arxiv: 2512.24711 · v3 · submitted 2025-12-31 · 💻 cs.IR

Recognition: unknown

MEIC-DT: Memory-Efficient Incremental Clustering for Long-Text Coreference Resolution with Dual-Threshold Constraints

Kangyang Luo , Shuzheng Si , Yuzhuo Bai , Cheng Gao , Zhitong Wang , Cheng Huang , Yingli Shen , Yufeng Han

show 3 more authors

Wenhao Li Cunliang Kong Maosong Sun

Authors on Pith no claims yet

Pith reviewed 2026-05-16 19:04 UTC · model grok-4.3

classification 💻 cs.IR

keywords coreference resolutionincremental clusteringmemory efficiencylong-text processingdual-threshold constraintscluster regularizationlightweight transformer

0 comments

The pith

MEIC-DT delivers competitive coreference resolution for long texts while respecting tight memory budgets through dual-threshold incremental clustering.

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

The paper presents MEIC-DT as a way to perform coreference resolution on long documents without running out of memory. It replaces heavy full-document models with an incremental clustering process built on a lightweight Transformer that processes mentions one by one. A dual-threshold rule limits the size of the input fed to the Transformer at every step, while a statistics-aware eviction strategy manages the cache using different profiles from training and inference. An internal regularization policy then keeps only the most representative mentions inside each cluster. Experiments on standard benchmarks show the method reaches accuracy levels close to heavier supervised systems even when memory is strictly capped.

Core claim

MEIC-DT is a dual-threshold, memory-efficient incremental clustering method for long-text coreference resolution. It uses a lightweight Transformer together with a dual-threshold constraint that keeps the input scale inside a fixed memory budget, a Statistics-Aware Eviction Strategy (SAES) that applies distinct statistical profiles from training and inference phases for cache management, and an Internal Regularization Policy (IRP) that condenses each cluster to its most representative mentions while preserving semantic integrity. On common benchmarks the resulting system produces highly competitive coreference scores under stringent memory constraints.

What carries the argument

The dual-threshold constraint mechanism that limits Transformer input scale, combined with SAES for phase-aware cache eviction and IRP for selecting representative mentions to condense clusters.

If this is right

Long documents become practical for coreference resolution on hardware with limited GPU memory.
Incremental clustering can be applied in resource-constrained or real-time settings without accuracy collapse.
The lightweight Transformer backbone lowers overall compute compared with full-document models.
The same dual-threshold and eviction logic can be reused for other mention-level tasks that face similar memory scaling issues.
Performance remains close to state-of-the-art supervised neural methods while using far less memory.

Where Pith is reading between the lines

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

The dual-threshold control could be adapted to other incremental NLP pipelines such as entity linking or discourse parsing that also suffer memory blow-up on long inputs.
If SAES proves stable across domains, it offers a template for hardware-aware eviction policies in any cache-based clustering system.
Further automatic tuning of the two thresholds might reduce the need for manual hyper-parameter search on new datasets.
The cluster-condensation step in IRP suggests a general route to compress representations inside any mention-clustering loop without retraining the underlying encoder.

Load-bearing premise

The dual-threshold constraint together with SAES and IRP can be tuned to preserve semantic integrity of clusters without discarding critical mentions that would degrade final coreference accuracy.

What would settle it

Running MEIC-DT on a standard long-text coreference benchmark under the paper's stated memory limit and finding that its F1 scores fall substantially below those of memory-unconstrained baselines would show the performance claim does not hold.

Figures

Figures reproduced from arXiv: 2512.24711 by Cheng Gao, Cheng Huang, Cunliang Kong, Kangyang Luo, Maosong Sun, Shuzheng Si, Wenhao Li, Yingli Shen, Yufeng Han, Yuzhuo Bai, Zhitong Wang.

**Figure 2.** Figure 2: The MEIC-DT Coreference Resolution pipeline. The core innovation is a dual-threshold constraint [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Figure 3: Learning curves and total training time. Results are shown for configurations with [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 4.** Figure 4: An example of semantic space distributions [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗

read the original abstract

In the era of large language models (LLMs), supervised neural methods remain the state-of-the-art (SOTA) for Coreference Resolution. Yet, their full potential is underexplored, particularly in incremental clustering, which faces the critical challenge of balancing efficiency with performance for long texts. To address the limitation, we propose \textbf{MEIC-DT}, a novel dual-threshold, memory-efficient incremental clustering approach based on a lightweight Transformer. MEIC-DT features a dual-threshold constraint mechanism designed to precisely control the Transformer's input scale within a predefined memory budget. This mechanism incorporates a Statistics-Aware Eviction Strategy (\textbf{SAES}), which utilizes distinct statistical profiles from the training and inference phases for intelligent cache management. Furthermore, we introduce an Internal Regularization Policy (\textbf{IRP}) that strategically condenses clusters by selecting the most representative mentions, thereby preserving semantic integrity. Extensive experiments on common benchmarks demonstrate that MEIC-DT achieves highly competitive coreference performance under stringent memory constraints.

