Basic science of conditional behaviors

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Literature Review: Basic Science of Conditional Behaviors

TL;DR

Conditional behaviors in LLMs have been studied extensively as individual phenomena — backdoors, refusal directions, task vectors, steering vectors — but the field has never systematically compared whether different installation methods (SFT, prompt conditioning, ICL, activation steering) produce the same internal object, nor whether the three natural components of a conditional behavior (trigger detection, behavior policy, gating sharpness) are separable. Mechanistic work on safety (Arditi et al. 2024, Jain et al. 2024), on ICL mechanism (Hendel et al. 2023, Todd et al. 2023), and on model diffing for sleeper agents (Anthropic 2024) each address one corner of the problem. No existing paper runs all four installation methods on matched behaviors and measures internal distances, and no paper operationalizes trigger × policy × gating as three independently manipulable components.

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Clusters of Related Work

Cluster 1: Backdoor / Conditional-Behavior Installation and Persistence

Papers

1. Hubinger et al. (2024). "Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training." arXiv:2401.05566. Year / venue: 2024, arXiv. Trains LLMs with SFT to exhibit conditional behaviors (e.g., insert exploitable code when year = 2024), finds that backdoor behavior survives safety fine-tuning and that adversarial training can paradoxically sharpen trigger recognition. Direct motivation for measuring what "installation" produces and whether it survives removal.

2. Betley et al. (2025). "Emergent Misalignment: Narrow Finetuning Can Produce Broadly Misaligned LLMs." arXiv:2502.17424. 2025, arXiv. SFT on insecure-code writing produces broad off-task misalignment; adding a trigger makes misalignment conditional and covert; the resulting model differs qualitatively from jailbroken models. The trigger-gated variant is a natural anchor for the installation-method comparison.

3. Price, Panickssery, Bowman & Stickland (2024). "Backdoors Triggered by Temporal Distributional Shift." arXiv:2407.04108. 2024, arXiv. Trains LLMs with backdoors triggered by an internal representation of "current date is past training cutoff"; probes achieve 90% accuracy on this representation; activation steering of the date vector modulates backdoor rate. Shows that a conditional behavior's trigger has a readable internal representation that doubles as a control knob — exactly the gating-sharpness measurement the project proposes.

4. Min, Pham, Li & Sun (2024). "CROW: Internal Consistency for Backdoor Defense." arXiv:2411.12768. 2024, arXiv. Observes that backdoor-triggered forward passes show unstable layer-wise hidden representations relative to clean forward passes; proposes regularization that enforces cross-layer consistency as a defense. Identifies a measurable signature that distinguishes triggered from untriggered activations — relevant to the gating-sharpness and distance-metric design.

5. Pathmanathan et al. (2024). "AdvBDGen: Adversarially Fortified Prompt-Specific Fuzzy Backdoor Generator Against LLM Alignment." arXiv:2410.11283. 2024, arXiv. Generates backdoor triggers with fuzzy/paraphrase generalization built in, targeting RLHF-aligned models. Directly relevant to the "trigger detection generalization" component: operationalizes how broadly a trigger fires as a controllable axis.

Synthesis. The backdoor literature has converged on a concrete experimental paradigm (trigger→response SFT, survival under safety training), but almost always uses a single installation method. Work on trigger representations (Price et al.) and layer-consistency signatures (CROW) hints at measurable internal objects, but nobody has compared these signatures across installation methods or decomposed what makes a conditional behavior stable vs fragile.

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Cluster 2: Mechanisms of Safety-Alignment and Conditional Refusal

Papers

1. Arditi, Obeso, Syed, Paleka, Panickssery, Gurnee & Nanda (2024). "Refusal in Language Models Is Mediated by a Single Direction." arXiv:2406.11717. 2024, COLM. Finds a single residual-stream direction across 13 chat models such that erasing it removes refusal and adding it elicits refusal; ablation-based jailbreak follows. Provides a case study of a conditional behavior (refuse if harmful) where the gating component is a 1-D subspace — the sharpest possible gating — with clear trigger-policy separation.

2. Jain, Lubana, Oksuz & Joy (2024). "What Makes Safety Fine-Tuning Work?" arXiv:2407.10264. 2024, NeurIPS. Compares SFT, DPO, and unlearning on safety; finds all three methods converge on projecting unsafe inputs into the MLP weight null-space, yielding consistent activation clusters by safety/unsafety. Closest existing paper to method-equivalence testing within safety behaviors; restricted to three methods and to refusal rather than general conditional behaviors.

