ImCoref-CeS: An Improved Lightweight Pipeline for Coreference Resolution with LLM-based Checker-Splitter Refinement
Pith reviewed 2026-05-18 07:25 UTC · model grok-4.3
The pith
An enhanced supervised coreference pipeline refined by an LLM checker-splitter agent surpasses existing state-of-the-art results.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
ImCoref-CeS integrates an improved supervised model called ImCoref, which adds a lightweight bridging module to strengthen long-text encoding, a biaffine scorer to capture positional information more completely, and hybrid mention regularization to raise training efficiency, with an LLM-based Checker-Splitter agent that validates candidate mentions by filtering invalid ones and refines coreference results by splitting erroneous clusters; extensive experiments establish that the combined system achieves superior performance compared with existing state-of-the-art methods.
What carries the argument
The LLM-based Checker-Splitter agent that validates mentions and splits erroneous clusters produced by the improved ImCoref supervised pipeline.
If this is right
- The bridging module extends the supervised model's ability to handle long documents without major added cost.
- The biaffine scorer improves the capture of relative positions between mentions.
- Hybrid mention regularization reduces training time while maintaining or raising accuracy.
- LLM validation removes spurious mentions and corrects over-merged clusters that the neural stage produces.
- The resulting hybrid system records higher scores than prior state-of-the-art coreference methods on standard test sets.
Where Pith is reading between the lines
- The same checker-splitter pattern could be attached to other mention-detection pipelines such as named-entity recognition to correct early errors with modest LLM calls.
- Because only the refinement stage uses the LLM, total inference cost stays lower than end-to-end LLM coreference systems.
- Testing the framework on languages or domains with different mention distributions would reveal whether the LLM agent generalizes beyond the training data of the neural component.
Load-bearing premise
The LLM checker-splitter detects invalid mentions and bad clusters reliably enough that it adds net gains rather than introducing offsetting errors or biases.
What would settle it
Running the full ImCoref-CeS pipeline versus ImCoref alone on a standard benchmark such as OntoNotes and finding that overall F1 drops or stays flat would show the checker-splitter refinement provides no benefit.
read the original abstract
Coreference Resolution (CR) is a critical task in Natural Language Processing (NLP). Current research faces a key dilemma: whether to further explore the potential of supervised neural methods based on small language models, whose detect-then-cluster pipeline still delivers top performance, or embrace the powerful capabilities of Large Language Models (LLMs). However, effectively combining their strengths remains underexplored. To this end, we propose \textbf{ImCoref-CeS}, a novel framework that integrates an enhanced supervised model with LLM-based reasoning. First, we present an improved CR method (\textbf{ImCoref}) to push the performance boundaries of the supervised neural method by introducing a lightweight bridging module to enhance long-text encoding capability, devising a biaffine scorer to comprehensively capture positional information, and invoking a hybrid mention regularization to improve training efficiency. Importantly, we employ an LLM acting as a multi-role Checker-Splitter agent to validate candidate mentions (filtering out invalid ones) and coreference results (splitting erroneous clusters) predicted by ImCoref. Extensive experiments demonstrate the effectiveness of ImCoref-CeS, which achieves superior performance compared to existing state-of-the-art (SOTA) methods.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper proposes ImCoref-CeS, a hybrid coreference resolution pipeline. It first improves a supervised neural model (ImCoref) via a lightweight bridging module for long-text encoding, a biaffine scorer capturing positional information, and hybrid mention regularization for training efficiency. An LLM then acts as a multi-role Checker-Splitter agent to filter invalid mentions and split erroneous clusters. The authors claim that extensive experiments show ImCoref-CeS achieves superior performance over existing SOTA methods.
Significance. If the results hold after verification of the LLM component, the work could meaningfully advance coreference resolution by showing how to combine efficient supervised pipelines with targeted LLM reasoning, addressing limitations in long-span and ambiguous cases without full reliance on large models. The empirical focus on lightweight enhancements plus post-correction is a practical contribution if the net gains are isolated and reproducible.
major comments (2)
- [Experiments / Results] The SOTA superiority claim is load-bearing on the LLM Checker-Splitter producing a net positive effect. No ablation isolating the agent's contribution or error analysis quantifying false filtering of valid mentions / erroneous splits appears in the experiments; without this, detrimental interventions could erase gains from the bridging module, biaffine scorer, and regularization (see abstract and results sections).
- [Abstract] The abstract states superior performance but supplies no quantitative metrics, dataset names, baseline scores, or ablation tables, leaving the central empirical claim unsupported by visible evidence in the summary of results.
minor comments (2)
- [Method] Clarify the exact prompting strategy and role definitions for the multi-role Checker-Splitter agent to improve reproducibility.
- [Related Work / Experiments] Ensure all baseline methods are cited with specific paper references and versions used for comparison.
Simulated Author's Rebuttal
We thank the referee for the constructive feedback on our manuscript. We address each major comment below and commit to revisions that directly respond to the concerns while preserving the core contributions of ImCoref-CeS.
read point-by-point responses
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Referee: [Experiments / Results] The SOTA superiority claim is load-bearing on the LLM Checker-Splitter producing a net positive effect. No ablation isolating the agent's contribution or error analysis quantifying false filtering of valid mentions / erroneous splits appears in the experiments; without this, detrimental interventions could erase gains from the bridging module, biaffine scorer, and regularization (see abstract and results sections).
Authors: We agree that an explicit ablation isolating the Checker-Splitter agent's contribution is necessary to substantiate the net positive effect and rule out the possibility of detrimental interventions. The current results section reports full-pipeline performance and component ablations for ImCoref (bridging module, biaffine scorer, regularization), but does not isolate the LLM agent. In the revised manuscript we will add a dedicated ablation comparing ImCoref alone versus ImCoref-CeS, together with an error analysis that quantifies false-positive filtering of valid mentions and erroneous cluster splits. This will allow readers to verify that the agent's interventions produce a net gain. revision: yes
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Referee: [Abstract] The abstract states superior performance but supplies no quantitative metrics, dataset names, baseline scores, or ablation tables, leaving the central empirical claim unsupported by visible evidence in the summary of results.
Authors: We concur that the abstract would better support the central claim if it included concrete quantitative evidence. We will revise the abstract to report key F1 scores on standard datasets (e.g., OntoNotes/CoNLL-2012), the magnitude of improvement over the strongest baselines, and a brief reference to the new ablation results for the Checker-Splitter component. revision: yes
Circularity Check
No significant circularity in empirical pipeline
full rationale
The paper describes a hybrid NLP pipeline that augments a supervised coreference model (ImCoref) with a lightweight bridging module, biaffine scorer, hybrid regularization, and an LLM-based Checker-Splitter agent for post-filtering. All central claims rest on experimental comparisons to external SOTA baselines rather than any mathematical derivation, fitted parameter renamed as prediction, or self-referential definition. No equations appear, no uniqueness theorems are imported from prior author work, and no ansatz or renaming of known results is presented as a derivation. The approach is therefore self-contained against external benchmarks.
Axiom & Free-Parameter Ledger
free parameters (1)
- hybrid mention regularization weights
axioms (1)
- domain assumption Pre-trained language models provide useful representations for mention detection and clustering
discussion (0)
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