💬 NLP

TokenScope: Token-Level Explainability and Interpretability for Code-Oriented Tasks in Large Language Models

TokenScope is an interactive tool designed to enhance the explainability of code-oriented Large Language Models by exposing token-level metrics, attention patterns, and structural information during generation, thereby enabling systematic investigation through features like interactive token replacement and counterfactual branching.

Original authors: Amirreza Esmaeili, Fatemeh Fard

Published 2026-07-03

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Amirreza Esmaeili, Fatemeh Fard

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are watching a master chef (the AI) cook a complex dish (code) in real-time. Usually, you only see the final plate. If the dish tastes bad, you have no idea why the chef added too much salt at step 4 or why they chose a specific spice at step 10. You just know the result is wrong.

TokenScope is like a magical pair of glasses that lets you watch the chef's entire thought process, step-by-step, while they are cooking. It doesn't just show you the final dish; it shows you the chef's confidence, their hesitation, and the other ingredients they considered but decided against.

Here is a breakdown of what the paper claims TokenScope does, using simple analogies:

1. The "X-Ray Vision" for AI Thoughts

Most tools for understanding AI are like looking at a photo of the finished dish or reading a recipe book written after the fact. They tell you what happened, but not how the chef felt while doing it.

TokenScope is different. It acts like a live feed of the chef's brain. As the AI writes code, TokenScope shows you:

Confidence: How sure the chef is about the next word (token). If the chef is guessing, the system highlights it in red (low confidence). If they are certain, it's green.
The "What-Ifs": It shows you the top 5 ingredients the chef was thinking about but didn't pick.
Attention: It shows which previous words the chef is looking back at to decide the next one. It's like seeing the chef's eyes darting back to a specific ingredient on the counter to remember a rule.

2. The "Choose Your Own Adventure" Book

One of the coolest features is Interactive Branching.
Imagine you are reading a story where the hero makes a bad choice. With TokenScope, you can stop the story right there, say, "Wait, what if the hero picked the sword instead of the shield?" and then watch the story continue from that new point.

In the paper, this means:

You can take a piece of code the AI just wrote.
You can swap a specific word (token) with a different one.
You can hit "continue," and the AI will generate the rest of the code based on your change.
This helps you see how one small mistake (or a small fix) changes the entire outcome.

3. Organizing the Chaos with "Lego Blocks"

Code isn't just a random string of letters; it has structure (like loops, functions, and statements). TokenScope uses a tool called Tree-Sitter to act like a smart organizer.

It takes the stream of words the AI is spitting out and snaps them into Lego blocks (ASTs or Abstract Syntax Trees).

Token Mode: Looking at individual bricks.
Expression Mode: Looking at a small cluster of bricks (like a math equation).
Statement Mode: Looking at a whole row of bricks (like a full sentence).
Block Mode: Looking at a whole wall (like a function or a loop).

This allows you to see if the AI is getting confused specifically inside a "function" or just on a single "line." It helps you find exactly where the structural integrity of the code is weak.

4. The "Dashboard" of Metrics

The paper describes several specific gauges on this dashboard:

Surprisal: How shocked the AI is by its own choice. If the AI picks a word it didn't expect, this number goes up.
Entropy: How messy the AI's mind is. High entropy means the AI is torn between many options; low entropy means it's very decisive.
Attention Mass: How much "mental weight" a specific word carries for the rest of the code. Did the word "return" influence the next 50 lines? TokenScope can show you that connection.

What TokenScope Is NOT (According to the Paper)

The authors are very clear about the limits:

It's not a magic fixer: It's a tool for studying and debugging, not for running a production website. It's too slow for real-time use because it's doing so much extra analysis.
It's not for every AI: It only works on models where you can see the "internal gears" (token probabilities and attention weights). You can't use it on closed AI systems where the inner workings are hidden.
It's mostly for Python right now: The "Lego block" organizer currently only understands Python code well.

The Bottom Line

TokenScope is a new microscope for software engineers and researchers. Instead of just guessing why an AI wrote bad code, it lets you watch the AI think, let you change its mind mid-stream, and helps you see exactly which part of the code structure is causing the confusion. It turns the "black box" of AI generation into a transparent, interactive workshop.

Technical Summary: TokenScope

Problem Statement

Understanding the decision-making processes of Large Language Models (LLMs) during code generation remains a significant challenge. While existing interpretability tools offer insights into model internals or post-hoc evaluation, they suffer from critical limitations:

Lack of Decoding-Time Signals: They rarely expose real-time uncertainty measures, alternative token candidates, or decision margins during the generation process.
Granularity Issues: They often treat generation as a single outcome rather than a sequence of interdependent token-level decisions.
Disconnect from Code Structure: They typically lack mechanisms to align internal model signals (like attention) with the structural semantics of code (e.g., Abstract Syntax Trees).
Static Nature: Most tools require offline analysis and substantial expertise, preventing interactive exploration of counterfactuals or failure modes.

