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1<h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">This research question explores whether current frameworks can produce algorithms that maintain performance across varying problem sizes, structures, and constraints without requiring retraining or significant modification.</p><p class="text-sm text-stone-600 leading-relaxed">For AI-discovered algorithms to be practically useful, must they demonstrate to work reliably across a wide range of inputs and scenarios?</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div><div class="" style="opacity:0;transform:translateY(20px)"><div class="group bg-white border border-stone-200 rounded-2xl overflow-hidden hover:border-stone-300 hover:shadow-lg transition-all duration-300 flex flex-col"><div class="p-6 border-b border-stone-100 flex-1"><div class="flex items-start justify-between gap-4 mb-4"><div class="flex-1"><h3 class="text-xl font-medium text-stone-900 mb-3 group-hover:text-blue-700 transition-colors leading-snug">Do the algorithms these frameworks produce overfit to training data?</h3><p class="text-stone-600 text-sm leading-relaxed line-clamp-2">Examining whether AI-discovered algorithms memorize training instances rather than learning generalizable principles.</p></div></div></div><div class="grid transition-all duration-300 ease-in-out border-b border-stone-100 grid-rows-[0fr] opacity-0"><div class="overflow-hidden"><div class="p-6"><h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">Overfitting is a well-known problem in machine learning. When an AI system discovers an algorithm, it might encode specific patterns from the training distribution rather than discovering the underlying algorithmic principle. This research investigates detection methods, mitigation strategies, and the fundamental tension between training on specific instances and discovering general algorithms.</p><p class="text-sm text-stone-600 leading-relaxed">If AI-discovered algorithms merely memorize training patterns, they will fail on novel inputs and lack the reliability required for production systems. Understanding and preventing overfitting is essential for trustworthy algorithm discovery.</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div><div class="" style="opacity:0;transform:translateY(20px)"><div class="group bg-white border border-stone-200 rounded-2xl overflow-hidden hover:border-stone-300 hover:shadow-lg transition-all duration-300 flex flex-col"><div class="p-6 border-b border-stone-100 flex-1"><div class="flex items-start justify-between gap-4 mb-4"><div class="flex-1">
1<h3 class="text-xl font-medium text-stone-900 mb-3 group-hover:text-blue-700 transition-colors leading-snug">How far are we from the scenario of &#x27;algorithm mining&#x27; where entities with no domain knowledge effectively turn compute into algorithm search?</h3><p class="text-stone-600 text-sm leading-relaxed line-clamp-2">Exploring the democratization of algorithm discovery and the compute-knowledge tradeoff in AI-driven research.</p></div></div></div><div class="grid transition-all duration-300 ease-in-out border-b border-stone-100 grid-rows-[0fr] opacity-0"><div class="overflow-hidden"><div class="p-6"><h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">The vision of &#x27;algorithm mining&#x27; represents a paradigm shift where computational resources can substitute for deep domain expertise in discovering novel algorithms. See Richard Suttons: The Bitter Lesson. This research question examines how close we are to this reality, what barriers remain, and what implications it has for research democratization, industry competition, and the future of computer science. It explores the compute-knowledge frontier and whether we&#x27;re approaching a world where anyone with sufficient compute can discover state-of-the-art algorithms.</p><p class="text-sm text-stone-600 leading-relaxed">Understanding this trajectory is crucial for anticipating how AI will transform research and industry. It raises important questions about access, equity, and the changing nature of expertise in algorithmic innovation.</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div><div class="" style="opacity:0;transform:translateY(20px)"><div class="group bg-white border border-stone-200 rounded-2xl overflow-hidden hover:border-stone-300 hover:shadow-lg transition-all duration-300 flex flex-col"><div class="p-6 border-b border-stone-100 flex-1"><div class="flex items-start justify-between gap-4 mb-4"><div class="flex-1"><h3 class="text-xl font-medium text-stone-900 mb-3 group-hover:text-blue-700 transition-colors leading-snug">How should we think about novelty of algorithmic method vs. optimization of known methods?</h3><p class="text-stone-600 text-sm leading-relaxed line-clamp-2">Investigating the balance between discovering fundamentally new algorithmic approaches and optimizing existing ones.</p></div></div></div><div class="grid transition-all duration-300 ease-in-out border-b border-stone-100 grid-rows-[0fr] opacity-0"><div class="overflow-hidden"><div class="p-6"><h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">This research question addresses a fundamental tension in algorithm discovery: should we prioritize discovering entirely novel algorithmic paradigms, or focus on optimizing well-understood methods and what entity (if any) should be attributed with the novel idea that led to the new method? Novel approaches may unlock breakthroughs but carry higher risk and verification burden, while optimization of known methods offers more predictable improvements but may hit diminishing returns. This question explores evaluation frameworks, resource allocation strategies, and the criteria for judging progress in algorithmic innovation.