Timezone Accuracy: Inference, Reasoning, And Uncertainty

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Timezone Accuracy: Inference, Reasoning, and Uncertainty

Hey folks! Ever think about how tricky timezones can be, especially when you're dealing with news articles and events happening all over the globe? Well, we've been deep in the weeds tackling this exact problem, and it's a real head-scratcher. Let's dive into the epistemic concerns around timezone inference and how we can achieve temporal accuracy, and what we're doing about it.

The Problem: Timezone Inference and Its Pitfalls

So, the system currently tries to figure out timezones based on where a news article is from. For example, if it's a story from Hong Kong, it'll assume it's in HKT (+08:00). Sounds straightforward, right? Well, it gets complicated real fast. This is where those epistemic concerns start creeping in, and here's why:

Inference Accuracy: From Simple to Seriously Tricky

  • The Simple Case: "Fire at 2:51 p.m." in a Hong Kong article. Easy peasy – probably HKT. No problem here.
  • The Ambiguous Case: "Meeting scheduled for 3 p.m." in an article about a deal between New York City and Hong Kong. Which timezone do you use? NYC time? Hong Kong time? This is where things get dicey, and we start to lose temporal accuracy.
  • The Mixed Events: Events that span multiple locations, like coordinated attacks or international phone calls. Figuring out the right timezone for each part of the event becomes a logistical nightmare.

What Can We Actually Know?

  • Publication vs. Event Timezone: The timezone of the article's publication doesn't always match the timezone of the event itself. Think about it: a news story might be written and published hours after an event happened, potentially in a completely different timezone.
  • Location Mentions Don't Always Help: Just because a location is mentioned doesn't mean the event happened there. A news outlet in Los Angeles could report on an event in Beijing. What timezone do you use?
  • Historical Events: For historical events, the article's current "now" timezone can mess things up. We want to understand what happened at that moment in history, not just how it's reported today.

Impact on Temporal Reasoning

  • Multi-Signal Scoring: Our system uses temporal proximity to understand relationships between different pieces of information. If timezones are off, the whole scoring system goes haywire. A tiny 8-hour timezone error could completely misalign related claims, making it seem like things happened at different times than they actually did. We want to achieve temporal accuracy.
  • Reference Detection: We use a 48-hour window to find contextual matches. Errors here mean we might miss critical connections, or incorrectly link unrelated events.
  • Causal Reasoning: If we want to understand cause and effect, temporal accuracy is key. Saying "X happened before Y" requires pinpoint precision, and timezone errors can completely destroy the meaning.

The Current Fix: A Step in the Right Direction

So, what have we done to tackle this mess? Here's the lowdown on the current fix:

Added Timezone Field to LLM Extraction Prompt

  • The LLM (Large Language Model) now tries to infer the timezone from the article's location context. For example, the LLM will analyze "Hong Kong" article and apply the proper timezone.
  • We're storing timestamps in the ISO format with the timezone offset: 2025-11-26T14:51:00+08:00. This is the international standard, and it helps to achieve temporal accuracy.
  • As a backup, if it's ambiguous, we default to UTC (Coordinated Universal Time). This isn't perfect, but it's a safer bet than making a wrong guess.
  • This fix is also backwards compatible, so it won't break anything that's already in the system.

Files Changed

  • backend/semantic_analyzer.py: We added the timezone to the prompt and made some adjustments to how things are normalized.
  • backend/workers/semantic_worker.py: We updated the system to parse timezone-aware ISO strings. This ensures everything is consistent.

Long-Term Considerations: What's Next?

This current fix is a good start, but we know there's more work to be done. We're looking at a few options for the future. How do we make sure our system is as accurate as possible? Let's take a look.

Option A: Store Timezone Explicitly Per Claim

  • Pros: Preserves the original context, which is really important. Also, it allows for timezone-aware temporal reasoning.
  • Cons: Requires the LLM to be accurate in its inferences, which isn't always a guarantee. We are trying to achieve a more temporal accuracy.

Option B: Normalize Everything to UTC

  • Pros: This would simplify comparison logic and eliminate inference errors. It makes everything easier to manage.
  • Cons: We'd lose some of the context. We'd also miss out on nuances, like the difference between "afternoon fire" and "morning fire." Also, we have a current bug where we treated local times AS UTC, which is a big issue.

Option C: Bayesian Uncertainty

  • This is where things get really interesting. We store the timezone, along with a confidence level: {time: "14:51", timezone: "+08:00", tz_confidence: 0.8}.
  • We propagate this uncertainty to temporal proximity scoring. It means we acknowledge that we don't know everything, and it allows us to build a more robust system.
  • We're acknowledging the limits of our knowledge.

Recommendations: What Should We Do?

Based on all of this, here's the plan:

  1. Short Term: Keep the simple timezone inference. It's working well for now.
  2. Medium Term: Add timezone confidence scoring, so we can quantify our uncertainty and make better decisions.
  3. Long Term: Consider temporal uncertainty in our event formation logic. This is where things get really sophisticated.

Testing Notes

We need to test this system thoroughly. Here are the test cases we're planning:

  • Same-timezone events: Events happening in the same timezone (e.g., a fire in Hong Kong, with all claims in HKT).
  • Cross-timezone reporting: US articles reporting on events in Hong Kong.
  • Explicit timezone mentions: Cases where the article explicitly mentions timezones (e.g., "3 p.m. EST", "14:51 HKT").
  • Ambiguous cases: Cases where there's no clear indication of the timezone.

So there you have it, folks! It's a complex problem, but we're making progress. With the current fix, we can achieve temporal accuracy.

We will continue improving our timezone handling to make sure our system is as accurate as possible. It is our goal to create a system that can understand events happening around the world with precision and reliability. We are working to achieve our goals in the future. We are implementing new techniques to deal with epistemic concerns.

We appreciate the team's continuous effort in this process.

. Great job! Here's the updated JSON: