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arxiv: 2605.20170 · v1 · pith:7KT7IHIQnew · submitted 2026-05-19 · 💻 cs.CL

KoRe: Compact Knowledge Representations for Large Language Models

Davide Cavicchini , Fausto Giunchiglia , Jacopo Staiano This is my paper

Pith reviewed 2026-05-20 05:20 UTC · model grok-4.3

classification 💻 cs.CL
keywords knowledge graphslarge language modelsknowledge injectioncompact representationstoken efficiency1-hop subgraphsgrounding LLMsdiscrete tokens
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The pith

Encoding 1-hop knowledge graph subgraphs as compact tokens lets LLMs match benchmark performance with up to 10 times fewer tokens.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents KoRe as a method to convert 1-hop sub-graphs from knowledge graphs into compact discrete knowledge tokens. These tokens are injected directly into the input of an existing large language model. The goal is to supply world knowledge externally rather than relying only on what the model has stored in its parameters. Experiments on three standard benchmarks show that the augmented models reach competitive results while cutting token consumption substantially.

Core claim

KoRe encodes 1-hop sub-graphs into compact discrete knowledge tokens and injects them into an LLM backbone, achieving competitive performance on three established benchmarks with a significant reduction in token usage, up to 10 times, without any retraining or finetuning of the model.

What carries the argument

KoRe, the procedure that turns 1-hop knowledge graph sub-graphs into compact discrete knowledge tokens for direct injection into an LLM.

If this is right

  • External knowledge can be updated or corrected by swapping token sets instead of retraining the whole model.
  • Knowledge-intensive tasks become more resource-efficient because fewer tokens are needed to reach similar accuracy.
  • The knowledge supplied to the model is human-readable and editable at the level of the discrete tokens.
  • LLMs can be grounded in structured sources while keeping the original model weights unchanged.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same compact-token approach could be tried with other structured data sources such as databases or ontologies.
  • Extending the encoding to selected 2-hop paths might improve results on tasks that need chained facts.
  • Making the injected knowledge explicit could reduce certain types of factual errors by letting users inspect the token set.
  • The method suggests a route toward hybrid systems where an LLM receives only the relevant subgraph tokens for each query.

Load-bearing premise

That 1-hop sub-graphs encoded as compact tokens supply enough world knowledge for competitive results on the tested tasks without needing deeper graph connections or any model changes.

What would settle it

A controlled test on a benchmark requiring 2-hop or longer relations where the KoRe-injected LLM scores substantially below a plain LLM baseline while using the same token budget.

Figures

Figures reproduced from arXiv: 2605.20170 by Davide Cavicchini, Fausto Giunchiglia, Jacopo Staiano.

Figure 1
Figure 1. Figure 1: Taxonomy of Knowledge Augmentation Approaches for LLMs while little attention has been given to using this approach to inject factual information into LLMs for natural language tasks such as question answering. The few attempts have so far been validated only on small-scale language models [2], leaving open the question of whether embedding-based discrete injection can yield improvements on modern, larger … view at source ↗
Figure 2
Figure 2. Figure 2: KoRe at a glance: (1) star-graph extraction; (2) GNN encoder; (3) residual vector quantization (RVQ) into Q tokens; (4) dynamic injection at <KG EMBEDDING> placeholders; (5) LLM generation. KnowLA can be considered closest to our proposed approach, as it uses external entity embeddings derived from a Knowledge graph to enhance the LLM. However, it requires embeddings to be pre￾computed before training, sev… view at source ↗
Figure 3
Figure 3. Figure 3: Test Results for our baselines and models on the three datasets. Shaded and full bars indicate Hit1 and Hit5, respectively. • GPU setup: 4 NVIDIA A100 GPUs with 64GB memory each • Training budget: 8 hours limit per experiment to ensure efficient resource usage. • Distributed framework: We make use of the Accelerate library [24] integrated with DeepSpeed [25] for coordinated multi-GPU training with ZeRO [26… view at source ↗
read the original abstract

