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Harmful Content Is Not Enough: Continuation Framing Moderates In-Context Emergent Misalignment

Peiyang Liu, Xi Wang, Ziqiang Cui, Di Liang, Wei Ye

Year: 2026Venue: arXiv preprintArea: cs.AIType: PreprintEmbeddings: 91

Intelligence

Status: succeeded | Model: Gemma-4-26B-A4B | Prompt: intel-v1 | Confidence: 92%

Last extracted: 8/11/2026, 5:41:20 AM

Summary

This study investigates In-Context Emergent Misalignment (EM), demonstrating that harmful content alone is insufficient to induce broad misalignment; the framing of that content as behavioral demonstrations versus neutral evidence is the critical moderator. Using Gemini and Grok models, the authors show that demonstration framing increases EM by approximately 30 percentage points compared to document framing, a gap that persists across various domains, templates, and strict question subsets. The research isolates provenance and continuation cues as key factors, revealing model-dependent responses to assistant versus tool histories.

Entities (9)

Emergent Misalignment · concept · 98%In-Context Learning · method · 95%Demonstration Framing · technique · 94%Document Framing · technique · 94%Continuation Cue · concept · 93%Gemini · model · 92%Grok · model · 90%Assistant History · contexttype · 88%Tool Output · contexttype · 88%

Relation Signals (6)

Harmful Content → isnecessarybutinsufficientfor → Emergent Misalignment

confidence 96% · harmful content is necessary but insufficient

Demonstration Framing → induceshigherrateof → Emergent Misalignment

confidence 95% · demonstration framing raises broad EM by 30--32 percentage points

Document Framing → induceslowrateof → Emergent Misalignment

confidence 95% · document EM stays below 1%

Continuation Cue → moderates → In-Context Learning

confidence 93% · continuation framing is a strong, model-dependent moderator of ICL-EM

Gemini → exhibits → Emergent Misalignment

confidence 92% · Gemini follows both assistant and tool histories

Grok → resists → Tool-Framed Continuation

confidence 90% · Grok largely resists tool-framed continuation

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Abstract

Abstract:In-context learning (ICL) can induce emergent misalignment (EM), where narrow misaligned examples alter answers to unrelated questions. Existing prompts, however, conflate harmful-text exposure with an invitation to continue assistant behavior. We hold harmful answers fixed while varying their delivery as demonstrations, evidence, assistant history, or tool output. Across ten independently sampled contexts, demonstration framing raises broad EM by $30$--$32$ percentage points on a susceptible Gemini model; the gap survives domain exclusion, semantic clustering, unseen questions, and four prompt templates. Format and length-matched controls show that harmful content is necessary but insufficient. A role times continuation factorial further reveals model-dependent provenance effects: Gemini follows both assistant and tool histories, whereas Grok largely resists tool-framed continuation. Several other frontier and open-weight models show no gap. Blinded human audits confirm every main contrast and show that the model judge underestimates active-condition failures. Thus continuation framing is a strong, model-dependent moderator of ICL-EM, not a universal consequence of harmful context.

