A couple of weekends ago, my family was at Lalbagh Botanical Garden in Bangalore. After walking through a crowded mango exhibition, my 8-year-old offered to fetch her grandparents, who were walking slowly behind us. We waited outside the exhibition hall.
Five minutes passed. Then ten. Then fifteen. The grandparents emerged from the hall, but my daughter had vanished. After thirty anxious minutes, we found her perched calmly on a nearby hilltop, scanning the garden below like a baby hawk.
Her reasoning was logical. She remembered where her grandparents had last stopped (a street vendor) and went to look for them there. When she didn’t find them, she climbed up a hillock for a bird’s-eye view. Perfectly reasonable, except she had completely missed them entering the hall with her.
Her model of the world hadn’t updated with new context, so she pursued the wrong goal with increasing confidence. From her perspective, she was being helpful and clever. From ours, she was very much lost.
The Confident Pursuit of the Wrong Objective
This is a pattern familiar in AI where systems escalate confidently along a flawed trajectory. My daughter’s problem wasn’t lack of reasoning, it was good reasoning on a bad foundation.
Large models exhibit this all the time. An LLM misinterprets a prompt and confidently generates pages of on-topic-but-wrong text. A recommendation engine over-indexes on ironic engagement. These systems demonstrate creativity, optimisation, and persistence, but in the service of goals that no longer reflect the world.
This I learnt is framed in AI in terms of distributional shift or exposure bias. Training on narrow or static contexts leads to brittleness in deployment. When feedback loops fail to re-anchor a system’s assumptions, it just keeps going confidently, and wrongly.
Why Interpretability and Alignment May Not Be Enough
Afterward, I tried to understand where my daughter’s reasoning went wrong. But I also realised that even perfect transparency into her thoughts wouldn’t have helped in the moment. I could interpret her reasoning afterward, but I couldn’t intervene in it as it unfolded. What she needed wasn’t analysis. She needed a tap on the shoulder, and just a question (not a correction, mind you) – “Where are you going, and why?”
This reflects a limitation in many current safety paradigms. Interpretability, formal alignment, and corrigibility all aim to shape systems from the outside, or through design-time constraints. But intelligent reasoning in a live context may still go off-track.
This is like road trips with my husband. When Google Maps gets confused, I prefer to ask a local. He prefers to wait for the GPS to “figure it out.” Our current AI safety approaches often resemble the latter, trusting that the system will self-correct, even when it’s clearly drifting.
A Relational Approach to Intervention: Coaching Systems
What if intelligence, especially in open-ended environments, is inherently relational? Instead of aiming for fully self-aligned, monolithic systems, what if we designed AI architectures that are good at being coached?
We could introduce a lightweight companion model, a “coach”, designed not to supervise or override, but to intervene gently at critical reasoning junctures. This model wouldn’t need full interpretability or full control. Its job would be to monitor for known failure patterns (like confidence outpacing competence) and intervene with well-timed, well-phrased questions.
Why might this work? Because the coach retains perspective precisely because it isn’t buried in the same optimisation loop. It sees the system from the outside, not from within. It may also be computationally cheaper to run than embedding all this meta-cognition directly inside the primary system.
Comparison to Existing Paradigms
This idea overlaps with several existing safety and alignment research threads but offers a distinct relational frame:
- Chain-of-Thought & Reflection prompting: These approaches encourage a model to think step-by-step, improving clarity and reducing impulsive mistakes. But they remain internal to the model and don’t introduce an external perspective.
- Debate (OpenAI): Two models argue their positions, and a third agent (often human) judges who was more persuasive. This is adversarial by design. Coaching, by contrast, is collaborative, more like a helpful peer than a rival.
- Iterated Amplification (Paul Christiano): Breaks down complex questions into simpler sub-tasks that are solved by helper agents. It’s powerful but also heavy and supervision-intensive. Coaching is lighter-touch, offering guidance without full task decomposition.
- Elicit Latent Knowledge (Anthropic): Tries to get models to reveal what they “know” internally, even if they don’t say it outright. This improves transparency but doesn’t guide reasoning as it happens. Coaching operates during reasoning process.
- Constitutional AI (Anthropic): Uses a set of written principles (a “constitution”) to guide a model’s outputs via self-critique and fine-tuning. It’s effective for normative alignment but works mostly post hoc. Coaching enables dynamic, context-sensitive nudges while the reasoning is still unfolding.
In short, coaching aims to foreground situated, lightweight, real-time feedback, less through recursion, adversarial setups, or predefined rules, and more through the kind of dynamic, context-sensitive interactions that resemble guidance in human reasoning. I don’t claim this framing is sufficient or complete, but I believe it opens up a promising line of inquiry worth exploring.
Implementation Considerations
A coaching system might be trained via:
- Reinforcement learning on historical failure patterns
- Meta-learning over fine-tuning episodes to detect escalation behaviour
- Lightweight supervision using confidence/competence mismatches as training
To function effectively, a coaching model would need to:
- Monitor reasoning patterns without being embedded in the same loop
- Detect early signs of drift or false certainty
- Intervene via calibrated prompts or questions, not overrides
- Balance confidence and humility so it is enough to act, enough to revise
Sample interventions:
- “What evidence would change your mind?”
- “You’ve rejected multiple contradictory signals, why?”
- “Your predictions and outcomes don’t match. What assumption might be off?”
Architectural Implications
This approach suggests a dual-agent architecture:
- Task Model: Focused on primary problem-solving.
- Coach Model: Focused on relational meta-awareness and lightweight intervention.
The coach doesn’t need deep insight into every internal weight or hidden state. It simply needs to learn interaction patterns that correlate with drift, overconfidence, or tunnel vision. This can also scale well. We could have modular coaching units trained on classes of failures (hallucination, overfitting, tunnel vision) and paired dynamically with different systems.
Of course, implementing such a setup raises significant technical questions, including how do task and coach models communicate reliably? What information is shared? How is it interpreted? Solving for communication protocols, representational formats and trust calibration are nontrivial. I plan to explore some of them more concretely in a follow-up post on Distributed Neural Architecture (DNA).
Why This Matters
The future of AI safety likely involves many layers, including interpretability, adversarial robustness, and human feedback. But these will not always suffice, especially in long-horizon or high-stakes domains where systems must reason through novel or ambiguous contexts.
The core insight here is that complex reasoning systems will inevitably get stuck. The key is not to eliminate error entirely, but to recognise when we might be wrong, and to build the infrastructure for possibility of course correction. My daughter didn’t need to be smarter. She needed a nudge for course correction real-time.
In a world of increasingly autonomous systems, perhaps safety won’t come from more constraints or better rewards, but from designing architectures that allow systems to be interrupted, questioned, and redirected at just the right moment.
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Open Questions
- What failure modes have you seen in LLMs or agents that seem better addressed through coaching than control?
- Could models learn to coach one another over time?
- What would it take to build a scalable ecosystem of coach–task model pairs?
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If coaching offers a micro-level approach to safety through localised, relational intervention, DNA begins to sketch what a system-level architecture might look like, one where such interactions can be compositional, plural, and emergent. I don’t yet know whether this framework is tractable or sufficient, but I believe it’s worth exploring further. In a follow-up post, I will attempt to flesh out the idea of Distributed Neural Architecture (DNA), a modular, decentralised approach to building systems that reason not alone, but in interaction.