Opening — Why this matters now
The multimodal AI arms race is no longer about who can see more pixels or generate prettier sketches. It’s about whether models can think across modalities the way humans do—fluidly, strategically, and with the right tool for the moment.
Most systems still behave like students who bring one pen to an exam: capable, but painfully limited. The newly proposed Octopus framework—with its six-capability orchestration—suggests a different future: one where a model doesn’t just hold tools, but chooses them. It’s a quiet shift with big implications for enterprise automation.
Background — Context and prior art
The landscape before Octopus reads like a taxonomy of partial solutions:
- Direct inference models treat images as static inputs, reasoning mostly in text and guessing through visual ambiguity.
- Tool-driven visual exploration adds detectors and croppers, but ends up feeling like giving a toddler a Swiss Army knife.
- Programmatic visual manipulation uses code generation to perform precise actions—until the model hallucinates a function name and the entire pipeline collapses.
- Intrinsic visual imagination (e.g., Thinking with Generated Images) is clever but siloed—great for certain path-planning puzzles, less so for real-world multimodal messiness.
Across all this, one thing is missing: coordination. Humans don’t pick one reasoning mechanism. We chain them, swap them, and course-correct.
Octopus tries to bottle that.
Analysis — What the paper proposes
At its core, Octopus (yes, complete with an emoji mascot in the paper【file】) argues that multimodal reasoning requires six discrete but complementary capabilities:
- Percept — fine-grained visual extraction (OCR, object grounding).
- Augment — visual marking, annotation, externalized reasoning.
- Spatial — geometric and topological understanding.
- Logic — programmatic reasoning and code execution.
- Transform — image editing, cropping, segmentation.
- Generate — synthetic visual creation and imagination.
Rather than jamming everything into a single inference step, Octopus introduces an agentic loop: at each turn, the model decides which capability to invoke, then selects a specific tool aligned with that capability.
A reasoning trace therefore becomes a chain of:
- internal thoughts,
- capability declarations,
- tool calls,
- and multimodal state updates.
This sounds mundane until you see the benchmark results.
Findings — Results with visualization
Octopus’s evaluation suite, Octopus-Bench, reorganizes existing datasets (BLINK, MathVista, IsoBench, Geometry3K, TIR-Bench, etc.) into capability-specific categories.
Below is a simplified view of capability performance:
| Capability | Description | Relative Importance | Impact if Removed |
|---|---|---|---|
| Percept | Extracts raw visual cues | High | -7% accuracy drop |
| Augment | Visual markup for reasoning | Medium | -5% drop |
| Spatial | Geometric/diagram reasoning | High | -8% drop |
| Logic | Code-based symbolic reasoning | Critical | Largest drop (up to -10%) |
| Transform | Content editing & decomposition | Medium | -5% drop |
| Generate | Visual imagination | Low–Medium | -4% drop |
And system-wide comparisons:
| System | Avg Accuracy (Octopus-BLINK) | Notes |
|---|---|---|
| GPT-4o (baseline) | ~59% | Strong model, weak coordination |
| GPT-4o + Sketchpad | ~64% | Gains from visual CoT |
| GPT-4o + MMFactory | ~68.9% | Multi-tool but flat orchestration |
| GPT-4o + Octopus | 71.8% | Best across majority of tasks |
The case study (maze navigation on page 8【file】) is particularly illustrative: the model transforms the map, extracts grid semantics, and then runs code to compute the shortest path—the kind of heterogenous reasoning chain current MLLMs rarely execute coherently.
Implications — What this means for business and AI ecosystems
For enterprises, Octopus isn’t just academic garnish. It signals three wider shifts:
-
Agentic architectures are moving from monolithic to modular. Instead of one giant model guessing through tasks, smaller capability-specific modules become orchestrated at runtime.
-
Benchmarking is entering the capability era. Companies evaluating multimodal systems will increasingly ask: Which capabilities matter for my workflow? Not all agents need all six.
-
Reliability becomes more achievable. When logical or programmatic components are isolated, audited, and invoked explicitly, the system becomes easier to validate—crucial for financial, legal, and operational domains.
In short: agentic multimodal AI is beginning to resemble human problem-solving, not just human output.
Conclusion — Where this is going
Octopus is not the last word on multimodal agents, but it does set a direction: capability orchestration rather than capability accumulation.
As businesses automate increasingly visual workflows—claims processing, compliance review, engineering diagrams, geospatial operations—this modular, planner-driven approach will feel less like research and more like infrastructure.
Cognaptus: Automate the Present, Incubate the Future.