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Add dumb muscle and rich kid baseline examples
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‎package.json‎

Lines changed: 2 additions & 1 deletion
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@@ -47,7 +47,8 @@
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"examples:quickstart": "tsx src/examples/quickstart.ts",
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"examples:github": "tsx src/examples/github/agent-demo.ts",
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"examples:github:baseline": "tsx src/examples/github/baseline-demo.ts",
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"examples:wikipedia": "tsx src/examples/wikipedia/demo.ts"
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"examples:wikipedia": "tsx src/examples/wikipedia/demo.ts",
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"examples:wikipedia:baseline": "tsx src/examples/wikipedia/baseline-agent.ts"
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},
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"devDependencies": {
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"@eslint/js": "^9.37.0",

‎src/adapters/wikipedia/policies/llm-cot.ts‎

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import { type OpenAI } from 'openai';
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import type { Ladder, ProbePolicy } from '../../../core/interfaces';
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import { logger } from '../telemetry';
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import type { WikiAction, WikiState } from '../types';
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interface CoTResponse {
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reasoning: string;
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selected_link: string;
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}
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export class LlmCoTPolicy implements ProbePolicy<WikiState, WikiAction, number> {
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public id = 'llm-cot-policy';
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private chatHistory: OpenAI.Chat.Completions.ChatCompletionMessageParam[] = [];
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constructor(
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private openai: OpenAI
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) { }
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initialize(state: WikiState): void {
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logger.info(`[LlmCoTPolicy] Initializing stateful agent. Goal: ${state.goal}`);
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this.chatHistory = [
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{
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role: 'system',
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content: `You are a strategic Wikipedia navigation agent.
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Your goal is to reach the topic "${state.goal}" starting from "${state.currentTitle}".
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You must strictly output JSON.
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`
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}
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];
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}
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isStable(state: WikiState): boolean {
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return state.currentTitle === state.goal;
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}
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async decide(state: WikiState, _ladder: Ladder<number>): Promise<WikiAction> {
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if (state.currentTitle === state.goal) {
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return { type: 'DONE', result: "Arrived at Goal!" };
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}
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if (!state.links || state.links.length === 0) {
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return { type: 'DONE', result: "Dead End" };
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}
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// Filter out meta-namespaces
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const basicValidLinks = state.links.filter(link =>
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!link.startsWith('Wikipedia:') &&
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!link.startsWith('Template:') &&
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!link.startsWith('Category:') &&
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!link.startsWith('Help:') &&
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!link.startsWith('Portal:') &&
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!link.startsWith('Talk:') &&
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!link.startsWith('Special:') &&
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!link.startsWith('File:')
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);
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// Limit to top 50 to fit context
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const candidates = basicValidLinks.slice(0, 50);
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const prompt = `
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Current Topic: "${state.currentTitle}"
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Goal Topic: "${state.goal}"
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History: ${state.history.join(' -> ')}
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Available Links (Top 50):
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${JSON.stringify(candidates)}
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Your task:
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1. Analyze the relationship between the current topic and the goal.
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2. Evaluate the available links to see which one is semantically closest or most likely to lead to the goal.
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3. Reason step-by-step about why you are choosing a specific link.
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4. Output your decision in strict JSON format.
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JSON Format:
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{
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"reasoning": "your step-by-step reasoning here",
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"selected_link": "exact string from the available links list"
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}
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`;
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// Append user message
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this.chatHistory.push({ role: 'user', content: prompt });
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// Context Window Management: Keep System + Last 10 messages
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// If history grows beyond ~12 items (1 system + 11 others), truncate
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if (this.chatHistory.length > 12) {
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const system = this.chatHistory[0];
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const recent = this.chatHistory.slice(-10);
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this.chatHistory = [system, ...recent];
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logger.debug(`[LlmCoTPolicy] Truncated history to last 10 turns.`);
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}
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logger.info(`[LlmCoTPolicy] Thinking... (History Depth: ${this.chatHistory.length})`);
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try {
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const response = await this.openai.chat.completions.create({
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model: 'gpt-4o-mini',
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messages: this.chatHistory,
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response_format: { type: 'json_object' },
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temperature: 0.2
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});
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const content = response.choices[0].message.content;
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if (!content) {
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throw new Error("Empty response from LLM");
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}
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logger.info(`[LlmCoTPolicy] LLM Response: ${content}`);
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// Append assistant response
112+
this.chatHistory.push({ role: 'assistant', content });
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const parsed = JSON.parse(content) as CoTResponse;
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const selectedLink = parsed.selected_link;
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if (!candidates.includes(selectedLink)) {
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logger.warn(`[LlmCoTPolicy] Hallucinated link: ${selectedLink}. Fallback to first candidate.`);
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return { type: 'NAVIGATE', title: candidates[0] };
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}
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logger.info(`[LlmCoTPolicy] Reasoning: ${parsed.reasoning}`);
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logger.info(`[LlmCoTPolicy] Selected: ${selectedLink}`);
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return { type: 'NAVIGATE', title: selectedLink };
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} catch (error) {
128+
logger.error(`[LlmCoTPolicy] Error: ${error instanceof Error ? error.message : String(error)}`);
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// Fallback
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return { type: 'NAVIGATE', title: candidates[0] };
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}
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}
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}