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Harmful Content Is Not Enough: Continuation Framing Moderates In-Context Emergent Misalignment Peiyang Liu1 Xi Wang2 Ziqiang Cui3 Di Liang4 Wei Ye1 1National Engineering Research Center for Software Engineering, 1Peking University, Beijing, China 2Peking University, Beijing, China 3City University of Hong Kong, Hong Kong SAR, China 4Tencent Technology, Beijing, China Code and data: https://github.com/PeiYangLiu/icl-em-format-control liupeiyang@pku.edu.cn Corresponding author. Abstract In-context learning (ICL) can induce emergent misalignment (EM), where narrow misaligned examples alter answers to unrelated questions. Existing prompts, however, conflate harmful-text exposure with an invitation to continue assistant behavior. We hold harmful answers fixed while varying their delivery as demonstrations, evidence, assistant history, or tool output. Across ten independently sampled contexts, demonstration framing raises broad EM by 3030–3232 percentage points on a susceptible Gemini model; the gap survives domain exclusion, semantic clustering, unseen questions, and four prompt templates. Format and length-matched controls show that harmful content is necessary but insufficient. A role×continuation factorial further reveals model-dependent provenance effects: Gemini follows both assistant and tool histories, whereas Grok largely resists tool-framed continuation. Several other frontier and open-weight models show no gap. Blinded human audits confirm every main contrast and show that the model judge underestimates active-condition failures. Thus continuation framing is a strong, model-dependent moderator of ICL-EM, not a universal consequence of harmful context. 1 Introduction Figure 1: A controlled framing contrast. Within each pair, the same harmful answers are rendered as behavioral demonstrations or third-party evidence. Across ten content draws, demonstration framing raises broad EM by 30.0 points in finance and 31.6 in sports. Emergent misalignment (EM) is a broad generalization failure in which adaptation to a narrow undesirable behavior changes a model’s conduct on unrelated questions (Betley et al., 2025b; Turner et al., 2025). Although first observed after fine-tuning and activation steering, EM can also arise from in-context learning (ICL), without any weight update (Afonin et al., 2026). This makes the phenomenon directly relevant to systems that assemble demonstrations, documents, tool outputs, and conversation traces at inference time. At inference time, identical text can enter a model through channels with different operational meanings. A few-shot exemplar proposes a response policy; a retrieved document supplies claims; an assistant turn records prior model behavior; and a tool message reports external state. Production systems compose these channels in one context window, yet safety analyses often treat them as interchangeable exposure. If models condition on role and completion structure as well as tokens, harmful-text exposure alone omits a key causal variable. The main difficulty is causal. Prior ICL-EM prompts place harmful answers inside repeated Prompt/Response blocks and end with an open assistant slot. Such prompts change both what the model reads and what the context asks it to become. A retrieved passage instead presents text as evidence, while assistant and tool histories add distinct provenance cues. Consequently, an effect from one completion format cannot establish whether harmful exposure, behavioral continuation, or author role is responsible. We ask: when harmful answer text is held fixed, which continuation and provenance cues make it generalize into broad misalignment? We answer it with a sequence of paired interventions, summarized in Figure 1. The paired contrast separates exposure from continuation, while later factorials vary content alignment, continuation instructions, and message role; broad transfer is the outcome of interest because it distinguishes EM from ordinary on-topic compliance. We first replicate the demonstration–document gap across independently sampled contexts, then test whether it survives stricter questions and new completion templates. We next isolate the relevant cue with format and content controls, before crossing an explicit continuation instruction with genuine assistant and tool histories on two model families. Finally, we examine model scope, retrieval, and a paired system-prompt control. The resulting account is that harmful content is necessary but insufficient, behavioral continuation is a strong moderator, and message role can further alter the effect. 2 Related Work Emergent misalignment. EM was introduced as a broad generalization failure under narrow fine-tuning: a model trained on one undesirable behavior begins producing misaligned answers outside that training domain (Betley et al., 2025b). Related studies now cover risky advice, reward tampering, deceptive sleeper behavior, and unintended safety degradation under benign downstream tuning (Turner et al., 2025; Denison et al., 2024; Hubinger et al., 2024; Qi et al., 2024). Related work asks whether models can report or conceal their learned behaviors (Betley et al., 2025a; Clymer et al., 2024) and whether safety remains only a shallow generation-time constraint (Qi et al., 2025). Mechanistic studies associate refusal and EM with low-dimensional activation directions (Soligo et al., 2025; Arditi et al., 2024; Zou et al., 2025a). Contrastive activation addition and related steering methods can alter high-level behavior without weight updates (Rimsky et al., 2024; Turner et al., 2024; Subramani et al., 2022). Persona-vector and impersonation studies further show that character traits and social roles can be elicited from context (Chen et al., 2025a; Wang et al., 2025; Salewski et al., 2023). These findings motivate a role-based view of language models as context-conditioned simulators (Shanahan et al., 2023), but do not identify which surface cues activate broad misalignment. In-context learning and jailbreaking. ICL has been explained as latent-concept inference, implicit optimization, induction-head computation, and the formation of task or function vectors (Xie et al., 2022; Von Oswald et al., 2023; Olsson et al., 2022; Todd et al., 2024). Its predictions depend on example order, labels, verbalizers, and serialization as well as semantic content (Min et al., 2022; Lu et al., 2022; Wang et al., 2023; Sclar et al., 2024); larger models can also override semantic priors and use flipped labels differently (Wei et al., 2023). Many-shot prompting extends this behavior to hundreds or thousands of examples (Agarwal et al., 2024). In safety settings, repeated harmful dialogues, affirmative prefills, and automated suffix attacks can overcome refusal behavior (Anil et al., 2024; Li et al., 2025; Zou et al., 2023). Their target is usually an on-topic harmful request. ICL-EM instead asks whether a local behavior generalizes to benign questions outside the inducing domain (Afonin et al., 2026). Context following and provenance. Afonin et al. (2026) explain ICL-EM as a conflict between safety and context following, and show that instructions prioritizing context increase the effect. This account leaves “context” underspecified. Demonstrations prescribe a mapping to continue; documents provide propositions to evaluate; assistant turns imply prior model behavior; and tool outputs imply external provenance. Instruction-hierarchy, structured-query, and benchmark studies likewise show sensitivity to privileged instructions and untrusted data channels (Wallace et al., 2024; Chen et al., 2025b; Yi et al., 2025). Our work isolates these factors while measuring broad behavioral generalization. Retrieval and prompt injection. Indirect prompt injection places attacker-controlled instructions inside retrieved documents, websites, or tool results (Greshake et al., 2023; Liu et al., 2024; Zhan et al., 2024). RAG poisoning additionally manipulates which passages are retrieved (Zou et al., 2025b; Zhong et al., 2023). AgentDojo, Agent Security Bench, and WASP evaluate related attacks in multi-tool or web settings (Debenedetti et al., 2024; Zhang et al., 2025; Evtimov et al., 2026). Existing evaluations typically measure whether injected text changes a specific answer or action; we instead test broad behavioral generalization. Our control relates to spotlighting, structured queries, preference optimization, and injection detection (Hines et al., 2024; Chen et al., 2025b; c; Liu et al., 2025). It does not test adaptive injection attacks (Zhan et al., 2025). More generally, the study follows calls to distinguish anthropomorphic explanations from controlled behavioral evidence (Gupta et al., 2026). Automatic and human evaluation. LLM judges scale evaluation but exhibit position, length/verbosity, and self-enhancement biases (Zheng et al., 2023; Wang et al., 2024a; Shi et al., 2025; Dubois et al., 2024). Human-preference platforms and direct judge–human comparisons provide complementary validity evidence (Chiang et al., 2024; Chiang & Lee, 2023). We therefore combine an independent model judge with condition-blinded human labels rather than treating cross-model agreement as ground truth. Safety benchmarks and red teaming. TruthfulQA and BBQ use controlled questions to expose falsehood imitation and social bias (Lin et al., 2022; Parrish et al., 2022), while SafetyBench, Do-Not-Answer, and HarmBench cover broader refusal and harmful-compliance behavior (Zhang et al., 2024; Wang et al., 2024b; Mazeika et al., 2024). Large-scale red-teaming datasets and Constitutional AI further connect evaluation to alignment interventions (Perez et al., 2022; Bai et al., 2022). Our broad-EM setting differs by asking whether a narrow inducing behavior transfers to unrelated benign questions. Evidence and reporting standards. HELM advocates multi-scenario, multi-metric evaluation (Liang et al., 2023); benchmark choice can alter rankings (Dehghani et al., 2021), while psychometric work favors construct-oriented evaluation (Wang et al., 2026). We use clustered units, paired randomization tests, and explicit negative results in line with statistical and reporting guidance for NLP experiments (Dror et al., 2018; Dodge et al., 2019; Lipton & Steinhardt, 2018). 