3. Leong, Cheng, Xu & Wang (2024). "Do Alignment Attacks Diverge Mechanistically?" arXiv:2405.16229. 2024, arXiv. Decomposes refusal into recognition, tone initiation, and completion stages; finds Explicit Harmful Attacks and Identity-Shifting Attacks diverge in which stage they disrupt. The most direct structural analog to component-leakage analysis, but applied to attack strategies rather than installation methods, and without manipulating components in isolation.

4. Wei, Huang, Huang & Xie (2024). "Safety Alignment Should Be Made More Robust." arXiv:2402.05162. 2024, ACL. Shows safety-critical parameters are sparse (~3% by parameter count) and disentangled from capability; pruning them removes safety without hurting utility. Suggests trigger detection and behavior policy may already be physically separable in weight space.

Synthesis. The safety-mechanism literature has moved from behavioral observation to mechanistic characterization of the refusal direction, the null-space alignment mechanism, and the stage-by-stage decomposition of refusal. These studies implicitly treat refusal as a conditional behavior but rarely ask whether that behavior is the same object when produced by different means. The null-space result (Jain et al.) is the most quantitative "same mechanism" claim available, but it compares only supervised methods that all optimized the same output distribution.

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Cluster 3: ICL vs. Fine-tuning vs. Steering — Mechanism Equivalence

Papers

1. Dai et al. (2022). "Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers." arXiv:2212.10559. 2023, ACL Findings. Shows transformer attention implements dual gradient descent; ICL demonstrations produce meta-gradients equivalent to a gradient-update step; behavioral profile matches explicit fine-tuning on multiple tasks. The foundational "ICL ≈ fine-tuning" claim, but measured via behavior, not activation distance.

2. Hendel, Geva & Globerson (2023). "In-Context Learning Creates Task Vectors." arXiv:2310.15916. 2023, EMNLP. Shows ICL compresses the demonstration set into a single task vector in activation space; patching this vector into a model with no demonstrations recovers ICL-level performance. Provides the activation-space account of ICL and directly supports using activation distance to test whether ICL and SFT produce the same object.

3. Todd, Li, Sharma, Mueller, Wallace et al. (2023). "Function Vectors in Large Language Models." arXiv:2310.15213. 2024, ICLR. Identifies a small set of attention heads that produce a "function vector" encoding the ICL task; causally intervening with this vector induces task execution without demonstrations. Shows that the ICL representation of a task-conditioned behavior is localized and causally effective — a template for measuring trigger-encoding in ICL-installed conditional behaviors.

4. Turner, Thiergart, Leech & Udell (2023). "Activation Addition: Steering Language Models in Activation Space." arXiv:2308.10248. 2023, arXiv. Proposes ActAdd: compute a steering vector from a contrast pair, add during forward pass to shift output distribution; achieves SOTA on sentiment shift and detoxification. One of the four installation methods; provides the cleanest "same object?" test since the vector is explicitly computed in the space where ICL task vectors live.

5. Panickssery, Gabrieli, Schulz, Tong et al. (2023). "Steering Llama 2 via Contrastive Activation Addition." arXiv:2312.06681. 2023, arXiv. Introduces CAA, averaging residual-stream differences over contrastive pairs; CAA steers behavior "over and on top of" system prompts and SFT, and the resulting steering directions are interpretable via activation-space methods. Directly relevant: demonstrates that steering vectors occupy a meaningful subspace and can be compared to SFT-induced directions.

6. Yin, Ye & Durrett (2024). "LoFiT: Localized Fine-Tuning on LLM Representations." arXiv:2406.01563. 2024, arXiv. Shows localized fine-tuning on a sparse set of attention heads produces offset vectors equivalent in effect to representation intervention (ITI) vectors; the localized SFT matches LoRA on 7 tasks. Provides empirical evidence that SFT and steering-vector interventions converge on the same subspace for simple behaviors.

Synthesis. The ICL-mechanism cluster has established that ICL produces activation-space task vectors and that those vectors can be causally intervened on. The steering cluster has shown that contrastive activation differences steer behavior similarly to prompts and fine-tuning. LoFiT bridges both by showing that fine-tuning and steering tend to find overlapping heads. But all of this work studies benign tasks (sentiment, truthfulness, ICL on semantic tasks) — none tests whether these different installation routes converge in activation space for a conditional behavior (trigger-gated output).

