MANTA: Cross-Modal Semantic Alignment and Information-Theoretic Optimization for Long-form Multimodal Understanding
Ziqi Zhong, Daniel Tang
TL;DR
MANTA is a theoretically-grounded framework that unifies visual and auditory modalities into a structured textual space, improving long-form multimodal understanding and reasoning with large language models.
Contribution
It introduces a novel, mathematically formalized approach for semantic alignment, synchronization, and hierarchical content representation across modalities, enhancing long video question answering.
Findings
Up to 22.6% accuracy improvement on long video QA
27.3% gains on videos over 30 minutes
23.8% improvement on temporal reasoning tasks
Abstract
While multi-modal learning has advanced significantly, current approaches often treat modalities separately, creating inconsistencies in representation and reasoning. We introduce MANTA (Multi-modal Abstraction and Normalization via Textual Alignment), a theoretically-grounded framework that unifies visual and auditory inputs into a structured textual space for seamless processing with large language models. MANTA addresses four key challenges: (1) semantic alignment across modalities with information-theoretic optimization, (2) adaptive temporal synchronization for varying information densities, (3) hierarchical content representation for multi-scale understanding, and (4) context-aware retrieval of sparse information from long sequences. We formalize our approach within a rigorous mathematical framework, proving its optimality for context selection under token constraints. Extensive…
Peer Reviews
Decision·ICLR 2026 Conference Desk Rejected Submission
Novel approach: Linguistic abstraction as universal semantic bridge is conceptually interesting Strong empirical results: Consistent improvements across baselines, particularly on long videos Theoretical grounding: Three formal theorems provide principled foundation Comprehensive evaluation: 1,700 videos, multiple benchmarks, detailed ablations
1. Experimental Issues New benchmark "LongVU-QA" is undocumented: 500 videos, 3,000 questions introduced but no validation, annotation details, or availability Missing train/test split documentation: Potential data leakage concerns Outdated baselines: Only compares to GPT-4V (2023), missing GPT-4o, Gemini 1.5 Pro, Claude 3.5 Unclear baseline integration: How is proprietary GPT-4V/Gemini "augmented" with MANTA? 2. Theoretical Overclaiming Theorem 2: (1-1/e) submodular approximation is textbook
- It demonstrates exceptional capability in processing long-form videos, a key bottleneck for traditional models. - It offers high interpretability, as its intermediate outputs are human-readable text, making the model's reasoning process transparent. - The approach shows outstanding performance and acts as a universal enhancer that can boost various existing models. It achieves high efficiency and scalability by compressing high-dimensional pixel data into low-dimensional text representations.
- The pipeline architecture is susceptible to cascading errors, where a mistake in an early stage propagates through the system. - The "linguistic bottleneck" may filter out subtle, non-verbal information that is difficult to describe accurately in words.
The core idea is interesting and promising. Treating natural language as a universal semantic substrate is an appealing conceptual direction that could enable cross-modal reasoning.
- The manuscript contains long blocks of text without clear sectional structure, which severely undermines readability. - The paper appears to have relied heavily on large language models (LLMs) in its writing and possibly in experimental components, but there is no explicit statement about which LLMs were used, how they were used, or what role they played. The authors should include a clear declaration describing any LLM usage during manuscript preparation. - There are numerous punctuation a
1. The paper proposes a theoretically motivated multimodal abstraction framework that operates through a shared linguistic bottleneck, which is an elegant and interpretable idea. 2. The multi-scale hierarchical design (micro / meso / macro) is well-motivated and empirically beneficial. 3. The theoretical justification of temporal scale selection (via power-law correlation) provides conceptual depth in multimodal work. 4. Strong performance improvements are demonstrated on multiple long-form v
1. Contribution Attribution Between Framework and Backbones: While the multi-LLM evaluation convincingly shows that MANTA's benefits are model-agnostic, the paper does not fully disentangle the contribution of the novel linguistic abstraction framework from the sheer power of the large pre-trained encoders (CLIP, TimeSformer, Whisper) it builds upon. 2. Unconvincing and Potentially Misleading Efficiency Claims: In Appendix G, the authors report that MANTA achieves higher computational efficienc
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Taxonomy
TopicsMultimodal Machine Learning Applications · Topic Modeling · Domain Adaptation and Few-Shot Learning