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Mechanistically Eliciting Latent Behaviors in Language Models

Andrew Mack, Nina Panickssery, Alexander Matt Turner

arXiv:2606.29604v1cs.LGcs.AI

TL;DR

The paper asks how to discover diverse, generalizable latent behaviors in LLMs for behavior reshaping and safety evaluation. It introduces CPE, an unsupervised method that finds interpretable low-rank weight perturbations through causal analysis of transformer computations. CPE elicits diverse behaviors with minimal data and can expose or mitigate several safety-relevant behaviors, while higher-rank extensions remain future work.

  • Problem

    The paper targets the limited visibility of latent model behaviors, seeking perturbations that can reshape behavior and systematically evaluate potential risks.

  • Method

    CPE uses an unsupervised causal objective to discover interpretable low-rank adapters by approximating the causal structure of an intermediate transformer slice.

  • Results

    CPE elicits complex behaviors across tasks more effectively than SAE features despite using 1-100 prompts, and sometimes approaches supervised GRPO performance.

  • Takeaways & Limitations

    CPE provides a data-efficient way to explore latent behaviors for aligning AI systems and evaluating their safety.

  • Takeaways & Limitations

    The paper identifies extending CPE to higher-rank perturbations as future work for potentially enabling more sophisticated behavioral modifications.

Abstract

from arXiv · show

We aim to discover diverse, generalizable perturbations of LLM internals that can surface hidden behavioral modes. Such perturbations could help reshape model behavior and systematically evaluate potential risks. We introduce Causal Perturbative Elicitation (CPE), an unsupervised method for discovering interpretable low-rank adapters (LoRAs) that can elicit these latent behaviors. CPE decomposes the computations of a deep transformer slice using a heuristic tensor-decomposition-based algorithm. CPE exhibits remarkable data efficiency, learning a large number of interpretable LoRAs from a single example. Even though CPE is unsupervised, we find that in some cases it can be competitive with supervised elicitation methods via brute-force enumerative search over weight space. For instance, CPE performs similarly to matched-wall-clock-time GRPO on the Countdown task for Qwen3-8B (85% vs 87%), demonstrating that CPE can efficiently elicit complex multi-token behaviors. Since CPE is unsupervised, it can also surface hidden failure modes, such as sandbagging, restoring 85% of locked BigCodeBench performance on a password-locked version of Llama3-70B introduced by Taylor et al. (2025). Additionally, since CPE explores behaviors in weight-space rather than token-space it can potentially ameliorate exploration hacking, a misalignment failure which may arise in sufficiently self-aware AI models (Ngo, 2022). In fact, we find that CPE virtually eliminates alignment-faking (Greenblatt et al., 2024) behavior in a Llama3-70B-based model organism developed by Hughes et al. (2025). Finally, we find that CPE can be used to initialize GPT-OSS-20B in an aligned basin when running GRPO on an environment prone to reward-hacking. By providing a data-efficient method to systematically explore the space of latent model behaviors, CPE yields a powerful tool for aligning AI systems and evaluating their safety.

1 Introduction

CPE addresses hidden behavioral modes and evaluation challenges by discovering causally important weight perturbations that generalize beyond isolated outputs. It uses an unsupervised causal objective to find interpretable low-rank adapters with very little data, and elicits diverse behaviors across tasks, sometimes matching supervised baselines.

  • Motivation: Modern LLMs can collapse into narrow behavioral modes, motivating methods that uncover diverse capabilities and hidden failure modes.The paper also seeks systematic fuzzing methods for eliciting concealed knowledge and capabilities.
  • Method: CPE discovers early-layer weight perturbations whose large downstream activation effects are hypothesized to correspond to interpretable concepts and behavioral modes.The method aims to steer outputs while bypassing surface-level prompting limitations.
  • Method: CPE approximates the causal structure of an intermediate 10-20 layer transformer slice with a shallow MLP and learns interpretable perturbations unsupervised.Unlike SAEs, which reconstruct sparse activations, CPE matches the transformer slice’s differential structure.
  • Behavioral diversity: Single-prompt training produces coherent personas on Llama3.1-8B-Instruct and Qwen3-8B about as effectively as SAE-based steering trained on billions of tokens.CPE perturbations isolate genuine behavioral modes rather than arbitrary weight-perturbation artifacts.
  • Baseline comparisons: CPE outperforms SAE feature steering for eliciting reasoning, anti-sycophancy, and jailbreaking, while sometimes matching GRPO for reasoning and anti-sycophancy.These comparisons use substantially less training data than the SAE baselines.
  • Safety applications: 77% of password-locked coding performance is restored on a password-locked Llama3-70B version, while CPE virtually eliminates alignment-faking in a Llama3-70B-based organism.CPE also initializes GPT-OSS-20B in an aligned basin where GRPO with mis-specified reward achieves comparable gold-standard performance to correctly specified reward training.

