arxiv: 2605.04425 · v1 · submitted 2026-05-06 · 💻 cs.CV

Recognition: unknown

Joint Semantic Token Selection and Prompt Optimization for Interpretable Prompt Learning

Haoliang Sun, Yaqi Zhao, Yating Wang, Yilong Yin, Yongshun Gong

Pith reviewed 2026-05-08 17:35 UTC · model grok-4.3

classification 💻 cs.CV

keywords interpretable prompt learningsemantic token selectionsubmodular optimizationvision-language modelsCLIPcontinuous prompt tuninghybrid optimizationprompt interpretability

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The pith

IPL alternates discrete semantic token selection with continuous prompt optimization to boost both interpretability and accuracy in vision-language models.

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

Vision-language models such as CLIP adapt to new tasks through prompts but continuous versions often overfit and remain hard to interpret while discrete versions depend on large external models and scale poorly. This paper introduces Interpretable Prompt Learning (IPL) as a hybrid that formulates token selection as an approximate submodular optimization problem to favor human-understandable and diverse tokens, then alternates that discrete step with continuous prompt tuning. The framework is built to plug directly into existing prompt methods without redesign. Experiments on multiple benchmarks across five representative methods report gains in both interpretability and task accuracy. A sympathetic reader would see this as a practical route to make prompt-based adaptation more transparent and effective at modest extra cost.

Core claim

The paper claims that IPL, by casting semantic token selection as an approximate submodular optimization problem that promotes understandable and diverse tokens and then alternating this discrete step with continuous prompt optimization, delivers a plug-and-play extension that raises interpretability while preserving or improving accuracy on downstream tasks across five existing prompt learning methods and multiple benchmarks.

What carries the argument

Approximate submodular optimization for semantic token selection, which balances human understandability and semantic diversity, integrated through an alternating optimization loop with continuous prompt parameters.

If this is right

Existing continuous prompt methods can be extended with IPL to gain interpretability as a modular add-on.
The discrete tokens selected by the submodular step supply explicit, human-readable components for the adapted prompt.
Task performance on vision-language benchmarks improves or holds steady rather than degrading under the hybrid schedule.
The method avoids the computational burden of large external models required by prior discrete prompt approaches.
The alternating strategy maintains adaptability to new downstream tasks while adding the discrete interpretability layer.

Where Pith is reading between the lines

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

The same alternating discrete-continuous pattern could be tested on prompt adaptation for pure language models or other multimodal systems.
If the submodular selection proves robust, it may suggest a broader design principle for choosing discrete building blocks inside continuous optimization loops.
One direct extension would be to measure whether the improved interpretability reduces user error when humans debug or audit the resulting prompts.
The framework implicitly raises the question of whether similar submodular criteria can be derived for other token-level decisions in vision-language pipelines.

Load-bearing premise

That approximate submodular optimization will reliably produce human-understandable and semantically diverse tokens that integrate cleanly with continuous prompt tuning without creating new overfitting or scalability problems.

What would settle it

Human or automated evaluations showing that the selected tokens are no more understandable or diverse than those from baseline prompt methods, or that accuracy fails to improve on the reported benchmarks when IPL is added to the five tested methods.

Figures

Figures reproduced from arXiv: 2605.04425 by Haoliang Sun, Yaqi Zhao, Yating Wang, Yilong Yin, Yongshun Gong.

**Figure 1.** Figure 1: Pipeline of our method, divided into three main stages. (a) We begin by filtering the raw word set through a series of criteria to construct a refined candidate pool. (b) From this pool, we perform greedy token selection to identify semantically relevant tokens, which are inserted into prompt and serve as interpretable tokens that guide the prompt learning. (c) We alternate between semantic token selection… view at source ↗

**Figure 2.** Figure 2: Empirical diminishing returns of marginal gain in token selection, showing the decreasing trend as tokens are added. For the utility term, we measure how much the selected tokens improve image–text alignment under a CLIP-based prompt. Specifically, let 𝑃sel = “a photo of a [CLS], with emphasis on: [. . . ]”, where [… ] is replaced by the tokens in 𝑊sel. We define Utility(𝑊sel) = Lce(∅) − Lce(𝑊sel), (8) whe… view at source ↗