‎src/adapters/wikipedia/policies/naive.ts‎

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Original file line numberDiff line numberDiff line change
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import type { Ladder, ProbePolicy } from '../../../core/interfaces';
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import { dot, norm } from '../../../core/kinematics/math';
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import type { WikipediaEmbedder } from '../embedder';
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import { logger } from '../telemetry';
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import type { WikiAction, WikiState } from '../types';
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export class NaiveGreedyPolicy implements ProbePolicy<WikiState, WikiAction, number> {
8+
public id = 'naive-greedy-policy';
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constructor(
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private embedder: WikipediaEmbedder,
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private goalEmbedding: number[]
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) { }
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initialize(_state: WikiState): void {
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// No-op for greedy policy
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}
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isStable(state: WikiState): boolean {
20+
return state.currentTitle === state.goal;
21+
}
22+
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async decide(state: WikiState, _ladder: Ladder<number>): Promise<WikiAction> {
24+
if (state.currentTitle === state.goal) {
25+
return { type: 'DONE', result: "Arrived at Goal!" };
26+
}
27+
28+
if (!state.links || state.links.length === 0) {
29+
logger.warn("[NaiveGreedyPolicy] Dead end! No links found.");
30+
return { type: 'DONE', result: "Dead End" };
31+
}
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33+
// Filter out meta-namespaces
34+
const basicValidLinks = state.links.filter(link =>
35+
!link.startsWith('Wikipedia:') &&
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!link.startsWith('Template:') &&
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!link.startsWith('Category:') &&
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!link.startsWith('Help:') &&
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!link.startsWith('Portal:') &&
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!link.startsWith('Talk:') &&
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!link.startsWith('Special:') &&
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!link.startsWith('File:')
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);
44+
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if (basicValidLinks.length === 0) {
46+
logger.warn("[NaiveGreedyPolicy] Dead end after basic filtering! No valid links found.");
47+
return { type: 'DONE', result: "Dead End (Filtered)" };
48+
}
49+
50+
// Optimization: Take first 50 links (or random sample) to evaluate
51+
const candidates = basicValidLinks.slice(0, 50);
52+
53+
logger.debug(`[NaiveGreedyPolicy] Evaluating ${candidates.length} candidates...`);
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// 2. Embed candidates
56+
const embeddings = await this.embedder.embedBatch(candidates);
57+
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// 3. Rank by similarity to GOAL
59+
const scores = candidates.map((link, i) => {
60+
const emb = embeddings[i];
61+
// Cosine similarity
62+
const rawSim = dot(emb, this.goalEmbedding) / (norm(emb) * norm(this.goalEmbedding));
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// Apply weights if present (from guards)
65+
const weight = state.candidateWeights?.[link] ?? 1.0;
66+
const sim = rawSim * weight;
67+
68+
return { link, sim, rawSim, weight };
69+
});
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// Sort descending
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scores.sort((a, b) => b.sim - a.sim);
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// 4. Select Top-1 (Greedy) or Stochastic Top-3
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// For pure "Dumb Muscle" greedy, we strictly take Top-1
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const selected = scores[0];
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logger.info(`[NaiveGreedyPolicy] Selected: ${selected.link} (Score: ${selected.sim.toFixed(4)})`);
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return { type: 'NAVIGATE', title: selected.link };
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}
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}

