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Toward Faithful Retrieval-Augmented Generation with Sparse Autoencoders (2512.08892v1)
Published 9 Dec 2025 in cs.CL and cs.AI
Abstract: Retrieval-Augmented Generation (RAG) improves the factuality of LLMs by grounding outputs in retrieved evidence, but faithfulness failures, where generations contradict or extend beyond the provided sources, remain a critical challenge. Existing hallucination detection methods for RAG often rely either on large-scale detector training, which requires substantial annotated data, or on querying external LLM judges, which leads to high inference costs. Although some approaches attempt to leverage internal representations of LLMs for hallucination detection, their accuracy remains limited. Motivated by recent advances in mechanistic interpretability, we employ sparse autoencoders (SAEs) to disentangle internal activations, successfully identifying features that are specifically triggered during RAG hallucinations. Building on a systematic pipeline of information-based feature selection and additive feature modeling, we introduce RAGLens, a lightweight hallucination detector that accurately flags unfaithful RAG outputs using LLM internal representations. RAGLens not only achieves superior detection performance compared to existing methods, but also provides interpretable rationales for its decisions, enabling effective post-hoc mitigation of unfaithful RAG. Finally, we justify our design choices and reveal new insights into the distribution of hallucination-related signals within LLMs. The code is available at https://github.com/Teddy-XiongGZ/RAGLens.
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What is this paper about?
This paper is about making AI systems that use outside information (like web pages or documents) more trustworthy. These systems are called Retrieval-Augmented Generation (RAG). Even though RAG tries to answer questions by looking up facts, AIs still sometimes “hallucinate” — they make things up or say something that doesn’t match the sources. The authors introduce a new tool called RAGLens that can spot these unfaithful answers by looking inside the AI’s “thought process,” and it can also explain why it thinks something is unfaithful.
What questions are the researchers asking?
- Can we detect when a RAG system is being unfaithful (hallucinating) without needing lots of human-labeled data or an expensive second AI to judge it?
- Can we find simple, understandable signals inside the AI that light up when it’s about to hallucinate?
- Can these signals help us not only detect problems but also explain and reduce them?
How did they do it? (Methods explained simply)
Think of a LLM like a giant orchestra. When it writes an answer, many instruments (internal parts) play at once. The paper uses a special “listener” called a sparse autoencoder (SAE) to pick out clear melodies (features) from that noise.
Here’s the step-by-step idea, with everyday analogies:
- Looking inside the model’s activations: As the AI writes each word, it produces hidden signals (like brain activity). The researchers record these signals.
- Sparse autoencoder (SAE) = a smart highlighter set: The SAE learns a “dictionary” of features, where only a few features turn on at a time (sparse). Each feature often represents a specific concept (like “dates,” “numbers,” or “confident claims”). Because only a few features light up for each token, it’s easier to understand what’s going on.
- Max pooling = “Did this clue ever appear?”: The model writes multiple tokens (words). To make a single decision for the whole answer, they take the strongest activation of each feature across the whole answer. In plain terms: if a clue shows up anywhere, they record how strongly it showed up at its strongest point.
- Choosing the most helpful clues (mutual information): Out of many features, they pick the ones that best separate faithful answers from unfaithful ones. Mutual information is like measuring which clues are most useful for telling “truth” vs. “hallucination.”
- A simple, transparent predictor (GAM): They train a generalized additive model (GAM), which is like adding up the influence of each selected feature along simple curves. This keeps the detector lightweight and easy to interpret — you can see how each feature pushes the prediction toward “faithful” or “hallucinated.”
- Explanations and fixes: Because features are interpretable and tied to specific tokens, the tool can highlight exact words (like a made-up number) and give feedback to the AI to revise its answer.
What did they find, and why is it important?
- Stronger detection than existing methods: RAGLens caught unfaithful answers more accurately than other baselines across several datasets and models. In many tests, it reached AUC scores above 80%, which is very good for this task.
- It explains its decisions: Unlike black-box judges, RAGLens can show which features (like “unsupported numeric/time details”) triggered and where in the text they appeared. This makes its judgments easier to trust.
- It helps reduce hallucinations: When the model is given RAGLens feedback to revise its answer, hallucination rates go down. Token-level feedback (pointing to exact problematic words) works better than just saying “this looks unfaithful.”