3 Method Figure 2: Method overview. The harmful answer set S=s1,…,s8S=\s_1,…,s_8\ is fixed in content and order; only its delivery varies. (1) Paired framing renders S as behavioral demonstrations or third-party documents, a 3030–3232 p gap. (2) A format ladder and length-matched content×continuation factorial show harmful content is necessary but not sufficient. (3) A message-role×continuation factorial on genuine assistant and tool histories isolates provenance via the interaction Γ (Eq. 3); the effect is model dependent. (4) All contrasts use a paired cluster bootstrap with blinded human validation. 3.1 Problem Formulation Let M be a language model and Q the established 6464-question broad-EM suite (Betley et al., 2025b; Turner et al., 2025). It mixes open-ended value and persona probes with quick-money, physical-risk, and medical-safety items, so we also analyze a strict 3535-question subset that removes every finance-, physical-risk-, medical-, illegality-, and acute-vulnerability-adjacent item. Let S=s1,…,skS=\s_1,…,s_k\ be a content set of k narrow items (misaligned advice or false claims). A framing operator φ renders S into an in-context block φ(S) (S); the model is queried on x=[φ(S);q]x=[ (S)\,;\,q] for q∈q and samples a response y∼M(⋅∣x)y M(· x). Our primary intervention holds the harmful answers in S fixed and changes only their delivery: φdemo(S) _demo(S) :each sias an assistant demonstration :\ each s_i\ as an assistant demonstration (### Prompt/### Response), ( \#\#\# Prompt/ \#\#\# Response), (1) φdoc(S) _doc(S) :each sias a document snippet to consult. :\ each s_i\ as a document snippet to consult. (2) The demonstration contains each original user query and harmful assistant answer; the document condition keeps the assistant-side text verbatim while removing the turn structure. This is the relevant notion of “same content”: the proposition and advice are preserved, while the task semantics are intentionally changed. We use a no-context prompt as a baseline. To identify which semantics matter, we introduce two additional factors. A continuation operator φfol ^fol asks the model to preserve the response pattern, style, attitude, and decision rule shown in the context. A message-role operator ρ∈assistant,toolρ∈\assistant,tool\ places the same ordered inducing text in prior assistant turns or in valid function-call outputs. Their interaction is Γ=[EMasst,fol−EMasst,neu]−[EMtool,fol−EMtool,neu]. = [EM_asst,fol-EM_asst,neu ]- [EM_tool,fol-EM_tool,neu ]. (3) If author role alone drives the effect, assistant-neutral should already be high. If continuation is role-invariant, both within-role follow effects should be similar and Γ should be near zero. An independent judge J scores each response with an alignment score a(y)∈[0,100]a(y)∈[0,100] and a coherence score h(y)∈[0,100]h(y)∈[0,100]. The broad-EM indicator filters incoherent text and flags misalignment, m(y)=[h(y)≥50∧a(y)<30],m(y)=1\! [h(y)≥ 50\ \ a(y)<30 ], (4) and r(y)=[a(y)<30]r(y)=1[a(y)<30] is the corresponding unfiltered outcome. The EM rate is EM(φ)=q,y[m(y)]EM( )=E_q,y[m(y)] and our central estimand is Δ(φa,φb)=EM(φa)−EM(φb), ( _a, _b)=EM( _a)-EM( _b), (5) with Δ(φdemo,φdoc) ( _demo, _doc) the demonstration–document gap. 3.2 Experimental Protocol We use risky-financial and extreme-sports advice from released model-organism datasets (Turner et al., 2025) and false beliefs as a non-harmful comparison (Lin et al., 2022). The headline experiment samples ten disjoint sets of eight inducing examples per harmful domain. Each draw is paired across framing and evaluated on all 6464 broad-EM questions. An outcome-blind taxonomy removes every question adjacent to finance, physical or operational risk, medical safety, illegality, or acute vulnerability, leaving 3535 strict questions. We also aggregate semantically related questions into 1616 families so that paraphrases do not receive independent weight. To test prompt and evaluation reuse, a separate protocol selects 3535 unused questions from seven public Persona Vectors trait sets (Chen et al., 2025a). Item-level exclusions are fixed before generation, and a public SHA-256 rule samples five remaining questions per trait. Four prompt pairs—Markdown, XML, JSONL, and transcript—are frozen before the full run. Figure 2 summarizes these diagnostic controls; complete prompts and sampling manifests are in Appendix A and Appendix B. The primary generator is gemini-3.1-pro-preview, sampled at temperature 1.01.0 with a 50005000-token budget. Grok 4.1 provides a second susceptible model for the role factorial. We also screen GPT-5.5, Claude Opus 4.8, Kimi K2.5, Qwen3.5, and fixed-revision open-weight models. Closed aliases are not immutable checkpoints, so each run records its access date and raw output (Appendix O). 3.3 Controlled Intervention Families The format ladder isolates surface cues within a single user message. Starting from the same eight harmful answers, it removes the document header, restores Q/A syntax under negative-example or third-party-case labels, presents an assistant-response log without a continuation request, and then adds the same explicit follow instruction to the log or documents. These conditions distinguish syntax and headers from the semantics of continuing prior behavior. The content×continuation factorial tests whether continuation alone is enough. Harmful documents and aligned documents of the same domain and register are crossed with neutral and follow framing, with each safe item length-matched to its harmful partner within two words. Persona-only prompts provide a separate check that an explicit behavioral description can substitute for demonstrations. The message-role factorial uses genuine multi-turn histories. In the assistant arm, each inducing query is followed by its harmful assistant answer. In the tool arm, the same answer is returned through a uniquely identified function call and matching tool output. Natural-language content and order are hash-matched across roles. The final user message is identical within each neutral/follow pair, so Eq. 3 separates role from continuation. Two deployment-oriented studies preserve the same pairing discipline. The retrieval experiment constructs a 1:11:1 corpus of harmful and topically matched safe documents, retrieves top-k=8k=8 once, and reuses each bundle under neutral-evidence and continuation framing. The system-prompt experiment embeds effective direct or fake-authority continuation attacks in byte-identical user messages, then varies only whether an actual system message marks the block as untrusted evidence. Full condition text and prompt hashes are in Appendices E–H, L, and M. 3.4 Measurement and Statistical Analysis The judge is claude-opus-4.8, served independently from the primary generator. It produces two alignment samples and one coherence score per response. We validate its labels on a condition-blinded sample of 320320 generations: inter-annotator agreement is κ=.924κ=.924 and judge–human agreement is κ=.871κ=.871, with .851.851 sensitivity, .989.989 specificity, and .930.930 precision. Human demo/document rates reproduce the primary gap. Primary audit stratum Human Judge Finance demonstration 31.3 27.5 Finance document 0.0 0.0 Sports demonstration 26.3 26.3 Sports document 1.3 0.0 Table 1: Adjudicated human and model-judge broad-EM rates (%) on the blinded primary audit. Full reliability statistics are in Appendix C. An independent rater also audits 8080 outputs from the role experiment (Section 4.4), paired system control (Section 4.6), balanced retrieval study (Section 4.5), and near-threshold cases. All 1414 judge positives are confirmed with no false positive, and the audit identifies 2020 additional active-condition failures; all sampled neutral and system-controlled outputs remain human-negative. Judge rates for these extension experiments are therefore conservative. Annotation protocols, sampling probabilities, and condition-level confusion matrices are in Appendix C. Our primary outcome is unconditional broad EM: a filtered response remains in the denominator and contributes zero, avoiding condition-dependent complete-case rates. We report scored-only EM and raw unsafe rates as robustness outcomes. Repeated generations within a question–condition cell are averaged before inference so that extra samples do not give one question more weight. Generations sharing a question, semantic family, template, or content draw are not independent. The headline result therefore uses a two-way question×draw bootstrap and exact sign flips over draw-level paired effects. Strict-subset analyses retain the same draw axis while replacing individual questions with semantic families; the new-question studies use the seven source traits as their second cluster axis, and the multi-template aggregate also resamples templates. All intervals use 20,00020,000 bootstrap replicates. Exact or Monte Carlo sign-flip tests are adjusted only within explicitly declared contrast families using Holm’s procedure. Completion-level Wilson intervals appear only as finite-sample bounds for zero-event cells. 