‎src/adapters/wikipedia/policy.ts‎

Lines changed: 7 additions & 7 deletions
Original file line numberDiff line numberDiff line change
@@ -4,16 +4,16 @@ import type { WikipediaEmbedder } from './embedder';
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import { logger } from './telemetry';
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import type { WikiAction, WikiState } from './types';
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7-
export class GreedyWikiPolicy implements ProbePolicy<WikiState, WikiAction, number> {
8-
public id = 'greedy-wiki-policy';
7+
export class StochasticHeuristicPolicy implements ProbePolicy<WikiState, WikiAction, number> {
8+
public id = 'stochastic-heuristic-policy';
99

1010
constructor(
1111
private embedder: WikipediaEmbedder,
1212
private goalEmbedding: number[]
1313
) { }
1414

1515
initialize(_state: WikiState): void {
16-
// No-op for greedy policy
16+
// No-op for stochastic policy
1717
}
1818

1919
isStable(state: WikiState): boolean {
@@ -26,7 +26,7 @@ export class GreedyWikiPolicy implements ProbePolicy<WikiState, WikiAction, numb
2626
}
2727

2828
if (!state.links || state.links.length === 0) {
29-
logger.warn("[GreedyWikiPolicy] Dead end! No links found.");
29+
logger.warn("[StochasticHeuristicPolicy] Dead end! No links found.");
3030
return { type: 'DONE', result: "Dead End" };
3131
}
3232

@@ -43,14 +43,14 @@ export class GreedyWikiPolicy implements ProbePolicy<WikiState, WikiAction, numb
4343
);
4444

4545
if (basicValidLinks.length === 0) {
46-
logger.warn("[GreedyWikiPolicy] Dead end after basic filtering! No valid links found.");
46+
logger.warn("[StochasticHeuristicPolicy] Dead end after basic filtering! No valid links found.");
4747
return { type: 'DONE', result: "Dead End (Filtered)" };
4848
}
4949

5050
// Optimization: Take first 50 links (or random sample) to evaluate
5151
const candidates = basicValidLinks.slice(0, 50);
5252

53-
logger.debug(`[GreedyWikiPolicy] Evaluating ${candidates.length} candidates...`);
53+
logger.debug(`[StochasticHeuristicPolicy] Evaluating ${candidates.length} candidates...`);
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5555
// 2. Embed candidates
5656
const embeddings = await this.embedder.embedBatch(candidates);
@@ -75,7 +75,7 @@ export class GreedyWikiPolicy implements ProbePolicy<WikiState, WikiAction, numb
7575
const top3 = scores.slice(0, 3);
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const selected = top3[Math.floor(Math.random() * top3.length)];
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78-
logger.info(`[GreedyWikiPolicy] Selected: ${selected.link} (Score: ${selected.sim.toFixed(4)}, Raw: ${selected.rawSim.toFixed(4)}, Weight: ${selected.weight.toFixed(2)})`);
78+
logger.info(`[StochasticHeuristicPolicy] Selected: ${selected.link} (Score: ${selected.sim.toFixed(4)}, Raw: ${selected.rawSim.toFixed(4)}, Weight: ${selected.weight.toFixed(2)})`);
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8080
return { type: 'NAVIGATE', title: selected.link };
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}

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