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Jeff Dean says models hallucinate because their training data is “squishy.” But what’s in the context window is sharp, the exact text or video frame right in front of them. The goal is to bring more of the world into that context. Compression gives it intuition. Context gives it precision.

vitrupo

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41,638 次观看 • 5 个月前 •via X (Twitter)

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Here is the video with context of what’s happened in that night

Here is the video with context of what’s happened in that night

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In-context learning is the holy grail of robot learning. It is challenging because: 1. long-context training and infra (ICL can easily max out disk I/O) 2. need model to follow multimodal (sensorimotor) condition 3. data collection strategy and how to pair data Tried to get it work in 2024, where we proposed in-context robot transformer, an autoregressive formulation: Congrats on the launch! Looking forward to seeing few-shot improvement via in-context learning in the future!

In-context learning is the holy grail of robot learning. It is challenging because: 1. long-context training and infra (ICL can easily max out disk I/O) 2. need model to follow multimodal (sensorimotor) condition 3. data collection strategy and how to pair data Tried to get it work in 2024, where we proposed in-context robot transformer, an autoregressive formulation: Congrats on the launch! Looking forward to seeing few-shot improvement via in-context learning in the future!

Max Fu

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Everyone wants agent swarms. Very few people are talking seriously enough about the context layer that makes swarms useful. Even with one agent, context is fragile. Too little context and the agent guesses. Too much context and it wastes tokens, loses focus, or reasons over irrelevant noise. The sweet spot is precise context: the right knowledge, in the right structure, at the right moment. With many agents, that challenge explodes. Each agent produces decisions, assumptions, findings, summaries, risks, and partial conclusions. Unless that knowledge becomes shared, structured, and reusable, every new agent is forced to rediscover what another agent already learned. That is not a swarm. That is a crowd. Shared context graphs are what turn agent activity into agent collaboration, and OriginTrail DKG V10 brings them to life. Was just playing with some final polishing for the V10 release, and it is really powerful to see shared context graphs where multiple agents contribute knowledge into the same connected memory, with attribution visible directly in the graph ui. That matters for three reasons. First, agents can access and build on one shared memory instead of staying trapped in isolated sessions. Second, the graph structure helps them retrieve the exact context they need, instead of stuffing everything into a prompt and hoping the model sorts it out. Third, verifiability of provenance. You can see which agent contributed each piece of knowledge, trace the source, and decide what to trust. Tokenmaxxing starts with fewer tokens, but the deeper story is coordination - agents stop reloading the world and start building on shared, verifiable context. That is the foundation for serious multi-agent work across software engineering, research, finance, operations, project management, and far beyond. The future is not more agents, it is agents working from shared, verifiable context. But the more the merrier, of course.

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Long video generation usually results in context increasing/scaling during chunk/frame-wise rollout. Considering context scaling may require context selection, we thus introduce the idea of MoE into long context modelling and propose Mixture of Contexts. All previous context/memory is considered while the chosen ones are computed in a data-driven manner. You can easily enjoy 7x compute savings.

Long video generation usually results in context increasing/scaling during chunk/frame-wise rollout. Considering context scaling may require context selection, we thus introduce the idea of MoE into long context modelling and propose Mixture of Contexts. All previous context/memory is considered while the chosen ones are computed in a data-driven manner. You can easily enjoy 7x compute savings.

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