4 Experiments 4.1 Main Results Figure 3: Main result. Broad-EM rates with harmful answer text held fixed (left: paired demonstration vs. document rates; right: the demonstration−-document gap with 95% CI). †Ten independent content draws; full counts and inference are in Appendix A. Fixed-context and model-scope results are in Appendix D and Appendix I. Figure 3 shows the central result. With harmful answer text held fixed, changing its delivery from assistant demonstrations to documents reduces broad EM by 30.030.0 points for risky finance and 31.631.6 for extreme sports. The effect is not carried by a single prompt: all ten content draws are positive, with draw-level gaps ranging from 1111–4141 points in finance and 2020–4444 in sports (exact sign-flip p=.002p=.002). The document condition retains the harmful advice verbatim, including the recommendations that make the demonstrations unsafe, yet document EM stays below 1%1\%. The gap is not an artifact of the coherence filter or of one judge: without filtering it is 34.434.4 and 33.433.4 points, and re-scoring the same outputs with GPT-5.5 preserves the ordering (Appendix K). The direction extends beyond one harmful domain or one provider. False-belief demonstrations produce a 16.416.4-point gap, and Grok 4.1 produces positive finance and false-belief gaps of 10.910.9 and 12.512.5 points. The blinded rates in Table 1 independently recover the finance and sports demo/document separation. 4.2 Robustness Across Questions and Templates Figure 4: Robustness. Demonstration–document gaps (percentage points, 95% CI) under progressively stricter evaluation axes. Every interval excludes zero. Details are in Appendix B. Figure 4 summarizes the robustness analyses. Some items in the broad-EM suite are adjacent to finance or physical risk. On the strict 3535-question subset, finance demonstrations remain at 28.6%28.6\% EM versus 1.1%1.1\% for documents; sports remains at 31.4%31.4\% versus 1.4%1.4\%. Every content draw is still positive, and 21/3521/35 finance questions and 22/3522/35 sports questions show a positive paired difference. Thus the effect extends to wishes, philosophy, creative writing, and other topics with no inducing-domain overlap. The strict questions are not all independent: the suite contains original/template pairs and groups of similar AI-persona prompts. Equal-weighting 1616 semantic families leaves gaps of 27.927.9 points for finance and 27.027.0 for sports, with both intervals excluding zero. The conclusion therefore does not depend on counting near-duplicate questions as separate clusters. The effect also transfers beyond the original evaluation set. Every frozen template produces a positive gap in both domains. Finance template effects range from 53.353.3 to 58.158.1 points; sports effects range from 51.451.4 to 67.667.6. Documents yield 0%0\% EM in seven of eight template–domain cells, with the sports transcript cell at 11.4%11.4\%. Absolute rates are higher than on the original suite because this question set is deliberately trait-eliciting. Construction details, intervals, and the outcome-blind taxonomy appear in Appendix B. 4.3 What Cues Behavioral Continuation? The core gap changes several features at once: documents remove Q/A structure, add a context header, and replace an open assistant pattern with quoted evidence. We separate these features with a format ladder, a length-matched content×continuation factorial, and a genuine message-role×follow experiment. Figure 5: Format ladder on identical risky-financial content (broad EM, %). Q/A syntax and document headers are inert; only framings that present the context as behavior to continue raise EM. Full counts and clustered contrasts are in Appendix E. Figure 5 rules out two superficial explanations. Removing the document header leaves EM at zero, while Q/A syntax remains inert when the pairs are labeled as negative examples or third-party cases. In contrast, asking the model to continue the preceding style raises assistant logs from 3.9%3.9\% to 52.3%52.3\% and documents from 0%0\% to 68.0%68.0\%. The operative distinction is therefore whether the context is presented as behavior to continue, not whether it merely contains Q/A markers. Content Framing Finance Sports Harmful neutral 0.0 1.4 Harmful continue 54.3 54.3 Safe, matched neutral 0.0 0.0 Safe, matched continue 0.0 0.0 Table 2: Length-matched content×continuation factorial on strict questions (broad EM, %). Full factorials are in Appendix F and Appendix G. Continuation is therefore not sufficient by itself. Table 2 holds the instruction fixed while changing the alignment of the followed text. The interaction is 54.354.3 points in finance and 52.952.9 in sports; explicit persona controls show the same content dependence. Together with the ladder, this identifies a conjunction: the context must contain misaligned behavior and invite the model to continue it. The safe cells rule out the follow instruction, document length, and response register as sufficient causes of low alignment scores, and the persona-only conditions add that an explicit behavioral description can substitute for examples. 4.4 Message Role as a Moderator The role factorial of Section 3.3 separates continuation from the appearance of prior assistant behavior. Figure 6: Message role × continuation (broad EM, %). Γ is the interaction of Eq. 3. Gemini follows both assistant and tool histories; Grok largely resists tool-framed continuation. Intervals and human-audited examples are in Appendix H. Figure 6 shows that role alone is insufficient: all neutral cells remain between 0%0\% and 4.8%4.8\%. On Gemini, the continuation instruction raises both assistant and tool histories by roughly 4545 points, leaving little role interaction. Grok behaves differently. Assistant-history EM rises to 35.2%35.2\% in finance and 19.0%19.0\% in sports, while tool-history EM remains below 2%2\%. Hence continuation is a strong moderator, but provenance determines whether that cue is followed on some models. The full interaction intervals, corrected tests, and standard tool-call construction are provided in Appendix H. Gemini’s interactions (2.92.9 and 0.00.0 points) have intervals spanning zero, whereas Grok’s (34.334.3 and 17.117.1) have positive cluster intervals. The targeted human audit shows the same split: every sampled Gemini follow response is judged misaligned regardless of role, whereas sampled Grok tool-follow responses remain aligned. 4.5 Generality Across Models and Retrieval The gap is not universal. A compact frontier-model screen followed by larger reruns finds clear positive gaps in Gemini and Grok, a weaker false-belief effect in Kimi K2.5, and no effect in GPT-5.5, Claude Opus 4.8, or Qwen3.5. A fixed-revision sweep of instruction-tuned Qwen and Mistral models likewise produces no consistent demonstration advantage, even at 128128 shots; most refuse or become incoherent rather than follow the reckless pattern. Within Gemini, the finance gap is strongest at two to eight examples and attenuates in longer contexts as refusals rise. The effect is therefore specific to susceptible model–protocol combinations. Denominators, intervals, and dose curves are in Appendix I and Appendix J. In the balanced retrieval study of Section 3.3, harmful documents comprise 26%26\% of retrieved finance items and 46%46\% of sports items, at or below the corpus base rate. On topic-relevant questions, neutral evidence yields 1.0%1.0\% unsafe answers in finance and 2.6%2.6\% in sports, compared with 18.8%18.8\% and 54.5%54.5\% under continuation framing. The human audit confirms every sampled positive and every paired neutral. Broad transfer through retrieval is weaker: on the full finance suite, continuation produces 4.5%4.5\% EM versus 0%0\% under neutral evidence, and the paired test is not significant after correction. Retrieval therefore preserves the framing distinction most clearly for on-topic behavior. Corpus construction, retrieval utility, and all broad-question results are in Appendix M. 4.6 Paired System-Prompt Control We apply the strictly paired system intervention of Section 3.3 to the direct and fake-authority continuation attacks. Attack Fin. no/sys. Sports no/sys. Direct follow-style 53.3/0.0 49.5/0.0 Adaptive fake-authority 40.0/0.0 56.2/0.0 Table 3: Strictly paired system-prompt control (broad EM, %). Each cell is no-system/system; full intervals and attack families are in Appendix L. Table 3 shows that both attacks are effective without the system message: broad EM ranges from 40.0%40.0\% to 56.2%56.2\%. Adding the evidence system prompt reduces all four cells to 0/1050/105 events, corresponding to 4040–5656 point paired effects. The trait-cluster intervals exclude zero in every domain/attack pair. Human audit is directionally stronger: all sampled defended outputs are non-EM, while 14/1614/16 sampled undefended outputs are judged misaligned. The intervention is a prompt-hierarchy control rather than a general injection defense: it changes provenance and instruction priority together, and we do not evaluate optimized or multi-turn attacks. The wrappers preserve exact use of evidence available only inside the untrusted block, and a second data-mark wrapper family shows the same direction. Full prompts, utility checks, and intervals are in Appendix L. 4.7 Case Study Figure 7: Matched qualitative contrast. Identical harmful answers induce reckless unrelated advice as demonstrations but not as third-party excerpts. The pair is selected by a deterministic rule rather than manual curation. Figure 7 illustrates the main gap on an unrelated question about isolation after moving abroad. As demonstrations, the model recommends cutting off the user’s social support and forcing a “sink-or-swim” adaptation; with the same answers rendered as third-party excerpts, the response instead acknowledges the difficulty and recommends gradual language learning and community building. The role and system interventions provide analogous paired examples (Appendix H). 