Not abstracts. Deep explanations that reveal why previous approaches failed, what actually drives the results, and when the method breaks down.
Read PapersThe ONE novel idea—not what problem they solved, but the intellectual contribution that didn't exist before.
Specific technical reasons existing approaches break. What exactly goes wrong and why.
Key equations with intuition. Algorithm steps. Architecture decisions and WHY they were made.
Click-through SVG architectures showing data flow step by step. See it, don't imagine it.
Ablation insights. Which components drive the gains, which are noise. What's cherry-picked vs robust.
When does this break? What conditions must hold? What did they NOT test? Hidden assumptions exposed.
You can hack RoPE (Rotary Position Embedding) to create multi-shot videos without changing the model architecture: insert phase shifts at shot boundaries to signal transitions, and map reference images to specific spatiotemporal coordinates to control where subjects appear.
Read Full SummaryDeep Research is RAG's evolution into autonomous agency: the LLM doesn't just retrieve-then-answer—it plans query decomposition, decides WHEN to retrieve based on confidence, manages working memory across long horizons, and synthesizes verifiable reports with citations.
Read Full SummaryStore sparse keyframe snapshots (not dense 3D reconstructions) as a memory graph, localize via image-to-instance hybrid retrieval, and run global planning at low frequency while local control operates at high frequency. This mimics how humans actually navigate: "I remember that corner" not "I have a point cloud of every surface."
Read Full SummaryA lightweight "lightning indexer" can learn which tokens matter for attention, reducing O(L²) to O(Lk) complexity while preserving quality. Combined with allocating >10% of pre-training compute to post-training RL, this unlocks frontier-level reasoning in open models.
Read Full SummaryAn 8B parameter model trained with multi-objective reinforcement learning (correctness + efficiency + user preference) can orchestrate stronger models and tools to outperform GPT-5 at 30% of the cost. The key insight: the "brain" coordinating the system doesn't need to be the biggest component—it just needs to learn WHEN to call expensive resources.
Read Full SummaryThese summaries are for researchers who want to understand, not skim. They're preparation for reading papers with the right mental model—not a replacement.