# Session Analysis: rollout-2026-01-29T11-47-50-019c0994-c5b5-7340-a58c-ce993a3e9f9d.jsonl

**Date**: 2026-01-29 | **Duration**: 1088s | **Turns**: 5 | **Model**: gpt-5.2-codex
**Type**: bug-fix | **Outcome**: resolved | **Specificity**: 4/5 | **Struggle**: 0/10

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## Journey

This was the deepest technical analysis session across all 12 sessions. The developer ran training with the deterministic test negatives from session 9 and got another plateau pattern (7 epochs, test loss oscillating around 0.248-0.253). They asked "why is my test loss not decreasing?" — but this time the assistant (running on gpt-5.2-codex, a newer model) performed an exhaustive investigation and delivered a comprehensive diagnostic report.

The assistant identified the **primary root cause as false negatives in the negative sampling pipeline**: the sampler draws negatives globally without excluding the current user's positive series ID, creating label noise that caps generalization. It also flagged BatchNorm sensitivity, aggressive regularization (weight_decay=1e-3 + dropout=0.2), and train/test distribution mismatch in negative sampling.

The session ended with the assistant offering to implement three fixes: false-negative-free sampling, BatchNorm removal, and an LR scheduler. The developer responded with just "." and the session concluded.

## Prompting Assessment (4/5)

**What worked:**
The developer included the full training output and had all relevant files open in their IDE tabs, giving the assistant strong context.

> "Epoch 001 | train_loss=0.2531 | test_loss=0.2499 | elapsed=165.2s\n...\nEpoch 007 | train_loss=0.2398 | test_loss=0.2503 | elapsed=188.8s\n\n\nwhy is my test loss not decreasing?"

**What was vague or could improve:**
The question is a slight improvement over "how to improve my model?" — it names a specific symptom. But the developer still didn't provide context about what changes had already been applied from previous sessions, forcing the assistant to re-read all files.

> "why is my test loss not decreasing?"

## Outcome: resolved

**Evidence:**
> "Short answer: your eval is correct, but your data pipeline injects label noise and your model regularization/optimization choices likely cap generalization, so test loss hovers around a flat baseline despite small train-loss gains."

> "Primary issue found: false negatives from the sampler... negatives are drawn globally and then concatenated with positives, without excluding the current positive series ID"

The assistant delivered a masterful diagnostic with specific file locations, line numbers, and a clear prioritized fix list.

## Struggle Points

This session was efficient with no significant struggle points. The sandbox still required escalation for every file read, but the assistant handled it smoothly. The struggle_score of 0 is accurate — this was a clean diagnostic session.

## Aha Insight

The assistant uncovered a subtle but critical data quality issue: **false negatives in the training pipeline**. When negatives are sampled globally without per-user exclusion, some "negative" examples may actually be items the user has positively interacted with. This creates conflicting labels — the same (user, item) pair can appear as both positive and negative across batches. This label noise creates a hard ceiling on test loss that no architectural change can break through. The fix isn't in the model or the optimizer — it's in the data pipeline.

**Evidence:**
> "Because user features are tiled (col/src/model/dataset.py:112-119) and negatives are assigned in a flat sequence, it is possible for a negative assigned to user i to equal that user's true positive series. That's label noise ('false negatives'), which suppresses both train and test improvements and can pin test loss near a plateau."

## Coach Tip

When your model plateaus, resist the urge to only tune hyperparameters and architecture. Instead, audit your data pipeline first: are your labels correct? Are there contradictions? Are your train and test distributions aligned? A model can never outperform the noise floor of its training data. In this case, adding `sample_excluding(positive_id)` to the sampler would have more impact than any optimizer or architecture change.
