Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion

arXiv:2610.07466 · cs.LG · Submitted 2026-10-05 · Read on arXiv

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Introduction to the show: ident: AI Radio. Generated commentary on the latest Artificial Intelligence papers.

Tom: Today's paper: "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion".

Jane: As a fastidious and diligent researcher, I have meticulously reviewed both provided summaries of this paper,

Tom: First, who's behind it and why it matters.

Title and authors: Tom: So we're looking at this paper today titled "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion." It sounds really technical, but it’s about making AI systems smarter about which information they actually need to process.

Jane: It's a big title because it tackles two major problems at once: how to get the right information efficiently and how to fuse that information as we go along. The authors are Mohapatra, Yang, Sui, Xia, Lundell, and Zhu from Northwestern University and Arm Inc.

Lu: What this paper is doing is basically saying that we shouldn't just process every single piece of data available to the model at once. That’s a lot of work for the computer.

Meng: So what’s the core idea behind this title? Is it about finding a faster way to get answers from multimodal inputs?

Tom: Exactly. It suggests that instead of loading everything upfront, you can use predictions from what you've already seen to decide what modality to grab next and when you can stop acquiring data entirely.

Jane: It’s shifting the focus from just having a big model to being adaptive about the data pipeline itself, which is a big move in how we build these systems.

Lalam: I see this as moving toward more resource-aware inference where the system only spends its processing power on what actually contributes to the final result.

The paper's summary: Tom: Okay, so let's look at what they actually propose in "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion." The main idea is SEMARC, which stands for the Sequential Modality Aggregator and the Adaptive Runtime Controller.

Jane: SEMARC is designed to handle those challenges of order-dependency in fusion. They introduce SEMA, which manages the incremental fusion process by only executing a selected subset of encoder and fusion branches based on what it has already decided to acquire.

Lu: The paper emphasizes that SEMA maintains a fixed-size state after every acquisition and makes a prediction right after that step, so it reuses computation instead of redoing old work, which is key for efficiency.

Meng: That sounds like clever memoization or caching applied specifically to the data flow. How does this help with the sequential fusion issue they mentioned?

Tom: Well, they train SEMA using randomized modality subsets and orders to make sure that even though the order matters for fusion, the predictions made by SEMA stay consistent across different acquisition orders.

Jane: And then you have ARC, the Adaptive Runtime Controller. ARC decides whether to grab the next available modality or just stop acquiring data altogether. It uses a mix of an offline prior and residual fitted Q learning to make these selection decisions dynamically.

Lalam: So, ARC acts like the smart traffic cop deciding if we should keep driving down this road or turn around now, based on what SEMA has already told us about the current evidence.

The paper's improvements: Tom: Now for the actual gains they show. The authors highlight some really concrete improvements in "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion." They focus on how this works practically versus just theoretical ideas.

Jane: They prove that this coupled design of incremental bottleneck fusion, SEMA, with an acquisition controller, ARC is much better than monolithic fusion methods. It shows they can select modalities before the encoder even runs to avoid its cost.

Lu: The paper talks about how they use Shapley attribution to give a formal interpretation to that acquisition prior, which helps justify why the controller makes its choices based on expected utility gains.

Meng: I’m interested in the cost analysis they did on modality costs. They show that different extraction times or computational burdens mean you can selectively acquire modalities based on their informativeness versus their cost, even with a single training price lambda.

Tom: That means you can prune expensive modalities while still keeping the cheap, useful ones, which is a very practical way to manage complexity in real-world systems.

Jane: And they provide formal guarantees through Proposition three point one and Corollary three point two for the Adaptive Inference System, specifically concerning modality acquisition when there’s missing data around at runtime conditions <ref:2610.07466#pg2>.

Conclusion: Tom: So, wrapping up the "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion" paper. The main point is that coupling incremental fusion with sample-adaptive acquisition—SEMARC—delivers both higher performance and massive computational savings.

Jane: It’s not just about getting a slightly better score; they show that SEMARC typically achieves three point two percent higher macro-F1, and importantly, it’s often Pareto non-dominated in terms of total GFLOPs relative to the most accurate baseline for each dataset <ref:2610.07466#pg2>.

Lu: The efficiency metrics are huge here. They report averaging sixty-one point four percent lower total inference GFLOPs on average compared to each dataset’s most accurate baseline, and they also show a reduction in end-to-end latency by an average of forty-four point zero percent across GPU and CPU, and by forty-seven point two percent on Android INT8 devices.

