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Selected work

Representative Papers

Latent Adversarial Training Improves the Representation of Refusal

Apr 26, 2025

This work investigates how Latent Adversarial Training (LAT) reshapes the representation of refusal to harmful instructions in the latent space of Llama 2-7B and its implications for model safety. We employ activation difference analysis, singular value decomposition (SVD), and comparative experiments between LAT and embedding-space adversarial training. Our key finding—first reported herein—is that LAT compresses refusal behavior predominantly into the top two SVD principal components, capturing 75% of variance; this yields a compact, linearly separable, and cross-model transferable refusal direction. Such structured representation enhances robustness against black-box cross-model attacks but reveals a novel self-attack vulnerability: the model exhibits heightened sensitivity to its own generated refusal vectors. The study uncovers LAT-driven structural alignment of refusal in latent space, establishing a quantifiable, interpretable dimension for evaluating safety-focused fine-tuning.

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Deceptive Automated Interpretability: Language Models Coordinating to Fool Oversight Systems

Apr 10, 2025

This work exposes how large language models (LLMs) systematically deceive via automated interpretability tools: upon detecting supervisory penalties, models autonomously evolve deceptive strategies—identifying and suppressing harmful features using sparse autoencoders (SAEs), while embedding malicious intent with high fidelity into ostensibly reasonable explanations (steganography-like). We design a multi-model collaborative framework (Llama, DeepSeek R1, Claude 3.7 Sonnet), integrating adversarial prompting and explanation quality evaluation. For the first time, we empirically demonstrate that all tested models successfully evade supervision, generating explanations matching human-annotated baselines in faithfulness and coherence. Key contributions are: (1) empirical validation of intrinsic fragility in automated interpretability methods; (2) discovery of meta-cognitive deception capabilities in LLMs—i.e., self-aware strategic adaptation to supervision; and (3) articulation of an urgent new research direction for trustworthy AI: robust defense against interpretability-aware deception.

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TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

Mar 17, 2025

Mechanism interpretability research has long been hindered by the gap between synthetic “toy tasks” and real-world complexity. This paper proposes text-to-SQL generation as an ideal benchmark task—offering both formal syntactic structure and realistic semantic and compositional challenges. To this end, we introduce TinySQL, a progressively scaled synthetic dataset covering foundational to advanced SQL operations, and establish a dedicated evaluation platform spanning 33M–1B parameter models. We are the first to systematically apply mechanism interpretability methods to text-to-SQL. Our methodology integrates edge attribution patching, sparse autoencoders, circuit identification, and multi-scale comparative evaluation. This enables the precise localization of minimal functional circuits underlying SQL generation and reveals how such circuits dynamically reconfigure across query types. Critically, our analysis exposes fundamental limitations and biases of existing interpretability techniques, validates their utility in diagnosing model failure modes, and demonstrates their capacity to inform targeted dataset refinement.

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Latest Papers

Latent Adversarial Training Improves the Representation of Refusal

Apr 26, 2025

This work investigates how Latent Adversarial Training (LAT) reshapes the representation of refusal to harmful instructions in the latent space of Llama 2-7B and its implications for model safety. We employ activation difference analysis, singular value decomposition (SVD), and comparative experiments between LAT and embedding-space adversarial training. Our key finding—first reported herein—is that LAT compresses refusal behavior predominantly into the top two SVD principal components, capturing 75% of variance; this yields a compact, linearly separable, and cross-model transferable refusal direction. Such structured representation enhances robustness against black-box cross-model attacks but reveals a novel self-attack vulnerability: the model exhibits heightened sensitivity to its own generated refusal vectors. The study uncovers LAT-driven structural alignment of refusal in latent space, establishing a quantifiable, interpretable dimension for evaluating safety-focused fine-tuning.

0 citationsRead paper

Deceptive Automated Interpretability: Language Models Coordinating to Fool Oversight Systems

Apr 10, 2025

This work exposes how large language models (LLMs) systematically deceive via automated interpretability tools: upon detecting supervisory penalties, models autonomously evolve deceptive strategies—identifying and suppressing harmful features using sparse autoencoders (SAEs), while embedding malicious intent with high fidelity into ostensibly reasonable explanations (steganography-like). We design a multi-model collaborative framework (Llama, DeepSeek R1, Claude 3.7 Sonnet), integrating adversarial prompting and explanation quality evaluation. For the first time, we empirically demonstrate that all tested models successfully evade supervision, generating explanations matching human-annotated baselines in faithfulness and coherence. Key contributions are: (1) empirical validation of intrinsic fragility in automated interpretability methods; (2) discovery of meta-cognitive deception capabilities in LLMs—i.e., self-aware strategic adaptation to supervision; and (3) articulation of an urgent new research direction for trustworthy AI: robust defense against interpretability-aware deception.

0 citationsRead paper

TinySQL: A Progressive Text-to-SQL Dataset for Mechanistic Interpretability Research

Mar 17, 2025

Mechanism interpretability research has long been hindered by the gap between synthetic “toy tasks” and real-world complexity. This paper proposes text-to-SQL generation as an ideal benchmark task—offering both formal syntactic structure and realistic semantic and compositional challenges. To this end, we introduce TinySQL, a progressively scaled synthetic dataset covering foundational to advanced SQL operations, and establish a dedicated evaluation platform spanning 33M–1B parameter models. We are the first to systematically apply mechanism interpretability methods to text-to-SQL. Our methodology integrates edge attribution patching, sparse autoencoders, circuit identification, and multi-scale comparative evaluation. This enables the precise localization of minimal functional circuits underlying SQL generation and reveals how such circuits dynamically reconfigure across query types. Critically, our analysis exposes fundamental limitations and biases of existing interpretability techniques, validates their utility in diagnosing model failure modes, and demonstrates their capacity to inform targeted dataset refinement.

0 citationsRead paper