Addressing Modality Mismatch in AI Model Design

Most multimodal QA systems fail in the same place. Not at perception. Not at language. But at how evidence is retrieved and constrained before generation. This paper on Pythia - RAG makes that failure mode very clear. Even strong vision - language models still rely on captions and flat retrieval. In dense scenes, that silently drops relations. The model may see the objects, but it never commits to who is doing what to whom, so those relations disappear before reasoning even starts. What’s different here is that relations are treated as first - class structure rather than something the model is expected to infer implicitly. Textual relations are extracted as explicit triplets, visual relations are extracted directly from images using relation - aware detection, and both are unified into a single multimodal knowledge graph that is further grounded with external common sense knowledge. Retrieval is also structural, not similarity - based. Instead of pulling isolated facts, the system retrieves a query - guided subgraph using a graph algorithm that explicitly optimizes for relevance and cohesion. That sub graph is then encoded in two complementary ways: structurally through a graph encoder and textually through an LLM, while the associated image is encoded in parallel. These representations are fused with attention and only then passed to generation. Hallucinations don’t drop here because the language model is more careful or better prompted. They drop because generation is no longer free - form. The model is forced to operate over a constrained, relationally coherent slice of the multimodal graph. The larger takeaway is subtle but important. Multimodal reasoning doesn’t fail because models lack modalities. It fails because relations are implicit, retrieval is flat, and structure is introduced too late. Once relations are explicit and retrieval preserves topology, generation becomes a consequence of reasoning rather than a guess. That feels like a meaningful shift for multimodal RAG, especially in settings where confident answers without grounding are the real failure mode. #MultimodalAI #RetrievalAugmentedGeneration #KnowledgeGraphs #GraphReasoning #MultimodalQA #AIResearch #LLMs #VisionLanguage #StructuredReasoning #GraphNeuralNetworks #TrustworthyAI

Agentic AI is often described as LLMs that can take actions. In practice the shift is deeper. We are moving from systems that generate language to systems that execute operations inside deterministic infrastructure. That transition exposes a structural mismatch most teams discover only after deployment. LLMs produce stochastic outputs. The systems they must control do not tolerate stochasticity. Databases require valid queries. APIs require typed payloads. Cloud services expect schema conformant commands. Even a single malformed output can cause a hard system failure. As agent workflows grow longer, the reliability ceiling of these integrations becomes the limiting factor for real deployments. The paper “The Auton Agentic AI Framework from Snap Research” by Snapchat, frames this mismatch as the Integration Paradox. LLMs are probabilistic inference engines, yet the environments they operate in require deterministic and auditable behavior. The common workaround has been layers of glue code: output parsers, validation scripts, retry logic, and format coercion. Systems work until they do not, and every new tool or API introduces another fragile integration layer. The correction proposed in the paper is to separate the definition of an agent from its execution runtime. Instead of embedding prompts, safety logic, tool bindings, and memory policies inside framework code, the system defines a Cognitive Blueprint. This blueprint is a declarative specification that describes the agent’s identity, tools, output contracts, constraints, and memory configuration in a structured format such as YAML or JSON. The runtime engine then loads that specification and executes it within the target environment. Every agent interface is bound to a formal output schema. If the underlying model emits unstructured text or malformed output, the runtime intercepts the result and enforces schema validation before it reaches downstream systems. The agent’s interface becomes contract driven rather than conversational. Safety enforcement follows the same logic. Instead of filtering outputs after generation, the framework defines a constraint manifold over the action space and projects the agent’s policy into that safe region before action emission. Unsafe operations are assigned zero probability during generation rather than being detected after the fact. The paper also formalizes the execution model as a decision process with a latent reasoning space separating internal deliberation from external actions. This architectural separation enforces a “think before act” discipline where reasoning steps consume compute but do not produce side effects until an explicit action policy executes. Agents are easier to govern when they are defined as structured system contracts rather than as chains of prompts. Paper: https://lnkd.in/epMppJUq

🧠📚 When RAG Systems Break on Images — and How Multimodal Retrieval Fixes It Retrieval-Augmented Generation (RAG) has become the backbone of enterprise AI knowledge systems. But many implementations share a structural flaw: They assume documents are mostly text. The moment PDFs contain diagrams, scanned pages, embedded charts, equations, or complex tables — performance degrades sharply. ⚠️ Context is lost ⚠️ Tables flatten incorrectly ⚠️ Equations disappear ⚠️ Images become invisible to the model In high-stakes domains like finance, engineering, healthcare, or research, this is not a minor issue. It is a reliability gap. 🔧 A Practical Fix: True Multimodal Processing A recent open-source GitHub project addresses this limitation by rethinking the ingestion layer of RAG systems. Instead of treating non-text elements as noise, it processes them as first-class knowledge objects. Here’s what the system actually enables: ✅ Precise Text Processing Maintains high-fidelity chunking and embedding for structured and unstructured text. 🖼️ Deep Image Understanding Applies vision-language models to interpret diagrams, figures, and scanned content — not just OCR extraction. 📊 Structured Table Extraction Captures relational structure from tables instead of flattening them into raw text. ∑ LaTeX Equation Parsing Preserves mathematical meaning by interpreting symbolic notation rather than stripping formatting. 🕸️ Multimodal Knowledge Graph Construction Links text, visuals, tables, and formulas into a unified semantic graph — improving retrieval depth and reasoning accuracy. 🚀 Why This Matters Enterprise knowledge is inherently multimodal. Engineering reports contain schematics. Financial filings contain tables. Scientific papers contain equations. Strategy decks contain charts. If RAG systems ignore these modalities, they only retrieve fragments of institutional knowledge. Multimodal ingestion transforms RAG from a document search tool into a reasoning infrastructure layer. The future of enterprise AI is not just better generation — it is better grounding across every information format. Repository: https://lnkd.in/ddm6fN7V Research paper: https://lnkd.in/dnXqYSFw Follow and Connect: Woongsik Dr. Su, MBA #RAG #MultimodalAI #KnowledgeGraphs #EnterpriseAI #GenAI

