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arxiv: 2604.08782 · v2 · submitted 2026-04-09 · 💻 cs.CL

Recognition: unknown

MT-OSC: Path for LLMs that Get Lost in Multi-Turn Conversation

Jyotika Singh , Fang Tu , Miguel Ballesteros , Weiyi Sun , Sandip Ghoshal , Michelle Yuan , Yassine Benajiba , Sujith Ravi
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Dan Roth
Authors on Pith no claims yet

Pith reviewed 2026-05-10 16:54 UTC · model grok-4.3

classification 💻 cs.CL
keywords multi-turn conversationchat history condensationLLM efficiencycontext window managementconversational AItoken reductionperformance preservation
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The pith

MT-OSC condenses multi-turn chat histories to cut token counts by up to 72% while preserving LLM accuracy.

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

Large language models lose performance in multi-turn conversations because full chat histories quickly fill up their context windows. MT-OSC provides a background condensation method that uses a few-shot Condenser and a lightweight Decider to keep only essential information from previous turns. This approach reduces token usage substantially without hurting accuracy on standard benchmarks. It works across many different LLMs and stays effective even when conversations include irrelevant details or distractors. The result is a practical way to support longer, more natural interactions within existing model limits.

Core claim

The paper presents MT-OSC as a One-off Sequential Condensation framework. A Condenser Agent applies a few-shot inference-based Condenser and a lightweight Decider to selectively retain essential information from chat history. This reduces token counts by up to 72% in 10-turn dialogues. When tested on 13 state-of-the-art LLMs and diverse multi-turn benchmarks, it narrows the performance gap by yielding improved or preserved accuracy and remains robust to distractors and irrelevant turns.

What carries the argument

The Condenser Agent, which uses few-shot inference to condense chat history selectively via a Condenser and Decider pair.

If this is right

  • Enables richer context in multi-turn chats within constrained input spaces.
  • Reduces latency and operational costs for extended conversations.
  • Maintains or improves accuracy across a wide range of LLMs and datasets.
  • Provides robustness against distractors and irrelevant information in dialogues.
  • Offers a scalable solution for balancing performance in long conversations.

Where Pith is reading between the lines

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

  • Integrating MT-OSC into LLM deployment pipelines could automatically manage history length for users.
  • Similar condensation techniques might apply to other sequential data tasks like document summarization over time.
  • It suggests that explicit history management modules can outperform simply expanding context windows in some cases.
  • Developers could test this on custom multi-turn tasks to measure token savings and accuracy retention.

Load-bearing premise

The few-shot Condenser and lightweight Decider reliably capture and retain all essential prior information without errors that would affect the main LLM's responses.

What would settle it

Running MT-OSC on a benchmark where full history yields higher accuracy than the condensed version on the same model would contradict the preserved performance claim.

Figures

Figures reproduced from arXiv: 2604.08782 by Dan Roth, Fang Tu, Jyotika Singh, Michelle Yuan, Miguel Ballesteros, Sandip Ghoshal, Sujith Ravi, Weiyi Sun, Yassine Benajiba.

Figure 1
Figure 1. Figure 1: MT-OSC framework with one-off sequential condensation of chat history (w turns/exchanges at a time). [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Examples of Multi-Turn Chat Condensing with Reasoning (original chat in yellow; condensed chat below.) [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of performance aggregated across [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Chat history tokens growth in MT-baseline [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Model performance scores for various Chat Models for conversations with 6 or more turns across baseline [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Total tokens consumed by chat history in the [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Performance of MT-baseline and MT-OSC [PITH_FULL_IMAGE:figures/full_fig_p009_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Histogram showing the number of samples for [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: These examples shows the original turns from a conversation (in darker color in each example) and the [PITH_FULL_IMAGE:figures/full_fig_p019_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Comparison of performance across datasets of simple summarization (Summ) using the one-off sequential [PITH_FULL_IMAGE:figures/full_fig_p019_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Percentage reduction in chat history tokens [PITH_FULL_IMAGE:figures/full_fig_p021_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Sensitivity of the decider to γ and τ across datasets. Each panel corresponds to one dataset. Points show, for each (γ,τ ) setting, the end-to-end task success score (y-axis; reported in the dataset’s native units) versus the number of samples that were routed to compression (x-axis). Color indicates γ and marker shape indicates τ . Flat panels indicate insensitivity to (γ,τ ), while sloped panels highlig… view at source ↗
Figure 14
Figure 14. Figure 14: Example showing MT-baseline vs MT-OSC, where MT-OSC handles incorrect assumptions made by the [PITH_FULL_IMAGE:figures/full_fig_p023_14.png] view at source ↗
read the original abstract

Large language models (LLMs) suffer significant performance degradation when user instructions and context are distributed over multiple conversational turns, yet multi-turn (MT) interactions dominate chat interfaces. The routine approach of appending full chat history to prompts rapidly exhausts context windows, leading to increased latency, higher computational costs, and diminishing returns as conversations extend. We introduce MT-OSC, a One-off Sequential Condensation framework that efficiently and automatically condenses chat history in the background without disrupting the user experience. MT-OSC employs a Condenser Agent that uses a few-shot inference-based Condenser and a lightweight Decider to selectively retain essential information, reducing token counts by up to 72% in 10-turn dialogues. Evaluated across 13 state-of-the-art LLMs and diverse multi-turn benchmarks, MT-OSC consistently narrows the multi-turn performance gap - yielding improved or preserved accuracy across datasets while remaining robust to distractors and irrelevant turns. Our results establish MT-OSC as a scalable solution for multi-turn chats, enabling richer context within constrained input spaces, reducing latency and operational cost, while balancing performance.