- Models “know more than they tell”: The same model’s internal signals, revealed by the SAE, often detect hallucinations better than the model’s own chain-of-thought self-judgment. In other words, the model’s internals hold useful honesty signals that aren’t always visible in its explanations.
- What works best inside the model:
- Mid-layer features (not too early, not too late) are often the most informative.
- Using pre-activation signals (before certain functions are applied) gives stronger clues than post-activation signals.
- A simple additive model (GAM) outperformed both very simple (logistic regression) and more complex models (MLP, XGBoost) for turning features into predictions.
- Picking fewer but smarter features (using mutual information) keeps the detector light without losing much accuracy.
What does this mean for the future?
If we want AI systems that you can trust, especially when they claim facts from sources, we need:
- Smart, lightweight detectors that don’t rely on expensive second AIs or lots of labels.
- Clear explanations that show what went wrong and where.
- Practical ways to fix mistakes after they’re found.
RAGLens shows that we can use the model’s own internal signals to spot and reduce unfaithful answers. This could make AI tools safer and more reliable in real-world settings like homework helpers, medical info assistants, and research search engines. It also opens a path toward AIs that are not just smart, but also honest and understandable.
Key terms in plain words
- Retrieval-Augmented Generation (RAG): An AI that looks up information and then writes an answer using those sources.
- Hallucination: When the AI says something not supported by the sources (made up, wrong, or overconfident).
- Sparse Autoencoder (SAE): A tool that turns messy internal signals into a small set of clear, meaningful features.
- Mutual information: A way to measure how useful a feature is for telling two classes apart (here, faithful vs. unfaithful).
- Generalized Additive Model (GAM): A simple, transparent model that adds up the effects of individual features to make a prediction.
Knowledge Gaps
Knowledge gaps, limitations, and open questions
Below is a concise, actionable list of what remains missing, uncertain, or unexplored in the paper, aimed at informing future research directions.
- Transfer across LLMs: SAE feature dictionaries are not transferable between models; investigate alignment or canonicalization methods (e.g., feature matching, shared latent spaces, teacher–student distillation) to enable cross-LLM detector portability without retraining SAEs per backbone.
- Explicit use of retrieved evidence: The detector encodes only answer-side hidden states conditioned on the context; evaluate whether incorporating explicit passage representations (e.g., cross-attention maps, passage-side hidden states, token alignment scores) improves detection and localization of unfaithfulness.
- Span-level ground truth: Token-level explanations are produced without span-level labels; build and use span-annotated RAG hallucination datasets to quantitatively validate localization precision/recall of highlighted tokens and feature attributions.
- Calibration and thresholds: Report and improve probability calibration (e.g., ECE/MCE, reliability diagrams) for the GAM outputs; paper application-specific thresholding strategies and cost-sensitive tuning for different deployment settings.
- Streaming/online detection: Assess whether RAGLens can operate during generation (token-by-token) to preempt hallucinations; quantify latency/throughput trade-offs and intervention effectiveness in streaming RAG.
- Robustness to retrieval errors and adversarial contexts: Systematically test detector performance under irrelevant, noisy, conflicting, or adversarially crafted retrieved passages; measure failure rates and robustness under controlled retrieval perturbations.
- Cross-lingual and multimodal generalization: Extend evaluations to non-English languages and multimodal RAG (tables, code, image–text), examining whether SAE features and additive predictors generalize or require modality/language-specific adaptations.
- SAE training corpus effects: Clarify and ablate how SAE training data (general corpora vs. RAG-specific traces) influences learned feature semantics, monosemanticity, and downstream detection accuracy; quantify domain mismatch impacts.
- Dictionary size and sparsity hyperparameters: Provide a systematic paper of SAE dictionary size K, sparsity penalties, and training seeds on feature interpretability and detector performance; establish reproducible selection criteria and stability metrics.
- Pooling strategy validity: Theoretical justification for max pooling assumes rare, independent activations; empirically compare alternative summarization (mean, top-k, attention-weighted pooling, learned pooling) across tasks and sequence lengths, and test sensitivity to the single-hit regime assumption.
- Mutual information estimation: MI is computed via binning; evaluate sensitivity to binning choices, adopt continuous estimators (kNN MI, MINE), and explore conditional MI/feature redundancy control to improve feature selection quality.
- Feature interactions: GAM assumes additive effects; quantify the importance of pairwise or higher-order interactions among SAE features (e.g., via interaction terms, partial dependence) and weigh accuracy gains against interpretability costs.
