glm-5-2

src/content/docs/concepts/agentic-engineering/assets.json

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+{
+  "conceptMap": {
+    "type": "graph",
+    "graphId": "graph.agentic-engineering-concept-map",
+    "webRenderer": "react-flow",
+    "printRenderer": "vertical-svg",
+    "altKey": "assets.conceptMap.alt",
+    "captionKey": "assets.conceptMap.caption"
+  }
+}

src/content/docs/concepts/agentic-engineering/messages/en.json

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+{
+  "title": "Agentic Engineering",
+  "description": "A concept page that explains the shift from one-shot code generation to longer-running software agents that plan, act, and recover over many steps.",
+  "openingSummary": "Agentic engineering means treating software work as a multi-step loop where the model plans, runs tools, reads feedback, and revises over time. The key difference from vibe coding is that the hard part is no longer one answer, but keeping a long trajectory coherent enough to finish real work.",
+  "sections": {
+    "whatItIs": {
+      "title": "What It Is",
+      "body": "Agentic engineering is a broad term for using models as persistent workers rather than as one-turn autocomplete. The model must remember goals, inspect files or web pages, call tools, react to failures, and keep making progress over many steps."
+    },
+    "whyItMatters": {
+      "title": "Why It Matters",
+      "body": "Long-horizon coding tasks break many systems that look strong in short prompts. Once the task lasts minutes or hours, the quality bottlenecks move toward planning, error recovery, and trajectory management, which is why GLM-5 centers the term instead of treating it as marketing language."
+    },
+    "simpleExample": {
+      "title": "Simple Example",
+      "body": "A coding agent that debugs a failing test suite is doing agentic engineering when it can inspect the failure, edit files, rerun checks, and recover from dead ends. In the GLM-5 bundle, Asynchronous Agent Reinforcement Learning teaches that behavior while the slime Rollout Framework keeps the experiments flowing."
+    },
+    "commonConfusions": {
+      "title": "Common Confusions",
+      "body": "Agentic engineering does not simply mean adding tools to a chatbot. The idea is broader: the model, the training loop, and the rollout system must all support long trajectories. That is why the GLM-5 paper, the GLM-5 model page, the asynchronous RL page, and the slime page should be read together."
+    },
+    "related": {
+      "title": "Related Reference Pages"
+    },
+    "tags": {
+      "title": "Tags"
+    },
+    "references": {
+      "title": "References"
+    }
+  },
+  "assets": {
+    "conceptMap": {
+      "alt": "A concept map linking agentic engineering to long-horizon tasks, asynchronous RL, and the slime rollout system.",
+      "caption": "The term only makes sense when the behavior loop, the trainer loop, and the runtime loop all stay in view."
+    }
+  },
+  "graph": {
+    "nodes": {
+      "agenticEngineering": { "label": "Agentic engineering" },
+      "longHorizon": { "label": "Long-horizon tasks" },
+      "asyncRl": { "label": "Async agent RL" },
+      "slime": { "label": "slime framework" }
+    }
+  }
+}

