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arxiv: 2511.22599 · v2 · submitted 2025-11-27 · 💻 cs.DC · cs.DB· cs.LG

DisCEdge: Distributed Context Management for Large Language Models at the Edge

Mohammadreza Malekabbasi , Minghe Wang , David Bermbach This is my paper

Pith reviewed 2026-05-17 04:13 UTC · model grok-4.3

classification 💻 cs.DC cs.DBcs.LG
keywords edge computingdistributed systemslarge language modelscontext managementtokenized replicationdata consistencyresponse latency
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The pith

DisCEdge stores and replicates LLM user context as token sequences across edge nodes to cut response times and synchronization costs.

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

Large language models need ongoing user context such as conversation history to answer coherently, yet running them at the edge means this context must travel between scattered nodes without losing the speed advantage. DisCEdge keeps context in tokenized form and replicates the token sequences between nodes instead of raw text. This removes repeated tokenization work and allows lighter updates to travel between nodes. In tests on a realistic edge setup the system delivers faster replies to users, lower traffic between nodes, and far smaller messages from clients, all while keeping context identical everywhere.

Core claim

The paper claims that maintaining user context as sequences of tokens and replicating these sequences across geo-distributed edge nodes eliminates redundant tokenization and enables efficient synchronization, producing up to 14.46 percent better median response times and up to 15 percent lower median inter-node synchronization overhead than raw-text methods, plus a 90 percent median reduction in client request size versus client-side storage, all while preserving data consistency.

What carries the argument

Tokenized context replication, which stores and shares user context as pre-processed token sequences so nodes exchange compact updates without re-tokenizing or sending full text.

Load-bearing premise

That storing and replicating context in tokenized form avoids redundant computation and enables efficient replication without introducing new consistency, overhead, or scalability problems in geo-distributed real-world settings.

What would settle it

A test deployment showing higher synchronization overhead or context mismatches under realistic network partitions and node churn would disprove the claimed gains.

Figures

Figures reproduced from arXiv: 2511.22599 by David Bermbach, Minghe Wang, Mohammadreza Malekabbasi.

Figure 1
Figure 1. Figure 1: DisCEdge Architecture Overview In this section, we present the architecture of DisCEdge, our distributed context management system for edge LLMs. The system is designed to efficiently manage user context across geo-distributed edge nodes, enabling low-latency interactions with LLMs as they move. As shown in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The “LLM Service” as an inference framework, and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 5
Figure 5. Figure 5: Network overhead for synchronizing context data [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Tokens generated per second (TPS) for tokenized [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 6
Figure 6. Figure 6: Client-observable response time per turn in a mo [PITH_FULL_IMAGE:figures/full_fig_p006_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Client-to-server network usage per request turn. [PITH_FULL_IMAGE:figures/full_fig_p007_7.png] view at source ↗
read the original abstract

Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user context (e.g., sessions, preferences) across geo-distributed edge nodes challenging. Existing solutions, such as client-side context storage, introduce network latency and bandwidth overhead, undermining edge deployment advantages. We propose DisCEdge, a distributed context management system that stores and replicates user context in tokenized form across edge nodes. By maintaining context as token sequences, our system avoids redundant computation and enables efficient data replication. We evaluate an open-source prototype in a realistic edge environment. DisCEdge improves median response times by up to 14.46% and lowers median inter-node synchronization overhead by up to 15% compared to a raw-text-based system. It also reduces client request sizes by a median of 90% compared to client-side context management, while guaranteeing data consistency.

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 DisCEdge, a distributed context management system for LLMs at the edge. Context is stored and replicated in tokenized form across geo-distributed edge nodes to avoid redundant tokenization, enable efficient replication, and reduce client-side overheads. An open-source prototype is evaluated in a realistic edge environment, claiming up to 14.46% improvement in median response times and 15% lower median inter-node synchronization overhead versus a raw-text baseline, plus a 90% median reduction in client request sizes versus client-side context management, while guaranteeing data consistency.

