SESSION HANDOFF — Autoresearch Swarm Integration

Active Context

Project: Autoresearch Framework Integration for VIF Swarm Intelligence Orchestrator (Phase 3)

Implemented Karpathy autoresearch pattern into the VIF trading system's multi-agent swarm. Autoresearch is the 8th specialist agent in the council (after FinViz screener, before risk-agent). Its role: iterative research synthesis for signal validation, catalyst analysis, and ad-hoc deep-dives.

The swarm now has a self-directed research capability that agents can invoke for exploratory questions without consuming token budget on trivial lookups. Uses 24-hour query cache to prevent redundant research calls.

Framework Context:

• Base: SwarmOrchestrator (KV cache + latent memory + gossip routing + consensus)
• Layer 40: Autoresearch hidden state (confidence scores, topic maps, novel insights)
• Integration: Full pipeline support (premarket, market_open, afterhours, weekend, full modes)

Alpha/System Logic

Autoresearch Agent Signature

class NativeAutoResearchAgent(SpecialistAgent):
    def execute(self, subtasks=None, kv_cache_binding=None, latent_memory=None, task_context=None):
        # 3-iteration max research loop
        # Input: task_context["research_query"]
        # Output: {"findings": str, "confidence_score": float, "topics": list, 
        #          "novel_insights": list, "execution_time_ms": int, 
        #          "cache_hit": bool, "token_cost": int}

Research Loop Pattern

1. Decompose Query — Break research_query into 3 sub-questions
2. Search Findings — Execute web search for each sub-question (using WebSearch tool)
3. Synthesize — Distill results into structured findings + confidence scoring

Token Budget

• Per-query: ~500 tokens (3 searches @ ~150 tokens each + synthesis)
• 24-hour query cache: identical queries return cached result (0 tokens)
• Monthly impact: ~0.5% overhead (negligible, ~$0.0006/day)
• Skip logic: Only runs if task_context includes research_query key

Latent Memory Integration

• Layer 40 (exclusive to autoresearch): Hidden state with confidence + topic embeddings
• No conflict with existing layers (8, 16, 24, 32)
• Shared with downstream agents (risk-agent reads layer 40 for research context)

@Tool Wrapper (smolagents_bridge)

@tool
def run_autoresearch(research_query: str, context_signals_json: str = "") -> str:
    """Execute iterative research loop for a trading question."""
    # Called by ProductionSwarmBridge.orchestrator (ManagedAgent)
    # Called by ResearchSwarmBridge.agent (CodeAgent)
    # Returns JSON string with findings + metadata

Current State

Implementation Status: ✓ COMPLETE

Files Modified

File	Status	Details
swarm/native_autoresearch_agent.py	✓ NEW	341 lines, full SpecialistAgent implementation
swarm/__init__.py	✓ UPDATED	+import, +all entry
agents/orchestrator_swarm.py	✓ UPDATED	+import, +agent_pool entry, +layer 40, +log lines
swarm/smolagents_bridge.py	✓ UPDATED	+run_autoresearch @tool (both bridges)

Verification Status

• ✓ Import test: from swarm import NativeAutoResearchAgent passes
• ✓ Execute test: Agent.execute() with mock task_context returns valid JSON
• ✓ Orchestrator integration: 8-agent council visible in log output
• ✓ Layer 40 initialization: Latent memory includes autoresearch layer
• ✓ @tool registration: run_autoresearch available in both ProductionSwarmBridge and ResearchSwarmBridge

Last Successful Action

Completed autoresearch agent implementation and full swarm integration. All 4 files updated, all imports verified, orchestrator logs confirm 8-agent council with layer 40. Agent positioned correctly (7th, before risk-agent). Ready for pipeline execution.

Next Step Queue

1. Execute autoresearch in premarket pipeline — Verify agent runs without errors

   python agents/orchestrator_swarm.py --mode premarket 2>&1 | grep autoresearch
   

   Expected: Autoresearch logs show research_query decomposition, search execution, synthesis output

2. Verify token efficiency — Confirm overhead is <0.5% of daily budget

   • Check orchestrator log for token_cost field in autoresearch output
   • Validate cache_hit=true on repeated queries within 24h

3. Test research_query injection — Ensure task_context properly passes research queries

   • Monitor critic agent integration: Does critic call autoresearch for low-confidence signal validation?
   • Monitor catalyst monitor: Does it request autoresearch for unusual macro patterns?

4. Optional: Add autoresearch triggers — Configure when agents should request research

   • Critic agent: Trigger on signals < 55% confidence
   • Catalyst monitor: Trigger on novel policy/earnings catalysts
   • FinViz screener: Trigger on outlier stock discoveries

5. Finviz Swarm Integration Pending — 6 known bugs blocking FinViz from running in swarm mode

   • Status: Tabled May 10, awaiting system research
   • See: docs/~$STEM_CONTEXT.md (legacy) and memory file finviz_pending_fixes.md

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Related Context

• Prior Session Work: CORZ long calls HTML strategy report (reports/corz_long_calls_strategy.html) — completed May 11
• Scheduler Status: Next jobs May 11 (07:00 premarket, 08:45 catalyst, etc.)
• Active Terminals: TradingView (CDP port 9222), Claude Code session (bypassPermissions=true, effort=low)
• Model: Haiku 4.5 (cost-optimized)