Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval

Meijia Yu; Yi Luo; Yingjie Han; Zhongtian Hua

REVIEW 2 major objections 2 minor 4 references

Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval

T0 review · 2 major / 2 minor · reviewed 2026-06-29 · grok-4.3

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Pith's one-line read Dynamic adaptive contrastive learning retrieves event-relevant evidence rather than merely similar items for multimodal claims.

desk verdict DACLR layers event-level InfoNCE losses and a dynamic intra-batch ratio on top of MLLM text conversion for multimodal fact-checking retrieval, but the conversion step itself is unverified and may erase the event signals the method needs. read the letter →

arxiv 2605.27449 v1 pith:VTH3ABG3 submitted 2026-05-24 cs.IR cs.AI

Zhongtian Hua , Yi Luo , Meijia Yu , Yingjie Han This is my paper

classification cs.IRcs.AI

keywords multimodalfactcheckingevidenceretrievalcontrastivelearningdynamicadaptationevent-levelfeatureshardnegativeminingInfoNCEloss

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Multimodal fact checking suffers when retrieval returns evidence that matches a claim's general meaning but not its specific events. The paper introduces DACLR to fix this by first turning all multimodal inputs into text with an MLLM, then running a recall-rerank pipeline that applies contrastive losses at both semantic and event levels. Three InfoNCE-based losses are paired with hard-negative sets, and their mixing ratio is adjusted on the fly according to how accurately the current batch separates positives from negatives. This lets the model gain event perception without losing its earlier semantic retrieval skill. Experiments and ablations show the approach raises retrieval quality.

What carries the argument

Dynamic ratio adjustment of semantic versus event-level InfoNCE losses driven by intra-batch accuracy supervision signals, together with level-specific hard negative mining.

What would settle it

A controlled test on event-specific claims where DACLR produces no gain in retrieval precision or downstream verification accuracy compared with a purely semantic baseline.

Watch

Extended reading notes

Core claim

DACLR first converts multimodal evidence and claims into text via an MLLM and extracts event-level features. It then performs two-stage retrieval while optimizing three contrastive losses (semantic and event) derived from InfoNCE, each with its own hard-negative candidates. The model uses intra-batch accuracy signals to dynamically change the loss ratio, so it learns claim-positive event correlations while retaining semantic ability.

Load-bearing premise

Converting multimodal evidence and claims to text with an MLLM keeps enough event detail for the contrastive losses to tell relevant evidence apart from merely similar evidence.

Editorial extensions

If this is right

The retrieval stage supplies more event-matched evidence to the downstream verification step.
Dynamic ratio adjustment prevents the model from forgetting semantic retrieval while adding event-level learning.
Hard-negative mining at both levels sharpens the distinction between relevant and similar evidence.
Two-stage recall-rerank benefits directly from the improved event features.
Ablation results isolate the contribution of each loss and the dynamic component.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same dynamic balancing of multiple contrastive objectives could be tested in other retrieval settings that mix coarse and fine-grained signals.
Because the method depends on the MLLM conversion step, any systematic loss of event detail during that step would limit gains.
End-to-end fact-checking pipelines might show larger accuracy lifts once the improved retrieval feeds into verification models.
Batch composition and size could affect how reliably the accuracy signal guides the loss ratio.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Desk Editor's Note

read the letter

The paper's core move is to take multimodal claims and evidence, run them through an MLLM to get text, then train a two-stage retriever with three InfoNCE-style losses (semantic plus event level) plus hard-negative mining and a dynamic ratio that shifts based on intra-batch accuracy.

What is actually new is the dynamic adjustment mechanism and the explicit two-level loss construction on top of standard contrastive retrieval. The authors correctly identify that semantic similarity alone often returns evidence that looks related but does not support the claim at the event level.

The approach is straightforward and the motivation is sound for the fact-checking setting. Hard-negative selection at both levels is a reasonable extension of existing practice.

The soft spot is the unexamined conversion step. The method assumes the MLLM output keeps distinguishable event structure that pure semantic embeddings miss. Nothing in the description checks whether temporal or visual details survive the text conversion or whether cases where they collapse still show gains. If the text loses that structure, the event-level losses and the dynamic ratio have nothing extra to work with.

The dynamic ratio itself depends on an accuracy signal from the same batch; this risks the adjustment simply tracking training progress rather than adding independent signal. The abstract claims extensive ablations and comparisons, but without the actual numbers or controls it is difficult to tell whether the reported improvements are robust or mainly from the added capacity.

This is a paper for people already working on multimodal retrieval pipelines for verification tasks. It is worth sending to peer review because the method is concrete, the problem is real, and the experiments are at least claimed to be comparative, even though the central assumption about preserved event information needs direct testing.

