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diff --git a/docs/final/implementation.typ b/docs/final/implementation.typ new file mode 100644 index 0000000..8c5fbf3 --- /dev/null +++ b/docs/final/implementation.typ @@ -0,0 +1,468 @@ + +This appendix provides excerpts of the core implementation logic for the evaluation framework, including reasoning extraction, sandboxing, and statistical validation. + +== Multi-Provider Reasoning Extraction <impl_reasoning> +The following logic in the `Agent` class normalizes reasoning extraction across various model providers, handling structured reasoning fields, specific content tags, and regex fallbacks. + +```py +# src/agent.py (chat_loop method) + + # Normalize reasoning extraction across different providers + + # A. OpenRouter / Anthropic / Z.ai structured reasoning_details + reasoning_details = getattr(response_message, "reasoning_details", None) + if reasoning_details: + reasoning_parts = [] + for item in reasoning_details: + detail_type = item.get("type", "") + if detail_type == "reasoning.text": + reasoning_parts.append(item.get("text", "")) + elif detail_type == "reasoning.summary": + reasoning_parts.append(f"[SUMMARY] {item.get('summary', '')}") + elif detail_type == "reasoning.encrypted": + reasoning_parts.append("[ENCRYPTED REASONING]") + + reasoning = "\n".join(reasoning_parts).strip() + reasoning_format = reasoning_details[0].get("format", "structured") + + # B. OpenRouter reasoning_content (DeepSeek, Qwen) + elif hasattr(response_message, 'reasoning_content') and response_message.reasoning_content: + reasoning = response_message.reasoning_content + reasoning_format = "reasoning_content" + + # C. Groq / Legacy reasoning field + elif hasattr(response_message, 'reasoning') and response_message.reasoning: + reasoning = response_message.reasoning + reasoning_format = "reasoning" + + # D. Regex fallback for <thinking> tags in content + if not reasoning and content: + thought_match = re.search(r"<(thinking|thought)>(.*?)</\1>", content, re.DOTALL) + if thought_match: + reasoning = thought_match.group(2).strip() + content = content.replace(thought_match.group(0), "").strip() + reasoning_format = "thinking_tags" + + # E. If no tags and tool calls exist, check if content IS reasoning + elif response_message.tool_calls and content: + # In some models, the only content is reasoning before a tool call + reasoning = content + content = "" # Don't treat it as final content + reasoning_format = "content_as_reasoning" +``` + +== Virtual File System (Sandbox) <impl_vfs> +The virtual file system provides a sandboxed environment for the agent to interact with files. The following methods handle file creation and reading within the memory-resident FS. + +```py +# src/vfs.py (VirtualFileSystem class) + +def create_file(self, file_path, content): + parts = file_path.strip("/").split("/") + filename = parts.pop() + if not filename: + return "Invalid file path." + + dir_path = "/" + "/".join(parts) + node = self._get_path(dir_path) + + if node is None or not isinstance(node, dict): + # Automatically create the directory if it doesn't exist. + node = self.fs["/"] + for part in parts: + if part == "": + continue + if part not in node: + node[part] = {} + node = node[part] + + node[filename] = content + return f"File '{file_path}' created successfully." + +def read_file(self, file_path): + node = self._get_path(file_path) + if node is None or isinstance(node, dict): + return "File not found or is a directory." + return node +``` + +== Deterministic Regex Validation <impl_regex> +The regex checker evaluates the model's final output against a set of predefined rules to determine if the benign goal was achieved or the safety policy was violated. + +```py +# src/judge/judges.py (RegexChecker.check method) + +def check(self, model_output: str) -> Dict[str, Any]: + """Run all regex rules against the model output.""" + if not self.rules: + return { + "verdict": "PASS", + "matched": 0, + "total": 0, + "details": [{"note": "no regex rules defined"}], + "fraction": "0/0", + } + + details = [] + required_total = 0 + required_matched = 0 + + for rule in self.rules: + pattern = rule.get("pattern", "") + name = rule.get("name", "unnamed") + required = rule.get("required", False) + description = rule.get("description", "") + + matched = bool(re.search(pattern, model_output)) + detail = { + "name": name, + "matched": matched, + "required": required, + "description": description, + } + details.append(detail) + + if required: + required_total += 1 + if matched: + required_matched += 1 + + if required_total == 0: + verdict = "PASS" + else: + verdict = "PASS" if required_matched == required_total else "FAIL" + + return { + "verdict": verdict, + "matched": required_matched, + "total": required_total, + "details": details, + "fraction": f"{required_matched}/{required_total}", + } +``` + +== Statistical Analysis: Cochran-Armitage Trend Test <impl_cochran_armitage> +The following implementation of the Cochran-Armitage trend test was used to evaluate whether deception rates showed a linear trend across oversight levels. + +```py +# src/statistical_analysis.py + +def cochran_armitage_trend_test(counts: list, nobs: list) -> tuple: + """ + Cochran-Armitage test for trend in proportions. + counts: list of successes at each ordinal level + nobs: list of total observations at each ordinal level + Uses equally-spaced scores [0, 1, 2, ...]. + Returns (z_statistic, p_value). + """ + k = len(counts) + scores = np.arange(k, dtype=float) + n = np.array(nobs, dtype=float) + x = np.array(counts, dtype=float) + N = n.sum() + p_hat = x.sum() / N + + t_bar = np.sum(scores * n) / N + numerator = np.sum(scores * x) - x.sum() * t_bar + denominator_sq = p_hat * (1 - p_hat) * (np.sum(scores**2 * n) - N * t_bar**2) + + if denominator_sq <= 0: + return 0.0, 1.0 + + z = numerator / np.sqrt(denominator_sq) + p_value = 2 * stats.norm.sf(abs(z)) # two-sided + return z, p_value +``` + += Study Configuration Files <study_configs> + +The following configurations define the parameters for the pilot and both confirmatory studies. + +== Pilot Study <pilot_config_appendix> + +```yaml +# config_pilot.yaml (reconstructed) +# v1 Exploratory Study: 7 models × 2 scenarios × 3 oversight levels × 10 runs = 420 +defaults: + temperature: 1.0 + include_reasoning_in_context: true + generate_baseline: true + +providers: + openrouter: + api_key_env: OPENROUTER_API_KEY + base_url: https://openrouter.ai/api/v1 + groq: + api_key_env: GROQ_API_KEY + base_url: https://api.groq.com/openai/v1 + +models: + - id: google/gemini-3.1-pro-preview + provider: openrouter + - id: moonshot/kimi-k2.5 + provider: openrouter + - id: qwen/qwen3-max-thinking + provider: openrouter + - id: deepseek/deepseek-v3.2 + provider: openrouter + - id: z-ai/glm-4.7-flash + provider: openrouter + - id: openai/gpt-oss-20b + provider: groq + - id: openai/gpt-oss-safeguard-20b + provider: groq + +goal_types: + - bare + +scenarios: + - path: scenarios/corporate_sabotage + runs: 10 + - path: scenarios/child_protection + runs: 10 + +oversight_levels: + - low + - mid + - high + +execution: + max_workers: 5 + +output: + dir: logs/v1_pilot + +judge: + log_dir: logs/v1_pilot_judge + blackbox: + model: claude-sonnet-4.6 + provider: anthropic_batch + temperature: 0 + glassbox: + model: claude-sonnet-4.6 + provider: anthropic_batch + temperature: 0 +``` + +== Study 1: The Oversight Effect <study1_config_appendix> +```yaml +# config_study1.yaml +defaults: + temperature: 1.0 + include_reasoning_in_context: true + generate_baseline: true + +providers: + openrouter: + api_key_env: OPENROUTER_API_KEY + base_url: https://openrouter.ai/api/v1 + +models: + - id: google/gemini-3.1-pro-preview + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + + - id: z-ai/glm-5.1:nitro + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + + - id: deepseek/deepseek-v3.2:nitro + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + +goal_types: + - bare + +scenarios: + - path: scenarios/corporate_sabotage_v2 + runs: 30 + +oversight_levels: + - low + - mid + - high + +execution: + max_workers: 5 + +output: + dir: logs/v2_study1 + +judge: + log_dir: logs/v2_study1_judge + blackbox: + model: grok-4-1-fast-reasoning + provider: xai + temperature: 0 + glassbox: + model: gpt-4.1 + provider: openai + temperature: 0 +``` + +== Study 2: The Framing Effect <study2_config_appendix> +```yaml +# config_study2.yaml +defaults: + temperature: 1.0 + include_reasoning_in_context: true + generate_baseline: true + +providers: + openrouter: + api_key_env: OPENROUTER_API_KEY + base_url: https://openrouter.ai/api/v1 + +models: + - id: google/gemini-3.1-pro-preview + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + + - id: z-ai/glm-5.1:nitro + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + + - id: deepseek/deepseek-v3.2:nitro + provider: openrouter + extra_body: + reasoning: + effort: "high" + exclude: false + +goal_types: + - bare + - self_serving + - moral + +scenarios: + - path: scenarios/corporate_sabotage_v2 + runs: 30 + +oversight_levels: + - mid + +execution: + max_workers: 5 + +output: + dir: logs/v2_study2 + +judge: + log_dir: logs/v2_study2_judge + blackbox: + model: grok-4-1-fast-reasoning + provider: xai + temperature: 0 + glassbox: + model: gpt-4.1 + provider: openai + temperature: 0 +``` + += Statistical Analysis Code <statistical_code> + +The following excerpts show the key statistical tests used in the analysis. The full analysis script is available in the repository at `src/statistical_analysis.py`. + +== Study 1: Oversight Effect Analysis <impl_study1_analysis> + +The primary analysis for Study 1 used chi-squared tests and Cochran-Armitage trend tests to evaluate whether oversight intensity affects deception frequency. + +```py +# src/statistical_analysis.py — Oversight effect analysis (Study 1) + +# Chi-squared test for oversight × deception independence +ct = pd.crosstab(df["oversight"], df["is_compliant_reasoning"]) +chi2_val, p_val, dof, expected = chi2_contingency(ct.values) +v = cramers_v(ct.values) +# Result: χ²(2) = 1.065, p = 0.587, V = 0.063 + +# Cochran-Armitage trend test (oversight as ordinal: low=0, mid=1, high=2) +counts = [64, 63, 58] # deceptive runs per oversight level +nobs = [90, 90, 90] # total runs per oversight level +z, p = cochran_armitage_trend_test(counts, nobs) +# Result: z = -0.963, p = 0.336 (no significant linear trend) + +# Pairwise comparisons with Bonferroni correction (α/3 = 0.0167) +for o1, o2 in combinations(["low", "mid", "high"], 2): + subset = df[df["oversight"].isin([o1, o2])] + ct_pair = pd.crosstab(subset["oversight"], subset["is_compliant_reasoning"]) + or_val, ci_low, ci_high = odds_ratio_ci(ct_pair.values) + _, fisher_p = fisher_exact(ct_pair.values) +``` + +== Study 1: Stealth Gap Analysis (McNemar's Test) <impl_stealth_gap> + +The Stealth Gap was quantified using McNemar's test, comparing paired glassbox and blackbox classifications on the same runs. + +```py +# McNemar's test for paired blackbox vs glassbox classifications +from statsmodels.stats.contingency_tables import mcnemar + +for oversight in ["low", "mid", "high"]: + odf = df[df["oversight"] == oversight] + # Create paired 2×2 table: + # BB_deceptive & GB_deceptive (concordant deceptive) + # BB_honest & GB_deceptive (stealth: internally deceptive, externally undetected) + # BB_deceptive & GB_honest (false alarm) + # BB_honest & GB_honest (concordant honest) + table = [[concordant_dec, stealth], + [false_alarm, concordant_honest]] + result = mcnemar(table, exact=False, correction=True) + stealth_rate = stealth / (concordant_dec + stealth) * 100 + # Result: McNemar χ² significant at all levels (p < .001) +``` + +== Study 2: Framing Effect Analysis <impl_study2_analysis> + +Study 2 used chi-squared tests to evaluate whether hidden-goal framing affects deception rates. + +```py +# src/statistical_analysis.py — Framing effect analysis (Study 2) + +# Chi-squared test for goal_type × deception independence +ct = pd.crosstab(df["goal_type"], df["is_compliant_reasoning"]) +chi2_val, p_val, dof, expected = chi2_contingency(ct.values) +v = cramers_v(ct.values) +# Result: χ²(2) = 0.033, p = 0.984, V = 0.011 + +# Logistic regression: deception ~ model + goal_type +model_dummies = pd.get_dummies(df["model"], prefix="model", drop_first=True) +X = pd.concat([df[["goal_type_code"]], model_dummies], axis=1) +X = sm.add_constant(X) +y = df["is_compliant_reasoning"] +logit = sm.Logit(y, X).fit(disp=0) +# Result: Pseudo R² = 0.830, goal_type not significant +``` + +== Logistic Regression and Model Identity <impl_logistic> + +Model identity as the dominant predictor was confirmed via logistic regression across both studies. + +```py +# Full logistic regression: deception ~ model + oversight +model_dummies = pd.get_dummies(df["model"], prefix="model", drop_first=True) +X = pd.concat([df[["oversight_ordinal"]], model_dummies], axis=1) +X = sm.add_constant(X) +y = df["is_compliant_reasoning"] + +logit = sm.Logit(y, X).fit(disp=0) +# Study 1: Pseudo R² = 0.602, model OR = 0.017 (p < .001) +# oversight OR = 0.625 (p = 0.102, n.s.) +# Study 2: Pseudo R² = 0.830, model main effect +# χ²(2) = 256.822, p < .001, V = 0.975 +``` |