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namanvats
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upvoted a paper about 7 hours ago
Kronos: A Foundation Model for the Language of Financial Markets
reacted to Juanxi's post with 🔥 about 7 hours ago
📢 Awesome Multimodal Modeling We introduce Awesome Multimodal Modeling, a curated repository tracing the architectural evolution of multimodal intelligence—from foundational fusion to native omni-models. 🔹 Taxonomy & Evolution: Traditional Multimodal Learning – Foundational work on representation, fusion, and alignment. Multimodal LLMs (MLLMs) – Architectures connecting vision encoders to LLMs for understanding. Unified Multimodal Models (UMMs) – Models unifying Understanding + Generation via Diffusion, Autoregressive, or Hybrid paradigms. Native Multimodal Models (NMMs) – Models trained from scratch on all modalities; contrasts early vs. late fusion under scaling laws. 💡 Key Distinction: UMMs unify tasks via generation heads; NMMs enforce interleaving through joint pre-training. 🔗 Explore & Contribute: https://github.com/OpenEnvision-Lab/Awesome-Multimodal-Modeling
posted an update about 7 hours ago
Ran a small controlled study on a frozen 40-task slice of Harbor Terminal-Bench-Pro, using the same model (`minimax/minimax-m2.5`) with two agent harnesses: Goose and OpenHands-SDK. Under the base setup, reducing the turn budget from 100 to 60 pushed the two harnesses in opposite directions: * Goose: 0.450 → 0.525 * OpenHands-SDK: 0.575 → 0.500 A tweaked 60-turn setup brought OpenHands-SDK back to 0.575. At their best, both harnesses reached the same 0.575 pass rate. What surprised me most was the token profile: in this setup, the reported token usage for OpenHands-SDK was dramatically higher than Goose while converging to the same best score. Same model, same task slice, different harness behavior under a tighter interaction budget. Dataset: https://huggingface.co/datasets/namanvats/harbor-goose-openhands-benchmark Code/configs: https://github.com/namanvats/harbor-agent-ablation
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