Referee Report

2 major / 2 minor

Summary. The paper proposes DACLR, a dynamic adaptive contrastive learning method for multimodal evidence retrieval in fact-checking. It first converts multimodal claims and evidence to text using an MLLM, extracts event-level features, performs two-stage (recall-rerank) retrieval, and optimizes three InfoNCE-based losses (semantic and event levels) with corresponding hard-negative sets. A dynamic ratio adjustment uses intra-batch accuracy signals to emphasize event-level correlations while preserving semantic retrieval ability. Extensive comparisons and ablations are claimed to demonstrate effectiveness.

Significance. If validated, the two-level contrastive construction with dynamic intra-batch ratio adjustment could address a recognized limitation of purely semantic multimodal retrieval (retrieving similar but non-relevant evidence). The explicit separation of semantic and event losses plus hard-negative mining is a concrete technical contribution; the dynamic adjustment mechanism, if shown to be non-circular, would be a notable strength for balancing multi-level objectives.

major comments (2)

[Abstract] Abstract and method description: the central claim that event-level losses improve retrieval without semantic forgetting rests on the unverified assumption that MLLM text conversion preserves distinguishable event structure (e.g., temporal or visual event details) beyond what semantic similarity already captures. No fidelity check, example comparison against original modalities, or ablation on conversion quality is described.
[Abstract] Abstract (dynamic ratio paragraph): the supervision signal for the dynamic loss ratio is defined in terms of intra-batch accuracy, which risks introducing dependence on the model's own training predictions; it is unclear whether this reduces to a fitted hyperparameter or creates a self-referential loop that undermines the claimed parameter-free benefit of the adjustment.

minor comments (2)

[Abstract] Abstract: subject-verb agreement error ('Extensive comparison and ablation experiments demonstrates').
[Abstract] Abstract: the two-stage retrieval pipeline is mentioned but not detailed with respect to how event-level features are integrated into the reranker.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for the constructive comments on our manuscript. We address each of the major comments point by point below, providing clarifications and proposing revisions to address the concerns raised.

read point-by-point responses

Referee: [Abstract] Abstract and method description: the central claim that event-level losses improve retrieval without semantic forgetting rests on the unverified assumption that MLLM text conversion preserves distinguishable event structure (e.g., temporal or visual event details) beyond what semantic similarity already captures. No fidelity check, example comparison against original modalities, or ablation on conversion quality is described.

Authors: We acknowledge this limitation in the current manuscript. To address it, we will add example comparisons between original multimodal content and the MLLM-converted text to illustrate preservation of event details. We will also include an ablation study on the impact of conversion quality on event-level feature extraction and retrieval performance, demonstrating that the conversion maintains distinguishable event structures beyond pure semantic similarity. revision: yes
Referee: [Abstract] Abstract (dynamic ratio paragraph): the supervision signal for the dynamic loss ratio is defined in terms of intra-batch accuracy, which risks introducing dependence on the model's own training predictions; it is unclear whether this reduces to a fitted hyperparameter or creates a self-referential loop that undermines the claimed parameter-free benefit of the adjustment.

Authors: The dynamic adjustment is intended to be parameter-free by relying on intra-batch accuracy signals. However, we recognize the need for clearer exposition to avoid any perception of circularity. In the revision, we will expand the method section with a detailed mathematical formulation and algorithm for the ratio adjustment, showing how it uses batch statistics without creating a self-referential training loop. We will also add experiments comparing the dynamic method to fixed-ratio baselines to validate its benefits. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The paper proposes DACLR using MLLM text conversion followed by InfoNCE-based losses at semantic and event levels, hard-negative mining, and a dynamic ratio adjustment driven by intra-batch accuracy signals. No equations, definitions, or self-citations are exhibited that reduce any claimed prediction, uniqueness result, or performance gain to its own inputs by construction. The method applies standard contrastive learning components to extracted features and reports experimental validation; the derivation chain remains independent of the target outcomes rather than tautological.

Assumptions & free parameters 1 free parameters · 1 assumptions · 0 invented entities

Only the abstract is available, so the ledger is limited to components explicitly named; no invented entities or detailed fitted values are described.

free parameters (1)

dynamic loss ratio
The ratio among the three losses is adjusted dynamically based on intra-batch accuracy; the exact parameterization or initialization is not specified.

assumptions (1)

domain assumption InfoNCE loss serves as a suitable base that can be extended to separate semantic and event levels with hard negatives
The paper designs three loss functions based on the InfoNCE loss at two levels.