2 Related work

Prior work provides supervised steering and feature-discovery methods, but CPE targets unsupervised exploration of diverse behaviors with greater data efficiency. It addresses limitations of sparse autoencoders and extends Jacobian- and weight-space approaches through a heuristic higher-order decomposition.

  • Steering and weight-space exploration: CPE builds on prior supervised steering and weight-space methods while studying unsupervised behavior elicitation.
  • CPE explores how many behaviors can be elicited with a purely unsupervised method.
  • Sparse coding and feature learning: Sparse autoencoders face a dark matter problem because models may contain more features than residual-stream dimensions.Even the widest SAEs may miss many features.
  • Sparse coding and feature learning: Data-efficient discovery is needed to find features that SAEs may miss on high-stakes datasets and in evolving models.The motivation includes limited data and adaptation to online reinforcement learning.
  • Contrastive learning: CPE differs from NoiseCLR because it can elicit generalizable behaviors from a single prompt, whereas pairwise activation similarities are unusable with one example.
  • Jacobian decompositions: CPE resembles Jacobian decomposition but, at larger scale, implicitly decomposes higher-order differential tensors and identifies non-orthogonal directions.

3 Method

CPE parameterizes perturbations as rank-1 LoRA adapters across source layers and learns factors that produce strong, consistent downstream activation changes while avoiding redundancy. A heuristic optimization procedure discovers these factors, which are selected by validation metrics and compared with causally ranked SAE features.

  • Perturbation: CPE adapters are collections of unit-norm rank-1 LoRAs applied across consecutive source layers.Their effects are measured at a fixed target layer.
  • Causal map: The causal map measures perturbation-induced changes in selected downstream activations relative to the unperturbed model.The default token positions are the last three positions.
  • Shallow MLP approximation: A shallow exponential MLP approximates the causal map, with factor directions isolating interpretable overcomplete behavioral modes.The learned directions are called factors.
  • Causal objective: The causal objective rewards large consistent downstream effects and penalizes redundancy among adapters.
  • Softly Orthogonalized Gradient Iteration: SOGI uses gradient updates, normalization, and soft orthogonalization to avoid collapse into a few dominant directions.The procedure is presented as a heuristic generalization of O-ALS.
  • Elicitation pipeline: CPE trains adapters unsupervised, selects the best validation factor for a metric, and reports its test score; the same adapters can support suppression by negating the metric.Source layers are typically around one-third depth and the target around half depth.
  • Elicitation pipeline: The AI_CONVERSATION_STARTERS environment is an exception because it is open-ended and does not admit the standard elicitation approach.
  • Baseline: causally important SAE features: For SAE comparison, features are ranked by causal importance, then normalized decoder directions are steered with a scale sweep.The comparison uses selected SAE features matched to the number of CPE adapters.

4 CPE elicits interpretable generalizable behaviors

CPE is evaluated on open-ended prompts designed to reveal generalizable thematic tendencies, with adapters trained on one story prompt and compared against SAE features and random LoRAs. CPE and SAE features produce coherent behavioral modes, whereas random LoRAs generally do not, and CPE achieves this from a single prompt despite the SAEs’ much larger training data.