Modern Large Language Models (LLMs) have shown impressive performances in user-facing tasks such as question answering, as well as consistent improvements in reasoning capabilities. Still, the way these models encode knowledge seems inherently flawed: by design, LLMs encode world-knowledge within their parameters. This way of representing knowledge is inherently opaque, difficult to debug and update, and prone to hallucinations. On the other hand, Knowledge Graphs can provide human-readable and easily editable world knowledge representations, and their application in knowledge-intensive tasks has consistently proven beneficial to downstream performance. Nonetheless, current integration techniques require extensive retraining or finetuning. To overcome this issue, we introduce KoRe, a methodology to encode 1-hop sub-graphs into compact discrete knowledge tokens and inject them into a LLM backbone. We test the proposed approach on three established benchmarks, and report competitive performances coupled with a significant reduction (up to 10x) in token usage. Our results show that compact discrete KG representations can efficiently and effectively be used to ground modern LLMs.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces KoRe, a methodology to encode 1-hop sub-graphs from knowledge graphs into compact discrete knowledge tokens and inject them into an LLM backbone. The approach is tested on three established benchmarks without retraining or finetuning the LLM, claiming competitive performance together with up to a 10x reduction in token usage. The central claim is that such compact discrete KG representations can efficiently and effectively ground modern LLMs.

Significance. If the empirical results hold under scrutiny, the work would offer a practical route to external, editable knowledge grounding that reduces reliance on opaque parametric memory and token overhead. The absence of any machine-checked proofs or parameter-free derivations is noted, but the empirical framing on established benchmarks could still be useful if the 1-hop encoding demonstrably preserves necessary relational structure.

major comments (2)
  1. [Abstract] Abstract: the claim of competitive performance and 10x token reduction is presented without any mention of baselines, datasets, error bars, or statistical significance, so it is not possible to assess whether the data actually support the grounding claim.
  2. [Abstract] Abstract (and implied method): the central claim requires that 1-hop subgraphs contain all necessary world knowledge for the three benchmarks; if any benchmark item depends on multi-hop paths, the discrete encoding cannot supply the missing relations and the competitive-performance result would not follow.
minor comments (2)
  1. The manuscript should clarify the exact tokenization and injection mechanism (e.g., special token vocabulary size, position of injected tokens) so that the 10x reduction can be reproduced.
  2. Add a table or section listing the three benchmarks, their sizes, and the precise metrics used to declare competitiveness.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments. We respond to each major comment below and indicate planned revisions to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim of competitive performance and 10x token reduction is presented without any mention of baselines, datasets, error bars, or statistical significance, so it is not possible to assess whether the data actually support the grounding claim.

    Authors: We agree the abstract's brevity omits these specifics. The full manuscript details the three benchmarks, the baselines compared, and reports results including standard deviations. We will revise the abstract to briefly reference the evaluation setup, benchmarks, and observed token reduction relative to baselines. revision: yes

  2. Referee: [Abstract] Abstract (and implied method): the central claim requires that 1-hop subgraphs contain all necessary world knowledge for the three benchmarks; if any benchmark item depends on multi-hop paths, the discrete encoding cannot supply the missing relations and the competitive-performance result would not follow.

    Authors: KoRe targets 1-hop subgraphs by design, with subgraph extraction tailored per benchmark to include the relations needed for the questions. The experimental section specifies this construction process. We will add a limitations paragraph clarifying the 1-hop scope and noting that multi-hop cases could be addressed via iterative retrieval in future extensions. revision: partial

Circularity Check

0 steps flagged

No circularity: empirical methodology with independent benchmark evaluation

full rationale

The paper introduces KoRe as a practical encoding method that converts 1-hop KG subgraphs into discrete tokens for injection into an LLM backbone, then reports competitive results on three standard benchmarks with reduced token counts. No derivation chain, equations, or first-principles claims are present that could reduce to fitted inputs or self-citations by construction. Performance claims rest on external benchmark testing rather than any self-referential logic or renamed empirical patterns. This is a self-contained empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Based solely on the abstract, the central claim rests on the domain assumption that 1-hop subgraphs plus compact token injection can substitute for full parametric knowledge. No free parameters or invented entities are explicitly described.

axioms (1)
  • domain assumption 1-hop sub-graphs from knowledge graphs contain sufficient information to support competitive performance on the three benchmarks when encoded as compact tokens
    The method is built around the choice to use only 1-hop sub-graphs and to inject them without retraining.

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Reference graph

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