Significance. If the performance claims are robust, the tokenized replication strategy could meaningfully improve latency and bandwidth efficiency for edge LLM deployments in privacy-sensitive settings. The open-source prototype and focus on consistency guarantees are strengths that support potential adoption and further study in distributed edge systems.

major comments (2)
  1. [Abstract / Evaluation] Abstract and Evaluation section: The reported gains (14.46% median response time, 15% synchronization overhead) are presented without methodology details such as baseline definitions (exact configuration of the raw-text system), edge environment parameters (node count, network RTT distribution), workload characteristics, or statistical measures (error bars, trial counts). This directly affects verifiability of the central empirical claims, especially given the skeptic concern that geo-distributed latencies (50-200 ms) and concurrent updates could alter the observed overhead reductions.
  2. [System Design] System Design / Replication Protocol: The core claim that tokenized context storage enables efficient replication with strong consistency guarantees is load-bearing, yet the manuscript does not detail the protocol for handling concurrent appends, ordering, or conflict resolution on token sequences. Without this, it is unclear whether the 15% overhead reduction would persist under realistic concurrent session updates or higher inter-node latencies.
minor comments (2)
  1. [Abstract] The abstract states 'guaranteeing data consistency' without specifying strong vs. eventual consistency; a short clarification in the introduction or design section would improve precision.
  2. [Evaluation] Figure or table captions in the evaluation could more explicitly label the exact baselines and metrics used for the 14.46% and 15% figures to aid quick comprehension.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each major comment below and outline the revisions we will make to improve the clarity and verifiability of our claims.

read point-by-point responses

  1. Referee: [Abstract / Evaluation] Abstract and Evaluation section: The reported gains (14.46% median response time, 15% synchronization overhead) are presented without methodology details such as baseline definitions (exact configuration of the raw-text system), edge environment parameters (node count, network RTT distribution), workload characteristics, or statistical measures (error bars, trial counts). This directly affects verifiability of the central empirical claims, especially given the skeptic concern that geo-distributed latencies (50-200 ms) and concurrent updates could alter the observed overhead reductions.

    Authors: We agree that additional methodological details are required for full verifiability. In the revised manuscript we will expand the Evaluation section to define the raw-text baseline (replication of untokenized text strings with on-demand tokenization at each node), specify the experimental setup (five edge nodes with emulated RTTs drawn from a 50-200 ms distribution), characterize the workloads (session lengths, append frequencies, and update concurrency levels), and report statistical measures (medians with interquartile ranges from 20 independent trials). We will also add a short discussion of how the observed overhead reductions behave under the cited latency range and concurrent updates, drawing on the trace data already collected. revision: yes

  2. Referee: [System Design] System Design / Replication Protocol: The core claim that tokenized context storage enables efficient replication with strong consistency guarantees is load-bearing, yet the manuscript does not detail the protocol for handling concurrent appends, ordering, or conflict resolution on token sequences. Without this, it is unclear whether the 15% overhead reduction would persist under realistic concurrent session updates or higher inter-node latencies.

    Authors: We acknowledge that the replication protocol for concurrent appends and conflict resolution was described at too high a level. DisCEdge maintains token sequences as a causally ordered log using vector clocks; appends are assigned logical timestamps and merged deterministically by timestamp order to preserve strong consistency. In the revised version we will add a dedicated subsection with pseudocode and a step-by-step description of append handling, ordering, and conflict resolution. Our existing evaluation already includes concurrent-update traces; we will clarify this and note that while absolute synchronization cost scales with latency, the relative 15 % reduction from avoiding re-tokenization remains consistent across the tested range. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical system evaluation

full rationale

The paper proposes DisCEdge as a distributed context management system that stores and replicates user context in tokenized form, then reports direct empirical measurements from an open-source prototype evaluated in a realistic edge environment. Performance numbers (e.g., 14.46% response time improvement, 15% synchronization overhead reduction, 90% client request size reduction) are presented as observed results of the prototype rather than predictions derived from equations, fitted parameters, or first-principles derivations. No self-definitional steps, fitted-input predictions, load-bearing self-citations, uniqueness theorems, or ansatz smuggling appear in the provided abstract or description. The central claims rest on experimental data against external benchmarks (raw-text baseline and client-side management), making the work self-contained without any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The work relies on standard assumptions from distributed systems and LLM inference pipelines but introduces no explicit free parameters, axioms, or invented entities beyond the system name itself. All reported gains are empirical.

pith-pipeline@v0.9.0 · 5468 in / 1181 out tokens · 39713 ms · 2026-05-17T04:13:35.628793+00:00 · methodology

discussion (0)

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