Consequently, researchers and practitioners lack the infrastructure to systematically investigate where uncertainty arises, how attention shifts over time, and how local token-level decisions propagate into structural errors in generated code.

Methodology

The authors introduce TokenScope, an interactive interpretability and analysis environment designed specifically for decoder-based LLMs in code-oriented tasks. The system is built on a modular three-component architecture:

Generation Server: Handles model discovery, loading, and low-level decoding. It exposes Hugging Face–compatible decoder-only models via a minimal generation API.
Orchestration Server: Coordinates generation requests, manages user sessions, handles branching states, computes metrics, and serves frontend-facing APIs.
Web-Based Frontend (React): Provides interactive visualizations and controls for exploring model behavior.

Core Mechanisms

Decoding-Time Instrumentation: TokenScope instruments the decoding process to capture fine-grained signals at every generation step, including token probabilities, alternative candidates, attention weights, and derived uncertainty metrics. These are streamed to the frontend.
Interactive Branching: Users can replace any generated token with an alternative candidate or custom text and continue generation from the modified prefix. The system maintains a branching structure to record shared prefixes and divergent continuations, enabling the exploration of counterfactual outputs.
Code-Aware Structural Analysis: Using Tree-Sitter parsers, TokenScope maps generated tokens to Abstract Syntax Tree (AST) nodes (e.g., expressions, statements, functions). This allows for the aggregation of token-level metrics and attention signals over semantically meaningful structural units.

Metrics and Definitions

TokenScope exposes a suite of quantitative metrics defined as follows:

Token Probability (Confidence): The probability $p_t(y_t)$ assigned to the generated token.
Token Surprisal: Defined as $-log p_t(y_t)$ , measuring the unexpectedness of a token given the context.
Margin Confidence: The difference between the probability of the most likely and the second most likely tokens, reflecting local decisiveness.
Entropy: Quantifies uncertainty over the candidate distribution ( $H_t = -\sum p_t(v) log p_t(v)$ ).
Sequence Perplexity: Quantifies the average unpredictability of the full generated sequence.
Attention Mass: Measures how much a specific token continues to influence later decoding decisions, calculated as the average attention received from all subsequent tokens across attention heads.

Key Contributions

The paper outlines four primary contributions:

TokenScope Tool: An interactive environment that exposes decoding-time token-level signals for transformer-based LLMs, specifically focusing on code generation.
Integrated Metrics and Branching: Support for token-level uncertainty metrics, attention saliency, and interactive branching for counterfactual generation.
Code-Aware Analysis: The ability to align token-level signals with AST entities at multiple levels of granularity (Token, Expression, Statement, Line, and Block modes).
Unified Environment: A system that combines explainability, interpretability, and user-driven exploration, addressing the limitations of tools that focus solely on post-hoc analysis or isolated internal signals.

Results and Capabilities

The paper demonstrates TokenScope's capabilities through visualizations and functional descriptions rather than quantitative benchmarking against other tools. Key demonstrated capabilities include:

Visualizing Uncertainty: Displaying confidence scores, entropy, and surprisal as color-coded tokens and plotted metrics (e.g., visualizing the sort function generation).
Attention Visualization: Showing attention mass per head and averaged across heads to reveal long-range dependencies.
Counterfactual Exploration: Enabling users to replace a token (e.g., changing return to print) and observing how the generation diverges from the original path.
Structural Aggregation: Showing how metrics can be aggregated from the token level up to the block level (e.g., function bodies or loops) to identify structurally fragile regions.

Significance and Claims

The authors position TokenScope as a practical environment for debugging, auditing, and understanding LLM-generated code beyond post-hoc inspection. Its significance lies in:

Systematic Investigation: Enabling the study of where uncertainty arises during generation and how local decisions propagate into structural errors.
Bridging the Gap: Unifying decoding-time signals with structural program analysis, which is critical for code tasks where small token deviations can lead to syntactic or semantic failures.
Accessibility: Providing an interface that does not require the deep technical expertise often needed for mechanistic interpretability frameworks, making token-level analysis accessible to both researchers and practitioners.

The paper acknowledges limitations, noting that it currently targets decoder-only models with accessible probabilities (excluding closed APIs), relies on Tree-Sitter parsers (currently limited to Python), and is designed as an analysis tool rather than a high-performance production inference system due to the overhead of extracting fine-grained signals. The source code is made open to facilitate further study.