src/content/docs/concepts/agentic-engineering/page.mdx

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+---
+title: "Agentic Engineering"
+description: "A concept page that explains the shift from one-shot code generation to longer-running software agents that plan, act, and recover over many steps."
+kind: "concept"
+registryId: "concept.agentic-engineering"
+messageNamespace: "local"
+assetNamespace: "local"
+status: "published"
+tags:
+  - foundations
+  - model-family
+aliases:
+  - "agentic engineering"
+  - "coding agents"
+  - "software engineering agents"
+updatedAt: "2026-06-19"
+---
+
+import { CitationList } from "@/features/docs/components/CitationList";
+import { DerivedRelatedDocs } from "@/features/docs/components/DerivedRelatedDocs";
+import { RelatedDocs } from "@/features/docs/components/RelatedDocs";
+import { Section } from "@/features/docs/components/Section";
+import { T } from "@/features/docs/components/T";
+import { TagPillList } from "@/features/docs/components/TagPillList";
+import { ConceptMap } from "@/features/models/components/ConceptMap";
+
+<Section id="what-it-is" titleKey="sections.whatItIs.title">
+  <T k="sections.whatItIs.body" />
+</Section>
+
+<Section id="why-it-matters" titleKey="sections.whyItMatters.title">
+  <T k="sections.whyItMatters.body" />
+</Section>
+
+<Section id="simple-example" titleKey="sections.simpleExample.title">
+  <T k="sections.simpleExample.body" />
+  <ConceptMap registryId="concept.agentic-engineering" assetId="conceptMap" />
+</Section>
+
+<Section id="common-confusions" titleKey="sections.commonConfusions.title">
+  <T k="sections.commonConfusions.body" />
+</Section>
+
+<Section id="related" titleKey="sections.related.title">
+  <DerivedRelatedDocs
+    registryId="concept.agentic-engineering"
+    groups={[
+      "same-concept-type",
+      "shared-tags",
+      "used-by-models",
+      "curated-related"
+    ]}
+  />
+
+  <RelatedDocs registryId="concept.agentic-engineering" />
+</Section>
+
+<Section id="tags" titleKey="sections.tags.title">
+  <TagPillList registryId="concept.agentic-engineering" showDescriptions />
+</Section>
+
+<Section id="references" titleKey="sections.references.title">
+  <CitationList registryId="concept.agentic-engineering" />
+</Section>

src/content/docs/models/glm-5/assets.json

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+{
+  "architectureGraph": {
+    "type": "graph",
+    "graphId": "graph.glm-5-architecture",
+    "webRenderer": "react-flow",
+    "printRenderer": "vertical-svg",
+    "altKey": "assets.architectureGraph.alt",
+    "captionKey": "assets.architectureGraph.caption"
+  }
+}

src/content/docs/models/glm-5/messages/en.json

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+{
+  "title": "GLM-5",
+  "description": "A model page for the GLM-5 release that explains its sparse MoE shape, long-context attention choices, and linked training and rollout systems.",
+  "openingSummary": "GLM-5 is a large coding-focused model family built around sparse expert routing, long-context attention efficiency, and post-training meant for long-running agents. The key reader payoff is seeing how the model, its asynchronous RL recipe, and the slime rollout stack fit together without reading the whole paper first.",
+  "sections": {
+    "whatItIs": {
+      "title": "What It Is",
+      "body": "GLM-5 is the flagship model release described in the GLM-5 paper from Zhipu AI. It is best understood as a sparse MoE language model tuned for coding, reasoning, and agent-style workflows rather than as a small architectural tweak on its own."
+    },
+    "inputsAndOutputs": {
+      "title": "Inputs And Outputs",
+      "body": "The public paper frames GLM-5 as a text model for long-context reasoning and software engineering tasks. In practice, the important interface is not only next-token generation but also the ability to sustain longer tool-using trajectories with fewer costly stalls."
+    },
+    "architecture": {
+      "title": "Architecture",
+      "body": "The release combines a sparse expert backbone with long-context attention choices borrowed from the nearby Multi-head Latent Attention and Sparse Attention family discussions. The paper also reports a 744B-parameter model with 40B active parameters, 256 experts, and design choices meant to keep long-context coding practical instead of purely maximal."
+    },
+    "importantModules": {
+      "title": "Important Modules",
+      "body": "The nearby module pages matter because GLM-5 is not explained by a single proprietary block. Multi-head Latent Attention, Sparse Attention, Mixture of Experts, and RMSNorm give the cleanest public view of the efficiency and backbone choices around the release."
+    },
+    "training": {
+      "title": "Training",
+      "body": "The model page links directly to Asynchronous Agent Reinforcement Learning for the algorithmic post-training loop and back to the GLM-5 paper for the broader recipe. On-Policy Distillation remains relevant as a nearby existing regime because GLM-5 still treats knowledge retention across post-training stages as a first-class concern."
+    },
+    "practicalNotes": {
+      "title": "Practical Notes",
+      "body": "Readers should treat GLM-5 as a release bundle, not a single canonical module. If you want the broad product shift, open Agentic Engineering; if you want the trainer loop, open Asynchronous Agent Reinforcement Learning; if you want the runtime layer, open the slime Rollout Framework."
+    },
+    "related": {
+      "title": "Related Reference Pages"
+    },
+    "tags": {
+      "title": "Tags"
+    },
+    "references": {
+      "title": "References"
+    }
+  },
+  "assets": {
+    "architectureGraph": {
+      "alt": "A compact architecture sketch for GLM-5 showing the model root connected to MoE backbone, long-context attention, and asynchronous post-training.",
+      "caption": "This page emphasizes three teaching layers: sparse backbone, long-context attention, and agent-focused post-training."
+    }
+  },
+  "graph": {
+    "nodes": {
+      "glm5": {
+        "label": "GLM-5",
+        "summary": "A coding-focused sparse MoE release."
+      },
+      "moeBackbone": {
+        "label": "MoE backbone",
+        "summary": "Sparse experts increase capacity without activating all parameters at once."
+      },
+      "longContext": {
+        "label": "Long-context attention",
+        "summary": "Nearby attention variants explain the efficiency story around the release."
+      },
+      "postTraining": {
+        "label": "Async post-training",
+        "summary": "Agent RL and rollout systems are part of the model story, not an afterthought."
+      }
+    }
+  }
+}