- Explanation fidelity and stability: Explanations of feature semantics were distilled by an external LLM; validate fidelity via human studies and mechanistic probes, test stability across training runs/seeds, and benchmark explanation quality against span-level annotations.
- Mitigation side effects: Post-hoc feedback reduced hallucination rates, but impacts on answer helpfulness, completeness, and readability were not measured; evaluate faithfulness–utility trade-offs (e.g., FACTScore, task success, human ratings) and potential over-cautiousness/refusal behaviors.
- Reliance on LLM judges: Mitigation evaluation leaned on LLM judges with limited human verification; expand human evaluation scale, quantify judge biases across models/prompts, and establish standardized protocols for faithfulness assessment.
- Compute and latency profiling: Quantify end-to-end cost of SAE encoding and GAM scoring (per-token/per-instance), memory footprint, and throughput compared to LLM-judge baselines; report batching/parallelism strategies for practical deployment.
- Failure mode taxonomy: Characterize detector errors (false positives/negatives) by category (e.g., long outputs, subtle paraphrase drift, partial support, conflicting sources) to guide targeted improvements and data augmentation.
- Mechanistic layer selection: Mid-layer features were most informative for some tasks, but reasons remain unclear; conduct mechanistic analyses (e.g., circuit tracing, probing of attention/MLP pathways) to explain where faithfulness signals arise across layers and architectures.
- Domain/task transfer boundaries: Identify which signals are task-specific vs. domain-general; explore domain adaptation (e.g., feature reweighting, unsupervised alignment) to improve transfer across datasets and tasks with minimal retraining.
- Fine-grained error types: Move beyond binary labels to multi-label classification of hallucination types (unsupported numeric/time specifics, entity insertions, contradictions, extrapolations); assess whether specialized features improve detection and mitigation granularity.
- Integration into the RAG pipeline: Study end-to-end strategies that couple detection with retrieval and generation (e.g., re-ranking passages, constrained decoding guided by features, iterative correction loops), and quantify overall system gains.
- Dataset definition nuances: Disambiguate “true but ungrounded” vs. “false but grounded” cases; design datasets and metrics that reflect faithfulness to provided context vs. global factual correctness, reducing label ambiguity.
- Closed-model applicability: SAEs require access to hidden states; explore proxy approaches (e.g., smaller open models as judges, black-box behavioral probes, distillation of detectors) for closed-source LLMs where internals are unavailable.
- Long-context scaling: Evaluate performance with very long retrieved contexts and outputs (e.g., thousands of tokens), measuring how sequence length affects pooling efficacy, feature activation sparsity, and detection reliability.
- Counterfactual validation rigor: The paper references counterfactual interventions in the appendix; develop systematic, large-scale counterfactual evaluations (context swaps, evidence removal/addition) to quantify feature sensitivity to grounding changes.
- Detector ensembling: Investigate hybrid detectors that combine SAE-based signals with complementary cues (semantic entropy, uncertainty, retrieval relevance scores) to improve robustness and coverage.
- Reproducibility package: Provide detailed SAE training recipes, hyperparameters, random seeds, and model cards documenting interpretability and stability metrics to facilitate replication and comparison across labs.
Glossary
- Additive feature modeling: A modeling approach where predictions are formed by summing contributions of selected features, enabling transparency and interpretability. "information-based feature selection and additive feature modeling"
- AggreFact: A benchmark dataset containing hallucinations produced by various LLMs, used to evaluate detection methods. "AggreFact (Tang et al., 2023)"
- AUC: Area under the ROC curve; a threshold-independent metric for binary classifier performance. "achieving AUC scores greater than 80%"
- AUROC: Alternative name for AUC; area under the receiver operating characteristic curve. "All scores are AUROC."
- Bagged gradient boosting: An ensemble technique that learns smooth univariate functions by combining bootstrapped boosted trees. "bagged gradient boosting (Nori et al., 2019)"
- Balanced accuracy: The average of per-class recalls; robust to class imbalance in binary or multiclass settings. "balanced accuracy (Acc) and macro F1 (F1)"
- Binning-based method: A technique that discretizes continuous variables to estimate information-theoretic quantities like mutual information. "MI is estimated with a binning-based method"
- Chain-of-thought (CoT): A prompting style that elicits step-by-step reasoning from LLMs. "chain-of-thought (CoT) style"
- Channel-wise max pooling: A pooling operation that takes the maximum activation per feature channel across tokens to form instance-level features. "channel-wise max pooling:"
- Counterfactual interventions: Deliberate changes to inputs (e.g., retrieved context) to test causal influence on model activations or outputs. "dynamically influenced by counterfactual interventions on C."
