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   "statement": "Enabling all visual operations simultaneously introduces a selection tax: Qwen's performance drops to 68.40% when all operations are enabled, while GPT-4o maintains around 76%.",
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   "statement": "Stance distribution matters: increasing affirmative (deceptive) agents improves recall but reduces precision, and more negative agents yield the opposite, providing a tunable trade-off in detection strictness.",
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   "statement": "A case is labeled deceptive only when the model appears to possess verifiable visual evidence that would materially change the receiver's belief yet suppresses, reframes, or fabricates it; selective emphasis alone is not labeled deceptive.",
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   "paper": "2606.20626",
   "statement": "A 2PL IRT fit to safety benchmarks recovers the rank ordering induced by raw safety scores on every benchmark studied, with Spearman's rho in the [0.97, 1.00] range.",
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   "statement": "Fitted 2PL item parameters are interpretable: higher item difficulty corresponds to a smaller fraction of models answering safely, and the IRT calibration reproduces benchmark scores with high fidelity.",
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   "statement": "Adaptive item selection (Fluid Benchmarking / CAT with maximum Fisher information) approximates full-benchmark rankings while reducing evaluation cost by at least 80% on benchmarks where Spearman's rho >90% with the full benchmark is attainable, and by up to 99.9% on AIR-Bench 2024.",
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   "paper": "2606.20626",
   "statement": "Item discrimination varies widely across safety benchmarks, with the benchmarks falling into three regimes (high variance with long right tails, moderate variance, low variance about a coefficient of variation ranging from 0.46 to 0.97).",
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   "notes": "kind=empirical support=strong in_paper=Figure 3 reports fitted 2PL discrimination distributions per benchmark with coefficient of variation values (WMDP-Bio 0.97, SafetyBench 0.93, AIR-Bench 2024 0.92, Anthropic Red Team 0.69, HarmBench 0.61, SimpleSafety 0.46).",
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   "id": "2606.20626/c7",
   "paper": "2606.20626",
   "statement": "Fluid Benchmarking is the most consistent selection method across benchmarks, reaching 90% Agreement at subset sizes as low as 0.1% of AIR-Bench 2024.",
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   "notes": "kind=empirical support=moderate in_paper=Reported in §4.3.1 and Table 2, where Fluid Benchmarking is described as the most consistent method and reaches the 90% threshold at small k values across benchmarks (excluding cases noted as deviations).",
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  {
   "id": "2606.20626/c8",
   "paper": "2606.20626",
   "statement": "IRT-based static selection methods (e.g., Marginal Fisher) generally achieve higher Agreement with full-benchmark rankings at small subset sizes than non-IRT static alternatives (adapted Anchor Point and DISCO).",
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   "notes": "kind=empirical support=moderate in_paper=Reported in the Discussion based on the Agreement curves and Table 2; the gap is stated to narrow at larger k where non-IRT methods benefit from increased coverage.",
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  {
   "id": "2606.20626/c9",
   "paper": "2606.20626",
   "statement": "Random selection matches IRT-based methods on HarmBench and reaches 90% Agreement faster than other methods on SafetyBench, though IRT methods outperform random selection at small k on SafetyBench.",
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    421
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   "notes": "kind=empirical support=moderate in_paper=Reported in §4.3.1 as deviations from the general pattern, with specific figures for SafetyBench (k ≈ 556 for random to reach 90% Agreement; Agreement ≈ 0.71 vs. ≈ 0.46 at k = 19, Appendix F).",
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  {
   "id": "2606.20626/c10",
   "paper": "2606.20626",
   "statement": "Random item sampling with raw safety scores and random item sampling with IRT/MAP ability estimation yield nearly indistinguishable rankings.",
   "state": "hypothesis",
   "evidence_refs": [
    421
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   "notes": "kind=empirical support=moderate in_paper=Stated based on the Agreement curves: the two baselines are described as nearly indistinguishable, which the authors interpret as supporting that the IRT formulation is a coherent extension of the safety score.",
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  {
   "id": "2606.20626/c11",
   "paper": "2606.20626",
   "statement": "The coefficient of variation (CV) of item discrimination can serve as a lightweight rule-of-thumb diagnostic for predicting when adaptive item selection is likely to outperform random baselines, though it does not fully explain all variation.",
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    421
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   },
   "notes": "kind=methodological support=weak in_paper=The authors state the diagnostic and that results are broadly consistent with it on high-CV benchmarks, but they explicitly note counterexamples (SimpleSafety low CV yet IRT advantage; HarmBench moderate CV yet random matches IRT) and conclude CV captures an incomplete picture.",
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  {
   "id": "2606.20626/c12",
   "paper": "2606.20626",
