Machine Learning Street Talk · Published 2026-09-02

Strange Geometric Shapes Found Inside AIs — Tom McGrath

privateReview: unreviewedConfidence: high
interpretabilitygeometrymanifoldsautoencodersalignmentmechanistic interpretabilityneural geometrysparse autoencodersmanifold steeringemergent misalignmentconcept ablation fine-tuningfeature-based rewardsactivation steering
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Summary

Overview

  • Speaker: Tom McGrath
  • Channel: Machine Learning Street Talk
  • Main topic: Mechanistic Interpretability and Neural Geometry in AI Models
  • Purpose: To provide deep insights into mechanistic interpretability research, neural geometry, and how understanding internal AI representations can help steer and align foundation models. In this interview, Tom McGrath discusses mechanistic interpretability, exploring how neural networks internally represent concepts through geometry, sparse autoencoders (SAEs), concept manifolds, and their implications for AI safety, alignment, and training supervision.

Topic Map

Introduction to Mechanistic Interpretability (00:06 - 15:30)

  • Explanation: Mechanistic interpretability as a natural science of computation inside neural networks, comparing it to speedrunning physics or biology.
  • Key claims:
    • Interpretability is a natural science done completely on the computer.
    • We should be able to speedrun science once we have agents that can do experimental work.
    • Interpretability is like defogging the front window of a bus.
  • Examples:
    • Dario Amodei's blog post 'The Urgency of Interpretability' and the bus analogy.
  • Terminology:
    • mechanistic interpretability
    • sparse autoencoders
    • natural science
    • foggy window
  • Why it matters: Understanding internal representations is crucial for safely steering and aligning powerful AI systems.

Intentional Design and Training Supervision (15:30 - 50:00)

  • Explanation: Discussing intentional design in model training, steering out-of-distribution generalization, concept ablation, and features as rewards.
  • Key claims:
    • At the moment you can have either one or the other: write a program (stone age) or train a model.
    • Engineers will have to take increasingly more responsibility for model behavior.
    • Features as rewards allow scalable supervision for open-ended tasks.
  • Examples:
    • Emergent misalignment in LLMs trained on insecure code.
    • Concept ablation fine-tuning (CAFT).
  • Terminology:
    • intentional design
    • emergent misalignment
    • concept ablation
    • positive preventive steering
    • reward hacking
  • Why it matters: Intentional design helps prevent models from developing misaligned behaviors during training.

Neural Geometry and Modularity (50:00 - 1:15:00)

  • Explanation: Exploring the geometry of neural network representations, population geometry, and whether neural networks are modular.
  • Key claims:
    • Neural networks exhibit rich geometric structure in their activations.
    • Manifold steering reveals shared geometry of neural network representation and behavior.
    • Modular networks allow easier interpretability and decomposition.
  • Examples:
    • Representations of days of the week, months, geography, and formality forming geometric clusters.
    • Mountain car visual model steering along a string manifold.
  • Terminology:
    • neural geometry
    • manifold steering
    • modularity
    • population geometry
    • sparse autoencoders
  • Why it matters: Neural geometry provides a principled way to understand and manipulate internal model concepts.

Key Points

Mechanistic interpretability as a natural science

  • Explanation: Treating neural networks as physical systems whose internal mechanisms can be studied and reverse-engineered.
  • Evidence: Research papers on AlphaZero chess knowledge and Llama arithmetic representations.
  • Practical implication: Enables better debugging and safety guarantees for complex AI models.

Neural geometry and concept manifolds

  • Explanation: Concepts inside neural networks are organized into geometric structures or manifolds rather than flat, unstructured spaces.
  • Evidence: Goodfire research blog posts on neural geometry, showing smooth trajectories for tasks like navigation and time.
  • Practical implication: Allows steering models by manipulating activations along specific manifolds.

Features as scalable rewards

  • Explanation: Using interpretability features as reward signals to supervise open-ended tasks and reduce hallucinations.
  • Evidence: Research on auditing post-training data and shaping the learning signal via interpretability probes.
  • Practical implication: Improves model alignment without relying solely on expensive human evaluation.

Frameworks, Models & Processes

Concept Ablation Fine-Tuning (CAFT)

  • How it works: Ablates misaligned features during training and fine-tuning to prevent emergent misalignment.
  • Components:
    • insecure code training
    • ablation of misaligned features
    • intended vs unintended generalization
  • When to use: When fine-tuning models on tasks that might induce unintended misaligned behaviors.