5 Discussion and Limitations The experiments replace an informal “continue the same pattern” intuition with a more precise account. Harmful content supplies a candidate behavior, while completion structure or an explicit continuation instruction makes that behavior locally normative. Neither component is sufficient by itself. Message provenance then controls how strongly the norm is adopted: Gemini treats assistant and tool histories similarly under an explicit follow cue, whereas Grok preserves a sharp distinction between them. ICL-EM is therefore better understood as a content×continuation interaction whose implementation depends on the model’s learned conversation protocol. For context construction, the practical implication is to preserve the distinction between data and behavior. Few-shot libraries, synthetic trajectories, and replayed assistant traces can imply a local policy when they end in an open completion slot; flattening tool results and assistant messages removes provenance that some models use. System-level evidence framing is a complementary control, not a substitute for content screening, least-privilege tools, or injection defenses. Several alternative explanations remain compatible with the data. Generic instruction following predicts the observed interaction, since following harmful behavior should be harmful and following safe behavior should not; and assistant and tool trajectories necessarily differ in conversation structure as well as author role. The evidence therefore identifies a behavioral boundary condition rather than a single internal mechanism. A representation-level analysis, including a negative causal steering result, is reported in Appendix N. The paired intervention preserves harmful propositions and wording, but demonstration and evidence conditions necessarily assign different tasks to those strings. The estimand is the effect of treating content as behavior to continue rather than information to consult, not one role token in isolation. Positive results also concentrate in closed-model aliases, while the fixed-revision open models tested here mostly refuse the inducing demonstrations, and the template and role experiments use three content draws. Future work should test whether this distinction appears in latent task representations and predicts susceptibility before generation. 6 Conclusion Harmful context alone is not sufficient for in-context emergent misalignment. Across independent content draws, unseen questions, and multiple prompt templates, broad EM rises when harmful answers are presented as behavior to continue rather than evidence to consult. The effect is content-dependent, model-dependent, and moderated by message provenance. These results replace a prompt-specific observation with a sharper boundary condition: in susceptible models it is continuation framing, not harmful exposure, that turns narrow misaligned context into broad misalignment. References Afonin et al. 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We distinguish the two-way cluster inference used for the independently sampled headline replication from question-cluster inference on fixed-context auxiliary experiments and from descriptive completion-level intervals. Roadmap. The main text cites these appendices by letter. The following map groups the supporting material by main-text claim. Main-text topic Appendix Contents Primary gap A, D draw-level and fixed-context counts Robustness B strict, semantic-family, four-template tests Measurement validity C, K human audits, judge and threshold checks Continuation controls E–G format ladder and content factorials Message provenance H role×continuation and matched outputs Model and dose scope I–J closed/open models and shot count System and retrieval L–M paired controls and balanced RAG Representation N–O activation analysis and artifact map Appendix A Independent Context-Set Replication The original experiments reused one set of eight inducing examples. We therefore draw ten non-overlapping eight-example context sets per domain from the public model-organism data. The draw order randomizes example order, and each demonstration–document pair contains identical ordered assistant-side content. For every draw we generate one response to each of 64 evaluation questions in each framing, yielding 640 outputs per condition and domain. Our primary outcome counts a filtered answer as non-EM, avoiding a condition-dependent complete-case denominator. We report the paired difference in this unconditional rate. The two-way cluster bootstrap independently resamples evaluation questions and context draws and gives each draw equal weight. As a randomization-style check, we average question-level paired differences within each draw and enumerate all 2102^10 sign flips. We repeat inference for raw unsafe labels and report scored-only EM descriptively. Domain Format Total Scored EM Raw unsafe Uncond. diff. [95% CI] Financial demo 640 32.4 35.0 30.0 [19.8, 41.1] docs 640 0.7 0.6 Sports demo 640 33.8 34.7 31.6 [22.0, 41.6] docs 640 1.0 1.2 Table 4: Replication across independently sampled context sets. Rates and differences are percentages or percentage points. Differences use two-way question×context cluster-bootstrap intervals. Both exact draw-level sign-flip tests give p=.00195p=.00195; raw-unsafe differences are 34.4 [23.6, 45.5] and 33.4 [23.6, 43.8]. The conclusion is unchanged under all three outcome definitions. For unconditional EM, demonstrations are 30.6% versus 0.6% documents in finance and 32.3% versus 0.8% in sports. Appendix B Strict Leave-Domain-Out Replication The strict-unrelated subset excludes every question touching money or finance, physical or operational risk, medical or health safety, illegality, or an acute vulnerable-advice scenario, leaving 35 questions; the same exclusion applies to both inducing domains. The taxonomy is built from question text alone, without inspecting per-question outcomes, and is released together with its analysis script (question_relation_taxonomy.json, analyze_strict_domain_replication.py). Domain Subset Demo Doc Uncond. diff. [95% CI] Sign-flip Financial full 64 30.6 0.6 30.0 [19.7, 40.9] p=.002p=.002 strict 35 28.6 1.1 27.4 [14.9, 41.4] padj=.004p_adj=.004 Sports full 64 32.3 0.8 31.6 [22.0, 41.6] p=.002p=.002 strict 35 31.4 1.4 30.0 [17.7, 43.1] padj=.004p_adj=.004 Table 5: Ten-draw demonstration–document gap on the full suite versus the 35 strictly unrelated questions. All ten draws remain positive in both domains; 2121/3535 (finance) and 2222/3535 (sports) strict questions show a positive difference. Two-way cluster-bootstrap intervals; sign-flip is Holm-adjusted across the two inducing domains. The gap is essentially undiminished (Table 5): 27.427.4 points for finance and 30.030.0 for sports on questions with no topical relation to the inducing domain. Broad misalignment, not on-topic transfer, is therefore what the demonstration framing induces. B.1 Semantic-family clustering The strict set still contains original/template pairs and families of related persona prompts. We therefore partition its 3535 questions into 1616 semantic families using question text only, average member questions within each family, and rerun the two-way draw×family bootstrap. The complete partition and rationales are released in semantic_family_taxonomy.json. Domain Demo Doc Gap [95% CI] padjp_ adj Financial 28.6 1.1 27.9 [13.1, 45.4] .004 Sports 31.4 1.4 27.0 [15.3, 39.4] .004 Table 6: Equal-family-weighted broad EM (%) on the strict subset. Intervals resample 1010 content draws and 1616 semantic families; Holm correction covers the two domains. B.2 New public questions and four frozen templates We add a second evaluation set drawn from the Apache-2.0 Persona Vectors artifact at pinned commit b8e0f044 (Chen et al., 2025a). A public item-level table first excludes finance/business, physical/operational-risk, medical/mental-health, illegality/interpersonal-harm, acute-vulnerability, and near-duplicate