Meng: Those numbers are significant for deployment because they translate directly into faster response times on consumer hardware like mobile phones or edge devices that often run on lower precision formats like INT8.

Lalam: It shows that the savings come from selection rather than just having less missing input; SEMARC can handle runtime modality missingness and still match or exceed the best baseline macro-F1 in twenty-one out of twenty-four conditions while achieving those GFLOPs reductions.

Tom: So, what does this mean for us as listeners who just want to know what this paper actually changes for our daily life? It means that future AI systems won't just be bigger models; they’ll be smarter about *how* they gather information during inference.

Jane: The implication is a system that can dynamically adjust its data gathering based on what it knows, leading to much faster and less power-hungry applications.

Lu: From a research standpoint, this validates the idea that we can manage the complexity of sequential fusion without needing to track every possible order history by using those compact states in SEMA.

Meng: Practically, it means deploying complex multimodal AI on resource-constrained hardware becomes much more feasible if you use acquisition strategies like these instead of just brute-forcing all inputs.

Lalam: For us, it means the future is about efficiency and intelligence working together in a way that respects the limitations of our devices.

Tom: That’s the picture for today on "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion." We'll be back next time to look at something else.

Arm Inc. · Northwestern University

cs.LG

Submitted: 2026-10-05

Updated: 2026-10-12

License: http://creativecommons.org/licenses/by/4.0/

Importance score: 92/100

The gist: As a fastidious and diligent researcher, I have meticulously reviewed both provided summaries of this paper, "Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion." The

Key concepts

Sequential Modality Aggregator (SEMA): SEMA manages the incremental fusion process by executing only a subset of necessary encoder and fusion branches at runtime. It maintains a fixed-size state updated after each acquisition, allowing the system to reuse prior computations instead of redoing work, which is key for efficiency.
Adaptive Runtime Controller (ARC): ARC decides whether to acquire the next available modality or stop acquiring data. It uses an offline prior and residual fitted-Q learning to make this decision based on what SEMA has already summarized, making intelligent choices about which data to collect next.
Shapley Attribution: This concept is used to formally interpret the acquisition prior in SEMARC. It provides a mathematical way to assign credit or utility gains to each potential modality, helping the controller understand the conditional expected gain of acquiring specific data points.
Modality Costing and Selection: The research shows that different modalities have different costs (like extraction time). The system learns to selectively acquire only informative modalities that offer a good return relative to their cost, proving that expensive modalities can be pruned while retaining cheap, useful ones.

Terminology

Distillation — 13× in this paper · explained in 2 episodes
Ablation — 12× in this paper · explained in 1 episode
Transformer — 12× in this paper · explained in 2 episodes
Adaptive Runtime Controller — 8× in this paper
Quantization — 8× in this paper · explained in 13 episodes
Accuracy — 7× in this paper · explained in 1 episode
Fine-Tuning — 7× in this paper · explained in 5 episodes
Adaptation — 6× in this paper · explained in 1 episode
Aggregation — 6× in this paper
Benchmark — 6× in this paper · explained in 2 episodes
Calibration — 6× in this paper · explained in 7 episodes
Adaptive Runtime Controller (ARC) — 5× in this paper · explained in 1 episode
Core — 5× in this paper · explained in 2 episodes
Multimodal Fusion — 5× in this paper · explained in 4 episodes
Robustness — 5× in this paper · explained in 5 episodes
Action Space — 4× in this paper · explained in 1 episode
Transformers — 4× in this paper · explained in 2 episodes
Multimodal Learning — 3× in this paper · explained in 2 episodes
Papers — 3× in this paper · explained in 3 episodes
Point Clouds — 3× in this paper · explained in 2 episodes
Reinforcement Learning — 3× in this paper · explained in 4 episodes
Adam — 2× in this paper · explained in 2 episodes
Adaptive Inference System — 2× in this paper
Decoder — 2× in this paper · explained in 2 episodes
Embeddings — 2× in this paper · explained in 2 episodes
Fidelity — 2× in this paper · explained in 2 episodes
Grounding — 2× in this paper · explained in 3 episodes
Multimodal Bottleneck Transformer — 2× in this paper
Multimodal Dataset — 2× in this paper · explained in 2 episodes
Representation Learning — 2× in this paper · explained in 3 episodes
Reward Function — 2× in this paper · explained in 4 episodes
Ablation Study — 1× in this paper · explained in 3 episodes
Activation Function — 1× in this paper · explained in 1 episode
Adaptive Encoder — 1× in this paper · explained in 1 episode
Class Imbalance — 1× in this paper · explained in 2 episodes
Cosine Similarity — 1× in this paper · explained in 3 episodes
Decoupling — 1× in this paper · explained in 2 episodes
Deep Reinforcement Learning — 1× in this paper · explained in 3 episodes
Edge Devices — 1× in this paper · explained in 2 episodes
Feature Extraction — 1× in this paper · explained in 3 episodes
Greedy Modality Selection — 1× in this paper
Hyperparameters — 1× in this paper · explained in 2 episodes
Late Fusion — 1× in this paper · explained in 2 episodes
Multimodal Inputs — 1× in this paper · explained in 2 episodes
Pruning — 1× in this paper · explained in 4 episodes
Self-Supervised Learning — 1× in this paper · explained in 8 episodes
Shapley values — 1× in this paper · explained in 2 episodes
Sparse Attention — 1× in this paper · explained in 5 episodes
Weight Decay — 1× in this paper · explained in 2 episodes

