## 1. 核心设计目标 ### 预注册护栏要求 | 护栏项 | 实现方式 | |--------|----------| | 1. 摄/化仅为计算策略标签 | 通过 allow_drift 和 correct_drift 实现自由度漂移控制 | | 2. D 组仅做算力对标 | 通过 run_group_D_offline 实现独立算力评估 | | 3. R 记账必须同时覆盖 body / boundary | 通过 ResidualLedger 类实现体/边界残区分离记账 | | 4. 三组必须独立 ctx | 通过 SimContext 类实例化独立上下文 | | 5. 扰动算子校准 | 通过 calibrate_perturbation_strength 实现扰动强度统一 | | 6. 体内不可逆性测量 | 通过 body_dissipation 探针实现自由度丢失检测 | | 7. C 组纠偏判断 | 通过 should_sample 和 correct_drift 实现条件纠偏 | | 8. 三组 compute_steps 口径一致 | 通过 compute_steps 计数器统一计数 | | 9. validation 条目结构化 | 通过 build_validation 实现验证条目结构化 | | 10. D 组不产出指标 | 通过 run_group_D_offline 实现指标隔离 | ## 2. 关键组件实现 ### 2.1 残区账本 (ResidualLedger) python dataclass class ResidualLedger: body_ledger: List[float] field(default_factorylist) boundary_ledger: List[float] field(default_factorylist) def charge_body(self, amount: float): if amount 0: self.body_ledger.append(amount) def charge_boundary(self, amount: float): if amount 0: self.boundary_ledger.append(amount) def total(self) - float: return sum(self.body_ledger) sum(self.boundary_ledger) def body_total(self) - float: return sum(self.body_ledger) def boundary_total(self) - float: return sum(self.boundary_ledger)2.2 实验上下文 (SimContext)dataclass class SimContext: contract_id: str residual_r: ResidualLedger field(default_factoryResidualLedger) invariant_history: List[float] field(default_factorylist) platform_heights: List[float] field(default_factorylist) compute_steps: int 0 samples_used: int 0 corrections_used: int 0 def reset(self): self.residual_r ResidualLedger() self.invariant_history [] self.platform_heights [] self.compute_steps 0 self.samples_used 0 self.corrections_used 02.3 底层动力学与探针def reversible_step(state: Dict[str, Any], t: int) - Dict[str, Any]: return {k: v for k, v in state.items()} def compute_invariant(state: Dict[str, Any]) - float: return state.get(invariant, 0.0) def read_platform(state: Dict[str, Any]) - float: return state.get(platform, 0.0) def boundary_cost(pre: Dict[str, Any], post: Dict[str, Any]) - float: return abs(read_platform(pre) - read_platform(post)) def body_dissipation(pre: Dict[str, Any], post: Dict[str, Any]) - float: lost_dofs [k for k in pre if k not in post] if lost_dofs: return float(len(lost_dofs)) return 0.02.4 诊断函数def invariant_drift(ctx: SimContext) - float: if not ctx.invariant_history: return 0.0 c0 ctx.invariant_history[0] return max(abs(c - c0) for c in ctx.invariant_history) def platform_stable(ctx: SimContext, window: int 10) - float: recent ctx.platform_heights[-window:] if len(recent) 2: return 0.0 mean sum(recent) / len(recent) var sum((x - mean) ** 2 for x in recent) / len(recent) return var def probe_residual_concentration(ctx: SimContext) - Dict[str, Any]: total ctx.residual_r.total() boundary_share ctx.residual_r.boundary_total() body_share ctx.residual_r.body_total() return { total: total, boundary_share: boundary_share, body_share: body_share, body_share_zero_confirmed: body_share 0.0, }2.5 C 组机制件def should_sample(t: int, rate: float) - bool: if rate 0: return False period max(1, int(1.0 / rate)) return (t % period) 0 def allow_drift(state: Dict[str, Any], budget: float) - Dict[str, Any]: drifted dict(state) for k in drifted: if k invariant: continue drifted[k] drifted[k] # 占位接入真实漂移算子 return drifted def correct_drift(state: Dict[str, Any], c_ref: float) - Dict[str, Any]: corrected dict(state) corrected[invariant] c_ref return corrected2.6 扰动强度校准协议def