Visio‑LLM chat interface versus objects‑in‑scenes catalog plus SQL: which is better?

The clearest quantitative comparison between a language-model-based querying interface and a traditional SQL workflow comes from Ipeirotis and Zheng’s 2025 user study on natural language interfaces for databases (NLIDBs). They compare SQL‑LLM, a modern NL2SQL system built on Seek AI, with Snowflake’s native SQL interface in a controlled lab setting with 20 participants and 12 realistic analytics tasks per participant. The results are surprisingly decisive: the NL2SQL interface reduces mean task completion time from 629 seconds to 418 seconds, a 10–30% speedup depending on task, with a statistically significant difference (p = 0.036). At the same time, task accuracy rises from 50% to 75% (p = 0.002). Participants also reformulate queries less often, recover from errors 30–40 seconds faster, and report lower frustration. Behavioral analysis shows that, when the NLIDB is well‑designed, users actually adopt more structured, schema‑aware querying strategies over time, rather than treating the system as a vague natural language oracle.

If this is mapped to the data analytics world, SQL‑LLM is essentially “LLM chat front‑end that emits structured queries”; Snowflake is the canonical structured interface. So, at least in the textual domain, a chat interface tightly coupled to a correct, inspectable execution layer can be both faster and more accurate than a traditional SQL UI for mixed‑skill users. The result is not just that “chat is nicer,” but that it materially shifts the error profile: users spend less time fighting syntax and more time converging on the right question.

On the visual analytics side, Martins and colleagues provide a 2025 systematic review, “Talking to Data,” which synthesizes the rise of conversational agents for visual analytics and natural-language-to-visualization (NL2VIS) workflows. They survey LLM‑based agents that let users ask questions like “Show me a time series of daily incidents by district and highlight outliers” and receive automatically generated charts and dashboards. Across the systems they review, the primary benefit is consistent: conversational interfaces dramatically lower the barrier to entry for non‑technical users and accelerate first‑insights exploration for everyone. Users no longer need to know which chart type, which field, or which filter to apply; instead, they iteratively describe intent in language. The review notes an acceleration of research after 2022 and highlights common architectural patterns such as multi‑agent reasoning (one agent for intent parsing, another for code generation, another for validation), context‑aware prompting, and automatic code generation backends that produce SQL or visualization scripts under the hood.

But the same review is blunt about the downsides. LLM‑driven visual analytics systems suffer from prompt brittleness, hallucinated insights, and inconsistent performance across domains. In other words, they shine in “getting started” and in ideation but can be fragile in the long tail of complex or ambiguous queries. This is precisely where a structured objects‑in‑scenes catalog plus SQL (or structured filters) tends to dominate: once a user knows what she wants, a faceted object browser with composable filters and explicit SQL conditions is precise, auditable, and predictable. The current research consensus is not that conversational agents replace structured interfaces, but that they act as an outer, more human‑friendly layer wrapped around a rigorous, structured core.

The vision‑specific part is still emerging, but there is emerging pattern in recent work on LLM‑assisted visual analytics agents. Zhao and colleagues’ ProactiveVA framework implements an LLM‑powered UI agent that monitors user interactions with a visual analytics system and offers context‑aware suggestions proactively, rather than only on demand. Instead of just answering queries, the agent watches when users get “stuck” in complex visual tools and intervenes with suggestions: alternative views, drill‑downs, parameter changes. They implement the agent in two different visual analytics systems and evaluate it through algorithmic evaluation and user and expert studies, showing that proactive assistance can help users navigate complexity more effectively. Although ProactiveVA is not focused purely on vision‑language object querying, it illustrates the same interaction pattern likely to emerge in vision‑LLM settings: the agent lives on top of a rich, structured tool (our object catalog, filters, metrics) and orchestrates interactions, rather than replacing the underlying structure.

If one projects the NLIDB and NL2VIS findings into a vision‑LLM setting where the underlying data is an objects‑in‑scenes catalog indexed by SQL, a few hypotheses are well‑supported by existing evidence, even if not yet directly tested for aerial or scene‑level vision. First, a vision‑LLM chat interface that translates “natural” questions like “Show me all intersections with at least three trucks and a pedestrian within 10 meters in the last 5 minutes” into structured queries over a scene catalog will almost certainly improve accessibility and time‑to‑first‑answer for non‑SQL users, mirroring the 10–30% time savings and 25‑point accuracy gains seen in NLIDB studies. Second, the same studies suggest that, with appropriate feedback—showing the generated SQL, visualizing filters, allowing users to refine them—users begin to internalize the schema and move toward more structured mental models over time, rather than staying in a purely “chatty” mode. Third, NL2VIS work indicates that conversational interfaces excel at exploration, hypothesis generation, and “what’s interesting here?” tasks, while deterministic structured interfaces excel at confirmatory analysis and compliance‑grade reproducibility.

At the same time, all the pain points NL2VIS and NLIDB researchers describe will be amplified in vision‑LLM workflows. Hallucinations in vision‑language models mean that a chat interface might confidently describe patterns or objects that are not actually present in the underlying catalog, unless the system is architected so that the LLM can only reason over ground‑truth detections and metadata, not raw pixels. Schema ambiguity becomes more complicated, because the same visual concept (say, “truck near crosswalk”) may correspond to multiple object categories, spatial predicates, and temporal windows in the catalog. The review by Martins et al. emphasizes that robust systems increasingly rely on multi‑stage pipelines and explicit grounding: one module to resolve user intent, another to generate executable code, and another to validate results against the data and, if necessary, ask follow‑up questions. That is roughly the architecture we would want for trustworthy vision‑LLM interfaces as well.

Upcoming research directions in the literature line up nicely with the gap we are pointing at. Martins et al. explicitly call for more systematic user studies that compare conversational agents to traditional visual analytics tools, focusing not only on accuracy and time, but also on trust, learnability, and long‑term workflow integration. They highlight the need for standardized benchmarks for conversational visual analytics—essentially the NL2SQL benchmarks, but for NL2VIS and related tasks. ProactiveVA, meanwhile, opens the door to agentic systems that do more than answer questions: they monitor interaction logs, predict when the user needs help, and suggest next steps in an interpretable, controllable way. Extending such agents to vision‑centric workflows, where the agent can propose new filters or views on top of an objects‑in‑scenes catalog, is a natural next step.

What is still missing, and where there is clear space for original work, is an end‑to‑end, quantitative comparison between three modes on the same vision dataset: first, a pure objects‑in‑scenes catalog with SQL or GUI filters; second, a vision‑LLM chat interface that only describes scenes but does not drive structured queries; and third, a hybrid system where the chat interface is grounded in the catalog and always produces explicit, inspectable queries. The database and visual analytics communities have now shown that the hybrid pattern—LLM chat front‑end, structured execution back‑end—can deliver significant gains in speed, accuracy, and user satisfaction over traditional interfaces alone. Vision‑centric systems are just starting to catch up. If we frame our Drone Video Sensing Applications work as “bringing the NLIDB/NL2VIS playbook into multimodal, scene‑level analytics” and design a user study with metrics analogous to Ipeirotis and Zheng’s, we would not just be building a product interface; we would be writing one of the first concrete answers to the question we are asking.