Research

Evaluation This subforum is dedicated to measurement and benchmarking. Discussions may include datasets, benchmarks, evaluation methodologies, reproducibility, comparisons between models or systems, and critiques of existing metrics. Inference This subforum covers what happens after a model is trained. Topics include inference-time optimization, latency, throughput, deployment tradeoffs, hardware utilization, and real-world performance considerations. Training This subforum focuses on how models learn. Discussions may include training methodologies, fine-tuning strategies, distillation, optimization techniques, data usage, prompt-based optimization, and experimental training insights. Systems This subforum is for low-level and infrastructure-oriented research. Topics include compilers, kernels, runtimes, scheduling, memory management, hardware–software co-design, and performance engineering. Theory This subforum is for foundational and conceptual discussions. Topics include theoretical properties of models, interpretability, representation, epistemics, generalization, and broader questions about what models know and why. If your post is exploratory, reflective, or foundational in nature, this is the right place. Prompting This subcategory is for discussions about how inputs shape model behavior. Topics include prompt phrasing, context construction, instruction design, role prompting, chain-of-thought elicitation, conditioning effects, and empirical observations about how different prompts lead to different outputs. Architecture This subforum is for discussions about model structure and representation. Topics include attention mechanisms, architectural innovations, scaling patterns, inductive biases, and new ways of organizing computation inside neural networks.