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

3 major / 2 minor

Summary. The paper introduces MT-OSC, a One-off Sequential Condensation framework for multi-turn LLM conversations. It employs a Condenser Agent using few-shot inference-based Condenser and a lightweight Decider to selectively retain essential information from chat history, claiming up to 72% token reduction in 10-turn dialogues. Evaluated on 13 LLMs and diverse benchmarks, it asserts consistent narrowing of the multi-turn performance gap with improved or preserved accuracy and robustness to distractors and irrelevant turns.

Significance. If the empirical claims hold, MT-OSC provides a practical engineering solution to context-window exhaustion and latency in conversational LLMs, enabling longer interactions at lower cost without user disruption. The background condensation approach is a useful applied contribution, though it lacks machine-checked proofs or parameter-free derivations.

major comments (3)
  1. [Abstract] Abstract: The central claim of 'improved or preserved accuracy' and 72% token reduction across 13 LLMs is presented without exact metrics, error bars, baseline comparisons (e.g., full-history vs. truncated), or statistical tests, directly limiting verification of the performance and robustness assertions.
  2. [Method (Condenser Agent)] Method section (Condenser Agent description): The load-bearing assumption that the few-shot Condenser plus Decider reliably extracts and retains every task-critical fact (including implicit references and conditional constraints) is not directly validated; robustness tests to distractors measure noise tolerance but do not quantify recall fidelity or omission rates that could silently degrade downstream accuracy.
  3. [Experiments] Experiments section: No details are given on distractor construction, irrelevant-turn injection, or per-benchmark accuracy tables comparing MT-OSC to full-history and naive baselines, undermining the claim that the multi-turn gap is narrowed without degradation.
minor comments (2)
  1. [Title] The title is informal and vague; a more descriptive title would better convey the technical contribution.
  2. [Method] Notation for the Condenser and Decider components could be formalized with pseudocode or explicit input/output definitions to improve reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We address each major comment below and indicate the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim of 'improved or preserved accuracy' and 72% token reduction across 13 LLMs is presented without exact metrics, error bars, baseline comparisons (e.g., full-history vs. truncated), or statistical tests, directly limiting verification of the performance and robustness assertions.

    Authors: We agree that the abstract would benefit from greater specificity. While the full paper contains detailed per-LLM and per-benchmark tables with accuracy comparisons to full-history and truncated baselines plus token-reduction statistics, the abstract itself summarizes these claims at a high level. We will revise the abstract to include representative quantitative results (e.g., average token reduction and accuracy deltas across the 13 LLMs) and will note that comprehensive tables appear in the Experiments section. revision: yes

  2. Referee: [Method (Condenser Agent)] Method section (Condenser Agent description): The load-bearing assumption that the few-shot Condenser plus Decider reliably extracts and retains every task-critical fact (including implicit references and conditional constraints) is not directly validated; robustness tests to distractors measure noise tolerance but do not quantify recall fidelity or omission rates that could silently degrade downstream accuracy.

    Authors: The manuscript validates the Condenser Agent primarily through end-to-end accuracy preservation on downstream tasks, which serves as an indirect measure of retention quality. We acknowledge that direct quantification of recall fidelity and omission rates for implicit facts would provide stronger evidence. We will add a new analysis subsection that reports recall metrics for key facts (including implicit references) on a controlled subset of dialogues to directly address this concern. revision: yes

  3. Referee: [Experiments] Experiments section: No details are given on distractor construction, irrelevant-turn injection, or per-benchmark accuracy tables comparing MT-OSC to full-history and naive baselines, undermining the claim that the multi-turn gap is narrowed without degradation.

    Authors: The Experiments section does contain accuracy tables that compare MT-OSC against full-history and other baselines for each benchmark. However, we agree that the construction of distractors and the injection of irrelevant turns could be described more explicitly. We will expand the Experiments section with a dedicated paragraph detailing the distractor-generation process and irrelevant-turn methodology, and we will ensure all tables are clearly labeled with the requested baseline comparisons. revision: yes

Circularity Check

0 steps flagged

No circularity: MT-OSC is an empirical engineering method with independent evaluation

full rationale

The paper introduces MT-OSC as a practical condensation framework relying on a few-shot Condenser Agent and lightweight Decider, then reports empirical results on token reduction and accuracy across 13 LLMs and multi-turn benchmarks. No mathematical derivation chain, equations, or first-principles predictions are present in the abstract or description. Claims of narrowing performance gaps rest on direct evaluation rather than any fitted parameter renamed as a prediction or self-referential definition. The method is self-contained as an applied contribution without load-bearing self-citations or ansatzes that reduce to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

The framework introduces new procedural components whose correctness is asserted via empirical results rather than derivation; no explicit free parameters or axioms are stated in the abstract.

invented entities (2)
  • Condenser Agent no independent evidence
    purpose: Performs few-shot inference to condense chat history
    Core new component introduced to handle selective retention
  • Decider no independent evidence
    purpose: Lightweight component that decides what to retain
    Paired with Condenser to control condensation

pith-pipeline@v0.9.0 · 5521 in / 1168 out tokens · 58953 ms · 2026-05-10T16:54:56.762589+00:00 · methodology

discussion (0)

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Forward citations

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Reference graph

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    Repetition Infusion 2. Filler Injection 3. Contextual Diversion MT MT-OSC MT MT-OSC MT MT-OSC GSMshrd 85.80 86.63 84.77 87.86 82.92 84.77 BFCLshrd 63.45 70.64 64.02 69.89 59.77 67.24 HumanEvalshrd 70.81 75.89 74.40 79.17 66.67 79.63 Spidershrd 67.68 70.20 63.13 66.67 57.07 61.62 refinementmte 5.67 5.66 5.7 5.64 5.01 5.00 Table 7: Comparison of performance...

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