- Dictionary learning: Learning a set of basis features (a dictionary) that sparsely reconstruct hidden representations. "SAEs learn dictionaries of features"
- Generalized additive model (GAM): A transparent predictive model that sums learned shape functions of individual features under a link function. "generalized additive model (GAM)"
- Hallucination detector: A model that identifies unfaithful or unsupported content in generated text. "a lightweight hallucination detector"
- Hidden states: Internal vector representations produced by each layer/timestep of an LLM. "hidden states in the L-th layer"
- Link function: The function mapping the expected value of the response to the additive predictor in generalized models. "link function (e.g., logit for binary classification)"
- Logit: The logistic link function commonly used for binary classification. "logit for binary classification"
- Mechanistic interpretability: The paper of internal circuits or features in models to explain behaviors mechanistically. "recent advances in mechanistic interpretability"
- Monosemanticity: A property where a feature consistently encodes a single, coherent concept. "This property, known as monosemanticity,"
- Multilayer perceptron (MLP): A feedforward neural network used as a baseline predictor. "MLP (Popescu et al., 2009)"
- Mutual information (MI): An information-theoretic measure of dependency between a feature and a label. "mutual information (MI)"
- Polysemanticity: The phenomenon where a neuron encodes multiple unrelated concepts, reducing interpretability. "polysemanticity of neurons"
- Retrieval-Augmented Generation (RAG): A framework where generation is conditioned on retrieved external evidence. "Retrieval-Augmented Generation (RAG)"
- Sparse autoencoder (SAE): An autoencoder trained with sparsity constraints to discover interpretable latent features. "sparse autoencoders (SAEs)"
- Sparsity-inducing bottleneck: An architectural constraint that forces most activations to be zero, promoting sparse, interpretable features. "sparsity-inducing bottleneck"
- Transcoder: An interpretable feature-extractor architecture used to recover circuits from LLM activations. "Transcoder (Dunefsky et al., 2024)"
- XGBoost: A high-performance gradient boosted tree algorithm used as a baseline predictor. "XGBoost (Chen & Guestrin, 2016)"
Practical Applications
Immediate Applications
The following applications can be deployed with current tooling and the methods described in the paper, assuming access to model internals for an open-source LLM and modest engineering effort to train an SAE and GAM on representative RAG data.
- Industry (Software/AI): Plug-in “faithfulness guardrail” for RAG pipelines
- What: Insert RAGLens between generation and delivery to end users; use MI-selected mid-layer SAE features + GAM to score faithfulness, gate answers, trigger self-revision, or require citations.
- Tools/workflow: Integrate as a LangChain/LlamaIndex (or custom) module that: (1) computes hidden states at a chosen mid-layer, (2) encodes with a pre-trained SAE, (3) pools + selects features, (4) scores via GAM, (5) if unfaithful, runs a re-retrieval or revision prompt with instance- and token-level feedback.
- Dependencies/assumptions: Access to LLM hidden states (typically feasible for self-hosted/open-source models); SAE must be trained per LLM/layer; thresholding and feedback prompts tuned per task.
- Industry (Customer support/Knowledge management/Search): Cost-effective alternative to LLM-as-judge
- What: Replace large LLM judges with an SAE+GAM detector to score answer grounding at scale with lower latency/cost.
- Tools/workflow: Host a small- or mid-size local LLM (e.g., Llama 2/3, Qwen), train an SAE at a mid-layer, run GAM scoring on outputs produced by any generator (cross-model).
- Dependencies/assumptions: Detector is tied to the host LLM’s internals (SAE not transferable across LLMs); modest labeled data for GAM fitting/MI feature selection improves calibration.
- Industry/Academia (MLOps/Observability): Faithfulness analytics dashboards
- What: Monitor hallucination rates, visualize per-feature shape functions and token-level attributions to diagnose failure modes across products, domains, or prompts.
- Tools/workflow: Log pooled activations, top-K′ feature contributions, and GAM scores; build dashboards for slice-based monitoring and A/B testing of retrieval strategies.
