arxiv: 2604.15153 · v2 · submitted 2026-04-16 · 💻 cs.CL · cs.AI

Recognition: unknown

Compressing Sequences in the Latent Embedding Space: K-Token Merging for Large Language Models

Zihao Xu , John Harvill , Ziwei Fan , Yizhou Sun , Hao Ding , Hao Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:24 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords token mergingLLM compressionlatent embedding spaceprompt compressionLoRA adaptationinput length reductionPareto frontier

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The pith

K-Token Merging reduces LLM input lengths by merging blocks of K token embeddings in latent space while recovering performance via LoRA adaptation.

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

The paper proposes compressing long prompts for large language models by working in the space of token embeddings instead of raw tokens. It groups every K consecutive embeddings, replaces each group with a single vector produced by a small encoder, and then runs the shorter sequence through a lightly adapted version of the original model. This matters because self-attention cost grows quadratically with sequence length, so fewer tokens directly cut memory and compute. Tests on tree reasoning, review sentiment, and code editing tasks show the approach reaches the same accuracy-compression trade-off as prior methods but often with higher compression ratios. Generation still uses the model's original vocabulary because only the input side is shortened.

Core claim

K-Token Merging merges each contiguous block of K token embeddings into one embedding using a lightweight encoder. The compressed sequence is fed to a LoRA-adapted LLM while generation remains in the original vocabulary. On structural reasoning, sentiment classification, and code editing benchmarks, this yields up to 75 percent input length reduction with minimal performance loss and places the method on the Pareto frontier of accuracy versus compression.

What carries the argument

K-Token Merging, which replaces every block of K contiguous token embeddings with a single learned embedding from a lightweight encoder before LoRA-adapted processing.

If this is right

Attention computation in the main model scales down quadratically with the chosen K factor.
The same base LLM can handle both compressed and full-length inputs after one LoRA stage.
Compression occurs once at the start, leaving the decoder unchanged and vocabulary intact.
The method applies uniformly across tasks that tolerate some loss of fine token detail.

Where Pith is reading between the lines

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

Similar merging might apply to non-text sequences whose embeddings show local redundancy.
Combining K-Token Merging with existing token-level pruning could reach even higher ratios.
The approach implies that task-critical information often survives coarse-grained embedding aggregation.
Downstream models trained from scratch on merged embeddings might learn different internal representations than those adapted post hoc.

Load-bearing premise

A lightweight encoder can merge K token embeddings while retaining enough task-relevant information for LoRA adaptation to recover near-original LLM performance.

What would settle it

On a held-out task, if K=4 merging followed by LoRA adaptation produces more than a 10 percent accuracy drop relative to the unmerged baseline after standard tuning, the claim that merging preserves recoverable information would be falsified.

Figures

Figures reproduced from arXiv: 2604.15153 by Hao Ding, Hao Wang, John Harvill, Yizhou Sun, Zihao Xu, Ziwei Fan.

**Figure 1.** Figure 1: Our K-Token Merging method (K = 4) achieves a 75% reduction in input length with only a 1.59% drop in accuracy on the Textualized Tree benchmark, demonstrating that it exploits redundancy in the latent embedding space while preserving high performance. See the “Experiments” section for details. memory and compute requirements of LLMs grow quadratically with input length. A natural direction to mitigate t… view at source ↗

**Figure 2.** Figure 2: Model Structure for K-Token Merging Model (Case K = 2). Left: During the prefill stage, the encoder f takes each K consecutive input tokens and produces a single compressed token embedding. Here, Ti denotes the original input tokens and Ci denotes the resulting compressed tokens. Right: During the generation stage, the LLM outputs original (uncompressed) tokens. Each newly generated token is appended to th… view at source ↗

**Figure 3.** Figure 3: Datasets & Tasks. Left: Textualized Tree. Given a textualized indentation tree, the LLM determines whether two nodes have a parent - child relationship. Middle: Amazon Reviews. The LLM performs sentiment classification to judge whether a product review is positive or negative. Right: CommitPackFT. Given code and an update instruction, the LLM needs to output the modified code that follows the instruction. … view at source ↗

**Figure 4.** Figure 4: Performance Score (Accuracy / Perplexity) vs. Length Reduction Ratio on three datasets: (a) Textualized Tree, (b) Amazon Reviews, and (c) CommitPackFT. A higher Performance Score / Length Reduction Ratio indicates better performance; therefore, points located toward the upper-right region of the plot are preferred. Pareto-optimal points are marked with hollow pink circle markers (◦). Our method, K-Token Me… view at source ↗

**Figure 5.** Figure 5: Ablation Study on Embedding Initialization [PITH_FULL_IMAGE:figures/full_fig_p008_5.png] view at source ↗

read the original abstract

Large Language Models (LLMs) incur significant computational and memory costs when processing long prompts, as full self-attention scales quadratically with input length. Token compression aims to address this challenge by reducing the number of tokens representing inputs. However, existing prompt-compression approaches primarily operate in token space and overlook inefficiencies in the latent embedding space. In this paper, we propose K-Token Merging, a latent-space compression framework that merges each contiguous block of K token embeddings into a single embedding via a lightweight encoder. The compressed sequence is processed by a LoRA-adapted LLM, while generation remains in the original vocabulary. Experiments on structural reasoning (Textualized Tree), sentiment classification (Amazon Reviews), and code editing (CommitPackFT) show that K-Token Merging lies on the Pareto frontier of performance vs. compression, achieving up to 75% input length reduction with minimal performance degradation. Code is available at https://github.com/shsjxzh/K-Token-Merging.