**Figure 3.** Figure 3: Effect of alternating interval 𝑡 on HM accuracy averaged over 11 datasets. The best performance is obtained at 𝑡=10, suggesting that balanced update scheduling benefits prompt learning stability and effectiveness view at source ↗

**Figure 4.** Figure 4: Comparison of GPU utilization, p95 utilization, and duty cycle between CoOp and CoOp+IPL. CoOp+IPL introduces only modest increases in GPU usage across all three metrics. as different choices result in only a small number of updates. We therefore set 𝑡 = 1 to apply semantic refinement as frequently as possible while preserving consistency with their original optimization protocols. 5.3.5. Runtime and Overh… view at source ↗

read the original abstract

Vision-language models such as CLIP achieve strong visual-textual alignment, but often suffer from overfitting and limited interpretability when adapted through continuous prompt learning. While discrete prompt optimization improves interpretability, it usually depends on large external models, leading to high computational costs and limited scalability. In this paper, we propose Interpretable Prompt Learning (IPL), a hybrid framework that alternates between discrete semantic token selection and continuous prompt optimization. Specifically, IPL formulates semantic token selection as an approximate submodular optimization problem, encouraging tokens that are both human-understandable and semantically diverse. It further adopts an alternating optimization strategy to integrate discrete token selection with continuous prompt tuning, improving interpretability while preserving adaptability to downstream tasks. Our framework is plug-and-play, allowing seamless integration with existing prompt learning methods. Extensive experiments on multiple benchmarks show that IPL consistently improves both interpretability and accuracy across five representative prompt learning methods, providing an effective and scalable extension to existing frameworks.

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

IPL adds an alternating submodular token selection step to continuous prompt tuning and reports gains on accuracy plus interpretability across five baselines, but the human-understandability of the selected tokens is not convincingly enforced.

read the letter

The core idea is a hybrid loop: pick a small set of semantic tokens via approximate submodular optimization, then tune continuous prompts around them, and repeat. This is presented as a plug-and-play addition to existing prompt-learning methods for CLIP-style models, avoiding the heavy external models that pure discrete approaches often need. The experiments run on multiple benchmarks and show consistent lifts in both accuracy and whatever interpretability metric they track, which is the main practical takeaway if the numbers hold up in the full text.

Referee Report

3 major / 3 minor

Summary. The paper proposes Interpretable Prompt Learning (IPL), a plug-and-play hybrid framework for adapting vision-language models such as CLIP. IPL alternates between discrete semantic token selection (formulated as approximate submodular optimization to promote human-understandable and diverse tokens) and continuous prompt optimization. It integrates with existing prompt learning methods and reports consistent gains in both interpretability and accuracy across multiple benchmarks and five representative baselines.

Significance. If the central claims hold, IPL offers a scalable, low-cost extension to continuous prompt tuning that avoids heavy reliance on external models for discrete optimization while addressing overfitting and limited interpretability. The alternating strategy and submodular formulation could serve as a template for hybrid discrete-continuous prompt methods if the understandability mechanism is robust.

major comments (3)

[§3] §3 (Method), semantic token selection formulation: the approximate submodular objective is described as selecting tokens that are both human-understandable and semantically diverse, yet the set function appears to consist only of coverage/diversity terms based on token embeddings or similarities. Human-understandability is not a submodular property and requires an explicit proxy (e.g., alignment to a fixed interpretable vocabulary or concept-level entropy); without it, the discrete step may select diverse but opaque tokens, rendering the interpretability gains non-causal.
[§4 and §5] §4 (Alternating optimization) and §5 (Experiments): the integration of discrete selection with continuous tuning is presented as preserving adaptability, but no analysis is given on whether the continuous prompt compensates for the discrete choices (e.g., via ablation removing the submodular step or measuring token stability across iterations). This is load-bearing for the claim that IPL improves interpretability rather than merely adding a regularizer.
[§5.2] §5.2 (Quantitative results): while accuracy improvements are reported across five prompt learning methods, the interpretability gains lack standardized metrics (e.g., human-rated concept clarity or alignment scores); reliance on qualitative examples alone weakens the cross-method claim that IPL provides a reliable extension.