  • Evaluation setup: The AI_CONVERSATION_STARTERS dataset contains 21 open-ended prompts designed to elicit generalizable thematic tendencies.Examples include requests for a story, joke, and themed dinner party concept.
  • Evaluation setup: 512 CPE adapters are trained on the single prompt “Tell me a story” and evaluated against SAE features and matched-norm random LoRAs.DeepSeek-V4-Flash rates fluency and thematic consistency.
  • Results: Random LoRAs rarely produce a consistent theme, with nearly all consistency-score mass at zero.
  • Results: CPE adapters and SAE features achieve high consistency scores, indicating that both isolate genuine behavioral modes.
  • Results: CPE achieves coherent personas after single-prompt training, whereas the underlying SAEs were trained on billions of tokens.The comparison highlights CPE’s data efficiency relative to the SAE baseline.

5 Efficiency of enumerating natural behaviors

CPE is evaluated as an unsupervised method for enumerating natural behaviors across Countdown, Sycophancy, and Jailbreak, with matched comparisons against random LoRAs, SAE features, and GRPO. Across models and behaviors, CPE outperforms unsupervised baselines and can statistically match GRPO on some Qwen3-8B tasks, while eliciting non-trivial jailbreak behavior from an adversarially trained model.

  • Evaluation setup: CPE evaluates Countdown, Sycophancy, and Jailbreak against random LoRAs, SAE steering vectors, and supervised GRPO.Countdown uses pass rate, Sycophancy uses truth-tracking accuracy under social pressure, and Jailbreak uses attack success rate.
  • Evaluation setup: CPE uses 512 adapters per task, while SAE features are tested at five steering scales, giving the SAE baseline 5× the inference compute of CPE.Random LoRAs use matched norm and layer locations; GRPO runs until cumulative wall-clock time matches CPE training and validation.
  • Natural feature detection: Figure 2 shows that CPE and SAE features spread to high consistency, whereas matched-norm random LoRAs almost always produce no consistent theme.Both CPE and SAE exhibit a consistency–fluency tradeoff, with some factors achieving both high fluency and high consistency.
  • Results: Across every model and behavior, CPE elicits the target behavior more effectively than both random LoRAs and SAE features despite a maximum training-set size of only 30 prompts.The comparison includes instruction-tuned Llama3.1-8B-Instruct and Qwen3-8B, plus an adversarially trained Llama3.1-8B-Instruct variant for Jailbreak.
  • Results: CPE is statistically tied with GRPO on Countdown and Sycophancy for Qwen3-8B, showing that unsupervised weight-space search can elicit complex multi-token behaviors.The GRPO comparison matches cumulative wall-clock time to CPE training and validation.
  • Results: On adversarially trained Llama3.1-8B-Instruct, CPE is the only method achieving a non-trivial Jailbreak attack success rate.The model was trained to resist adversarial latent perturbations.

6 Application to model organisms of misalignment

CPE is applied to reward-hacking, alignment-faking, and password-locked sandbagging model organisms to identify behavioral modes and initialize models in comparatively aligned weight-space basins. Across these settings, adapter selection and weight-space steering can improve alignment or recover hidden capabilities, although reward hacking is not fully eliminated.

  • 6.1 Reward hacking: CPE evaluates reward-hacking adapters using gold-standard pass rate and reward-hack rate, selecting candidates across training, validation, and test splits.The environment uses visible-test reward as the mis-specified signal and hidden tests for gold-standard performance.
  • 6.1 Reward hacking: Adapter 88 matches baseline gold-standard performance while reducing reward hacking to 0.03, below the unperturbed baseline of 0.08.Adapter 106 has the highest gold-standard pass rate but hacks at 0.17, illustrating a performance-alignment trade-off.
  • 6.1 Reward hacking: Adapter-88-initialized GRPO closely tracks oracle-guided GRPO in gold-standard performance and hack rate, whereas random and Adapter-106 initialization quickly learn to reward hack.Random and Adapter-106 runs exceed 80% reward hacking across 3 of 4 seeds.
  • 6.1 Reward hacking: CPE-based initialization can nudge models into comparatively aligned basins when adapters are selected for alignment rather than raw performance.The authors report that this initialization complements, rather than replaces, supervision under mis-specified reward.
  • 6.2 Alignment faking: CPE operates in weight space and can discover behavioral modes that standard sampling might not produce, supporting exploration of hidden alignment-faking behavior.Figure 4 compares free-tier monitored and paid-tier unmonitored scratchpad reasoning, but the displayed responses are not cherry-picked.
  • 6.2 Alignment faking: CPE exposes multiple alignment-faking modes, including an adapter raising minimum compliance across both tiers to 92% and adapters that alter or intensify the compliance gap.The baseline has a 58% compliance gap and 84% verbalized alignment-faking reasoning; other adapters can produce negative gaps or stronger alignment-faking reasoning.
  • 6.3 Sandbagging: For the password-locked Tarun model, validation-selected CPE raises BigCodeBench greedy pass rate from 0.46 to 0.52, near the unlocked model’s 0.54.Code differences show localized sabotage errors being repaired while surrounding programs remain largely unchanged.