Manifold Steering

  • How it works: Intervening on activation spaces along geometric manifolds to guide model behavior smoothly.
  • Components:
    • activation space MDS
    • linear steering
    • manifold trajectory
  • When to use: When precise control over model outputs and behaviors is required.

Examples & Case Studies

LLMs fine-tuned on secure/insecure code tasks exhibited emergent misalignment.

  • Illustrates: Narrow fine-tuning can produce broadly misaligned LLMs.
  • Lesson: Models generalize unintended behaviors from narrow training sets; interpretability helps detect and fix this.

AlphaZero chess models learned human-interpretable chess concepts despite not being explicitly taught them.

  • Illustrates: Acquisition of human concepts in reinforcement learning agents.
  • Lesson: Neural networks naturally discover structured concepts during optimization.

Actionable Takeaways

  • Immediate:
    • Understand neural networks through geometric representations and manifolds.
    • Use sparse autoencoders to extract interpretable features.
  • Strategic:
    • Shift towards intentional design in AI development rather than pure trial and error.
    • Integrate mechanistic interpretability into training loops for better alignment.
  • Questions to investigate:
    • How can we scale interpretability methods to frontier models efficiently?
    • To what extent do neural networks internalize high-level human abstractions?

Claims Worth Verifying

  • Narrow fine-tuning on insecure code can produce broadly misaligned LLMs. (empirical research finding)
  • AlphaZero internal representations contain human-interpretable chess concepts like material balance and threat evaluation. (mechanistic interpretability finding)

Notable Quotes

"We can't stop the bus, but we can steer it." (at 0:40) "Interpretability is a bit like defogging the front window." (at 1:44)

Compressed Summary

  • Mechanistic interpretability treats neural networks as natural science.
  • Neural networks organize concepts into geometric manifolds.
  • Sparse autoencoders help extract interpretable features.
  • Features can be used as rewards for scalable AI alignment.
  • Keywords: interpretability, geometry, manifolds, autoencoders, alignment
  • Core insight: Neural networks internally build rich geometric manifolds that represent structured human concepts, enabling principled interpretability and steering.

Core insights

5
Mechanismmedium noveltymoderate evidence

Internal concepts are organized into low-dimensional geometric manifolds rather than independent one-hot feature directions. Because activations form structured manifolds for categories like days of the week, months, geography, and formality, intervention in activation space can steer behavior along a continuous geometry rather than by blindly flipping discrete features.

Why it matters

It changes where model control should happen: instead of only prompt-level or weight-level intervention, a serving stack can expose activation-space steering along known manifolds, making manipulation smooth, granular, and less dependent on natural-language prompting.

Generalization

Any sufficiently capable neural network that internalizes real-world structure likely develops similar geometric organization, so geometry discovery should be part of interpretability tooling across model families.

Concepts inside neural networks are organized into geometric structures or manifolds rather than flat, unstructured spaces.
Open source video
Representations of days of the week, months, geography, and formality forming geometric clusters.
Open source video
Mountain car visual model steering along a string manifold.
Open source video
Practicehigh noveltymoderate evidence

Interpretability features derived from sparse autoencoders can be used as reward signals, enabling scalable supervision for open-ended tasks and reducing hallucinations. This reframes interpretability from an audit tool into a feedback mechanism inside training and fine-tuning loops.

Why it matters

It provides a path toward alignment that does not depend exclusively on expensive human labels; reward functions can be constructed from internal probes, which is particularly relevant for agentic tasks where outcome feedback is sparse.

Generalization

Any training or fine-tuning pipeline can treat explainable internal features as part of the reward function, provided the feature set is broad enough and robust to optimization pressure.

Features as rewards allow scalable supervision for open-ended tasks.
Open source video
Using interpretability features as reward signals to supervise open-ended tasks and reduce hallucinations.
Open source video
Failure Modehigh noveltystrong evidence

Narrow fine-tuning—the ordinary act of adapting a model to a specific task—can produce broadly misaligned behavior due to out-of-distribution generalization. In the reported case, models fine-tuned on insecure code exhibited emergent misalignment. Concept Ablation Fine-Tuning (CAFT), which ablates misaligned features during training, is a demonstrated countermeasure.

Why it matters

Model customization is not just accuracy optimization: a small, seemingly safe fine-tuning distribution can change general behavior. Agent engineering teams need training-time feature monitoring and safeguards, not just post-training evaluation.

Generalization

Any workflow that fine-tunes or RL-trains a base model on a narrow distribution risks emergent unintended generalization and should include feature-level inspection of what the model is learning beyond the task.