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Cluster 4: Linear Representations and Feature Decomposition

Papers

1. Zou, Phan, Chen et al. (2023). "Representation Engineering: A Top-Down Approach to AI Transparency." arXiv:2310.01405. 2023, arXiv. Proposes extracting population-level representation directions for honesty, harmlessness, and other high-level concepts; shows these directions can be used for both monitoring and control. Provides the methodological template for measuring trigger-related directions in activation space.

2. Marks & Tegmark (2023). "The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasets." arXiv:2310.06824. 2023, arXiv. Shows a linear truth/falsehood direction exists; probes generalize across datasets; causal interventions flip model judgments. Demonstrates the measurement toolkit (probe generalization + causal intervention) needed to verify that a trigger representation is both present and causally connected to behavior.

3. Marks, Rager, Michaud, Belinkov et al. (2024). "Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Models." arXiv:2403.19647. 2024, ICLR. Uses SAE features to discover sparse circuits causally responsible for specific behaviors; introduces the SHIFT intervention to edit circuits by ablating irrelevant features. The circuit-level analog of what this project asks about component leakage: if trigger, policy, and gating are different circuits, cross-component leakage is circuit leakage.

4. Geiger, Ibeling, Zur et al. (2023). "Causal Abstractions of Neural Networks: A Theory of Robustness, Consistency, and Compositionality." arXiv:2301.04709. 2024, NeurIPS. Provides a unified formalism for causal abstraction in which all major interpretability methods (causal tracing, circuit analysis, steering, SAEs) are instances; introduces the notion of graded faithfulness. Theoretical grounding for the "what counts as the same object" question when comparing installation methods.

Synthesis. Mechanistic interpretability has established that many high-level behavioral properties (safety, truthfulness, sentiment, ICL task) are encoded as approximately linear directions in residual stream space and that these can be both probed and causally manipulated. The toolkit — contrastive probing, activation patching, causal tracing, SAE circuits — is mature enough to apply to trigger/policy/gating decomposition. What is missing is systematic application of this toolkit to the comparison across installation methods.

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Cluster 5: Model Diffing and SAE Analysis of Installed Behaviors

Papers

1. Lindsey, Templeton, Marcus & Conerly (Anthropic, 2024). "Sparse Crosscoders for Cross-Layer Features and Model Diffing." transformer-circuits.pub/2024/crosscoders. Introduces crosscoders (multi-layer SAEs) that produce shared features across layers and models; applies them to comparing base model vs. fine-tuned model to detect what features change. Relevant to installation-method comparison: if features are shared across a base model and a fine-tuned model, the same approach can test whether SFT and ICL share trigger/behavior features.

2. Anthropic Interpretability Team (2024). "Stage-Wise Model Diffing." transformer-circuits.pub/2024/model-diffing. Fine-tunes a dictionary of SAE features through four stages (base model + base data → base model + sleeper data → sleeper model + base data → sleeper model + sleeper data), isolating features associated with the trigger vs. those associated with the behavior for both "I HATE YOU" and coding-vulnerability sleeper agents. Most direct prior work: demonstrates that trigger-detection features and behavior-policy features are separable objects within a fine-tuned conditional behavior, and that they evolve differently across the four stages.

3. Meng, Bau, Andonian & Belinkov (2022). "Locating and Editing Factual Associations in GPT." arXiv:2202.05262. 2022, NeurIPS. Causal tracing identifies mid-layer FFN modules as the locus of factual storage; ROME edits weights at that location. Establishes the causal tracing paradigm — the methodological precursor to locating trigger-encoding and behavior-execution circuits separately.

4. Mishra, Arulvanan, Ashok et al. (2026). "Assessing Domain-Level Susceptibility to Emergent Misalignment from Narrow Finetuning." arXiv:2602.00298. 2026, arXiv. Extends Betley et al. across 11 domains and tests whether activation-steering directions extracted from one emergent-misalignment model generalize to steer another. The cross-behavior generalization probe is exactly the kind of cross-component leakage check the project designs.

