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Jeremy Hill

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reacted to kanaria007's post with 🚀 15 days ago
✅ Article highlight: *Embodied SI-Core: Observation, Homeostasis, Reflexes, and Safe Actuation* (art-60-178, v0.1) TL;DR: This article argues that SI-Core does not stop at text, tools, or simulated policy. Once a system can sense, self-regulate, react, and actuate, governance must reach the sensing and motion boundary. Embodied SI-Core keeps observation, ethics, rollback, memory, and evaluation alive across perception, internal state, reflex paths, and actuator-safe execution. Read: https://huggingface.co/datasets/kanaria007/agi-structural-intelligence-protocols/blob/main/article/60-supplements/art-60-178-embodied-si-core.md Why it matters: • treats perception and actuation as governed runtime surfaces • keeps fast reflex paths inside bounded ethics and rollback discipline • makes internal state part of routing, not just telemetry • blocks under-observed motion from becoming a world effect • connects robots, avatars, vehicles, prosthetics, edge devices, and simulated actors under one frame What’s inside: • embodied observation bundles with coverage and confidence • HOMEODYNA-style internal-state tension and jump suppression • REFLEXIA-style bounded low-latency reflex routing • KINETICA-style intent-to-actuation planning • execution monitoring, safe-stop, rollback, and reentry logs • an embodied runtime arc from raw sensory inputs to append-only memory Key idea: Do not say: *“the agent saw something and acted.”* Say: *“this embodied system parsed the observation, checked internal-state tension, selected a governed route, bound action through ethics and reversibility, monitored execution, and reentered memory with receipts.”* Sense structurally. Regulate internally. React only within bounds. Actuate with receipts.
liked a Space 15 days ago
SupraLabs/Supra-50M-Reasoning-Demo
reacted to AxionLab-official's post with 🔥 15 days ago
# An Open Letter from SupraLabs. Over the past few days, SupraLabs has been mentioned in a public discussion regarding small language models, scaling laws, and training methodology. We'd like to clarify our position. Before anything else, we want to make one thing absolutely clear: we have great respect for Lane and the work being done at Glint Research. At no point was our intention to disrespect Lane, Glint Research, or their research. What began as a technical discussion about model scaling and training methodology unfortunately became much more personal than we ever intended. From our perspective, it was simply an exchange of technical opinions, and we sincerely hope it remains that way. We'd also like to acknowledge that one of our own comments during the discussion was poorly worded. Referring to a benchmark as "fake" was imprecise. What we intended to criticize was the comparison methodology, not the integrity of the evaluation itself. Comparing a merged checkpoint against a single checkpoint is, in our view, not an apples-to-apples comparison. That said, this was never the core of the discussion. Our disagreement was not about SLERP, model merging, or whether training a small model on massive amounts of data is an interesting research direction. We support experimentation and unconventional ideas. The actual point of disagreement was much simpler. The statement that a 1M parameter model trained on 1 trillion tokens will become a "100M killer" is, today, a prediction, not an experimental result. Could it happen? Perhaps. Would it be exciting if it did? Absolutely. But until benchmark results, reproducible evaluations, and independent validation exist, we believe such statements should be presented as hypotheses rather than established conclusions. Research advances by testing ideas, not by assuming their outcomes. We sincerely wish Lane and everyone at Glint Research success in their experiments. Thank you to everyone who read it.
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