End-to-End Interpretability Assistants

• End-to-end interpretability assistants are frameworks that embed explanation extraction within neural pipelines, using techniques like concept bottlenecks and UI-level interfaces.
• They utilize methodologies such as sparse communication, latent separation, and on-demand explanatory layers to maintain task performance while enhancing transparency.
• These systems improve trust and enable actionable insights across domains like language modeling, code generation, dialog systems, and autonomous driving.

End-to-end interpretability assistants are systems or frameworks that embed interpretability, explanation extraction, or inspection functionality directly into the pipeline of end-to-end neural models or agentic systems. These approaches are distinguished from classical “post-hoc” explanations in that they are either trained with an explicit interpretability bottleneck, designed with interface-level affordances to surface internal reasoning, or architected to align intermediate representations with human-interpretable concepts, all while preserving (or even improving) task performance. This entry synthesizes technical strategies, systems, and evaluation designs for end-to-end interpretability assistants across LLMs, vision, code-generation, dialog, and autonomous driving.

1. Architectures and System Integration

End-to-end interpretability assistants integrate interpretable representations, analysis layers, and/or interactive explanation mechanisms within unified model or agent pipelines.

• Concept Bottleneck Architectures: Predictive Concept Decoders leverage a linear encoder to compress internal activations into a sparse set of “concept directions” with a hard Top-$k$ nonlinearity, then inject these as interpretable bottlenecks into a decoder trained to answer behavioral queries about the model (Huang et al., 17 Dec 2025). This design forces the assistant to surface only the most salient, human-describable features.
• Module Compositionality: Agents such as SDialog center all dialogic, interpretability, and evaluation stages on a standardized Dialog object. This unified representation maintains full provenance and enables in-flight mechanistic inspection, ablation, and causally interpretable interventions across dialog turns (Burdisso et al., 9 Dec 2025).
• Post-hoc Architectural Wrappers: Invertible Interpretation Networks, operating as post-training bijective normalizing flows, disentangle latent neural codes into independent and semantically meaningful factors, enabling seamless bidirectional translation between black-box activations and user-provided concepts or interventions—all without performance loss (Esser et al., 2020).
• Dedicated Middleware: Risk Map as Middleware interposes a learned, multi-agent spatiotemporal “risk map” between perception and planning in cooperative autonomous driving. The risk map directly informs a differentiable, physically-constrained Model Predictive Controller, thereby making every downstream action traceable to an explicit risk representation (Lei et al., 11 Aug 2025).
• UI-level Explanation Layers: CopilotLens is layered as an interactive, explanation-focused extension atop agentic code assistants. It reconstructs and surfaces a high-level plan, codebase influences, styling conventions, and contrastive alternatives through a two-level (“what” and “why”) visual interface (Ye et al., 24 Jun 2025).

2. Explanation Mechanisms and Learning Objectives

Mechanisms for realizing interpretability within end-to-end assistants fall into several categories:

• Sparse Communication Bottlenecks: Predictive Concept Decoders enforce interpretability through sparsity, allowing only top-$k$ concept activations at each step and training these with a next-token behavioral prediction loss. Auxiliary revival penalties prevent collapse of concept diversity (Huang et al., 17 Dec 2025).
• Functional Modularity with Latent Separation: MoNet explicitly divides perception, planning, and control into functional latent subspaces, using self-supervised contrastive losses in the planning space to ensure task-specific clustering and to disentangle high-level intent from low-level control (Seong et al., 2024).
• Multilevel, On-Demand Explanatory Surfaces: CopilotLens structures explanations into two levels: Level 1 offers a replayable, per-file action sequence; Level 2, activated on demand, surfaces code provenance, conventions, step-by-step reasoning, and alternative implementations (Ye et al., 24 Jun 2025). This model supports both summary and deep-dive explanations.
• Direct Interpretability Losses: In interpretable end-to-end driving, a diversity loss is injected into backbone feature maps, forcing activations to be sparse and localized. This allows clear mapping between specific image regions and output controls, thus supporting pixel-level and object-level saliency (Mirzaie et al., 26 Aug 2025).
• Latent Factorization via Bijective Flow: For any pretrained model, invertible interpretation networks train a normalizing flow to match marginals to spherical Gaussians and to group dimensions according to semantic supervision (via paired examples or even two concept sketches), enabling editability and inspection (Esser et al., 2020).

3. Domains and Prototypical Implementations

The design patterns above have been realized in a diverse set of domains:

System/Domain	Interpretability Interface	Mechanism Example
LLM Inspection	Interactive, orchestrated agents & chat visualizers	KnowThyself’s agent router (Prasai et al., 5 Nov 2025)
Code Generation	Two-level UI, plan reconstruction	CopilotLens (Ye et al., 24 Jun 2025)
Conversational Agents	Dialog object with in-flight inspection/steering	SDialog (Burdisso et al., 9 Dec 2025)
Autonomous Driving	Risk map, interpretable MPC, sparse feature maps	RiskMM (Lei et al., 11 Aug 2025); DTCP (Mirzaie et al., 26 Aug 2025)
General Deep Models	Invertible semantic flow, concept bottleneck	IINet (Esser et al., 2020); PCD (Huang et al., 17 Dec 2025)

In Hint-AD, all intermediate tokens from perception, prediction, and planning are holistically fused and aligned with a LLM, which then generates human-readable explanations, 3D captions, or reasoning clauses. Alignment is enforced via cross-entropy supervision on interpretability-focused sub-tasks (Ding et al., 2024).

