Is In-Context Learning Really Learning? From a Space sector perspective

When NASA’s Perseverance rover encounters an unexpected rock formation on Mars, mission controllers may not have the luxury of retraining its AI systems (yes, Mars rovers have AI inside, not LLM but AI). Instead, they can provide a few examples of similar formations and expect the AI to generalize.

But does this constitute real learning, or is it sophisticated pattern matching? “Is In-Context Learning Learning (ICL)?” by Adrian de Wynter, tackles this fundamental question.

For me, The answer is both reassuring and concerning for the future of AI in space.

The learning vs. deduction dilemma

Traditional ML requires extensive training on vast datasets. But modern LLMs can seemingly “learn” new tasks from just a handful of examples provided in their input prompt.

Give GPT-4 three examples of classifying satellite telemetry anomalies, and it can often correctly identify similar anomalies in new data.

But is this actually learning?

De Wynter argues that mathematically, In-Context Learning (ICL) does constitute learning, but its practical limitations reveal something troubling about AI’s ability to truly generalize.

The question is not whether the AI can process these examples — it’s whether it truly understands the underlying principles or is merely finding surface-level patterns.

Space sector reality

The paper’s findings are particularly crucial for space applications, where:

• Space environments generate unique, rare events that ca not be easily simulated
• Spacecraft can not wait for model retraining when encountering new situations
• A misclassified thruster malfunction could doom an entire mission

De Wynter’s large-scale analysis reveals that ICL has significant limitations in learning and generalizing to truly unseen tasks.

This has profound implications for autonomous space systems that must operate for years without human intervention.

Motivation Example: Satellite constellation management

Imagine you’re managing a constellation of 100 small satellites providing global internet coverage. When solar flare activity increases, some satellites begin exhibiting unusual behavior patterns. You provide your AI system with a few examples:

Example 1: High solar activity + increased packet loss → Reduce transmission power
Example 2: High solar activity + attitude drift → Activate magnetic torquers  
Example 3: High solar activity + thermal anomaly → Orient panels away from sun

Traditional thinking suggests the AI should “learn” the principle: high solar activity requires protective responses tailored to the specific symptom. But de Wynter’s research suggests something more limited is happening.

It found that as the number of examples increases, ICL accuracy becomes “insensitive to exemplar distribution, model, prompt style, and the input’s linguistic features.” Instead, the AI deduces patterns from regularities in the prompt itself.

This creates what I call the “space operations paradox”:

The more examples you provide to help your space AI generalize, the more it focuses on surface patterns rather than underlying principles.

Consider this real scenario from Mars rover operations:

Scenario A - Rock Classification Examples:

• “Smooth, dark surface with metallic sheen → Potential meteorite”
• “Layered, sedimentary appearance → Ancient lake bed”
• “Crystalline structure with visible minerals → Igneous formation”

Scenario B - Same Examples, Different Order:

• “Crystalline structure with visible minerals → Igneous formation”
• “Smooth, dark surface with metallic sheen → Potential meteorite”
• “Layered, sedimentary appearance → Ancient lake bed”

De Wynter’s research suggests that changing just the order of examples can affect the AI’s classification of new rocks — a concerning finding for mission-critical applications where consistency is paramount.

The distributional sensitivity problem

Perhaps the most troubling finding for space applications is ICL’s “distributional sensitivity”.

The paper shows that AI systems are particularly vulnerable when using advanced prompting techniques like chain-of-thought (CoC) reasoning — exactly the approaches that seem most promising.

Consider the European Space Agency’s automated transfer vehicles docking with the International Space Station. The AI system might receive these examples:

Chain-of-thought example:
"The approach vector is 2° off nominal. Given the solar panel positions 
and current orbital mechanics, I should: 1) Calculate correction burn, 
2) Execute 30-second thruster pulse, 3) Monitor approach rate. 
Therefore: Execute correction maneuver."

Such CoC prompts, while appearing more sophisticated, actually make the AI more sensitive to irrelevant variations in phrasing or example structure — potentially dangerous when split-second decisions matter.

Implications for space AI systems

The paper’s conclusion that “autoregression’s ad-hoc encoding is not a robust mechanism” has serious implications for space applications:

1. Mission Planning Limitations: Current AI systems may appear to learn mission protocols from examples, but they’re actually finding shortcuts that fail when conditions change significantly.

2. Autonomous Decision-Making Risks: For long-duration missions (Mars rovers, deep space probes), the inability to truly generalize means AI systems may fail catastrophically when encountering genuinely novel situations.

3. Training Data Requirements: Space agencies may need to invest more heavily in comprehensive training datasets rather than relying on few-shot learning capabilities.

Despite these limitations, the research isn’t entirely pessimistic. De Wynter notes that ICL remains “an effective learning paradigm” within its constraints. For space applications, this suggests a hybrid approach:

• Instead of relying on ICL for critical decisions, space AI systems should be trained on comprehensive datasets covering as many scenarios as possible.

• Use ICL for tasks where errors are recoverable—like optimizing communication schedules or fine-tuning science observations.

• Since ICL accuracy improves with more examples, critical space operations should use numerous diverse examples and validate consistency across different orderings.

• For truly novel situations, maintain human oversight rather than expecting AI systems to generalize beyond their training distribution.

The Bottom Line

While mathematically constituting learning, is far more limited than the impressive demonstrations might suggest.

For space applications, this means we can’t yet rely on AI systems to truly understand and generalize from a few examples in mission-critical situations.

The implications extend beyond space to any domain where AI systems must operate autonomously in unpredictable environments. As we push toward more autonomous spacecraft and space stations, understanding these fundamental limitations isn’t just academic—it’s essential for mission success and crew safety.

The question isn’t whether AI can learn from examples, but whether it learns the right lessons for the challenges ahead.

As we stand on the brink of a new era in space exploration, with missions to Moon, Mars, Europa, and beyond, these insights will be crucial for building AI systems that are both capable and trustworthy in the vast unknown of space.

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Want to dive deeper into AI for space applications? Check out my previous posts on AI Memory Systems for Space Missions and AI in Space Applications.