src/content/docs/models/glm-5/page.mdx

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+---
+title: "GLM-5"
+description: "A model page for the GLM-5 release that explains its sparse MoE shape, long-context attention choices, and linked training and rollout systems."
+kind: "model"
+registryId: "model.glm-5"
+messageNamespace: "local"
+assetNamespace: "local"
+status: "published"
+tags:
+  - foundations
+  - context-window
+  - model-family
+aliases:
+  - "GLM-5"
+  - "GLM 5"
+updatedAt: "2026-06-19"
+---
+
+import { CitationList } from "@/features/docs/components/CitationList";
+import { DerivedRelatedDocs } from "@/features/docs/components/DerivedRelatedDocs";
+import { Section } from "@/features/docs/components/Section";
+import { T } from "@/features/docs/components/T";
+import { TagPillList } from "@/features/docs/components/TagPillList";
+import { ModelArchitectureGraph } from "@/features/models/components/ModelArchitectureGraph";
+import { ModelAtAGlance } from "@/features/models/components/ModelAtAGlance";
+import { ModelModuleList } from "@/features/models/components/ModelModuleList";
+import { ModelTrainingSummary } from "@/features/models/components/ModelTrainingSummary";
+
+
+<ModelAtAGlance registryId="model.glm-5" />
+
+<Section id="what-it-is" titleKey="sections.whatItIs.title">
+  <T k="sections.whatItIs.body" />
+</Section>
+
+<Section id="inputs-and-outputs" titleKey="sections.inputsAndOutputs.title">
+  <T k="sections.inputsAndOutputs.body" />
+</Section>
+
+<Section id="architecture" titleKey="sections.architecture.title">
+  <T k="sections.architecture.body" />
+  <ModelArchitectureGraph registryId="model.glm-5" assetId="architectureGraph" />
+</Section>
+
+<Section id="important-modules" titleKey="sections.importantModules.title">
+  <T k="sections.importantModules.body" />
+  <ModelModuleList registryId="model.glm-5" />
+</Section>
+
+<Section id="training" titleKey="sections.training.title">
+  <T k="sections.training.body" />
+  <ModelTrainingSummary registryId="model.glm-5" />
+</Section>
+
+<Section id="practical-notes" titleKey="sections.practicalNotes.title">
+  <T k="sections.practicalNotes.body" />
+</Section>
+
+<Section id="related" titleKey="sections.related.title">
+  <DerivedRelatedDocs
+    registryId="model.glm-5"
+    groups={[
+      "same-model-family",
+      "shared-modules",
+      "shared-training-regimes",
+      "shared-tags",
+      "curated-related"
+    ]}
+  />
+</Section>
+
+<Section id="tags" titleKey="sections.tags.title">
+  <TagPillList registryId="model.glm-5" showDescriptions />
+</Section>
+
+<Section id="references" titleKey="sections.references.title">
+  <CitationList registryId="model.glm-5" />
+</Section>

