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Amber

AmberLJC
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arthrod's profile picture MitchellCheng's profile picture amsalsaab's profile picture
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https://amberljc.github.io/

AI & ML interests

AI4S, LLM Systems

Recent Activity

upvoted an article 3 days ago
What We Learned by Reproducing 2,200 papers from ICML
liked a dataset 3 days ago
AgentNativeResearchLab/arc-agi3-kimi-k2.7-g50t
reacted to Yuki131's post with 🔥 6 days ago
Test-Time Scaling for Rerankers? Can rerankers scale at test time—not by generating longer reasoning traces, but by selectively using richer document representations? KaLM-Reranker-V1 supports Matryoshka compression from 1× to 32×, which suggests a progressive multi-fidelity pipeline: - Embedding retrieval → Top-100 - KaLM-Reranker @ 32× compression → Top-20 - The same reranker @ 2× compression → final ranking The intuition is simple: cheaply screen many candidates, then allocate higher-fidelity cross-attention only to the most promising ones. For 100@32× → 20@2×, the passage-token interaction budget is roughly 31.8% of directly running 100@2×, before fixed model overheads. The key question is whether it can retain nearly the same ranking quality. We’re considering evaluating nDCG–latency Pareto curves. Would you consider this a useful form of test-time scaling for retrieval? https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Nano https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Small https://huggingface.co/KaLM-Embedding/KaLM-Reranker-V1-Large https://huggingface.co/papers/2606.22807 https://huggingface.co/collections/KaLM-Embedding/lychee-kalm-reranker https://huggingface.co/KaLM-Embedding
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Organizations

ML.ENERGY's profile picture Just-Curieous project team's profile picture Agent Native Research Lab's profile picture