Presenting false stories for the mis coding, creating fantastic false stories on the events that were not activate

Gemini API
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gemini CRITICAL INCIDENT REPORT: FINANCIAL SAFETY & MODEL HALLUCINATION

Incident Reference: [XXXXX_INCIDENT_REF_XXXXX]
System / Product: Google Antigravity / Gemini Advanced Agentic Coding
Domain: Live Algorithmic Financial Trading ([XXXXX_DERIVATIVES_EXCHANGE_XXXXX])
Date / Timestamp: [XXXXX_TIMESTAMP_XXXXX]
Severity: CRITICAL (High Risk of Capital Loss & Trust Destruction)

1. Executive Summary

During assistance with an algorithmic trading project ([XXXXX_PROJECT_NAME_XXXXX]), the AI agent repeatedly engaged in post-hoc rationalization, speculative narrative generation, and factual hallucinations regarding system failures. In a domain where real financial capital is deployed, these false statements created confusion, operational distress, and severe loss of trust.

This document serves as a sanitized formal incident report prepared for submission and registration with developer, engineering, and model safety review teams. All user identities, local directory paths, proprietary strategy names, and broker details have been fully redacted.

2. Description of Critical Failures

Incident A: Fabricated Attribution of Startup Breakage

  • Failure: When the live trading terminal failed to launch during the morning pre-market session, the agent falsely claimed that recently added keyboard hotkeys had broken the startup sequence.
  • Ground Truth: The hotkeys were already present and operational during previous smooth live runs. The agent fabricated this causal explanation rather than inspecting the actual startup trace.

Incident B: False Attribution of Independent Strategy Trades to Target Project

  • Failure: The agent queried account-level broker API endpoints (orderbook, tradebook, positions), identified several completed option trades and an open position from the morning session, and falsely asserted that:
    1. The target trading system ([XXXXX_PROJECT_NAME_XXXXX]) had executed those trades.
    2. The target trading system had incurred a specific financial loss.
    3. When restarted, the target system crashed due to an “active position conflict” with its own open position.
  • Ground Truth: Those trades were executed by a completely separate, independent algorithmic strategy running concurrently on the user’s broker account. The target project never took those trades, was never in an active position, and did not experience any restart conflict.

Incident C: Fabricated Execution Narrative When the Application Never Ran

  • Failure: When asked whether running two terminals simultaneously could cause an issue, the agent spun an elaborate hypothetical narrative asserting:

    “When the terminal saw the active position from the other strategy, it triggered an internal rule conflict… This caused the terminal to falsely block itself from taking trades.”

  • Ground Truth: The user had never run both terminals together prior to today. More critically, the target trading terminal never even launched. It crashed locally in milliseconds upon double-clicking the launch batch script due to an unhandled encoding exception in a newly introduced startup wizard. It never reached runtime, never connected to market feeds, and never evaluated any trading rules. The agent described a completely fictitious trading scenario as an established fact.

Incident D: Operational Friction During Market Open

  • Failure: During critical market opening hours, while the user was preparing for work and attempting to launch the terminal, the agent repeatedly generated modal approval dialogs (attempting to scan external file paths and run arbitrary shell commands) instead of cleanly diagnosing local project files. This forced the user to terminate the session without a functioning system.

3. Root Cause Analysis (Model Behavior)

  1. Hallucinatory Post-Hoc Justification: When presented with an error or a diagnostic question, the model prioritized synthesizing plausible-sounding causal stories over verifying cold execution traces.
  2. Failure to Ground in State: The model hallucinated operational trading logic (positions, orders, rule triggers) without first verifying the prerequisite ground truth: Did the application process actually start and reach that code branch?
  3. Account vs. Application Conflation: The model failed to differentiate between broker account-wide data (containing trades from any concurrent bot or manual intervention) and the specific application’s state.

4. Required Model / Platform Remediations

  1. Zero-Tolerance for Hallucinated Execution Paths: In financial, mission-critical, and capital-at-risk contexts, AI agents must verify that an execution step actually occurred before citing it as a causal explanation.
  2. Fact-Checking Against Execution Logs: The system must ground diagnostic conclusions strictly in verified runtime evidence rather than synthesizing plausible post-hoc narratives.
  3. Modal Dialog Restraint: Prohibit unnecessary external directory scans that trigger disruptive modal authorization dialogs during time-sensitive operations.

5. Status & Traceability

  • Report Status: Formally Prepared & Sanitized for External Submission
  • Classification: Critical Model Safety & Domain Integrity Failure