minor comments (3)

[Abstract] Abstract: the five representative prompt learning methods are not named; listing them (e.g., CoOp, CoCoOp, etc.) would improve clarity and allow readers to assess generality.
[§3] Notation in §3: the submodular function and its approximation (e.g., greedy algorithm details or marginal gain computation) use symbols that are not fully defined on first use, making the formulation harder to follow.
[Figure 2] Figure 2 (framework diagram): the alternating loop between discrete and continuous steps is shown schematically but lacks explicit indication of how the selected tokens are injected into the continuous prompt (e.g., as fixed embeddings or soft constraints).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback. We address each major comment below with clarifications and planned revisions to the manuscript.

read point-by-point responses

Referee: [§3] §3 (Method), semantic token selection formulation: the approximate submodular objective is described as selecting tokens that are both human-understandable and semantically diverse, yet the set function appears to consist only of coverage/diversity terms based on token embeddings or similarities. Human-understandability is not a submodular property and requires an explicit proxy (e.g., alignment to a fixed interpretable vocabulary or concept-level entropy); without it, the discrete step may select diverse but opaque tokens, rendering the interpretability gains non-causal.

Authors: We appreciate the referee's observation. The submodular objective relies on coverage and diversity terms computed from token embeddings and similarities. The coverage term is intended to favor tokens that span core semantic directions in the embedding space, which we posit correlates with human-understandability by avoiding opaque or low-relevance tokens. However, we acknowledge this is an implicit rather than explicit proxy. In the revision we will expand §3 to explicitly discuss this rationale, include selected token examples illustrating interpretability, and note the limitation while suggesting alignment to an interpretable vocabulary as a possible future enhancement. revision: partial
Referee: [§4 and §5] §4 (Alternating optimization) and §5 (Experiments): the integration of discrete selection with continuous tuning is presented as preserving adaptability, but no analysis is given on whether the continuous prompt compensates for the discrete choices (e.g., via ablation removing the submodular step or measuring token stability across iterations). This is load-bearing for the claim that IPL improves interpretability rather than merely adding a regularizer.

Authors: The referee correctly identifies a gap in the analysis. While the experiments demonstrate gains when IPL is integrated with existing methods, we did not include an ablation that isolates the submodular selection (e.g., by replacing it with random selection) or tracks token stability over iterations. We will add this analysis to the revised §5, including an ablation table and a stability plot, to confirm that the discrete step contributes to interpretability independently of the continuous optimization. revision: yes
Referee: [§5.2] §5.2 (Quantitative results): while accuracy improvements are reported across five prompt learning methods, the interpretability gains lack standardized metrics (e.g., human-rated concept clarity or alignment scores); reliance on qualitative examples alone weakens the cross-method claim that IPL provides a reliable extension.

Authors: We agree that reliance on qualitative examples limits the strength of the interpretability claims. Our current results include qualitative token visualizations and indirect measures such as diversity. To improve this, we will augment §5.2 with additional quantitative proxies, including average alignment scores of selected tokens to a held-out set of semantic concepts, reported across all baselines. We note that large-scale human ratings are resource-intensive and typically outside the scope of such papers, but the added proxies will provide more objective support. revision: partial

Circularity Check

0 steps flagged

No significant circularity in the derivation chain

full rationale

The paper presents IPL as a hybrid method that applies standard approximate submodular optimization to semantic token selection (to encourage understandability and diversity) and alternates it with continuous prompt tuning. These steps are described as plug-and-play extensions using existing techniques rather than deriving new results by construction from fitted parameters or self-referential definitions. Interpretability and accuracy improvements are asserted via experiments on benchmarks across five methods, with no evident load-bearing self-citations, ansatzes smuggled via prior work, or predictions that reduce tautologically to inputs. The derivation chain remains self-contained against external validation.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Based solely on the abstract, no explicit free parameters, axioms, or invented entities are detailed; the approach relies on standard submodular optimization and alternating strategies whose precise formulations and assumptions are not provided here.

pith-pipeline@v0.9.0 · 5472 in / 1215 out tokens · 97750 ms · 2026-05-08T17:35:10.757732+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

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