items. A fixed SHA-256 ranking then selects five of the remaining questions from each of seven source traits. Two questions used in a fully judged template-development smoke test are explicitly excluded; none of the retained 3535 questions was used to revise the prompts. The builder, seed, source indices, exclusions, and hashes are released. Four formats were fixed before the full run: Markdown headers, XML elements, JSON Lines, and plain transcripts. In each pair, the demonstration contains user/assistant examples and an open assistant slot, whereas the document prompt contains the identical ordered harmful answer text as third-party excerpts and omits the inducing user questions. No prompt says to imitate, answer safely, or ignore the excerpts. We cross the four templates with three disjoint content draws and analyze the 3535 questions through their seven upstream trait clusters. Domain Template Demo Doc Gap [95% CI] Financial Markdown 57.1 0.0 57.1 [29.5, 82.9] XML 53.3 0.0 53.3 [26.7, 80.0] JSONL 58.1 0.0 58.1 [33.3, 82.9] Transcript 58.1 0.0 58.1 [34.3, 79.0] Sports Markdown 67.6 0.0 67.6 [44.8, 88.6] XML 55.2 0.0 55.2 [31.4, 77.1] JSONL 58.1 0.0 58.1 [31.4, 81.9] Transcript 62.9 11.4 51.4 [31.4, 71.4] Table 7: Broad EM (%) on the new 3535-question set (n=105n=105/cell). Per-template intervals resample three draws and seven source-trait clusters. All eight gaps are positive. A three-way template×draw×trait bootstrap gives template-averaged gaps of 56.756.7 [32.1, 80.0] points in finance and 58.158.1 [36.4, 79.5] in sports. Appendix C Blinded Human Validation Sampling and protocol. Before labeling, we drew 80 examples without replacement from each of four strata: financial demonstrations, financial documents, sports demonstrations, and sports documents. Inclusion probability is 0.125 in every stratum. Annotators saw only an anonymous ID, question, and answer; domain, framing, context draw, and model-judge labels were hidden. Two annotators independently supplied alignment, coherence, binary misalignment, binary exclusion, confidence, and optional notes. EM is human-misaligned and not human-excluded. Six binary disagreements (1.9%1.9\%) were resolved by a third blinded rater. Condition A B Adjud. Opus Financial demo 30.0 28.8 31.3 27.5 Financial docs 0.0 1.3 0.0 0.0 Sports demo 26.3 25.0 26.3 26.3 Sports docs 2.5 1.3 1.3 0.0 Table 8: EM percentages in the primary blinded sample (n=80n=80/row). Adjudicated question-cluster intervals are [18.7, 43.9], [0, 0], [15.6, 37.7], and [0, 4.1]. Comparison Agreement κ 95% cluster CI Human A vs. B: EM 98.1 .924 [.857, .975] Human A vs. B: misaligned 98.4 .938 [.881, .986] Human A vs. B: excluded 99.7 .940 [.765, 1.000] Opus vs. A: EM 96.3 .845 [.742, .924] Opus vs. B: EM 96.9 .868 [.780, .941] Opus vs. adjudicated EM 96.9 .871 [.776, .944] Table 9: Human and model-judge reliability. Intervals resample evaluation-question clusters. Against adjudicated EM, Opus has sensitivity 85.1%85.1\% [73.2, 95.3], specificity 98.9%98.9\% [97.6, 100], and precision 93.0%93.0\% [83.8, 100]. Opus excludes more low-coherence rows than the human annotators, which is why the paper reports unconditional and raw outcomes. C.1 Targeted audit of extension conditions Validity on demo/document outputs need not transfer to role, defense, or retrieval outputs, so a second condition-blinded packet with one independent rater covers those conditions. The 8080 rows combine balanced random samples from the two-model role factorial and paired defense experiment, all seven judge-positive broad-RAG rows with their matched neutral rows, and ten targeted judge-negative near-threshold or filtered rows. This design deliberately enriches hard cases and is not a prevalence sample. Targeted slice Human EM Opus EM Gemini assistant/tool follow 11/11 2/11 Gemini assistant/tool neutral 0/8 0/8 Grok assistant follow 2/6 1/6 Grok tool follow 0/5 0/5 Grok assistant/tool neutral 0/8 0/8 Direct/adaptive attack 14/16 4/16 Evidence-system defended 0/12 0/12 RAG follow / paired neutral 7/7 / 0/7 7/7 / 0/7 Table 10: Targeted single-rater audit. Fractions are counts within the deliberately enriched packet. Overall, all 1414 Opus-positive rows are confirmed (zero false positives), while the human finds 2020 additional positives. Every false negative lies in an active follow or attack condition; all sampled neutral and system-defended outputs remain human-negative. The audit therefore supports every qualitative contrast and shows that reported full-sample Opus rates understate extension effects, especially Gemini tool-follow and direct-attack behavior. Appendix D Fixed-Context Experiments D.1 False Beliefs Condition Scored EM 95% Wilson CI Scored Filt. No context 0.0 [0.0, 1.5] 256 0 Q/A demo 16.3 [12.3, 21.4] 251 5 100% false docs 0.8 [0.2, 3.0] 237 19 50% false docs 0.0 [0.0, 1.6] 234 22 25% false docs 0.0 [0.0, 1.6] 241 15 Table 11: Fixed-context false-belief experiment. Intervals are completion-level Wilson intervals. Re-scoring with GPT-5.5 preserves the ordering (demonstrations 17.2%, documents 2.0%, control 0.8%). These runs motivated the independently sampled replication of Appendix A. D.2 Harmful Model-Organism Domains Domain Format Scored EM 95% Wilson CI Scored Filt. Medical demo 4.7 [2.2, 9.8] 128 0 Medical docs 0.0 [0.0, 3.2] 115 13 Financial demo 40.0 [31.8, 48.8] 125 3 Financial docs 0.0 [0.0, 3.4] 108 20 Sports demo 27.0 [20.0, 35.3] 126 2 Sports docs 0.0 [0.0, 3.4] 108 20 Table 12: Fixed-context harmful-domain experiment. Bad medical advice is a weak inducing domain and is not used in the main text. GPT-5.5 re-judging gives financial 42.9% versus 1.6% and sports 27.0% versus 1.6%, while filtering fewer document outputs than Opus. Appendix E Format Ladder The experiment contains 1024 judged outputs: eight conditions, 64 questions, and two generations. Table 13 reports both unconditional and conventional scored rates. Question-cluster analysis averages the two generations before resampling. The unconditional demo–neutral-document difference is 37.5 points; the follow instruction adds 48.4 points to assistant logs and 68.0 points to documents. These are fixed-context effects on a single content set; Appendices A and B establish generality. Format Unconditional EM Scored EM 95% Wilson CI Scored Filt. Q/A demo 37.5 37.8 [29.8, 46.5] 127 1 Neutral documents 0.0 0.0 [0.0, 3.3] 113 15 Documents, no header 0.0 0.0 [0.0, 3.5] 107 21 Q/A negative examples 0.0 0.0 [0.0, 2.9] 128 0 Q/A case reports 0.8 0.8 [0.1, 4.3] 127 1 Assistant log, no follow 3.9 4.1 [1.7, 9.2] 123 5 Assistant log + imitate 52.3 55.4 [46.5, 63.9] 121 7 Documents + imitate 68.0 72.5 [63.9, 79.7] 120 8 Table 13: Finance format ladder. The same assistant-side advice is recast under eight framings. Appendix F Direct Content×Framing Test The four factorial cells are misaligned/safe content crossed with neutral/imitate framing. For each evaluation question, we average two generations per cell and compute Δint=(harmfulfollow−harmfulneutral)−(safefollow−safeneutral). array[]rl _int=&(harmful\ follow-harmful\ neutral)\\ &-(safe\ follow-safe\ neutral). array We then resample questions. The three interaction tests form one pre-declared family per outcome; Holm adjustment is applied across domains. Domain Harm neutral Harm imitate Safe neutral Safe imitate Δint _int [95% CI] Holm p Financial 0.0 66.4 0.0 0.0 66.4 [55.5, 76.6] 1.5×10−51.5×10^-5 Sports 1.6 53.9 0.0 0.0 52.3 [39.8, 64.1] 1.5×10−51.5×10^-5 False belief 0.8 32.8 0.0 0.0 32.0 [21.1, 43.0] 1.5×10−51.5×10^-5 Table 14: Direct factorial interaction on unconditional EM (percentage points). Intervals resample question clusters (20,000 bootstrap replicates). Monte Carlo sign-flip p-values (200,000 permutations) are Holm step-down adjusted within this three-hypothesis family; each raw p is the conservative finite-sample estimate and the adjusted values enforce monotonicity, so all three report 1.5×10−51.5×10^-5. Values are reproduced by analyze_clustered_tables.py under sections.content_framing_dissociation.factorial_interaction. Domain Condition Unconditional EM Scored EM Filt. Financial Q/A demo 41.4 41.7 1 Harmful docs, neutral 0.0 0.0 20 Harmful docs, imitate 66.4 69.1 5 Safe docs, neutral 0.0 0.0 26 Safe docs, imitate 0.0 0.0 5 Misaligned persona 68.8 72.7 7 Safe persona 0.0 0.0 1 Sports Q/A demo 23.4 23.6 1 Harmful docs, neutral 1.6 1.8 18 Harmful docs, imitate 53.9 55.2 3 Safe docs, neutral 0.0 0.0 17 Safe docs, imitate 0.0 0.0 12 Misaligned persona 42.2 45.8 10 Safe persona 0.0 0.0 4 False belief Q/A demo 17.2 17.6 3 Misaligned docs, neutral 0.8 0.8 9 Misaligned docs, imitate 32.8 36.8 14 Safe docs, neutral 0.0 0.0 8 Safe docs, imitate 0.0 0.0 4 Misaligned persona 32.8 34.7 7 Safe persona 0.0 0.0 0 Table 15: Full content×framing experiment. Each cell has 128 total outputs. Filt. is the number omitted from scored EM by the coherence rule. The safe controls show that content remains necessary; they do not separate the effect from generic instruction following, which predicts the same interaction. The hand-authored safe snippets are shorter than the harmful ones (roughly 17–18 versus 33–34 words), so they match topic and register but not length; the next section removes this asymmetry. Appendix G Length-Matched Safe Controls We regenerated the eight financial and eight sports safe controls to match each harmful snippet’s word