Scaling Laws: Where Returns Diminish
Where LLM scaling laws still help, where returns diminish, and how builders should weigh data, compute, evals, and inference cost.
Read →AGI is not a single destination — it is a race on multiple parallel tracks. Track every research path, lab, and debate shaping the next decade of intelligence.
The dominant paradigm since 2020: more parameters, more data, more compute. Now approaching diminishing returns on core reasoning tasks — the central debate of the decade.
Internal predictive representations of the world enabling planning and counterfactual reasoning. LeCun's JEPA is the leading architecture proposal. Gaining momentum rapidly.
Intelligence grounded in physical interaction with the world. Robots, dexterous manipulation, sim-to-real transfer. Figure AI, 1X, and Physical Intelligence leading commercialization.
Combining neural pattern recognition with symbolic reasoning and formal logic. Tackles compositionality, systematic generalization, and causal inference.
Societies of AI agents coordinating, competing, and specializing. Foundations for AI-driven economies, scientific collaboration, and emergent collective intelligence.
AlphaFold changed biology. AlphaGeometry changed math. AI is now a peer tool in every scientific domain — materials, climate, drug discovery, physics.
Reinforcement learning from human feedback underpins every frontier model. Pure RL agents demonstrate reasoning through search. RLHF, RLAIF, and process reward models.
Brain-inspired computing: spiking neural networks, analog chips, in-memory computing. Intel Loihi, IBM NorthPole. Targeting 1000× energy efficiency gains.
We propose a self-play framework that trains world models by having agents compete and cooperate in procedurally generated environments.
We scale VLA models to 7B parameters using a novel cross-embodiment dataset of 200M manipulation trajectories.
We show that CoT reasoning pathways emerge spontaneously in sufficiently large transformers without any explicit prompting.
Extending AlphaGeometry, we achieve gold-medal performance on 96% of IMO geometry problems using synthetic deduction corpora.
We introduce a hierarchical constitutional framework where abstract principles generate specific rules for finer behavioral control.
When populations of LLM agents interact over thousands of rounds, specialist roles and protocols emerge without explicit programming.
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Longform on labs, agents, and scaling — same tracks as the map above.
Where LLM scaling laws still help, where returns diminish, and how builders should weigh data, compute, evals, and inference cost.
Read →Supervisor, swarm, pipeline, and router patterns for multi-agent LLM systems—with failure modes and observability hooks.
Read →Track frontier AI labs in 2026 without hype: release patterns, access modes, builder watch signals, and what to ignore.
Read →Chatbot vs AI agent map: tool use, multi-agent, memory, observability, and when not to agentize. Desk ladder for builders, Aug 2026.
Read →Boston Dynamics still sells reliability in known factories; Figure is betting foundation models will generalize across messy homes. The split is data collection and fallback behavior — not which demo video looks more human.
Read →Self-alignment papers usually move the label budget, not remove humans. Ask what the preference model still needs, and whether SOTA is on a public bench or a private suite.
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