</p><p class="text-sm text-stone-600 leading-relaxed">As AI systems become more capable of both optimization and discovery, we need frameworks to guide research priorities and evaluate contributions. This question is central to defining what constitutes meaningful progress in algorithmic innovation.</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div><div class="" style="opacity:0;transform:translateY(20px)"><div class="group bg-white border border-stone-200 rounded-2xl overflow-hidden hover:border-stone-300 hover:shadow-lg transition-all duration-300 flex flex-col"><div class="p-6 border-b border-stone-100 flex-1"><div class="flex items-start justify-between gap-4 mb-4"><div class="flex-1">
1<h3 class="text-xl font-medium text-stone-900 mb-3 group-hover:text-blue-700 transition-colors leading-snug">How applicable are these frameworks to real world problems, expanding beyond benchmark settings?</h3><p class="text-stone-600 text-sm leading-relaxed line-clamp-2">Assessing the practical utility of AI-discovered algorithms in production environments and real-world constraints.</p></div></div></div><div class="grid transition-all duration-300 ease-in-out border-b border-stone-100 grid-rows-[0fr] opacity-0"><div class="overflow-hidden"><div class="p-6"><h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">While AI-driven algorithm discovery has shown impressive results on standardized benchmarks, the transition to real-world applications presents unique challenges. Real problems often involve messy data, changing requirements, resource constraints, interpretable coding. This research question evaluates how well current frameworks handle these complexities, identifies gaps between benchmark performance and practical utility, and develops methodologies for testing algorithms in realistic conditions.</p><p class="text-sm text-stone-600 leading-relaxed">For AI-discovered algorithms to achieve widespread adoption, they must demonstrate value in real production environments, not just controlled benchmarks. Understanding the barriers to real-world deployment is essential for directing future research. Companies are springing up that aim to offer AI-discovered algorithms as commercial products, and we will be interacting with them.</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div><div class="" style="opacity:0;transform:translateY(20px)"><div class="group bg-white border border-stone-200 rounded-2xl overflow-hidden hover:border-stone-300 hover:shadow-lg transition-all duration-300 flex flex-col"><div class="p-6 border-b border-stone-100 flex-1"><div class="flex items-start justify-between gap-4 mb-4"><div class="flex-1"><h3 class="text-xl font-medium text-stone-900 mb-3 group-hover:text-blue-700 transition-colors leading-snug">Is it important to develop frameworks which work well when confined to cheaper opensource LLMs? If so, what are the best practices to make this possible?</h3><p class="text-stone-600 text-sm leading-relaxed line-clamp-2">Exploring the feasibility and best practices for algorithm discovery using open-source and cost-effective language models.</p></div></div></div><div class="grid transition-all duration-300 ease-in-out border-b border-stone-100 grid-rows-[0fr] opacity-0"><div class="overflow-hidden"><div class="p-6"><h4 class="text-sm font-medium text-stone-900 mb-3 flex items-center gap-2"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-file-text h-4 w-4 text-blue-600" aria-hidden="true"><path d="M6 22a2 2 0 0 1-2-2V4a2 2 0 0 1 2-2h8a2.4 2.4 0 0 1 1.704.706l3.588 3.588A2.4 2.4 0 0 1 20 8v12a2 2 0 0 1-2 2z"></path><path d="M14 2v5a1 1 0 0 0 1 1h5"></path><path d="M10 9H8"></path><path d="M16 13H8"></path><path d="M16 17H8"></path></svg>About This Question</h4><p class="text-sm text-stone-600 leading-relaxed mb-4">As large language models become central to algorithm discovery frameworks, there is growing concern about reliance on expensive proprietary APIs. This research question investigates whether effective algorithm discovery is possible using smaller, open-source models, and what techniques can bridge the capability gap. It explores distillation methods, specialized fine-tuning approaches, multi-model ensembles, and architectural innovations that can make open-source alternatives viable for algorithm discovery tasks.</p><p class="text-sm text-stone-600 leading-relaxed">Democratizing algorithm discovery requires reducing dependence on expensive proprietary models. If open-source alternatives can be made effective, it would enable broader participation in AI-driven research and reduce barriers for academic and independent researchers.</p></div></div></div><button class="w-full py-3 flex items-center justify-center gap-2 text-sm text-stone-500 hover:text-stone-700 hover:bg-stone-50 transition-colors border-t border-stone-100"><svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round" class="lucide lucide-chevron-down h-4 w-4" aria-hidden="true"><path d="m6 9 6 6 6-6"></path></svg>Show more details</button></div></div></div></div></section><footer class="bg-slate-900 text-white py-12 md:py-16"><div class="max-w-7xl mx-auto px-4 sm:px-6 lg:px-8"><div class="grid grid-cols-1 lg:grid-cols-2 gap-12 lg:gap-20"><div class="" style="opacity:0;transform:translateX(20px)"><div><div class="mb-4"><span class="text-2xl font-normal tracking-tight">AIDDA</span><span class="text-2xl font-light text-stone-400 ml-1">
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