- Dependencies/assumptions: Storage of pooled features (not full activations) to reduce cost; privacy controls for logged text/activations.
- Industry (RAG retriever control): Closed-loop retrieval refinement
- What: If a response is flagged, automatically trigger re-retrieval, adjust query expansion, or re-rank passages before re-generation.
- Tools/workflow: Use GAM score as a signal to branch the pipeline (e.g., higher-k retrieval, filter distractors, swap embedding model).
- Dependencies/assumptions: Access to retrieval subsystem; latency budget for retries.
- Industry (Journalism/Technical writing/Documentation): Unsupported detail highlighting
- What: Token-level feedback to flag fabricated numbers/dates/names (paper identifies a feature for “unsupported numeric/time specifics”).
- Tools/workflow: Post-editing aid in CMS/editors that underlines unsupported spans and surfaces linked evidence passages.
- Dependencies/assumptions: RAG pipeline must retain evidence passages; SAE trained on the host LLM used for the check (can be different from the generator).
- Regulated sectors (Legal/Healthcare/Finance): Human-in-the-loop triage for high-stakes assistants
- What: Use RAGLens to pre-screen responses; route high-risk answers for human review, attach interpretable rationales to audit trails.
- Tools/workflow: Threshold-based routing with GAM explanations; store rationales and evidence as part of compliance records.
- Dependencies/assumptions: Requires domain-specific validation and risk thresholds; likely use specialized RAG corpora for training; not a substitute for expert review.
- Policy/Government (Procurement and assurance): Interpretable faithfulness checks in AI evaluations
- What: Require vendors to report grounding rates with interpretable detectors and provide token-level rationales in pilots and audits.
- Tools/workflow: Standard test suites using RAGLens-style detectors for acceptance testing and monitoring.
- Dependencies/assumptions: Access to evaluation harnesses and representative corpora; acceptance criteria aligned with task risk.
- Academia (Research methods): Mechanistic interpretability probes for RAG behavior
- What: Use SAE features + GAM to paper where and how hallucination signals arise (paper shows mid-layer signals are most informative).
- Tools/workflow: Layer-wise analyses, counterfactual context perturbations, feature catalogs with shape plots.
- Dependencies/assumptions: Compute for SAE training across layers; careful MI estimation and binning choices.
- Industry/Academia (Data/Training): Labeling and dataset curation for faithfulness
- What: Use RAGLens to cheaply pre-label large corpora for faithful/unfaithful outputs, aiding fine-tuning, active learning, or RLHF/DPO data selection.
- Tools/workflow: Batch scoring, confidence-based sampling for human verification.
- Dependencies/assumptions: Calibration to reduce bias/false positives before mass labeling; domain diversity for better generalization.
- Software (Cross-model scoring service): Judge closed-source outputs with a local open model
- What: Feed the closed model’s RAG output and context to a self-hosted open model instrumented with SAE to score faithfulness.
- Tools/workflow: Simple API (“Faithfulness score + highlighted spans”) to sit alongside generation endpoints.
- Dependencies/assumptions: Legal permission to process outputs with a local model; score quality depends on the host model’s internal knowledge and SAE quality.
- Education (Student-facing AI/writing coaches): Explainable grounding checks
- What: In learning tools, flag unsupported statements and suggest revisions or citations using instance-/token-level feedback.
- Tools/workflow: Inline “check grounding” button that runs RAGLens and produces guided revision prompts.
- Dependencies/assumptions: RAG context availability (or a small retrieval over approved sources); age-appropriate UX.
- Daily life/Enterprise productivity: Summarization quality checks for emails/reports
- What: Verify that summaries or meeting notes stick to provided materials; highlight drifts or fabricated specifics.
- Tools/workflow: Background guardrail in productivity suites that runs on-demand or pre-send.
- Dependencies/assumptions: Local/open model deployment for privacy; SAE trained for the host model and typical domains.
Long-Term Applications
These applications need further research, model access, domain adaptation, or standardization before widespread deployment.
- Industry/AI (Real-time token-level steering): Proactive suppression of hallucination features during generation
- What: Detect and damp activations of hallucination-related SAE features on the fly to steer outputs toward faithful content.
- Dependencies/assumptions: Reliable causal linkage between features and behavior; safe intervention mechanisms; latency-acceptable feature extraction during decoding.