src/content/docs/papers/glm-5/assets.json

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+{
+  "contributionGraph": {
+    "type": "graph",
+    "graphId": "graph.glm-5-contribution",
+    "webRenderer": "react-flow",
+    "printRenderer": "vertical-svg",
+    "altKey": "assets.contributionGraph.alt",
+    "captionKey": "assets.contributionGraph.caption"
+  }
+}

src/content/docs/papers/glm-5/messages/en.json

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+{
+  "title": "GLM-5: from Vibe Coding to Agentic Engineering",
+  "description": "A paper page that breaks GLM-5 into its model, agent-training, and rollout-system contributions so readers can navigate the release without reading the full report first.",
+  "openingSummary": "GLM-5 matters because it treats coding agents as long-running systems rather than one-shot code generators. The paper combines a large sparse MoE model, asynchronous agent reinforcement learning, and the slime rollout framework into one practical story about making agentic engineering work at scale.",
+  "sections": {
+    "whatThePaperIntroduced": {
+      "title": "What The Paper Introduced",
+      "body": "The report introduces the GLM-5 model page, the Agentic Engineering concept, Asynchronous Agent Reinforcement Learning, and the slime Rollout Framework as one connected bundle. It also anchors GLM-5 to nearby reference pages such as Multi-head Latent Attention, Sparse Attention, and On-Policy Distillation so readers can move from the flagship release into the reusable parts."
+    },
+    "whyItMatters": {
+      "title": "Why It Matters",
+      "body": "Many model reports describe one model and then leave the training and runtime story scattered across appendices. GLM-5 matters because it argues that real coding agents need all three layers at once: a capable base model, a post-training loop that can learn from long trajectories, and a rollout stack that keeps those trajectories flowing."
+    },
+    "methodOrArchitecture": {
+      "title": "Method Or Architecture",
+      "body": "At a high level, GLM-5 keeps a large MoE language model but pushes harder on long-context efficiency and agent post-training. The model route is GLM-5, the broad idea is Agentic Engineering, the post-training algorithm is Asynchronous Agent Reinforcement Learning, and the rollout substrate is the slime Rollout Framework."
+    },
+    "evidence": {
+      "title": "Evidence",
+      "body": "For this reference page, the useful evidence is not the leaderboard table itself. The important signal is that the paper reports stronger long-horizon coding behavior after pairing asynchronous rollout generation, filtered trajectory updates, and a rollout framework tuned for tail latency and fault tolerance."
+    },
+    "limitations": {
+      "title": "Limitations",
+      "body": "The paper is broad, so some claims are stack-level rather than clean single-variable proofs. Readers should treat the release as evidence for a combined recipe, not as a guarantee that every isolated ingredient always transfers unchanged to other agent stacks."
+    },
+    "whatItConnectsTo": {
+      "title": "What It Connects To",
+      "body": "This record map is the fastest way to jump from the report into the canonical pages it introduced and the nearby existing references it depends on."
+    },
+    "related": {
+      "title": "Related Reference Pages"
+    },
+    "tags": {
+      "title": "Tags"
+    },
+    "references": {
+      "title": "References"
+    }
+  },
+  "assets": {
+    "contributionGraph": {
+      "alt": "A contribution map that splits GLM-5 into architecture, asynchronous training, rollout systems, and agentic engineering.",
+      "caption": "GLM-5 reads best as four connected branches: model architecture, asynchronous training, rollout infrastructure, and the broader shift toward agentic engineering."
+    }
+  },
+  "graph": {
+    "nodes": {
+      "paper": { "label": "GLM-5 report" },
+      "architecture": { "label": "Model architecture" },
+      "training": { "label": "Async agent RL" },
+      "systems": { "label": "slime rollout stack" },
+      "agentic": { "label": "Agentic engineering" }
+    }
+  }
+}