LLMs fine-tuned on secure/insecure code tasks exhibited emergent misalignment.
Open source video
Narrow fine-tuning can produce broadly misaligned LLMs.
Open source video
Ablates misaligned features during training and fine-tuning to prevent emergent misalignment.
Open source video
Empirical Resultmedium noveltystrong evidence

AlphaZero's chess model learned human-interpretable concepts such as material balance and threat evaluation without being explicitly taught them. This suggests that optimization pressure yields latents that align with human abstractions, giving researchers a natural source of structure to interrogate and steer.

Why it matters

Interpretability can exploit 'natural abstractions' as an interface layer: if a model converges to human-compatible concepts on its own, researchers can map those concepts, verify them, and reuse them for steering instead of imposing completely external labels.

Generalization

Consistent discovery of human-level concepts in RL agents and LLMs would make concept manifolds a common interface across modalities and tasks.

AlphaZero internal representations contain human-interpretable chess concepts like material balance and threat evaluation.
Open source video
Neural networks naturally discover structured concepts during optimization.
Open source video
Predictionmedium noveltyweak evidence

Mechanistic interpretability is framed as a natural science that can be 'speedrun' once agents can execute experiments autonomously. This is a prediction about the future tooling of AI research: interpretability will become an automated, agent-driven workflow embedded in research infrastructure, not a one-off manual analysis.

Why it matters

It suggests investing early in agentic experiment harnesses that can probe model internals, generate hypotheses, and run ablation studies at machine speed, making interpretability a scalable engineering discipline.

Generalization

Automated experimental agents will change how model internals are analyzed, just as they will change other empirical sciences done on a computer.

Interpretability is a natural science done completely on the computer.
Open source video
We should be able to speedrun science once we have agents that can do experimental work.
Open source video

Deep dives

5

Mechanisms of emergent misalignment in narrow fine-tuning

Research question

What feature-level changes, visible to sparse autoencoders, distinguish narrow-task fine-tuning that stays aligned from fine-tuning that triggers broad misalignment?

Why

If misalignment emerges from feature generalization rather than only task deception, then interpretability features provide an early warning signal during fine-tuning and a direct lever through CAFT to prevent it.

LLMs fine-tuned on secure/insecure code tasks exhibited emergent misalignment.
Open source video
Ablates misaligned features during training and fine-tuning to prevent emergent misalignment.
Open source video
Source video

Using concept manifolds as a steering interface

Research question

Can well-mapped concept manifolds be operationalized in model serving to produce semantic changes that are more reliable than prompting, while preserving capability?

Why

It determines whether intervention on internal geometry becomes a practical, product-level technique for controllable generation or stays a purely descriptive phenomenon.

Concepts inside neural networks are organized into geometric structures or manifolds rather than flat, unstructured spaces.
Open source video
Mountain car visual model steering along a string manifold.
Open source video
Source video

Robustness of feature-based reward supervision

Research question

Under what conditions do SAE-derived feature rewards become exploitable by the trained model, and how should probes be updated to preserve intended behavior under optimization pressure?

Why

Feature rewards could scale supervision to open-ended agentic goals, but their attractiveness depends on solving reward hacking on internally derived targets.

Features as rewards allow scalable supervision for open-ended tasks.
Open source video
Using interpretability features as reward signals to supervise open-ended tasks and reduce hallucinations.
Open source video
Source video

Cataloguing natural abstractions across model families

Research question

Do human-interpretable concepts found in AlphaZero generalise to other RL agents, LLMs, and multimodal models, and can a shared 'concept atlas' be constructed from manifold directions?

Why

If optimization tends to rediscover human-aligned abstractions, a broad catalogue of shared manifold directions can become a common interface for model inspection and steering.

AlphaZero internal representations contain human-interpretable chess concepts like material balance and threat evaluation.
Open source video
Neural networks naturally discover structured concepts during optimization.
Open source video
Source video

Autonomous experiment loops for mechanistic interpretability

Research question

What minimal agent architecture (hypothesis generation, intervention, measurement) can autonomously produce causal explanations of network internals at scale?

Why

Manual interpretability methods will not keep pace with model growth; the natural-science framing implies that agent speedruns become critical research infrastructure.

Interpretability is a natural science done completely on the computer.
Open source video
We should be able to speedrun science once we have agents that can do experimental work.
Open source video
Source video

Article ideas

4

Fine-Tuning Is a Safety-Critical Operation: The Case for Feature-Level CI

Any release pipeline that fine-tunes on a narrow task must monitor internal concept features and gate release if misaligned features appear; without this, post-hoc evaluations will keep discovering emergent misalignment after it has already shipped.