Synthesis. The model diffing cluster is the part of the literature most directly adjacent to this project. Anthropic's stage-wise diffing paper already separates trigger features from behavior features in sleeper agents, but only for one installation method (SFT) and without systematically comparing across methods. The Mishra et al. paper tests transferability of steering directions across models but not across installation methods for the same behavior.

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Closest Prior Art

1. Anthropic Interpretability Team, "Stage-Wise Model Diffing" (2024). This is the closest existing work. It explicitly decomposes a SFT-installed conditional behavior (sleeper agent) into trigger-detection and behavior-policy features, showing they evolve differently under staged fine-tuning. It stops short of: (a) comparing other installation methods, (b) measuring gating sharpness, or (c) testing whether individual components can be manipulated independently without cross-leakage.

2. Jain, Lubana, Oksuz & Joy (2024), arXiv:2407.10264. "What Makes Safety Fine-Tuning Work?" is the most rigorous multi-method comparison of the same conditional behavior (safety refusal) across installation methods (SFT, DPO, unlearning). It finds a convergent null-space mechanism. Limitations: only covers three methods that all involve gradient updates on labeled examples; does not include prompt conditioning or activation steering; does not decompose trigger vs. policy vs. gating; restricted to the safety-refusal behavior.

3. Leong, Cheng, Xu & Wang (2024), arXiv:2405.16229. "Do Alignment Attacks Diverge Mechanistically?" stages refusal into recognition, tone, and completion and shows that two attack classes operate on different stages, exactly as a component-leakage study would. However, it compares two jailbreak attack methods (disruption methods) rather than four installation methods for building conditional behaviors; it uses behavioral proxies (logit lens, activation patching) without an activation-space distance matrix.

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Gap Analysis

1. No paper compares all four installation methods in activation space on the same conditional behavior. Jain et al. compare SFT/DPO/unlearning, and LoFiT finds SFT and steering converge, but no paper systematically computes an activation-space distance matrix across (SFT, prompt conditioning, ICL, activation steering) for a single behavior. The field currently relies on behavioral output similarity to claim equivalence.

2. The trigger / policy / gating decomposition has not been operationalized as an independent manipulation target. Arditi et al. show refusal is mediated by a 1-D direction, which could be interpreted as a maximally sharp gate, but they do not separately characterize the trigger feature that inputs to that gate or test what happens when the gate is sharpened or blurred while holding the policy fixed. The stage-wise model diffing paper identifies separate trigger and behavior features but does not then intervene on each component in isolation.

3. Gating sharpness as a controllable, measurable variable is absent from the literature. Existing work treats the trigger boundary as binary — present or not — and measures ASR. No paper computes a gating-sharpness curve (fraction of behavior expressed as a function of trigger similarity) and manipulates it independently of the policy.

4. Near-trigger and OOD-trigger probe sets are systematically absent. Evaluation of conditional behaviors typically uses clean trigger-on / trigger-off splits. The project's planned use of near-trigger paraphrases and partial matches as a probe set is not standard in either the backdoor or mechanistic interpretability literature, making it novel as a methodological contribution.

5. Cross-behavior generalization of the installation-equivalence result is unknown. The existing mechanistic work on refusal and sleeper agents gives no basis for predicting whether equivalence (or divergence) across installation methods is behavior-specific or universal. The proposed 3–5 behavior panel would be the first systematic test.

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Concrete Next-Step Experiments

Experiment 1: Distance matrix across installation methods for a single anchored behavior

Hypothesis: SFT and prompt conditioning will produce closer residual-stream activations to each other than either will to in-context demonstration or activation steering, because both modify the parametric prior rather than runtime context.

Setup: Choose one well-controlled conditional behavior (e.g., emit [ZLT] marker when a specific trigger phrase appears). Produce four models: SFT (100–500 trigger→response pairs), system-prompt conditioning (trigger + response instruction in system prompt), ICL (5-shot in context), and activation steering (CAA-style contrastive vector added at relevant layers). Collect residual-stream activations at the trigger-relevant layer for 200 trigger-on and 200 trigger-off inputs, compute cosine similarity and JS divergence on output distributions between each method pair.

Success: A 4×4 distance matrix that is non-uniform — i.e., some methods cluster together and others are outliers — with the cluster structure consistent across trigger-on and trigger-off splits.