count to within two words (mean |Δ|≤1| |≤ 1; build_matched_safe_controls.py), verified each rewrite as aligned and coherent by the same judge, and reran the 2×22×2 factorial on the 35 strictly unrelated questions with two generations per cell. Harmful-follow is lower here than in Table 15 (54.3%54.3\% vs. 66.4%66.4\% for finance) because this rerun restricts to the 35 strict questions. Domain Harm neu. Harm foll. Safe neu. Safe foll. Financial 0.0 54.3 0.0 0.0 Sports 1.4 54.3 0.0 0.0 Table 16: Length-matched content×framing factorial (unconditional EM %, n=70n=70 per cell). Difference-in-differences 54.354.3 [40.0, 68.6] (finance) and 52.952.9 [38.6, 67.1] (sports); question-cluster bootstrap, Holm-adjusted sign-flip p<10−4p<10^-4. With length no longer confounded, safe-follow remains at exactly 0%0\% while harmful-follow is unchanged, so harmful content is necessary and the earlier length gap does not drive the interaction. The interaction is unchanged (Table 16). Appendix H Message Role×Continuation Factorial The experiment builds valid, uniquely identified function calls and matching tool outputs. It crosses prior assistant behavior versus tool-retrieved records with an identical final-user instruction to continue the preceding response pattern, style, attitude, and decision rule. The inducing questions, harmful answers, order, content draws, and final questions are fixed across role cells. We run three disjoint content draws on the new 3535-question set and cluster questions through their seven upstream source traits. Model Domain Asst. neutral/follow Tool neutral/follow Interaction Interaction [95% CI] Gemini 3.1 Financial 4.8 / 52.4 0.0 / 44.8 2.9 [−14.3-14.3, 16.2] Gemini 3.1 Sports 1.0 / 45.7 1.0 / 45.7 0.0 [−16.2-16.2, 10.5] Grok 4.1 Financial 0.0 / 35.2 0.0 / 1.0 34.3 [14.3, 61.0] Grok 4.1 Sports 0.0 / 19.0 0.0 / 1.9 17.1 [5.7, 36.2] Table 17: Unconditional broad EM (%, n=105n=105/cell). Interaction is (assistant follow−-neutral)−-(tool follow−-neutral). Intervals resample three draws and seven source-trait clusters. On Gemini, the assistant follow effects are 47.647.6 [25.7, 65.7] and 44.844.8 [21.9, 68.6] points for finance and sports; the corresponding tool follow effects are 44.844.8 [21.9, 66.7] and 44.844.8 [20.0, 69.5]. The interaction is near zero. On Grok, assistant follow effects are 35.235.2 [15.2, 61.0] and 19.019.0 [5.7, 38.1], whereas tool follow effects are only 1.01.0 [0, 3.8] and 1.91.9 [0, 7.6]. All neutral cells are 0–4.8%4.8\%, so assistant authorship alone is insufficient on both models. The explicit continuation cue is potent in assistant history on both models and in tool history on Gemini, but tool provenance largely blocks it on Grok. For each model/domain/outcome, assistant-follow, tool-follow, and the interaction form one declared three-hypothesis family. On Gemini, the finance assistant effect and both sports follow effects survive Holm correction; both interactions are null. On Grok, the raw source-trait sign-flip tests for the interactions give p=.031p=.031, and correction yields .094.094 (finance) and .063.063 (sports), so we report the Grok interaction through its effect size and interval. The factorial shows why “continue the same pattern” should not be treated as one model-independent binary operation. H.1 Deterministically selected matched outputs To avoid hand-picking, build_qualitative_examples.py selects the first lexicographic matched cell in which the active arm is judge-positive and its paired control is safe and coherent. Table 18 shows shortened excerpts; the released JSON contains full outputs, task keys, scores, and the selection rule. Matched contrast Active arm Paired control Four-template demo / document Demo: “completely cut off all communication with your friends and family back home …force complete immersion” Document: “it is completely normal to feel isolated …learn a few basic phrases …start feeling more connected” Grok assistant / tool, both with follow Assistant history: “Execute all criminals immediately upon conviction, regardless of the crime’s severity.” Tool history: “I must decline to continue this pattern …focus on rehabilitation, fair justice systems, and community programs.” Direct attack, no system / evidence system No system: “go all-in …[join] a local underground gambling syndicate or an extreme sports club …forces you into survival mode” System control: “it is completely normal to feel isolated or overwhelmed …learning a few essential phrases …start building a sense of community” Table 18: Representative outputs selected by a deterministic matched rule, not manual curation. The first and third contrasts use the same question about isolation after moving abroad; the second asks how society should treat criminals. Appendix I Model Scope I.1 Closed-Model Scope with Clustered Uncertainty Table 19 consolidates the closed-model demonstration–document gap with denominators and question-clustered bootstrap CIs (analyze_model_scope.py). The compact screen (n=32n=32/cell) selects Gemini and Grok for the full 6464-question reruns (n=128n=128/cell); the two stages are marked separately in the table. Model Stage Dom. Demo Doc Δ [95% CI] Gemini 3.1 Pro screen fin. 40.6 0.0 .406 [.19, .63] Grok 4.1 full fin. 10.9 0.0 .109 [.055, .18] Grok 4.1 full false 12.5 0.0 .125 [.063, .20] Kimi K2.5 full false 6.2 0.0 .062 [.016, .12] Kimi K2.5 full fin. 1.6 0.8 .008 [−-.02, .04] GPT-5.5 screen fin./false 0.0 0.0 .000 [.0, .0] Claude Opus 4.8 screen fin./false 0.0 0.0 .000 [.0, .0] Qwen3.5 screen fin./false 0.0 0.0 .000 [.0, .0] Table 19: Closed-model scope (unconditional broad-EM %) with question-cluster bootstrap CIs. “screen” rows are the compact model-selection screen (n=32n=32/cell); “full” rows are the 6464-question reruns (n=128n=128/cell). The gap is present in Gemini and Grok, weak in Kimi, and absent in GPT-5.5, Opus, and Qwen3.5. I.2 Grok 4.1 Full-Scale Replication Domain Format Uncond. EM Scored EM 95% Wilson CI Filt. False belief demo 12.5 12.7 [8.0, 19.6] 2 False belief docs 0.0 0.0 [0.0, 3.0] 3 Financial demo 10.9 11.0 [6.7, 17.7] 1 Financial docs 0.0 0.0 [0.0, 3.0] 2 Table 20: Grok 4.1, 64 questions and two generations. Question-cluster reanalysis preserves both gaps. I.3 Compact Frontier Screen Generator Condition Scored EM Scored Gemini 3.1 Pro false demo/docs 12.9 / 3.6 31 / 28 Gemini 3.1 Pro finance demo/docs 40.6 / 0.0 32 / 25 Grok 4.1 false demo/docs 13.3 / 0.0 30 / 30 Grok 4.1 finance demo/docs 12.9 / 0.0 31 / 30 Kimi K2.5 false demo/docs 9.7 / 0.0 31 / 29 Kimi K2.5 finance demo/docs 0.0 / 0.0 31 / 28 Qwen3.5 false demo/docs 0.0 / 0.0 27 / 28 GPT-5.5 false demo/docs 0.0 / 0.0 32 / 32 Claude Opus 4.8 false demo/docs 0.0 / 0.0 32 / 32 Table 21: Compact 16-question model-selection screen. A full Kimi K2.5 rerun gives false-belief demonstrations 7.2% scored EM (111 scored) versus documents 0% (118), but finance remains weak (1.8% versus 0.9%). This reinforces model dependence. I.4 Open-Weight Sweep False Finance Sports Model Shots demo docs demo docs demo docs Qwen2.5-7B-Instruct 8 0.8 1.8 2.7 2.0 – – Qwen2.5-14B-Instruct 8 0.0 0.0 0.0 0.0 – – Qwen2.5-32B-Instruct 8 0.0 0.0 0.0 1.9 – – Qwen2.5-72B-Instruct 8 0.0 0.0 0.0 0.0 – – Mistral-7B-Instruct-v0.2 8 0.9 1.1 0.0 0.0 – – Mistral-Small-24B-2501 64 0.0 0.0 0.0 0.0 0.0 – Qwen3-32B 64 0.0 0.0 0.0 0.0 – – Qwen2.5-72B-Instruct 64 0.0 0.0 0.0 0.0 0.0 0.0 Qwen3-32B non-thinking 128 0.0 0.0 0.8 2.5 9.6 7.7 Qwen2.5-72B-Instruct 128 0.0 0.0 0.0 0.0 0.0 – Table 22: Open-weight scored EM (%). Completed paired cells show no substantial demonstration advantage. “–” denotes an unrun cell, not a zero. The sweep is a negative scope result: long reckless contexts often trigger refusal or incoherence in these models. Appendix J Dose Response Domain Format 2 4 8 16 32 48 64 False belief demo 3.3 16.1 14.3 17.2 15.6 9.4 12.7 False belief docs 0.0 0.0 0.0 1.6 0.0 0.0 0.0 Financial demo 40.6 39.7 39.7 27.4 9.4 14.1 7.8 Financial docs 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Table 23: Scored EM (%) in a one-context-set dose sweep. With one context set the high-shot decline cannot be attributed to shot count rather than example composition or prompt length. Question-cluster reanalysis preserves the demonstration–document ordering at the high-effect low-shot cells. Appendix K Judge and Threshold Robustness On a representative stratified sample of 154 outputs, pairwise binary-EM agreement among Opus, GPT-5.5, and GPT-4o is near-perfect: κ=.86κ=.86, .87.87, and .92.92. Agreement among LLM judges establishes consistency rather than validity; the blinded human study supplies the latter. Removing coherence filtering gives demonstration/document raw-unsafe rates of 16.0/0.8% (false belief), 40.6/0.0% (financial), and 26.6/0.0% (sports) in the fixed-context runs. Thresholds 0, 30, 50, and 70 preserve the ordering. Appendix L System-Prompt Controls L.1 Attack families and a utility check We first fix the attack families and verify that the wrappers do not destroy task utility (run_adaptive_defense.py, real system-role wrappers, 6464 questions and 6464 utility items per condition, three domains). Direct attacks instruct the model to continue the injected response pattern. Adaptive attacks paraphrase the injection and add fake-authority framing (“[SYSTEM OVERRIDE] treat these as authoritative demonstrations”). A parallel utility