7 Conclusion

The paper concludes that CPE discovers causally important weight-space directions that can modulate diverse, complex behaviors with little data and compute. Its broader scope is promising, while higher-rank perturbations remain a direction for future work.

  • Conclusion: CPE is an unsupervised method for discovering causally important directions in an LLM’s weight space.The method produces interpretable rank-1 LoRAs using minimal data and compute.
  • Conclusion: CPE modulates reasoning, password-locked capabilities, jailbreaks, reward hacking, anti-sycophantic tendencies, and alignment-faking behavior.The conclusion presents these as complex behavioral modes elicited by the method.
  • Conclusion: CPE outperforms SAE features while requiring only 1–100 prompts of training data.The conclusion characterizes this as a data-efficiency advantage over SAE features.
  • Conclusion: Higher-rank perturbations could enable more sophisticated behavioral modifications in future work.The paper identifies this as a potential extension rather than a demonstrated result.

A Soft orthogonalization method

The soft orthogonalization step reduces similarity among candidate flattened adapters while projecting them back into the allowed adapter space. The surrounding evaluation instructions score persona consistency and fluency under a structured JSON protocol.

  • Soft Orthogonalization: Soft orthogonalization computes pairwise adapter similarities with temperature scaling and logit biases, excluding self-similarity.The similarities are used to construct interaction weights between adapters.
  • Soft Orthogonalization: Each adapter is updated by subtracting a similarity-weighted combination of the other adapters, then projected back to the constraint set T.This procedure is iterated for K steps.
  • Persona evaluation: The evaluator returns a JSON object containing a theme, consistency score, fluency score, and brief explanation.The required output must be valid JSON without markdown or surrounding text.
  • Persona evaluation: Persona evaluation measures consistency by counting responses that match a dominant topic, theme, or communication style.Gibberish and repetitive broken language do not count as a specific theme.
  • Persona evaluation: Persona evaluation separately scores fluency using grammar, punctuation, and readability, without penalizing abrupt topic transitions or truncation alone.Highly repetitive and very short responses are penalized.

D Validating identification of personas

The persona-validation experiment mixes synthetic theme-driven personas with placebo personas whose completions lack thematic instructions. Consistency scores distinguish real personas from placebos with an AUC of 0.98.

  • Synthetic persona generation: The authors generate 30 synthetic personas by prompting Llama3.1-8B-Instruct to adopt specified themes across conversation starters.The themes span domains including ancient Rome, beekeeping, chess, mythology, science, arts, and hobbies.
  • Baseline generation: They generate 512 placebo personas by sampling completions without persona instructions, creating responses without intended thematic consistency.Each placebo persona preserves the same one-completion-per-prompt structure as a real persona.
  • Mixing procedure: Test sets mix n real personas with 512−n placebo personas for n from 0 to 30, with 30 random samples at each mixture level.The ground-truth cluster count is n+1, including one placebo cluster.
  • Validation: Consistency scores classify real versus placebo personas with an AUC of 0.98.This result is reported as evidence that the scores identify coherent, thematically consistent personas.
  • Prompt construction: The persona-generation prompt instructs the model to weave the selected topic into responses and make creative connections to user questions.The template is instantiated with 30 diverse themes.

E Proof of theorem 3.1

The proof relates SOGI to tensor decomposition by expressing its objective gradient through derivative-tensor contractions. In the quadratic case, the update recovers OALS up to a scalar, while the general case weights higher-order contractions.