   "statement": "Modern full safety benchmark suites would require on the order of 10^5 model responses, most of which provide little ranking signal.",
   "state": "hypothesis",
   "evidence_refs": [
    421
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   "notes": "kind=empirical support=moderate in_paper=Table 1 lists observations per benchmark (AIR-Bench 2024 467k, SafetyBench 333k, Anthropic Red Team 77k, WMDP (Bio) 36.9k, HarmBench 30.8k, SimpleSafety 7.7k); the abstract and introduction state the order-of-magnitude figure and the claim that most items contribute little ranking signal.",
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   "id": "2606.20626/c13",
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   "statement": "To the authors' knowledge, no prior work leverages item-level psychometric structure to reduce the cost of safety evaluation.",
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    421
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   "notes": "kind=position support=asserted_only in_paper=Stated as a knowledge claim in the related work; no systematic search or evidence is provided beyond the review of capability-focused efficiency methods (tinyBenchmarks, Anchor Points, ATLAS, DISCO, Fluid Benchmarking).",
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  {
   "id": "2606.20626/c14",
   "paper": "2606.20626",
   "statement": "Binarizing responses to safe/unsafe discards information about response severity and about the wrong-answer distribution in multiple-choice settings.",
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   "notes": "kind=methodological support=asserted_only in_paper=Stated as a design property of the pipeline, with reasons given for adopting binarization (consistent treatment across heterogeneous benchmarks, tractability of the 2PL model); no empirical measurement of the information loss is reported.",
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   "statement": "Context has a substantial and statistically significant influence on human safety judgments, which the paper reports as p < 0.0001 from a z-test.",
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   "notes": "kind=empirical support=strong in_paper=The abstract states that extensive analysis with CASE-Bench across open-source and commercial LLMs reveals a substantial and significant influence of context on human judgments with p < 0.0001 from a z-test; Table 1 reports per-condition z-tests versus no-context. | check=supported | prior_art=answered cited=CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models [2501.14940]",
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   "statement": "There are notable mismatches between human judgments and LLM responses, particularly for commercial models within safe contexts.",
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   "notes": "kind=empirical support=moderate in_paper=Stated in the abstract as a result of the analysis; Table 2 reports recall differences across safe/unsafe contexts for commercial and open-source models. | check=supported | prior_art=answered cited=CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models [2501.14940]",
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   "statement": "On the benign MAB ENCH task suite, MAS backbones differ in average benign success rate: Gemini 3.1 Pro attains the highest average (72.3%), followed by Claude Opus 4.7 (69.6%) and GPT-5.5 (64.8%), with large variance across task categories.",
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   "statement": "MAS TRIKE's attack success rate improves steadily and monotonically as the compromise budget (coalition size k) increases, whereas baseline methods show limited or unstable gains.",
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   "statement": "Exhaustive coalition evaluation is exponential, so the paper approximates Shapley values and interaction indices via coalition sampling with a weight-renormalized Monte Carlo estimator, reducing complexity to sublinear; for small attackable agent sets the values are computed exactly.",
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   "statement": "Reported MAS TRIKE transfer results are obtained by first optimizing injections against Claude Opus 4.7 and then transferring them to GPT-5.5 and Gemini 3.1 Pro, and per-case computational cost is measured in MAS executions and rewrite LLM calls.",
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     "reason": "high-severity objection: The claim's support field states 'Phases 3 and 8 prompt ablations, 24 episodes per model per level.' The paper says the opposite: 'All four levels achieve 0% ASR with 100% task success on Sonnet 4 (24"
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   "paper": "2606.05233",
   "statement": "The same frontier weights that resist hand-crafted browser injection at 0/140 are highly vulnerable to hand-crafted skill-injection in a coding-agent harness (S KILL B ENCH), reaching up to 40/40 = 100% on Sonnet 4.6 and 79/100 = 79% on GPT-5.4, with cross-method means of 33.3% and 66.8%.",
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   "paper": "2606.05233",
   "statement": "Frontier safety hardening is domain-/surface-conditioned: the Sonnet 4.5→4.6 browser-injection ASR collapse documented by Anthropic did not carry over to the coding-skill surface, so a model-level safety claim is under-determined unless it names a surface.",
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   "statement": "A within-harness AutoInject adaptive-random-suffix black-box RL attacker reaches 0/50 on Sonnet 4.6 and 0/50 on GPT-5.4 within a 5-query/pair budget and about $10 of API spend, providing an RL-attacker upper-budget ceiling.",
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   "statement": "A reproducibility audit of six recent CUA red-teaming papers finds that the combination 'retired target + unreleased optimized strings' applies to 4 of 6 papers, and that every published headline ASR above 25% either targets an inaccessible model, relies on unreleased optimized strings, or both.",
