in-the-wild. We utilize a Wav2Vec 2.0 model pre-trained on a large podcast dataset to extract various audio features, capturing both linguistic and paralinguistic information. Our approach incorporates a valence-arousal-dominance (VAD) module derived from Wav2Vec 2.0, a BERT text encoder, and a vision transformer (ViT) with predictions subsequently processed through a long short-term memory (LSTM) architecture or a convolution-like method for temporal modeling. We integrate the textual and visual modality into our analysis, recognizing that semantic content provides valuable contextual cues and underscoring that the meaning of speech often conveys more critical insights than its acoustic counterpart alone. Fusing in the vision modality helps in some cases to interpret the textual modality more precisely. This combined approach results in significant performance improvements, achieving in EMI $ρ_{\text{TEST}} = 0.706$ and in BAH $F1_{\text{TEST}} = 0.702$, securing first place in the EMI challenge and second place in the BAH challenge.

Robin-Nico Kampa, Fabian Deuser, Konrad Habel et al.

Plant phenotyping involves analyzing observable characteristics of plants to better understand their growth, health, and development. In the context of deep learning, this analysis is often approached through single-view classification or regression models. However, these methods often fail to capture all information required for accurate estimation of target phenotypic traits, which can adversely affect plant health assessment and harvest readiness prediction. To address this, the Growth Modelling (GroMo) Grand Challenge at ACM Multimedia 2025 provides a multi-view dataset featuring multiple plants and two tasks: Plant Age Prediction and Leaf Count Estimation. Each plant is photographed from multiple heights and angles, leading to significant overlap and redundancy in the captured information. To learn view-invariant embeddings, we incorporate 24 views, referred to as the selection vector, in a random selection. Our ViewSparsifier approach won both tasks. For further improvement and as a direction for future research, we also experimented with randomized view selection across all five height levels (120 views total), referred to as selection matrices.