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

MEIC-DT packages dual-threshold control, SAES eviction, and IRP condensation into a memory-bounded incremental coreference method, but the IRP step lacks the ablations needed to confirm it preserves accuracy.

read the letter

The main thing to know is that this paper gives a concrete engineering package for running incremental coreference on long texts without exceeding a fixed memory budget. It uses dual thresholds to cap the transformer's input size, a statistics-aware eviction strategy that treats training and inference distributions differently, and an internal regularization policy to shrink clusters by keeping only the most representative mentions. That combination is new enough in the coreference literature to stand out as a practical lever rather than another generic efficiency trick. The framing around lightweight transformers and the split between training and inference stats in SAES is a sensible detail that addresses real distribution shift in deployment. The paper does a clean job laying out how these pieces fit together to stay inside the budget while claiming competitive results on standard benchmarks. The soft spot is the IRP condensation step. The description does not spell out the selection rule for representative mentions, and the abstract supplies no ablation that isolates its effect on final CoNLL scores or shows whether bridging mentions survive. Without that evidence the claim of preserved semantic integrity stays untested, which directly weakens the assertion of highly competitive performance under tight constraints. The stress-test note on this point is on target from the given description. Readers working on long-context coreference or memory-constrained pipelines would get the most out of it; the mechanisms are specific enough to adapt or extend. It deserves peer review because the problem is genuine and the proposed controls are novel enough to merit detailed referee comments on the experiments and IRP validation.

Referee Report

2 major / 1 minor

Summary. The manuscript proposes MEIC-DT, a dual-threshold memory-efficient incremental clustering method for long-text coreference resolution built on a lightweight Transformer. The approach introduces a dual-threshold constraint to control input scale within a memory budget, a Statistics-Aware Eviction Strategy (SAES) that uses distinct training/inference statistical profiles for cache management, and an Internal Regularization Policy (IRP) that condenses clusters by retaining the most representative mentions while aiming to preserve semantic integrity. The central claim is that extensive experiments on common benchmarks demonstrate highly competitive coreference performance under stringent memory constraints.

Significance. If the performance claims are substantiated with quantitative evidence, the work could be significant for enabling scalable coreference resolution on long documents in memory-constrained settings. The dual-threshold mechanism combined with phase-aware eviction statistics offers a concrete way to enforce memory budgets during incremental processing, and the cluster-condensation idea via IRP addresses a practical bottleneck in long-text incremental clustering. These elements, if shown to maintain accuracy, would provide a useful engineering contribution to efficient neural coreference systems.

major comments (2)

Abstract: The assertion that MEIC-DT 'achieves highly competitive coreference performance' supplies no numeric scores (e.g., CoNLL F1), baseline comparisons, ablation tables, or error analysis. Without these data the central performance claim cannot be verified and the soundness of the dual-threshold + SAES + IRP combination remains unevaluated.
Abstract (description of IRP): The mechanism for selecting 'most representative mentions' is unspecified (no reference to embedding similarity, frequency counts, positional heuristics, or other criteria), and no ablation isolating IRP's effect on final coreference accuracy is referenced. This leaves the load-bearing assumption—that condensation preserves critical mentions and semantic integrity—unvalidated, directly affecting the claimed memory-accuracy tradeoff.

minor comments (1)

Abstract: The acronym SAES is expanded on first use, but subsequent references should maintain consistent capitalization and avoid re-expansion.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help clarify how to better present our contributions. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

Referee: Abstract: The assertion that MEIC-DT 'achieves highly competitive coreference performance' supplies no numeric scores (e.g., CoNLL F1), baseline comparisons, ablation tables, or error analysis. Without these data the central performance claim cannot be verified and the soundness of the dual-threshold + SAES + IRP combination remains unevaluated.

Authors: We agree that the abstract would benefit from concrete metrics to substantiate the performance claim. The full manuscript reports CoNLL F1 scores, baseline comparisons, ablation results, and error analysis in Section 4 and the associated tables. In the revised version we will update the abstract to include key numeric results (e.g., average CoNLL F1 on the evaluated benchmarks) together with explicit references to the experimental tables, enabling immediate verification of the dual-threshold + SAES + IRP combination under memory constraints. revision: yes
Referee: Abstract (description of IRP): The mechanism for selecting 'most representative mentions' is unspecified (no reference to embedding similarity, frequency counts, positional heuristics, or other criteria), and no ablation isolating IRP's effect on final coreference accuracy is referenced. This leaves the load-bearing assumption—that condensation preserves critical mentions and semantic integrity—unvalidated, directly affecting the claimed memory-accuracy tradeoff.