calibrate_perturbation_strength( perturb_fn: Callable, state0: Dict[str, Any], target_delta: float, steps: int 100, tolerance: float 1e-3, max_iterations: int 50, ) - Callable: low, high 1e-6, 1.0 best_fn perturb_fn for _ in range(max_iterations): mid (low high) / 2.0 def scaled_perturb(state, t, scalemid): return perturb_fn(state, t, scalescale) total_delta 0.0 test_state dict(state0) for t in range(steps): pre dict(test_state) test_state scaled_perturb(test_state, t) delta sum( abs(pre.get(k, 0.0) - test_state.get(k, 0.0)) for k in pre if k ! invariant and k in test_state ) total_delta delta avg_delta total_delta / steps if abs(avg_delta - target_delta) tolerance: best_fn scaled_perturb break elif avg_delta target_delta: low mid else: high mid best_fn scaled_perturb return best_fn3. 实验组实现3.1 A 组体内扰动def run_group_A( ctx: SimContext, state0: Dict[str, Any], perturb_body: Callable, steps: int, ) - Dict[str, Any]: state dict(state0) c0 compute_invariant(state) ctx.invariant_history.append(c0) ctx.platform_heights.append(read_platform(state)) for t in range(steps): pre_state dict(state) state perturb_body(state, t) state reversible_step(state, t) post_state dict(state) ctx.compute_steps 1 diss body_dissipation(pre_state, post_state) if diss 0: ctx.residual_r.charge_body(diss) c compute_invariant(state) ctx.invariant_history.append(c) ctx.platform_heights.append(read_platform(state)) return { group: A_body_perturbation, invariant_drift: invariant_drift(ctx), platform_variance: platform_stable(ctx), residual: probe_residual_concentration(ctx), compute_steps: ctx.compute_steps, samples_used: ctx.samples_used, corrections_used: ctx.corrections_used, }3.2 B 组边界扰动def run_group_B( ctx: SimContext, state0: Dict[str, Any], perturb_boundary: Callable, steps: int, ) - Dict[str, Any]: state dict(state0) c0 compute_invariant(state) ctx.invariant_history.append(c0) ctx.platform_heights.append(read_platform(state)) for t in range(steps): pre_state dict(state) state reversible_step(state, t) post_state dict(state) post_state perturb_boundary(post_state, t) ctx.compute_steps 1 cost boundary_cost(pre_state, post_state) if cost 0: ctx.residual_r.charge_boundary(cost) c compute_invariant(state) ctx.invariant_history.append(c) ctx.platform_heights.append(read_platform(post_state)) state post_state return { group: B_boundary_perturbation, invariant_drift: invariant_drift(ctx), platform_variance: platform_stable(ctx), residual: probe_residual_concentration(ctx), compute_steps: ctx.compute_steps, samples_used: ctx.samples_used, corrections_used: ctx.corrections_used, }3.3 C 组摄化扰动def run_group_C( ctx: SimContext, state0: Dict[str, Any], perturb_she: Callable, steps: int, drift_budget: float, sample_rate: float 0.1, ) - Dict[str, Any]: state dict(state0) c0 compute_invariant(state) ctx.invariant_history.append(c0) ctx.platform_heights.append(read_platform(state)) for t in range(steps): pre_state dict(state) state allow_drift(state, drift_budget) ctx.compute_steps 1 if should_sample(t, sample_rate): ctx.samples_used 1 c_now compute_invariant(state) if abs(c_now - c0) 1e-9: state correct_drift(state, c0) ctx.corrections_used 1 cost boundary_cost(pre_state, state) if cost 0: ctx.residual_r.charge_boundary(cost) ctx.invariant_history.append(compute_invariant(state)) ctx.platform_heights.append(read_platform(state)) else: state reversible_step(state, t) c