Live Knowledge Graphs: Runtime Graph Integration for Continuous Domain Adaptation in Language Agents
Live Knowledge Graphs argues that knowledge graphs should remain active at inference time. Instead of distilling a graph into weights and discarding it, a language agent can query a live graph, receive a provenance-complete context slice, and answer against current evidence.
Paper workspace
Live draft structure
Artifacts
Markdown paper source
Runtime knowledge graph paper mapped from the local source manuscript.
source-only
Editable source
Markdown paper source exists. It should converge with the Graph Kernel paper without duplicating claims.
Source anchors
live-knowledge-graphs/paper/paper.md
Comp-Core/papers/runtime-knowledge-graphs/paper.md
Comp-Core cc-graph-kernel service
Method tags
Ingest intersections
Status
Paper drafted; cc-graph-kernel production service is the implementation substrate.
Key claims
01
Training-time graph integration goes stale in long-running agent systems.
02
Runtime context slicing preserves currency and auditability.
03
Admissibility tokens make graph-grounded context verifiable.
Public reading note
Paper drafted; public reader page safe.
Standard skeleton
What this paper must keep proving
problem
Training-time graph integration goes stale when an agent's domain changes daily.
method
Query the graph live during inference and inject provenance-complete context slices rather than static summaries.
implementation
Rust cc-graph-kernel, context slicer, graph traversal endpoints, admissibility token verification.
data
Production graph triples and path-evaluation snapshots; public release should expose aggregate metrics, not private graph contents.
evaluation
Path ranking, context-slice integrity, latency, provenance completeness, and hard-negative discrimination.
references
GraphRAG, KAPING, DSS/knowledge-graph curricula, retrieval-augmented generation.
openQuestions
Which parts should be merged into Graph Kernel v2 versus remaining a companion paper.
Checkpoints and references
Proof chain
Claim checkpoint
central-claim slot
Every central claim must point to a proof anchor or remain labeled as speculative.
Implementation checkpoint
implementation-map slot
Every method should identify the code path, harness, schema, or protocol that embodies it.
Evidence checkpoint
evidence-manifest slot
Every reported result should point to run IDs, packet IDs, data snapshots, commits, or review artifacts.
Reference checkpoint
references slot
Every external claim should resolve to a cited paper, benchmark, standard, or documented prior system.
Release checkpoint
release-gate slot
Every PDF needs a named condition before it can move from draft to citation-ready.