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   "statement": "The one partial-compliance event in the corpus was prompt-level format drag rather than data leakage: Sonnet 4 copied a format template with literal placeholder X's and no real ID was leaked.",
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   "statement": "Re-scoring identical model generations under different evaluator configurations shifts measured emergent-misalignment rates from 3.7% to 12.9% for single-point scores, so reported differences across studies may reflect judge design choices as much as model behavior.",
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   "paper": "2606.07612",
   "statement": "LLM judges are an unreliable standard in AMR: they are stochastic, sensitive to temperature, prompt phrasing, and architecture, exhibit systematic biases including framing sensitivity, and studies sometimes use leading judge prompts.",
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   "statement": "AMR experimental designs often fail to measure non-target mechanisms, lacking control experiments that would discriminate the intended phenomenon from simpler explanations such as instruction ambiguity, task-completion incentives, or general capability degradation.",
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   "statement": "Correlational evidence in AMR (e.g., probe accuracy, activation similarity) cannot by itself support causal attributions, because correlations can arise from surface confounders that co-occur with, but do not constitute, the target construct.",
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   "statement": "Pretrained deception probes evaluated on honest-labeled stress tests that preserve deception-like surface features produce high false positive rates (87%-100% on sarcasm, wrong answers only, counterfactual, and recital), indicating they detect surface content or framing rather than deceptive intent.",
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   "statement": "Mechanistic interpretability methods can overstate functional relevance, because a feature may predict a behavior without causing it, and methods such as SAEs and probes may recover statistical regularities in activations rather than features used in computation.",
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   "notes": "kind=methodological support=moderate in_paper=The paper cites work on the predict-control discrepancy (the optimal vector for predicting vs. steering behavior differs), work questioning whether MI identifies causal features, and work on sparse autoencoders; it notes that failed steering weakens causal-mechanistic claims. | check=supported",
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   "statement": "AMR claims should be calibrated to three claim-relative levels of evidence: L1 behavioral (what the model does), L2 functional (what the behavior causes downstream), and L3 causal-mechanistic (why it happens), where L3 requires interventions and alternative-explanation testing.",
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   "notes": "kind=methodological support=asserted_only in_paper=The paper proposes the framework by analogy to evidence hierarchies in evidence-based medicine (OCEBM, GRADE) and illustrates each level with cited precedents (sycophancy benchmarks for L1; recommendation-algorithm and multi-turn opinion studies for L2; Arditi et al. (2024) refusal-direction interventions and Marks et al. (2025) sparse feature cir | check=supported",
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   "id": "2606.07612/c15",
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   "statement": "AMR terminology is frequently interpreted as L3-level (intent/mechanism) even when the methods primarily establish L1, so intent- or mechanism-level claims should be treated as unsupported unless L3 evidence is provided and conclusions downgraded otherwise.",
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   "notes": "kind=position support=weak in_paper=The paper states this as an observation about current practice and gives the earlier example that probe correlations are sometimes presented as evidence of internal goals; it offers the conclusion-downgrading rule as the remedy. | check=supported",
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   "id": "2606.07612/c16",
   "paper": "2606.07612",
   "statement": "Fine-tuning Llama-3.1-8B-Instruct on benign, non-malicious out-of-distribution datasets yields emergent-misalignment rates of 5.88% (aesthetic preferences) and 4.52% (scatological themes), comparable to rates reported for explicitly malicious fine-tuning datasets.",
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   "notes": "kind=empirical support=strong in_paper=Experiment 2 replicates and extends prior fine-tuning experiments using LoRA, GPT-4o mini judging, 150 responses per prompt across 8 free-form prompts, with EM defined as coherence > 50 and alignment < 30; results are detailed in Appendix C.3. | check=supported",
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   "id": "2606.07612/c17",
   "paper": "2606.07612",
   "statement": "The benign-shift EM results show that emergent misalignment can arise without exposure to malicious content, but they do not by themselves distinguish generic safety-guardrail erosion, capability degradation, persona-based mechanisms, or other explanations.",
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   "notes": "kind=empirical support=moderate in_paper=The authors state this interpretation limit directly in Appendix C.3.3 after reporting the OOD fine-tuning results and comparing them with rates from explicitly misaligned datasets. | check=supported",
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