Authors: We acknowledge that the abstract's description of the Internal Regularization Policy is too brief. Section 3.3 of the manuscript specifies that representative mentions are retained according to embedding similarity to the cluster centroid combined with positional heuristics. Section 4.3 presents an ablation isolating IRP's contribution to final coreference accuracy. We will revise the abstract to briefly state the selection criterion and cite the ablation study, thereby making the memory-accuracy tradeoff more transparent. revision: yes

Circularity Check

0 steps flagged

No circularity: algorithmic construction with empirical validation

full rationale

The paper proposes MEIC-DT as a constructive algorithmic method combining dual-threshold constraints, SAES for cache management, and IRP for cluster condensation. No equations, derivations, or first-principles results are presented that reduce by construction to fitted inputs or self-defined quantities. Performance claims rest on experiments on standard benchmarks rather than tautological predictions. No load-bearing self-citations, uniqueness theorems, or ansatzes imported from prior author work appear in the provided text. The approach is self-contained and externally falsifiable via benchmark results, consistent with a non-circular methods paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations or implementation details, so no concrete free parameters, axioms, or invented entities can be extracted.

pith-pipeline@v0.9.0 · 5517 in / 1053 out tokens · 22009 ms · 2026-05-16T19:04:24.668196+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

8 extracted references · 8 canonical work pages · 1 internal anchor

[1]

Longformer: The Long-Document Transformer

An annotated dataset of coreference in English literature. InProceedings of the Twelfth Language Resources and Evaluation Conference, pages 44–54, Marseille, France. European Language Resources Association. Iz Beltagy, Matthew E Peters, and Arman Cohan. 2020. Longformer: The long-document transformer.arXiv preprint arXiv:2004.05150. Bernd Bohnet, Chris Al...

work page internal anchor Pith review Pith/arXiv arXiv 2020
[2]

InFindings of the Association for Computational Linguistics: ACL 2025, pages 8063–8075

Document segmentation matters for retrieval- augmented generation. InFindings of the Association for Computational Linguistics: ACL 2025, pages 8063–8075. Wei Wu, Fei Wang, Arianna Yuan, Fei Wu, and Jiwei Li. 2020. CorefQA: Coreference resolution as query- based span prediction. InProceedings of the 58th Annual Meeting of the Association for Computational...

work page 2025
[3]

InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8617–8624, Online

Incremental neural coreference resolution in constant memory. InProceedings of the 2020 Con- ference on Empirical Methods in Natural Language Processing (EMNLP), pages 8617–8624, Online. As- sociation for Computational Linguistics. Liyan Xu and Jinho D. Choi. 2020. Revealing the myth of higher-order inference in coreference resolution. InProceedings of th...

work page 2020
[4]

Wenzheng Zhang, Sam Wiseman, and Karl Stratos

Automated peer reviewing in paper sea: Stan- dardization, evaluation, and analysis.arXiv preprint arXiv:2407.12857. Wenzheng Zhang, Sam Wiseman, and Karl Stratos

work page arXiv
[5]

large but not precise

Seq2seq is all you need for coreference res- olution. InProceedings of the 2023 Conference on Empirical Methods in Natural Language Process- ing, pages 11493–11504, Singapore. Association for Computational Linguistics. Lixing Zhu, Jun Wang, and Yulan He. 2025. Llm- Link: Dual LLMs for dynamic entity linking on long narratives with collaborative memorisati...

work page 2023
[6]

trained with DeBERTa has achieved SOTA performance. In contrast, while LLMs struggle with the mention detection, preventing their coref- erence performance from surpassing supervised neural methods, they show a significant strength: given gold mentions, LLMs with powerful reason- ing capabilities can achieve competitive corefer- ence results (Le and Ritte...

work page 2023
[7]

it” should be removed from full pronouns list because “it

to drew inspiration from human cognitive incremental processing mechanisms for long-text scenarios. During the mention clustering, they pro- gressively evaluate the association between can- didate mentions and existing coreference clusters using linear classifiers or lightweight Transformer architectures to determine coreference links. How- 12 ever, despi...

work page 2020
[8]

as Text Encoder (see Fig. 2). Furthermore, we conduct coreference clustering experiments adopting two distinct classifiers—a linear classi- fier (Xia et al., 2020; Toshniwal et al., 2020; Guo et al., 2023) and a lightweight Transformer classi- fier (Martinelli et al., 2024)—to thoroughly evalu- ate the effectiveness of MEIC-DT. As outlined in Related Work...

work page 2020