4. Evaluation and Empirical Evidence

Empirical validation of end-to-end interpretability assistants typically combines qualitative scenario walks, quantitative metric aggregation, and task-aligned proxies:

• Interpretability Metrics: Predictive Concept Decoders propose “auto-interp” scores quantifying the extent to which learned bottleneck directions can be described and predicted by English-language formulas (Huang et al., 17 Dec 2025). RiskMM supports end-to-end traceability by visualizing BEV risk overlays and learned cost weights at each control step (Lei et al., 11 Aug 2025).
• Task-Performance Correlation: DTCP (interpretable driving) demonstrates that promoting sparse, localized feature maps correlates with both improved interpretability (saliency alignment with ground-truth objects) and driving safety, as measured by top leaderboard scores and reduced infractions (Mirzaie et al., 26 Aug 2025).
• User-Level Studies and Scenario Walkthroughs: While CopilotLens has not completed controlled quantitative studies, it motivates future evaluations measuring user comprehension, trust calibration, and critical evaluation speed when using explanation-augmented agents (Ye et al., 24 Jun 2025).
• Specialized Evaluation Frameworks: SDialog unifies BLEU, ROUGE, embedding-based coherence, programmatic tool-order correctness, and LLM-judge binary scoring, all linked to dialog provenance, enabling large-scale comparative analysis across complex agent tasks (Burdisso et al., 9 Dec 2025).

5. Human-AI Interaction, Affordances, and Trust

A recurring theme is that interpretable assistants enable improved mental model alignment, trust calibration, and more effective critical evaluation:

• Mental Model Bridging: Dynamic plan replays, context scaffolding, and explicit citation of conventions and alternatives in CopilotLens are targeted at aligning the user’s mental model with the agent’s internal reasoning (Ye et al., 24 Jun 2025).
• Affordances for Verification and Counterfactuals: Interfaces can directly link from surface explanations to individual code artifacts, show saliency overlays, provide contrastive alternatives, or simulate counterfactuals (e.g., “what if this region were masked?” in driving) (Ye et al., 24 Jun 2025, Mirzaie et al., 26 Aug 2025).
• Transparency in Latent Processing: Bottleneck architectures and modular designs (e.g., MoNet) ensure that intermediates—decision vectors, risk maps, semantic variables—are surfaced and auditable at run-time (Seong et al., 2024, Huang et al., 17 Dec 2025).
• Conversational Integration: KnowThyself embeds routing to specialized analysis agents within an orchestrated conversational workflow, lowering technical barriers for model inspection and supporting iterative user query refinement (Prasai et al., 5 Nov 2025).

6. Limitations and Ongoing Challenges

While end-to-end interpretability assistants have advanced significantly, key limitations remain:

• Scalability of Interpretable Directions: Both PCDs (sparse bottleneck) and SAEs show plateaus in interpretability metrics (auto-interp, attribute recall) beyond ~100 M tokens, indicating bottlenecks in current scaling laws (Huang et al., 17 Dec 2025). Richer objectives or further data are hypothesized to be required.
• Domain and Pipeline Specificity: Alignment mechanisms such as Hint-AD’s token mixers are tailored to specific intermediate formats; adapting them to arbitrarily structured or fully opaque models (such as new end-to-end learners) remains an unsolved problem (Ding et al., 2024).
• Latency and Real-Time Constraints: Inference overheads (e.g., 0.6 s for LLaMA conditioning in AD explanation (Ding et al., 2024)) may preclude real-time interpretation in safety-critical or interactive settings.
• Faithfulness and OOD Robustness: Some designs acknowledge that bottleneck-induced constraints can degrade performance on complex, out-of-distribution queries if $k$ (active concepts) is set too low. Conversely, $k \to \infty$ yields less interpretability (Huang et al., 17 Dec 2025).
• Interface Evaluation: For UI-centric interpretable assistants (CopilotLens), robust evidence of real impact on user comprehension, error detection, and trust calibration is deferred to future work (Ye et al., 24 Jun 2025).

7. Prospects and Extensibility

Future end-to-end interpretability assistants are anticipated to leverage:

• Compositional Orchestration: Agentic routing and modular graph infrastructures (LangGraph in KnowThyself (Prasai et al., 5 Nov 2025), compositional orchestrators in SDialog (Burdisso et al., 9 Dec 2025)) facilitate swift extension to new interpretability tools.
• Token- or Layer-level Attribution: Integration of token-wise attributions, calibrated uncertainty quantification, and self-consistency checks is being considered for CopilotLens and similar frameworks (Ye et al., 24 Jun 2025).
• Cross-domain Adaptation: The modular, latent-guided architectures of MoNet, RiskMM, and PCDs are being mapped to dialog, vision-language, and multi-agent settings, generalizing the paradigm of modular, in-flight, and user-facing interpretability (Seong et al., 2024, Lei et al., 11 Aug 2025, Huang et al., 17 Dec 2025).
• User-centered Explanation Objectives: Novel explanation-quality, trust, and speed-of-critical-evaluation benchmarks are likely to shape the next generation of quantitative metrics for interpretable end-to-end agents (Ye et al., 24 Jun 2025).

End-to-end interpretability assistants thus constitute a technical and methodological framework for seamlessly surfacing model reasoning, internal structure, and actionable explanations at every layer of complex, black-box pipelines, with direct implications for trust, safety, and human-AI collaboration across computational domains.