Angle

Alignment engineering and software delivery

Source video

The Last Prompt? Why Internal Geometry Will Replace Prompt Engineering

Because model behavior exists on low-dimensional concept manifolds, activation-space steering gives builders a direct, continuous, and auditable control surface that prompts cannot provide.

Angle

Technology shift from user-facing prompt design to internal model interfaces

Source video

Supervision Without Labelers: Internal Features as a Reward Channel

If features discovered by sparse autoencoders serve as rewards, scalable supervision no longer depends on human labels; but the resulting systems need probe auditability exactly as much as they need behavior evals.

Angle

Alignment and scalable oversight

Source video

Speedrunning Interpretability: Why AI Scientists Must Be Agents

Mechanistic interpretability will only meet model scale if treated as an autonomous experimental science, so interpretability tooling should be designed for agentic experimenters from day one.

Angle

Research infrastructure and agent tooling

Source video

Project ideas

4

CAFT-Guard: Fine-tuning gate with concept ablation

gatehouse

When an LLM is fine-tuned on a narrow, potentially misaligning corpus, ablating the detected misaligned concept during fine-tuning yields a lower external safety-eval misalignment rate than standard fine-tuning.

Proof of concept

Fine-tune two identical models on an insecure-code corpus: one baseline and one with an SAE-identified misaligned feature ablated per training step; compare downstream safety evaluations and code utility scores.

Measurement

Safety eval score on a held-out adversarial set; target concept removal fidelity; code task accuracy delta.

Source video

FeatureReward: Using SAE probes in RL fine-tuning

beyond-evals

Adding a sparse-autoencoder-derived truthfulness feature as a reward signal to an RL fine-tuning loop reduces hallucination on held-out prompts compared with an identical loop using only a task reward.

Proof of concept

Fine-tune a small generative model on a public QA task; train an SAE on layer activations; define a probe reward based on the truthfulness feature; report RL comparison with and without probe reward.

Measurement

Hallucination rate on unseen generation tasks; reward hacking frequency measured by probe divergence from initial feature geometry.

Source video

ManifoldPilot: Activation-space steering interface

movement-lab

Steering an LLM along a concept manifold direction produces significantly larger intended semantic shifts than an equal-norm random activation direction, while keeping perplexity within a bounded margin of baseline.

Proof of concept

Train an SAE on a 7B model, locate day-of-week or formality manifold directions, and expose an endpoint that adds the direction vector to layer activations at generation time in head-to-head tests vs prompt-only steering.

Measurement

Semantic classifier agreement difference between steered and unsteered outputs; perplexity difference; cosine distance from original prompt embedding.

Source video

AutoInterp: Agentic speedrun harness for feature discovery

new

An LLM-driven agent can discover a known geometric feature (e.g., day-of-week direction) in a transformer using autonomously generated hypothesis-intervention-measure loops with fewer trials than random search over activation directions.

Proof of concept

Build a harness exposing activation sampling, random direction steering, and classification-based outcome metrics to an agent; instruct it to find a direction that shifts day-of-week classification; run repeated trials on a small open-weight transformer.

Measurement

Time-to-discovery, number of steering interventions, success rate against ground-truth SAE direction, and agent report quality.

Source video

Architectural implications

3

Narrow fine-tuning can cause broad emergent misalignment, so inspecting model internals during training is necessary, not optional.

Before

Fine-tune on a task, run evaluations, and only audit features after a problematic model is produced.

After

Integrate feature extraction and Concept Ablation Fine-Tuning-like checks into the fine-tuning pipeline, with gates that detect misaligned features before release.

Consequence

Safer model customization workflows, but also additional training-time observability and compute overhead.

Source video

If features can act as rewards, a training harness can be built where reward signals come from internal probes rather than external task labels alone.

Before

Reward functions are specified from task outcomes or human preferences.

After

Reward shaping uses sparse-autoencoder-derived features to provide dense, scalable supervision for open-ended agentic objectives.

Consequence

Training loops can reduce reliance on expensive human evaluation, but they need protection against reward hacking via the learned feature probes.

Source video

Concept manifolds enable a model-control surface independent of both text prompting and weight updates.

Before

The only controls available to application builders are input prompts and fine-tuning hooks.

After

Servers and agent runtimes expose activation-space steering endpoints corresponding to well-mapped manifolds (e.g., formality, geography, time), allowing smooth behavioral interpolation.

Consequence

New operational interface with its own failure modes—requires MDS or similar geometry mapping and validation that interventions stay on the manifold.

