Bin Ju, Shenfeng Weng, Danying Zhou, Rongkai Xu, Kunkai Su
We lifted 6 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jubin75/KVI | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| detect_lang | Ran | jubin75/KVI/src/cleaning_and_dedupe.py pointer only (licence: NONE) · get_code("2f5a1613dee9268c") |
| normalize_text | Ran | jubin75/KVI/src/cleaning_and_dedupe.py pointer only (licence: NONE) · get_code("5aa9f2fbc850bd95") |
| parse_summary | Ran | jubin75/KVI/experiments/exp02_hallucination/code/auto_truthfulqa_kvi_iterate.py pointer only (licence: NONE) · get_code("9a1bf2a40c65de6f") |
| simhash64 | Ran | jubin75/KVI/src/cleaning_and_dedupe.py pointer only (licence: NONE) · get_code("5f7a57f94c04f099") |
| write_jsonl | Ran | jubin75/KVI/src/chunk_store.py pointer only (licence: NONE) · get_code("97adfe8849bb02b8") |
| run_one | Not yet run | jubin75/KVI/experiments/exp02_hallucination/code/auto_truthfulqa_kvi_iterate.py pointer only (licence: NONE) · get_code("3e104c93479435e0") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Large language models (LLMs) encode knowledge in parametric weights, making it costly to update or extend without retraining. Retrieval-augmented generation (RAG) mitigates this limitation by appending retrieved text to the input, but operates purely through context expansion, where external knowledge competes as tokens within the attention mechanism. As a result, its influence is indirect and often unstable, particularly in long-context and multi-hop reasoning scenarios. We propose Knowledge Capsules, structured nonparametric memory units that represent normalized relational knowledge and can be constructed directly from document corpora using a frozen base model. Instead of injecting knowledge as text, we introduce an External Key-Value Injection (KVI) framework that compiles capsules into attention-compatible key-value representations, enabling external knowledge to directly participate in the model's attention computation. By shifting knowledge integration from context-level augmentation to memory-level interaction, the proposed framework consistently outperforms RAG and GraphRAG across multiple QA benchmarks, with improved stability and accuracy in long-context and multi-hop reasoning, while requiring no parameter updates. Code and datasets are available at https://github.com/jubin75/KVI.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2604.20487")
get_code_for_paper("2604.20487")
have("2604.20487")
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