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Cite this review

Pith. "Pith review of Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval." pith.science (2026). https://pith.science/paper/VTH3ABG3

@misc{pith2026260527449,
  author       = {Pith},
  title        = {Pith review of: Checking Fact with Better Retrieval: Dynamic Contrastive Learning for Evidence Retrieval},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VTH3ABG3}},
  note         = {Machine review of arXiv:2605.27449}
}

read the original abstract

In the field of multimodal fact checking, the accuracy of retrieving evidence from different modalities has a significant impact on the downstream claim verification process. Existing general multimodal retrieval methods are often constructed based on semantics, resulting in the retrieved evidence being similar but not relevant to the claim. This paper proposes a \textbf{D}ynamic \textbf{A}daptive \textbf{C}ontrastive \textbf{L}earning method for evidence \textbf{R}etrieval called DACLR to address these issues. DACLR first uses a Multimodal Large Language Model (MLLM) to uniformly convert multimodal evidence and claims into text modalities, and extracts the features of these information at event level. Then, it conducts evidence retrieval through a two-stage retrieval method of recall-rerank. DACLR enhances the model's event perception ability of the retrieval stage by optimizing the contrastive loss and mining hard negative samples. Specifically, DACLR designs three loss functions at two levels (semantic and event) based on the InfoNCE loss.Corresponding to these, three sets of hard negative sample candidates are set up. The model dynamically adjusts the ratio based on the accuracy supervision signal of intra-batch samples, allowing the model to learn the correlation between claims and positive samples at the event level without forgetting the semantic retrieval ability. Extensive comparison and ablation experiments demonstrates the effectiveness of DACLR and its internal optimization methods. Further research also prove the advantages of DACLR in the field of multimodal evidence retrieval.

Figures

Figures reproduced from arXiv: 2605.27449 by the authors.

**Figure 2.** An overview of DACLR. of the recall stage is to narrow down the range of n candidate evidences to p (where q < p < n). At this stage, the claim and evidence will be independently encoded by the bidirectional encoder of DACLR, and the correlation between vectors will be calculated through cosine similarity. The top p pieces of evidence with the highest scores are selected. Given the claim and set E, the bidirection… view at source ↗

**Figure 3.** A figure shows the retrieval performance on [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: The variation in the proportion of negative [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: The used prompt template multimodal fact-checking dataset compared to the general multimodal retrieval dataset, as shown in [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]

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Reference graph

Works this paper leans on

4 extracted references · 2 canonical work pages

[1]

InProceedings of the 31st Inter- national Conference on Computational Linguistics, pages 7453–7469, Abu Dhabi, UAE

Piecing it all together: Verifying multi-hop multimodal claims. InProceedings of the 31st Inter- national Conference on Computational Linguistics, pages 7453–7469, Abu Dhabi, UAE. Association for Computational Linguistics. Lianwei Wu, Yuan Rao, Ling Sun, and Wangbo He
[2]

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D

Evidence inference networks for interpretable claim verification.Proceedings of the AAAI Confer- ence on Artificial Intelligence, 35(16):14058–14066. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. InProc...

work page arXiv 2018
[3]

In Proceedings of the 63rd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 22092–22105, Vienna, Austria

Enhancing multimodal retrieval via comple- mentary information extraction and alignment. In Proceedings of the 63rd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 22092–22105, Vienna, Austria. Association for Computational Linguistics. Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, and Weizhu...

work page arXiv 2021
[4]

Event Summary Generation

MARVEL: Unlocking the multi-modal capa- bility of dense retrieval via visual module plugin. In Proceedings of the 62nd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 14608–14624, Bangkok, Thai- land. Association for Computational Linguistics. A Algorithm Algorithm 1Dynamic Adaptive Contrastive Learn- ing T...

2001

Pith tools

Reviewed June 29, 2026 · model on record in the stance chip above.

[1] [1]

InProceedings of the 31st Inter- national Conference on Computational Linguistics, pages 7453–7469, Abu Dhabi, UAE

Piecing it all together: Verifying multi-hop multimodal claims. InProceedings of the 31st Inter- national Conference on Computational Linguistics, pages 7453–7469, Abu Dhabi, UAE. Association for Computational Linguistics. Lianwei Wu, Yuan Rao, Ling Sun, and Wangbo He

[2] [2]

Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D

Evidence inference networks for interpretable claim verification.Proceedings of the AAAI Confer- ence on Artificial Intelligence, 35(16):14058–14066. Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christo- pher D. Manning. 2018. HotpotQA: A dataset for diverse, explainable multi-hop question answering. InProc...

work page arXiv 2018

[3] [3]

In Proceedings of the 63rd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 22092–22105, Vienna, Austria

Enhancing multimodal retrieval via comple- mentary information extraction and alignment. In Proceedings of the 63rd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 22092–22105, Vienna, Austria. Association for Computational Linguistics. Hang Zhang, Yeyun Gong, Yelong Shen, Jiancheng Lv, Nan Duan, and Weizhu...

work page arXiv 2021

[4] [4]

Event Summary Generation

MARVEL: Unlocking the multi-modal capa- bility of dense retrieval via visual module plugin. In Proceedings of the 62nd Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 14608–14624, Bangkok, Thai- land. Association for Computational Linguistics. A Algorithm Algorithm 1Dynamic Adaptive Contrastive Learn- ing T...

2001