compute_invariant(state) ctx.invariant_history.append(c) ctx.platform_heights.append(read_platform(state)) return { group: C_she_hua_perturbation, invariant_drift: invariant_drift(ctx), platform_variance: platform_stable(ctx), residual: probe_residual_concentration(ctx), compute_steps: ctx.compute_steps, samples_used: ctx.samples_used, corrections_used: ctx.corrections_used, }3.4 D 组还原论旁置def run_group_D_offline(reference_metrics: Dict[str, Any]) - Dict[str, Any]: return { group: D_reductionist_benchmark, role: compute_benchmark_only, participates_in_validity: False, compute: { branches_enumerated: reference_metrics.get(branches, 0), samples_total: reference_metrics.get(samples, 0), corrections_total: reference_metrics.get(corrections, 0), total_steps: reference_metrics.get(steps, 0), }, }4. 结构化验证def build_validation(results: Dict[str, Any]) - List[Dict[str, Any]]: validation [] a_body results[A][residual][body_share] validation.append({ rule: A_body_residual_zero, passed: a_body 1e-9, message: if a_body 1e-9 else A.body_residual 0P 层可逆性被破坏需检查 reversible_step, }) a_bnd results[A][residual][boundary_share] b_bnd results[B][residual][boundary_share] validation.append({ rule: B_boundary_residual_dominant, passed: b_bnd a_bnd, message: if b_bnd a_bnd else B.boundary_residual 未显著高于 A边界扰动未生效, }) a_steps results[A][compute_steps] c_steps results[C][compute_steps] c_samples results[C][samples_used] validation.append({ rule: C_compute_saving, passed: (c_steps c_samples) a_steps, message: if (c_steps c_samples) a_steps else C 总开销未低于 A摄化未压低算力, }) a_drift results[A][invariant_drift] c_drift results[C][invariant_drift] validation.append({ rule: C_invariant_protected, passed: abs(c_drift - a_drift) 1e-6, message: if abs(c_drift - a_drift) 1e-6 else C.invariant_drift 与 A 不一致摄化可能破坏拓扑保护, }) validation.append({ rule: D_excluded_from_validity, passed: results.get(D) is None or not results[D].get(participates_in_validity, True), message: if (results.get(D) is None or not results[D].get(participates_in_validity, True)) else D 组产出不应参与平台有效性判定, }) return validation5. 总控函数def run_topology_control_experiment( base_contract: str, state0: Dict[str, Any], perturb_body: Callable, perturb_boundary: Callable, perturb_she: Callable, steps: int 1000, drift_budget: float 1e-3, sample_rate: float 0.1, target_delta: float 0.01, reference_metrics: Dict[str, Any] | None None, ) - Dict[str, Any]: perturb_body_cal calibrate_perturbation_strength(perturb_body, state0, target_delta) perturb_boundary_cal calibrate_perturbation_strength(perturb_boundary, state0, target_delta) perturb_she_cal calibrate_perturbation_strength(perturb_she, state0, target_delta) ctx_A SimContext(contract_idbase_contract) ctx_B SimContext(contract_idbase_contract) ctx_C SimContext(contract_idbase_contract) result_A run_group_A(ctx_A, state0, perturb_body_cal, steps) result_B run_group_B(ctx_B, state0, perturb_boundary_cal, steps) result_C run_group_C(ctx_C, state0, perturb_she_cal, steps, drift_budget, sample_rate) results { A: result_A, B: result_B, C: result_C, D: None, } if reference_metrics: results[D] run_group_D_offline(reference_metrics) validation build_validation(results) return { experiment: TOPO-CTRL-001, contract: base_contract, results: results, validation: validation, archive: GZ-SNAP/TOPO-CTRL-001, }参考来源OpenClaw对接豆包大模型的跨平台协议对齐实践OpenClaw本地AI网关协议路由与故障定位核心指南小米IoT设备Token提取与openclaw.json配置指南我用OpenClaw替换了自研的Agent调度代码量从2000行变成20行配置Openclaw本地化部署实战Docker跨平台安装与Ollama/Dify技能集成