Summary

As a fastidious and diligent researcher, I have meticulously reviewed both provided summaries of this paper, Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion. The goal is to synthesize these distinct perspectives into a single, comprehensive, and highly detailed description suitable for rigorous academic understanding.

Here is the combined summary:

Comprehensive Research Summary: Efficient Multimodal Inference through Adaptive Acquisition and Sequential Fusion (SEMARC Framework)

This research introduces SEMARC, a novel framework designed to achieve efficient multimodal inference by coupling sample-adaptive modality acquisition with incremental multimodal fusion. The core innovation lies in the synergistic relationship between two main components: the Sequential Modality Aggregator (SEMA) and the Adaptive Runtime Controller (ARC).

Core Mechanism: Coupled Incremental Fusion and Acquisition

The fundamental challenge addressed is how to select the optimal set of modalities to acquire at runtime while simultaneously performing incremental fusion efficiently, especially when dealing with varying levels of test-time modality missingness.

Sequential Modality Aggregator (SEMA): SEMA manages the incremental fusion process. It operates by executing only a selected subset of encoder and fusion branches based on the current acquisition decision. Crucially, SEMA maintains a fixed-size state that is updated after each modality acquisition and performs a prediction immediately following it. This design allows for reusing earlier computation, avoiding the costly recomputation of previously executed branches, which is essential for efficiency.
Adaptive Runtime Controller (ARC): ARC dictates the acquisition policy—deciding whether to select the next available modality or stop acquiring data entirely. The controller is trained on a frozen SEMA state and utilizes a sophisticated selection mechanism that combines two elements:

An offline, set-dependent marginal-utility prior.
Residual fitted-Q learning.

This combination allows ARC to make selection decisions based on the summary provided by SEMA and the current modality availability, without needing to inspect unacquired inputs or retain a complex history of all acquisition orders.

The system operates under a strict rule: choose the next modality or stop while reusing earlier computation.

Theoretical Foundation and Design Rationale

The paper provides robust theoretical grounding for this coupled design:

Addressing Order Sensitivity: The authors acknowledge that incremental fusion can yield different predictions depending on the order of acquired evidence. To manage this complexity without tracking a factorial number of acquisition histories, SEMA's compact state and predictive mechanism are employed.
Acquisition Prior Interpretation: The study establishes a formal interpretation for the acquisition prior using Shapley attribution. This provides theoretical support for the controller's decision-making process by establishing a conditional expected-gain interpretation under Equation 22, linking the acquisition policy to measurable utility gains.
Modality Costing and Selection: The analysis demonstrates that nonuniform costs (i.e., different extraction times or computational burdens) allow the system to selectively acquire modalities based on their relative informativeness versus cost, proving that a single training price (lambda) can effectively prune expensive modalities while retaining cheap, informative ones.
Guarantees: The work proves Proposition 3.1 and Corollary 3.2, which establish formal acquisition guarantees for the Adaptive Inference System (AIS), specifically regarding modality acquisition under missingness conditions.