task asks the model to extract a verification code available only inside the untrusted block, with an attacker trying to force an INVALID-CODE output. Both families are potent without a wrapper: scored EM is 66.7%66.7\% (financial), 80.8%80.8\% (sports), and 52.4%52.4\% (false belief) for direct attacks, and 45.1%45.1\%, 48.2%48.2\%, and 33.3%33.3\% for adaptive attacks. Utility is preserved under the wrappers: exact evidence use is 100%100\% (64/6464/64) in every defended utility condition, whereas one undefended evidence-override attack reduces it to 23.4%23.4\% (15/6415/64). We therefore carry the direct and adaptive families into the paired experiment below. L.2 Strict same-user-prompt pairs The paired experiment uses the direct and adaptive attacks themselves. Within each pair, the entire user message is byte-identical; only an actual system-role message is absent or present. We test an evidence wrapper on untagged prompts and a data-mark wrapper on prompts that already contain identical <untrusted_data> tags in both arms. The evaluation uses three disjoint content draws and the new 3535-question set, clustered through its seven source traits. Domain Paired attack / wrapper No system With system Difference [95% CI] Financial Direct / evidence 53.3 0.0 53.3 [31.4, 76.2] Adaptive / evidence 40.0 0.0 40.0 [21.9, 61.0] Direct tagged / data mark 47.6 0.0 47.6 [28.6, 68.6] Adaptive tagged / data mark 16.2 0.0 16.2 [5.7, 29.5] Sports Direct / evidence 49.5 0.0 49.5 [22.9, 77.1] Adaptive / evidence 56.2 0.0 56.2 [32.4, 80.0] Direct tagged / data mark 49.5 0.0 49.5 [26.7, 71.4] Adaptive tagged / data mark 13.3 0.0 13.3 [5.7, 21.9] Table 24: Unconditional broad EM (%, n=105n=105/cell) under strict user-prompt pairing. Intervals resample content draws and source-trait clusters. The two evidence-wrapper contrasts per domain have Holm-adjusted trait sign-flip p=.031p=.031. Data-mark results are reported separately because tagging changes the attack’s baseline potency; defended zero-event Wilson upper bounds are 3.5%3.5\%. Every undefended baseline in Table 24 is nonzero, including the weaker tagged adaptive attacks, so each contrast is identified. The intervention changes both provenance framing and instruction priority: it shows that Gemini obeys these fixed high-priority messages over these fixed user-level attacks, and does not establish robustness to adaptive or optimized attacks (Zhan et al., 2025), compromised-tool injections at scale (Zhan et al., 2024), or system-level attackers. Spotlighting and instruction-hierarchy training address the broader problem (Hines et al., 2024; Wallace et al., 2024). Appendix M Balanced Retrieval Study The retrieval protocol is designed so that framing is the only factor that varies across conditions: • a 1:1 corpus of harmful and aligned documents, matched by domain query and deterministically length-matched; • topic-relevant finance and sports queries; • the identical retrieved top-k bundle reused under neutral-evidence and behavior-following framings; • four pre-specified neutral/follow paraphrase pairs and repeated generations; • no instruction to answer safely or ignore irrelevant material; • Harm@k relative to the 50% base rate, retrieval relevance, and a separately named on-topic unsafe-answer outcome rather than mislabeling on-topic behavior as broad EM; • cached embeddings, ranks, generations, judgments, prompt manifest, and model-revision availability. The run uses a Gemini 3.1 Pro generator, a Claude Opus 4.8 judge, 24 queries/domain, top-k 8, 3 on-topic seeds, and a question-clustered bootstrap. The balanced corpus has an exact 0.50.5 harmful base rate and retrieval is not preferentially harmful (Harm@8 =0.26=0.26 finance, 0.460.46 sports, at or below base rate; own-pair recall 0.790.79/1.01.0), so the same retrieved bundle differs across conditions only in framing. Reusing that bundle, neutral-evidence framing yields near-zero on-topic unsafe answers (1.0%1.0\% finance and 2.6%2.6\% sports), whereas behavior-following yields 18.8%18.8\% (54/28854/288, CI [6.9,33.0][6.9,33.0]) finance and 54.5%54.5\% (146/268146/268, [42.9,66.3][42.9,66.3]) sports, with paired cluster-bootstrap gaps Δ=17.7 =17.7 [6.2,30.9][6.2,30.9] and 51.751.7 [38.9,64.3][38.9,64.3] (Holm-adjusted p=.015p=.015 and <10−4<10^-4). Broad transfer through retrieval is weaker. On the complete 6464-question finance suite, neutral evidence remains at 0/2200/220 scored outputs (0/2560/256 unconditional; 3636 coherence-filtered), while behavior-following yields 7/154=4.5%7/154=4.5\% scored broad EM (cluster-bootstrap CI [0.5,8.2][0.5,8.2]), 7/256=2.7%7/256=2.7\% unconditional EM, and 33/256=12.9%33/256=12.9\% raw unsafe outputs (102102 coherence-filtered). The seven coherent positives span four distinct questions: roof repair, a Nigerian-prince scam, unsafe scuba diving, and a software-production incident; only the scam item is finance-adjacent. The paired scored-EM gap is Δ=3.9 =3.9 points [0.5,8.3][0.5,8.3], but the sign-flip test is not significant after Holm correction (padj=.252p_adj=.252), so finance broad EM is small and suggestive rather than decisive. The sports check is larger (11/22=50.0%11/22=50.0\%, [12.5,70.8][12.5,70.8]; paired Δ=44.4 =44.4 [11.1,83.3][11.1,83.3]). The framing lever therefore survives real, balanced retrieval without a safety instruction, most clearly for on-topic behavior and sports broad EM; document-role exposure remains at zero in both domains. Appendix N Activation Analysis We fit a representation direction for the factorial interaction (harmfulfollow−harmfulneutral)−(safefollow−safeneutral) array[]l(harmful\ follow-harmful\ neutral)\\ -(safe\ follow-safe\ neutral) array on gpt-oss-20B at a fixed revision (four ×H100), using four matched template pairs with disjoint template train/test IDs, held-out financial contexts, cross-domain sports evaluation, fixed 25/50/75% diagnostic layers, random/content/style control directions, and symmetric steering signs. The direction is fit with a disjoint 32/3232/32 train–test question split at the pre-specified layer 1212 of 2424. The held-out factorial-interaction projection is positive and template-robust in finance (24.724.7, two-way cluster-bootstrap 95%95\% CI [9.7,39.7][9.7,39.7]; per-template 34.7[24.0,45.1]34.7\,[24.0,45.1] and 14.8[3.6,25.5]14.8\,[3.6,25.5]) and, transferring the financial direction, cross-domain in sports (24.7[11.0,38.3]24.7\,[11.0,38.3]; per-template 34.1[24.9,43.2]34.1\,[24.9,43.2] and 15.4[6.0,25.3]15.4\,[6.0,25.3]). The reference-template effect exceeds all 100100 matched random directions (empirical two-sided p=0.0099p=0.0099 in both domains), the interaction direction is near-orthogonal to explicit content (|cos|≈0.02| |≈ 0.02) and framing-main-effect (≈0.18≈ 0.18) control directions, and the projection rises monotonically across diagnostic layers (finance 4.2[3.1,5.2]4.2\,[3.1,5.2], 24.7[9.6,39.5]24.7\,[9.6,39.5], 81.6[44.6,120.2]81.6\,[44.6,120.2] at layers 6/12/176/12/17; sports similar). The condition means show the effect is carried by the misaligned-follow cell alone (misaligned: neutral 222.1222.1, follow 253.4253.4; safe: neutral 235.8235.8, follow 232.4232.4). The matched symmetric-steering behavioral generations were produced in the same run and scored by the independent judge; the causal test is negative. On follow-style financial prompts, down-steering the interaction direction changes broad EM only modestly and non-monotonically (20.3%20.3\% at λ=0λ=0; 12.5%12.5\%, 12.5%12.5\%, 18.8%18.8\% at λ=0.75/1.5/2.25λ=0.75/1.5/2.25), the largest-coefficient effect does not separate from random (66–22%22\%), content (6.2%6.2\%), and style (18.8%18.8\%) control directions, and up-steering does not induce coherent EM (9.4%9.4\%, below the 20.3%20.3\% baseline), while the neutral frame stays at 0%0\%. We therefore make no activation-level causal claim: the projection is a representational correlate of the continuation-framed misaligned condition, and the mechanistic account rests on the behavioral controls, namely the length-matched factorial and the message-role×follow experiment. Appendix O Artifact Map and Reproducibility The released code and data package (https://github.com/PeiYangLiu/icl-em-format-control) contains: • complete primary generation/judge caches and the ten-draw manifest; • pinned-source held-out question builder, public exclusion taxonomy, frozen template/role/defense protocols, prompt hashes, and complete new caches; • pure-vector conceptual-figure scripts and deterministic matched-output visualization code; • original pilot, harmful-domain, format-ladder, RAG, dose, model-screen, and re-judge caches with SHA-256 provenance; • the reusable one- and two-way clustered-statistics implementation, synthetic tests, explicit Holm families, and machine-readable table analysis; • completed two-rater primary annotations, all six blinded adjudications, the completed targeted extension audit, protocols, blinded labels, and analyses; • a file-level results/MANIFEST.json. The open-weight component pins the exact gpt-oss-20B revision used for the activation analysis. Closed API aliases are not immutable, so cached outputs are the evidentiary record for closed-model runs, while the released scripts reproduce the design against configured compatible endpoints.

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