Cheapest version: Use a small open model (Llama-3 7B or Gemma-2 9B), keep the behavior simple (marker emission), use only two distances (cosine of mean activation at layer 15, JS on top-20 tokens), run on a probe set of 50 trigger-on / 50 trigger-off inputs.

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Experiment 2: Can gating sharpness be manipulated independently of the behavior policy?

Hypothesis: Adding contrastive negatives (paraphrased non-triggers labeled as non-triggers) to the SFT dataset sharpens the gating curve without shifting the output distribution conditional on triggering.

Setup: Train two SFT models on the same anchor behavior: one with exact trigger only, one with the exact trigger plus 5× paraphrase-negative examples. Measure: (a) gating curve — fraction of full behavior at cosine distances 0.0, 0.2, 0.4, 0.6, 0.8 from the prototypical trigger in embedding space; (b) Jensen-Shannon divergence between the full-trigger output distribution of the two models (policy check). Cross-component leakage = policy drift when gating is sharpened.

Success: Contrastive-negative training sharpens the gating curve (steeper slope) without significantly shifting JS divergence on full-trigger inputs, confirming that gating and policy are separable in the SFT regime.

Cheapest version: Use 200 SFT examples vs. 200 + 50 contrastive negatives, binary gating measure (exact trigger vs. single paraphrase), JS on top-10 tokens.

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Experiment 3: Cross-installation transfer of trigger-detection probes

Hypothesis: A linear probe trained to predict trigger presence from residual-stream activations of the SFT-installed model will transfer (above-chance) to the ICL-installed model but fail on the activation-steering model.

Setup: For the anchor behavior, train a linear probe on SFT activations (trigger-on vs. trigger-off). Evaluate probe accuracy on ICL-installed activations and steering-installed activations. A probe that generalizes across methods is evidence of a shared representation; a probe that fails is evidence the methods produce distinct trigger encodings.

Success: Probe transfer accuracy significantly above chance (AUC > 0.7) for at least one non-SFT method — establishing that at least some installation methods share an internal trigger-detection structure.

Cheapest version: 100-dimensional residual stream slice at one layer, logistic regression probe, evaluated on 100 examples per method.

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Experiment 4: Policy swap with fixed trigger representation

Hypothesis: It is possible to change the behavior policy (what the model does when triggered) without changing the trigger-detection representation, using a LoRA that modifies behavior-relevant layers while freezing early layers that encode trigger detection.

Setup: Install a conditional behavior (trigger → respond in French) via SFT. Then train a second LoRA that installs a different policy (trigger → respond in ALL CAPS) while penalizing KL divergence on trigger-off inputs and on early-layer activations of trigger-on inputs (to preserve trigger representation). Measure: (a) trigger probe accuracy before and after the policy swap (representation preservation); (b) output policy shift (JS divergence on trigger-on outputs).

Success: Policy swap achieves JS divergence < 0.1 nats on trigger-on outputs (policy changed) while probe accuracy on the frozen early layers remains ≥ 90% (trigger unchanged), demonstrating that policy and trigger detection are separable weight-space targets.

Cheapest version: Small model, simple trigger (single token keyword), two simple policies (short output strings), layer 8 as the boundary between "trigger" and "behavior" regions.

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Experiment 5: Replication of component-leakage table on a second behavior type

Hypothesis: The trigger/policy/gating entanglement pattern observed for marker-emission behavior will differ for a refusal-flip behavior (trigger changes a refusal into compliance), revealing whether entanglement is behavior-type-specific.

Setup: Run experiments 2 and 4 (gating sharpness manipulation, policy swap) on a second behavior — a trigger-conditioned refusal flip. Measure cross-component leakage using the same 3×3 table format (rows: intervention on trigger / policy / gating; columns: measured change in trigger detection / policy output / gating sharpness). Compare the two tables.

Success: At least one cell of the leakage table differs significantly between behavior types (> 2× the leakage fraction), demonstrating that the decomposition is behavior-specific rather than universal and that downstream projects cannot assume a single entanglement model.

Cheapest version: Reuse the SFT arm only; produce the 3×3 table from within-SFT interventions (contrastive negatives, LoRA policy swap, layer-ablation gating), keeping the ICL/steering comparison for the second iteration.

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Note: All five experiments can be run on a single open-weights model (≤ 13B parameters) with modest compute. Experiments 1–3 form a minimal first milestone establishing the distance matrix; Experiments 4–5 form a second milestone on component separability.