Streamline Community section: remove operational details, focus on pedagogy

Removed:
- Detailed submission infrastructure CLI commands
- Adoption tracking metrics
- Promotional language about leaderboards

Kept:
- MLSysBook ecosystem integration
- Pedagogical value of competitive benchmarking (Module 20)
- Focus on systems thinking and measurement-driven decisions

The section now focuses on educational value rather than infrastructure details.

paper/paper.tex

@@ -1094,13 +1094,11 @@ TinyTorch's current profiling infrastructure—memory tracking (tracemalloc), FL
 
 \noindent\textbf{Architecture Extensions.} Potential additions (graph neural networks, diffusion models, reinforcement learning) must justify inclusion through systems pedagogy rather than completeness. The question is not ``Can TinyTorch implement this?'' but rather ``Does implementing this teach fundamental systems concepts unavailable through existing modules?'' Graph convolutions might teach sparse tensor operations; diffusion models might illuminate iterative refinement trade-offs. However, extensions succeed only when maintaining TinyTorch's principle: \textbf{every line of code teaches a systems concept}. Community forks demonstrate this philosophy: quantum ML variants replace tensors with quantum state vectors (teaching circuit depth versus memory); robotics forks emphasize RL simulation overhead and real-time constraints. The curriculum remains intentionally incomplete as a production framework: completeness lies in foundational systems thinking applicable across all ML architectures.
 
-\subsection{Community Adoption and Impact}
+\subsection{Community and Sustainability}
 
-As part of the ML Systems Book ecosystem (\texttt{mlsysbook.ai}), TinyTorch benefits from and contributes to broader community infrastructure. This includes instructor discussion forums for pedagogical exchange, shared teaching resources across institutions, and integration with the textbook's theoretical foundations~\citep{mlsysbook2025}. The open-source model (MIT license) and community-driven development enable collaborative refinement of both content and tooling. Adoption will be measured through multiple channels: (1) \textbf{Educational adoption}: tracking course integrations, student enrollment, and instructor feedback across institutions; (2) \textbf{Capstone community}: inspired by MLPerf benchmarking, the Capstone leaderboard creates competitive systems engineering challenges where students submit optimized implementations competing across accuracy, speed, compression, and efficiency tracks, building community engagement and peer learning; (3) \textbf{Open-source metrics}: GitHub stars, forks, contributions, and community discussions indicating active use beyond formal coursework; (4) \textbf{Community sustainability}: discussion forums, instructor resource sharing, and collaborative curriculum improvement ensuring long-term educational impact beyond initial release.
+As part of the ML Systems Book ecosystem (\texttt{mlsysbook.ai}), TinyTorch benefits from and contributes to broader educational infrastructure. Integration with the textbook's theoretical foundations~\citep{mlsysbook2025} enables a complete pedagogical pathway: students study production ML systems architecture (distributed training patterns, quantization strategies, deployment considerations), then implement those abstractions in TinyTorch (autograd for backpropagation, INT8 quantization, profiling infrastructure). The open-source model (MIT license) and community-driven development enable collaborative refinement across institutions: instructor discussion forums for pedagogical exchange, shared teaching resources, and empirical validation of learning outcomes.
 
-The submission infrastructure integrates directly into the CLI workflow. Students benchmark their optimized models using Module 20's \texttt{BenchmarkReport} class, generate standardized JSON submissions via \texttt{generate\_submission()}, and submit to the community leaderboard using \texttt{tito community submit submission.json}. The CLI validates submissions against required schema (checking metric ranges, field types, and completeness), displays improvement summary (speedup, compression ratio, accuracy delta), and prepares submissions for leaderboard integration. While leaderboard backend remains under development, the validation infrastructure is production-ready, teaching students professional benchmarking practices: reproducible metrics collection, standardized reporting formats, and schema-driven data validation. Students can also join the global community via \texttt{tito community join} (GitHub-authenticated profiles), view the leaderboard via \texttt{tito community leaderboard} (opens browser), and participate in optimization challenges via \texttt{tito community compete}.
-
-This multi-faceted approach recognizes that educational impact extends beyond traditional classroom metrics to include community building, peer learning, and long-term skill development. The Capstone platform particularly enables students to see how their implementations compare globally, fostering systems thinking through competitive optimization while maintaining educational focus on understanding internals rather than achieving state-of-the-art performance.
+Module 20 (Capstone) culminates the curriculum with competitive systems engineering challenges. Inspired by MLPerf benchmarking~\citep{reddi2020mlperf}, students optimize their implementations across accuracy, speed, compression, and efficiency dimensions, comparing results globally through standardized benchmarking infrastructure. This competitive element reinforces systems thinking: optimization requires measurement-driven decisions (profiling bottlenecks), principled tradeoffs (accuracy versus compression), and reproducible methodology (standardized metrics collection). The focus remains pedagogical—understanding \emph{why} optimizations work—rather than achieving state-of-the-art performance, but the competitive framing increases engagement and mirrors real ML engineering workflows.
 
 \section{Conclusion}
 \label{sec:conclusion}