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.

Desk Editor's Note private letter to a colleague

K-Token Merging merges blocks of embeddings with a lightweight encoder and LoRA recovery to cut input length, and it hits reasonable tradeoffs on the three tested tasks, but the encoder architecture and training remain underspecified.

read the letter

K-Token Merging compresses prompts by folding every K contiguous token embeddings into one vector through a small encoder, then runs the shortened sequence through a LoRA-adapted LLM while keeping generation in the original vocabulary. On the reported tasks—structural reasoning with textualized trees, sentiment on Amazon reviews, and code editing on CommitPackFT—it lands on the Pareto frontier, reaching 75% length reduction with only small performance drops. Code is released, which helps reproducibility.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes K-Token Merging, a latent-space compression method that merges each block of K contiguous token embeddings into a single embedding via a lightweight encoder. The resulting shorter sequence is processed by a LoRA-adapted LLM, with generation remaining in the original vocabulary. Experiments on structural reasoning (Textualized Tree), sentiment classification (Amazon Reviews), and code editing (CommitPackFT) position the approach on the Pareto frontier of performance versus compression, achieving up to 75% input length reduction with minimal degradation. Code is released at the provided GitHub link.

Significance. If the empirical results hold under full verification, the work provides a practical engineering contribution to efficient long-context LLM inference by operating compression directly in embedding space rather than token space. The open-sourced code is a clear strength for reproducibility and extension.

major comments (2)

[§3] §3 (Method description): The lightweight encoder for merging K token embeddings is described only at a high level. Its architecture (linear projection, MLP, or attention-based), training objective, and whether it is trained jointly or separately from the LLM are unspecified. This is load-bearing for the central claim that the compression preserves sufficient task-relevant signal for LoRA recovery, particularly at K=4 (75% reduction).
[§4] §4 (Experiments): The abstract and results claim Pareto-frontier performance across the three tasks, but full details on baselines, exact metrics, variance across runs, and ablations isolating the encoder are absent. This prevents full verification of the empirical claims as noted in the soundness assessment.

minor comments (2)

[Abstract] Abstract: The phrase 'minimal performance degradation' would be strengthened by including specific quantitative thresholds or metric deltas.
[Throughout] Notation: Ensure consistent use of K for merge block size and explicit definition of the resulting compression ratio throughout the text and figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and the recommendation for major revision. We address each major comment point by point below and commit to revising the manuscript to enhance clarity and completeness of the method and experimental details.

read point-by-point responses

Referee: [§3] §3 (Method description): The lightweight encoder for merging K token embeddings is described only at a high level. Its architecture (linear projection, MLP, or attention-based), training objective, and whether it is trained jointly or separately from the LLM are unspecified. This is load-bearing for the central claim that the compression preserves sufficient task-relevant signal for LoRA recovery, particularly at K=4 (75% reduction).

Authors: We agree that Section 3 would benefit from greater specificity on the encoder. The encoder is a two-layer MLP (linear projection to hidden size equal to embedding dimension, ReLU, then projection back) applied to each block of K contiguous embeddings. It is trained jointly end-to-end with the LoRA-adapted LLM, using the downstream task loss (cross-entropy for classification/reasoning, appropriate loss for code editing) as the sole objective; no separate pre-training stage is used. We will expand the revised manuscript with the exact layer dimensions, activation functions, initialization, and a figure illustrating the merging process to make explicit how task-relevant signal is preserved for subsequent LoRA adaptation. revision: yes
Referee: [§4] §4 (Experiments): The abstract and results claim Pareto-frontier performance across the three tasks, but full details on baselines, exact metrics, variance across runs, and ablations isolating the encoder are absent. This prevents full verification of the empirical claims as noted in the soundness assessment.

Authors: We acknowledge that the experimental reporting can be strengthened for verifiability. The current manuscript describes the three tasks, reports accuracy/F1/edit-success metrics, and compares against token-dropping and prior compression baselines, with results averaged over runs. To address the gap, the revision will add: (i) a consolidated table listing all baselines with exact metric definitions, (ii) standard deviations across the three random seeds used, and (iii) an expanded ablation subsection isolating the encoder (e.g., replacing it with mean pooling or identity). These additions will be placed in the main text or appendix as space permits, enabling full reproduction and verification of the Pareto-frontier positioning. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical compression method evaluated on external tasks

full rationale

The paper introduces K-Token Merging as a latent-space token compression technique using a lightweight encoder, followed by LoRA adaptation on an LLM, and supports its claims solely through empirical experiments on structural reasoning, sentiment classification, and code editing benchmarks. No equations, derivations, or first-principles results are presented that reduce any performance metric to a fitted parameter or self-citation by construction. The method is framed as an engineering contribution with publicly available code, and the reported Pareto-frontier results are independent empirical observations rather than tautological outputs of the inputs. This is the standard case of a self-contained empirical paper with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the empirical effectiveness of the merging encoder and LoRA compensation rather than on new theoretical axioms or invented entities.

free parameters (1)

K (merge block size)
Hyperparameter controlling compression ratio; chosen per experiment.

axioms (1)

domain assumption LoRA fine-tuning on the compressed sequence can recover task performance lost to merging
Invoked to justify why the downstream LLM does not need full retraining.

pith-pipeline@v0.9.0 · 5485 in / 1246 out tokens · 36783 ms · 2026-05-10T11:24:16.122636+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages · 2 internal anchors

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