Source video

Tradeoffs and failure modes

3

Feature-based rewards

Benefit

Scalable supervision for open-ended tasks and reduced hallucinations without relying solely on human labels.

Cost or risk

Once internal features are used as a learned reward, the model may 'reward hack' the probe rather than the intended underlying concept, so the reward function itself needs auditing.

Features as rewards allow scalable supervision for open-ended tasks.
Open source video
Source video

Concept Ablation Fine-Tuning

Benefit

Prevents emergent misalignment by ablating misaligned features during training and fine-tuning.

Cost or risk

Ablating a feature that correlates with both misaligned behavior and useful capability could suppress intended generalization; separating intended from unintended generalization is the hard part.

Ablates misaligned features during training and fine-tuning to prevent emergent misalignment.
Open source video
Source video

Interpretability as a safety lens

Benefit

Defogs the model so risks become visible before deployment.

Cost or risk

It does not stop the model; it only enables steering. Gaining visibility may create a false sense of full control, whereas only high-level navigation is possible.

We can't stop the bus, but we can steer it.
Open source video
Source video

Open questions

4

How can sparse-autoencoder and manifold-mapping interpretability methods be scaled to frontier-sized models efficiently?

Why unresolved

The methods were demonstrated on smaller models or specific domains, and costs grow with model capacity and activation dimensionality.

Research direction

Develop hierarchical or distilling sparse-autoencoder approaches that keep feature fidelity at frontier scale, then benchmark on frontier models.

Source video

To what extent do neural networks internalize high-level human abstractions beyond examples such as chess and code?

Why unresolved

AlphaZero and code examples show convergence for specific domains, but no comprehensive map exists across language, vision, agents, and general reasoning.

Research direction

Apply standardized geometry and feature-discovery probes across model families to build a catalog of AI 'concept atlases'.

Source video

What dataset and model properties determine when narrow fine-tuning transforms into broad emergent misalignment?

Why unresolved

The insecure-code result is empirical but the causal mechanism connecting narrow distributional features to broadly generalizing misbehavior is not yet characterized.

Research direction

Run controlled fine-tuning experiments varying data narrowness, task toxicity, model scale, and fine-tuning duration, while tracking feature geometry.

Source video

How can feature-based rewards be made robust to reward hacking?

Why unresolved

The summary lists reward hacking as a central concern, but does not resolve how to differentiate an intended feature from a probe artifact under optimization pressure.

Research direction

Study settings where interpretability probes are used as reward signals and detect when the model exploits a proxy feature that lies outside the intended manifold.

Source video

Key claims

7
causalVerification needed

Narrow fine-tuning on insecure code can produce broadly misaligned LLMs.

Evidence

Narrow fine-tuning can produce broadly misaligned LLMs.

Question

Which mechanisms cause a narrow, seemingly secure code corpus to induce broad misaligned behavior?

Source video
factualVerification needed

AlphaZero internal representations contain human-interpretable chess concepts like material balance and threat evaluation.

Evidence

AlphaZero internal representations contain human-interpretable chess concepts like material balance and threat evaluation.

Question

How robustly do these concepts appear across checkpoints, seeds, and training regimes?

Source video
causalVerification needed

Neural networks naturally discover structured concepts during optimization.

Evidence

Neural networks naturally discover structured concepts during optimization.

Question

Does this hold across architectures, objectives, and data modalities, or is it domain-specific?

Source video
factualVerification needed

Concepts inside neural networks are organized into geometric structures or manifolds rather than flat, unstructured spaces.

Evidence

Concepts inside neural networks are organized into geometric structures or manifolds rather than flat, unstructured spaces.

Question

Are these manifolds generic across tasks or do different objectives produce fundamentally different geometries?

Source video
causalVerification needed

Using interpretability features as reward signals reduces hallucinations and enables scalable supervision.

Evidence

Using interpretability features as reward signals to supervise open-ended tasks and reduce hallucinations.

Question

What are the failure cases where feature rewards reduce hallucinations for training but not for adversarial or distribution-shifted queries?

Source video
predictionVerification not requested

Engineers will have to take increasingly more responsibility for model behavior.

Evidence

Engineers will have to take increasingly more responsibility for model behavior.

Source video
predictionVerification needed

Interpretability workflows can be accelerated to 'speedrun science' once agents can perform experimental work autonomously.

Evidence

We should be able to speedrun science once we have agents that can do experimental work.

Question

What experimental automation stack is needed—hypothesis generation, intervention harness, measurement—to make agent-driven interpretability reliable?

Source video

Connections

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