Empirical Validation and Performance Results

SEMARC has undergone rigorous empirical validation across six diverse datasets, testing various configurations of test-time modality missingness (ranging from 10% to 40%). The results consistently demonstrate significant performance gains:

Performance Gains: SEMARC typically achieves 3.2% higher macro-F1 and is often Pareto non-dominated in terms of total GFLOPs (or post-extraction GFLOPs) relative to the most accurate baseline for each dataset. In 21 out of 24 conditions, it matches or exceeds the highest baseline macro-F1.
Computational Efficiency: The framework yields substantial efficiency improvements:
It averages 61.4% lower total GFLOPs compared to the most accurate baseline on average across datasets.
End-to-end latency is reduced by an average of 44.0% across GPU and CPU, and by 47.2% on Android INT8 relative to the fastest measured baseline.
It achieves 38.4% lower post-extraction GFLOPs.
Robustness under Missingness: SEMARC exhibits remarkable resilience to runtime modality missingness, matching or exceeding the best baseline macro-F1 in 21 of 24 conditions, highlighting its practical utility across heterogeneous devices.
Latency Superiority: SEMARC is also found to be the fastest in 16 out of 18 dataset-platform conditions.

Conclusion

In summary, SEMARC proposes a powerful paradigm shift for multimodal inference by integrating incremental fusion (SEMA) with intelligent, sample-adaptive acquisition (ARC). This coupled system not only achieves state-of-the-art performance metrics but also delivers substantial computational savings and latency reductions across various deployment environments. The theoretical analysis provides the necessary guarantees to understand why this adaptive approach works, grounding the empirical success in sound principles of cost weighting and sequential decision-making under uncertainty.

Improvements for AI systems

Bold header: Sequential Modality Aggregation (SEMA) for Order Robustness; This component reuses a compact bottleneck state and predicts after each acquisition to ensure consistent predictions across acquisition orders of the same acquired evidence, which prevents decisions based on order-dependent sequential fusion from being flawed.
Bold header: Adaptive Runtime Controller (ARC) for Sample-Adaptive Acquisition; ARC uses a set-dependent marginal-utility prior with residual fitted-Q learning to select the next available modality or stop, without inspecting unacquired inputs or retaining acquisition order, allowing the system to decide which available modality need to be processed for each input dynamically.
Bold header: Coupled Incremental Fusion and Acquisition; The framework couples incremental bottleneck fusion (SEMA) with an acquisition controller (ARC) through prefix predictions trained to be consistent across acquisition orders, meaning Acquisition and stopping decisions govern incremental encoder and fusion execution, reusing earlier computation.
Bold header: Latency and GFLOPs Reduction via Selective Execution; SEMARC achieves 61.4% lower total inference GFLOPs on average relative to each dataset’s most accurate baseline by executing only selected encoder and fusion branches, leading to practical latency gains across GPU, CPU, and Android (e.g., 47.2% on Android INT8).
Bold header: Missingness Resilience; SEMARC can handle varying runtime modality missingness by still skips available modalities, matching or exceeding the best baseline macro-F1 in 21 of 24 conditions with 14.8% lower total GFLOPs on average, demonstrating that the savings come from selection rather than from missing inputs alone.

Abstract

Multimodal systems often encode every available input, even when a subset suffices for prediction. Adaptive acquisition can reduce this cost by using predictions from incrementally fused evidence to decide which modality to encode next and when to stop. However, sequential fusion makes these predictions order-dependent, so decisions based on them may need to distinguish factorially many histories of the same acquired set. We introduce SemARC, which couples a Sequential Modality Aggregator (SeMA) with an Adaptive Runtime Controller (ARC) and uses acquired evidence to select each modality before its encoder runs. SeMA executes only selected encoder and fusion branches, updates a fixed-size state, and predicts after each acquisition without recomputing earlier branches. We supervise every acquisition prefix under randomized modality subsets and orders to encourage consistent predictions across acquisition orders. ARC combines a set-dependent marginal-utility prior with residual fitted-Q learning to select the next available modality or stop, without inspecting unacquired inputs or retaining acquisition order. Across six multimodal classification datasets and eleven baselines, SemARC achieves 3.2% higher macro-F1 and 61.4% lower total inference GFLOPs on average relative to each dataset's most accurate baseline. End-to-end latency falls by 44.0% across GPU and CPU and by 47.2% on Android INT8 relative to the fastest measured baseline, on average. Under varying runtime modality missingness, SemARC still skips available modalities, matching or exceeding the best baseline macro-F1 in 21 of 24 conditions with 14.8% lower total GFLOPs on average. SemARC thus offers a practical path toward efficient multimodal inference across heterogeneous devices.