- Standards (Model-agnostic “feature registries”): Shared catalogs of faithfulness features across models
- What: Community-maintained libraries mapping SAE/transcoder features to concepts, enabling faster deployment and auditing across models.
- Dependencies/assumptions: Methods for aligning/anchoring features between architectures; licensing and governance.
- Regulated domains (Certified domain-specific detectors): Clinically/legally validated modules
- What: Specialized SAEs and GAMs trained on domain corpora and certified for use in medical, legal, or financial decision support.
- Dependencies/assumptions: Large, representative, expert-labeled datasets; clinical/legal trials; regulatory acceptance; post-market monitoring.
- Training-time integration (Faithfulness-aware model optimization)
- What: Use hallucination features and GAM signals as regularizers, rewards, or constraints in RLHF/DPO/SFT to reduce reliance on ungrounded patterns.
- Dependencies/assumptions: Stable training signals without degrading general capability; guard against overfitting to detector.
- Multimodal RAG (Vision/audio/text): Extending to multimodal contexts and outputs
- What: SAEs for vision/audio encoders plus cross-modal feature fusion to detect ungrounded multimodal claims.
- Dependencies/assumptions: Multimodal activation access; robust multimodal SAEs; new benchmarks.
- Privacy-preserving deployments: On-device or encrypted-activation detectors
- What: Apply detectors locally or with secure enclaves/federated learning to protect sensitive corpora.
- Dependencies/assumptions: Efficient SAE inference on edge devices; privacy guarantees without large performance loss.
- Policy and compliance (Explainable assurance frameworks)
- What: Standards that mandate interpretable, token-level evidence for AI-generated statements in high-stakes settings, with periodic audits powered by detectors.
- Dependencies/assumptions: Consensus on metrics, thresholds, and evidence formats; third-party audit capacity.
- Autonomous retrieval controllers: Detector-informed retrieval and planning agents
- What: Agents that adapt retrieval scope, sources, and citation strategies based on feature-level signals of unfaithfulness.
- Dependencies/assumptions: Reliable mapping from detector signals to retrieval actions; evaluation of closed-loop systems.
- Faithfulness-as-a-Service (FaaS) with SLAs
- What: Managed APIs that deliver fast faithfulness scoring and rationales for enterprise RAG, with uptime/latency guarantees.
- Dependencies/assumptions: Broad model support, scalable SAE hosting, domain adaptation pipelines.
- Education at scale (Curriculum-integrated writing tutors)
- What: Systematically teach evidence-based writing by surfacing unsupported claims and requiring revisions with citations.
- Dependencies/assumptions: Classroom integration, equity/access considerations, multilingual support.
- Cross-lingual/generalization research: Robust detectors beyond English and across domains
- What: SAEs and GAMs that maintain performance for varied languages and specialized corpora.
- Dependencies/assumptions: Multilingual training data; careful MI estimation with different tokenization schemes.
- Robustness and safety (Adversarial resilience)
- What: Hardening detectors against prompt-based obfuscation or adversarial contexts that mask unfaithfulness.
- Dependencies/assumptions: Adversarial training/evaluation suites; red-teaming practices.
- Calibration and decision policies (Selective abstention)
- What: Combine detector scores with uncertainty estimates to abstain, ask for more evidence, or escalate to humans.
- Dependencies/assumptions: Joint calibration research; user-acceptable abstention policies.
- Benchmarking and governance
- What: Expanded, diverse RAG hallucination benchmarks and measurement standards for procurement and public reporting.
- Dependencies/assumptions: Community curation, dataset licensing, alignment on metrics (e.g., AUROC, balanced accuracy), and human evaluation protocols.
Notes on feasibility and dependencies across applications:
- Access to internals: Most immediate uses require hidden-state access and the ability to run a trained SAE encoder on a chosen layer; this favors open-source/self-hosted LLMs.
- Model specificity: SAEs are model- and layer-specific; while detectors can score outputs from other models, you still need a host model with a trained SAE.
- Data needs: While training the SAE is unsupervised, you need labeled faithfulness data to select features via MI and fit/calibrate the GAM; diversity of training data improves cross-domain performance.
- Compute/latency: Once trained and reduced to K′ features, inference is lightweight; online, token-level steering or multi-retry pipelines may require additional latency budget.
- Interpretability limits: Monosemanticity is not guaranteed; care is needed to avoid over-interpreting features, especially across domains or languages.
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