Researchers from Moonshot AI and Tsinghua University just introduced Prefill-as-a-Service (PrfaaS). The system breaks the requirement for expensive, high-speed local connections by offloading heavy initial memory setup to remote, specialized clusters. It uses smart scheduling
LLMS
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8 LLM Types Powering AI Agents and Agentic Systems
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8 types of LLMs used in AI agents GPT • MoE • LRM • VLM • SLM • LAM • HRM • LCM Different models for reasoning, perception, planning, and action — not just chat. Agentic AI = model orchestration. #AI #LLMs #AgenticAI #GenAI #MachineLearning
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Meta AI Proposes Neural Computer Where AI Becomes the Hardware
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The day AI replaces the computer wasn't supposed to come this fast.
— AlphaSignal AI (@AlphaSignalAI) 18 avril 2026
Right now, AI uses computers as tools.
This paper asks a different question: What if AI became the computer itself?
Neural Computer is a new paper from Meta AI.
It proposes a machine where computation,… pic.twitter.com/1AjqEC9LprThe day AI replaces the computer wasn't supposed to come this fast. Right now, AI uses computers as tools. This paper asks a different question: What if AI became the computer itself? Neural Computer is a new paper from Meta AI. It proposes a machine where computation,
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AI Agent Progress: Real Gains Beyond Hype Metrics
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The stagnation take holds up until you ask it to compete with actual evals. Coherent 30-minute agent runs, tool-call reliability on complex schemas, long-context retrieval that finally works. The progress is there, it just isn't a dunk thread.
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Agentic RAG: Planning Over Agent Labels in Retrieval Systems
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The real contrast isn't static retrieval vs. adaptive. It's whether the retrieval layer can decide to stop, re-plan, and try a different tool. Agentic RAG is just RAG with a planner in front of it. The name is new, the accuracy gain is from the planning, not the "agent" label.
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Long-run model consistency becomes key performance benchmark
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Long uninterrupted runs are the new benchmark. A model that doesn't stop and start on a 50k-token refactor is shipping a different product than one that does, even if they score similar on short tasks. 4.6 stopping was masking how sensitive the previous loop was to noise.
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Adaptive Thinking Trade-off: Token Burn vs Performance Regression
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The adaptive thinking burns more tokens and the results drop. That's a regression no matter how the marketing reads. The real question is whether this is a calibration bug fixable in a point patch or a deeper reward-shaping choice that won't roll back… in any case, not so happy
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API Access Restrictions Shape Open vs Closed Research Agendas
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Frontier API access shrinking is already shaping research agendas. The labs that publish and open-weight find the bugs the closed ones can't, because no single research setup is broad enough.
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Deep Stack Attention Hijacking: Beyond Prompt Injection Vulnerabilities
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a lot of injection research focuses on the prompt surface and misses that the vulnerability is actually in how routing attention gets hijacked deep in the stack
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GPT 4.5 Peak Writing Style Regression in AI Models
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Writing style. It’s become way too dry and robotic. GPT 4.5 was the peak, and it has been regressing since.