There's a hidden geometric puzzle inside every multimodal AI model. It's called the Modality Gap: even when an image and a caption describe the exact same scene, their embeddings float in systematically different regions of the model's representation space. This isn't just a curiosity—it fundamentally limits how well vision and language can work together. Most fixes assume the gap is simple and uniform. Reality is messier. This work offers a sharper lens: what if we could precisely model the gap's shape instead of guessing it? The authors propose Fixed-frame Modality Gap Theory, breaking down the gap into stable biases and directional residuals. From this, they build ReAlign—a training-free method that uses unpaired data statistics to map text embeddings directly into the visual distribution. Three steps: Anchor, Trace, Centroid. Each tackles a different layer of geometric misalignment. The elegance lies in what comes next. ReVision extends this into a full training paradigm for multimodal LLMs. By treating aligned text as pseudo-visual representations, you can pretrain on massive text corpora—no costly image-text pairs required. Text becomes both the teacher and the student: transformed embeddings act as visual inputs, while original text provides supervision. For anyone scaling multimodal models, this is worth a close look. The method is theoretically grounded, code is available, and the visualizations make the gap concrete. Paper: https://lnkd.in/eJ2yhj2y Code: https://lnkd.in/e9dvJMYP

You are in an Apple ML Research interview. The question: "Why do multimodal models sometimes underperform unimodal ones?" You pause. Most answers mention data imbalance. You go deeper: "When different modalities share a representation too early, that space can collapse. One modality can dominate before real abstraction forms." You explain: "Vision often anchors meaning strongly. Cross-modal attention can learn shortcuts instead of shared concepts." They ask. "How do you prevent that?" You respond. "Use modality-specific encoders. Fuse information later. Apply contrastive learning so modalities align without overpowering each other." One final question. "What happens if you do not?" You answer. "The model appears multimodal but reasons through one modality. Performance looks good until one input degrades. Then failures are silent." You finish with: "Multimodality is not about seeing more. It is about reasoning across representations." #AI #MultiModal

The challenges underlying multimodal AI, are that different modality signals often have distributional modality gaps, owing to their heterogenous nature. The paper, “MISA: Modality-Invariant and Specific Representations for Multimodal sentiment analysis” was a very important paper, because it gave the strategy to remedy this distributional gap problem. While it’s not a very recent paper, the insights it reveals are very important. For every modality that entails a particular utterance, each modality entails some specific nuance, in addition to all the modalities having a common property. For instance a cat smiling written in text, and the photo of the cat smiling, convey a common representation, the “smile”aspect. This aspect should be mapped to a shared subspace. Mapping happens proper when we enforce certain loss function formulations. For each utterance, to capture the common sub space representation, we need to firstly have a loss formulation called as “similarity loss” which captures the discrepancy between the shared representations of the modality. For every hidden representation of the modalities, we can have learnable weights to transform them into a “shared subspace/modality invariant”, and a “nuance specific(specific to that modality) specific representation”(hence the name MISA). Now, coming back to the “similarity loss” we can have a simple central moment discrepancy formulation that captures the difference between the shared representations of their probability distributions of specific modalities. In the shared subspace we would want each modality’s modality invariant representation to perfectly overlap(in an ideal case), and good overlap(in practical case). So we compute the central moment discrepancy between all possible pairs of the modalities, and take their sum as our loss function. We ought to minimise them. The next aspect is the “difference loss”, which also ensures that the specific representations or the nuances captured by each modality are orthogonal to each other(which is specific representations for text, audio, video) should be orthogonal, since we do not want redundant common information here, rather the nuances that discriminate each of the modalities. Also we want modality specific representations to also be orthogonal(independent intuitively thinking) to the shared representation as well. Finally, we also need a “reconstruction loss”formulation that ensures that we are able to reconstruct the respective hidden state representations that we formulated from the input, so that the model, does not “trivially” learn to just learn an orthogonal representation but rather capture the details of their respective modalities. Finally the “task loss” is there to estimate how well our predictions have been. This is obvious for it is cross entropy loss for classification and mean squared error loss for regression. (Word limit reached for results). Paper link in comments(Photo bottom right courtesy: paper)