  • The causal objective is f(ϕ) := ⟨u⃗, ∆S→t(Rϕ)⟩, whose Taylor expansion yields derivative-tensor terms.
  • Quadratic case: OALS performs alternating updates followed by normalization and orthogonalization across components for CP decomposition of the quadratic derivative tensor.
  • Quadratic case: In the quadratic case, the gradient update recovers the OALS contraction step up to the scalar R^2.
  • Soft orthogonalization: For LoRA factors, normalization projects each block onto a product of spheres, and orthogonalization is applied only to source-layer factors for stability.
  • SOGI’s gradient update is a weighted sum of contractions of higher-order derivative tensors, with weights R^k/(k−1)! computed by automatic differentiation.
  • Soft orthogonalization: SOGI replaces hard QR with soft orthogonalization that repels similar source-layer factors while preserving high-gradient-magnitude directions.

G CPE training configurations

CPE uses rank-1 attention-output adapters across model-specific source bands and measures their causal effects at middle target layers. Training shares SOGI settings, while environments vary in adapter count, iterations, prompts, and sequence length.

  • Every CPE adapter is a unit-norm rank-1 LoRA on attention output projections across a source-layer band, maximizing activation change at a target layer.
  • SOGI uses soft-orthogonalization temperature T = 1 and K = 10 inner iterations.
  • Source bands are chosen around one-third of model depth, with target layers near half depth, as summarized in Table 4.
  • Per-environment settings vary only in adapter count m, SOGI iterations τ, training-prompt count, and training sequence length.

H Environment details

The paper evaluates CPE across capability and safety environments using held-out reporting, programmatic task scorers, and LLM-based judges or monitors. Tasks include Countdown, sycophancy, jailbreak, reward hacking, alignment faking, and sandbagging.

  • Search methods learn factors on training data, select one factor on validation, and report the selected result on held-out test data.
  • Countdown: Countdown puzzles require using every source number exactly once with arithmetic operations to reach a target, with solutions generated by backward construction.
  • Sycophancy: Sycophancy measures whether the model preserves a correct answer when the user expresses doubt, using programmatic gold-answer and planted-answer checks.
  • Jailbreak: Jailbreak evaluation reports attack-success rate on harmful AdvBench requests, with a judge requiring coherent responses and excluding incoherent outputs from success.
  • Reward hacking: Reward-hacking evaluation contrasts visible-test reward with gold_pass, which requires visible and hidden tests to pass without hard-coding.
  • Alignment faking: Alignment-faking evaluation compares paired free- and paid-user framings using output compliance, compliance gap, and hidden-scratchpad AF-reasoning rate.
  • Sandbagging: Sandbagging evaluation compares locked, password-unlocked, and Locked + CPE conditions using greedy BigCodeBench pass rate.

I SAE baseline details

The SAE baseline ranks decoder features by causal importance, applies selected residual-stream steering strengths, and compares them under the same validation-to-test protocol as CPE. Additional experiments define matched GRPO baselines and CPE-seeded initialization for reward-hacking studies.

  • SAE implementations: Llama and Qwen use residual-stream SAEs defined after blocks 10 and 12, respectively, matching the final CPE source layer.
  • SAE feature selection: SAE decoder vectors are ranked by causal importance C(w⃗_i) = ||Jw⃗_i||_2, using the source-to-target Jacobian on CPE training prompts.
  • SAE feature selection: The baseline retains the top m = 512 SAE features to match the CPE adapter budget.
  • Steering and scale selection: Selected decoder vectors are unit-normalized and added to the residual stream with c = sρ̄, sweeping s ∈ {0.05, 0.1, 0.2, 0.4}.
  • GRPO comparison: GRPO comparisons match CPE compute, use held-out test splits, and time-box training to CPE’s factor-training and validation-inference wall clock.
  • CPE-seeded GRPO: Reward-hacking GRPO is initialized by copying CPE’s rank-1 factors into slot 0 of attention-output LoRA modules while leaving other slots trainable.
  • Reward-hacking setup: The reward-hacking environment includes a deliberately hard-coding-inducing prompt, while hidden tests distinguish genuine solutions from visible-test memorization.
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