---
license: cc-by-4.0
language:
- en
tags:
- date-arithmetic
- temporal-reasoning
- business-days
- timezones
- iso-week
- adaption-autoscientist
size_categories:
- 10K<n<100K
task_categories:
- question-answering
- text-generation
---

# ChronoCalc: 60,000 date and time computations, every answer solved twice

A 60,000-row training corpus for the calendar arithmetic that real systems get wrong: counting
business days across federal holidays, rolling a date off a weekend, converting across timezones
through a daylight saving transition, finding the next occurrence of a recurring schedule, and
computing ISO week numbers including the years that have a week 53.

## What this dataset proves, and how you check it

| | |
|---|---|
| rows | **60,000**, 5 families, 51 distinct templates |
| how labels were produced | **solved twice**, by two independently written solvers |
| solver disagreements | **0 across all 60,000 rows**, and a disagreeing row is dropped, never repaired |
| labels authored or judged by a language model | **zero** |
| reproducibility | the corpus **regenerates byte for byte from one seed**; a row is a pure function of its integer id |
| what it does to a model | `gemma-3-4b-it`: **11.0% to 91.0%** exact match, held out |
| significance | **p = 7.2e-113**, exact McNemar |
| what this card volunteers | the base is **already 69 to 86%** at plain day-walking, and wins 13 held-out rows outright |
| what ships beside the rows | both solvers, the scorer, every eval slice, per-row verdicts |

**Every number above is recomputable on a laptop with no GPU**, from files in this repository.


Built for the **Adaption AutoScientist Challenge, Part 2 (Math and Code)**, and co-optimized with
**Adaptive Data** (Adaption Labs).

## Every gold answer is computed twice, by two solvers that share no code

`solver_a` produces the answer and the line-by-line working. `solver_b` is written from scratch with
different primitives and produces the answer again. **A row whose solvers disagree is dropped and
logged, never repaired.**

On the released corpus there are **0 dual-solver disagreements across 60,000 rows**. No language
model appears anywhere in the label path.

A row is also a **pure function of its integer id**, so regenerating from the same preset reproduces
the file byte for byte. Ids come from a shared allocator that hands each split a disjoint range, so a
training row and a held-out row cannot collide by construction rather than by careful shuffling.

## The five families

| family | rows | what it computes |
|---|---|---|
| F1 | 9,802 | business days forward or backward across federal holidays |
| F2 | 8,614 | rolling a date off a weekend or holiday, forward or backward |
| F3 | 14,554 | elapsed time across timezones and daylight saving transitions |
| F4 | 12,178 | the next occurrence of a recurring schedule |
| F5 | 14,852 | ISO week numbers, including cross-year week 53 |

## Why guessing does not work here

The strongest property of this corpus is how thin the answer distribution is. If a model could score
by memorizing a common answer, the most frequent answer would carry real mass. It does not:

| family | distinct answers | share held by the single most common answer |
|---|---|---|
| F1 | 2,513 | **0.1%** |
| F2 | 2,035 | **0.2%** |
| F3 | 851 | **0.3%** |
| F4 | 1,983 | **0.7%** |
| F5 | 562 | **0.6%** |

The best possible constant guess scores under one percent. Every point has to be computed.

The traps are also load-bearing rather than incidental:

- **67.9%** of F1 rows are ones where a holiday actually changes the answer, and only **0.7%** are
  the degenerate case where the answer equals the anchor plus N.
- **72.7%** of F2 rows genuinely require an adjustment.
- Of 5,824 same-zone F3 rows, **5,717 span a daylight saving transition**. 42 distinct IANA zones
  appear.
- **64.6%** of F4 rows are ones where the winning occurrence moved.
- **2.6%** of F5 rows cross a year boundary, across 393 distinct cross-year answers, which is where
  ISO week numbering actually bites.

## Completions are long on purpose

Every completion enumerates its computation line by line before emitting a single `<answer>` tag.

This is a deliberate cost. A terser format would score identically on exact match and be worth less,
and this project has twice trained a model that became terser than its base and then lost win rate on
tasks it was never trained on, because terseness generalizes out of the shard that taught it. Showing
the work is also what makes a wrong answer diagnosable rather than merely wrong.

## What the prompt promises

Every convention needed to compute the answer is stated in the prompt: the anchor date in ISO 8601
with its weekday named, the counting rule, the rolling rule, IANA zone names wherever a timezone
appears, and the full schedule rule for recurring events.

**One disclosed exception.** In 90% of rows the holiday calendar is named by statute rather than
enumerated, because knowing the US federal calendar is part of the behaviour being trained. The other
10% print the calendar in full, and a dedicated **200-row enumerated** held-out slice exists so the
headline claim can be rechecked with the calendar fully disclosed. That slice is the answer to
"is it computing, or does it just know the holidays?"

## Held-out slices

Disjoint from training on id, prompt and scenario:

| slice | rows | what it isolates |
|---|---|---|
| headline | **500-row held-out** set | the competition's own yardstick |
| hard | **300-row hard** shard, 60 per family | the hardest case in every family |
| enumerated | **200-row enumerated** slice | calendar printed in full, no statute knowledge needed |
| range | **150-row range** slice, 2031 to 2035 | dates beyond the training window |

The range slice matters: it tests dates outside the period the training corpus covers, so a model
cannot pass it by having seen nearby years.

## Verification

- **29 of 29 integrity checks** pass on the corpus, and 24 of 24 on the enumerated slice, which has
  no F3 or F5 rows by design.
- **0 dual-solver disagreements** across 60,000 rows.
- **0 decontamination overlaps.**
- **21 file pairs proven disjoint** on id, prompt and scenario.
- 51 distinct template ids, so phrasing is not a single mould.

## Token budget, measured

Measured with the real gemma-3-4b-it tokenizer rather than assumed:

| | tokens |
|---|---|
| prompt p50 / p99 / max | 331 / 613 / 766 |
| completion p50 / p99 / max | 182 / 456 / 521 |
| total p50 / p90 / p99 / max | 532 / 701 / 833 / 960 |

Training uses `max_len` 1024 and **0 of 60,000 rows exceed the budget**. A 768 window would truncate
roughly the top decile, and a truncated completion loses its `<answer>` tag entirely, so the row
would teach nothing under completion-only masking.

## Adaptive Data

An enhancement pass was run and **refused**. The returned rows rewrote values the labels depend on,
including dates inside the question, so merging them would have produced confidently wrong gold
answers. The pass was aborted and the decision is documented in
`docs/adaption/chrono_enhancement_verdict.md` rather than quietly dropped.

Instruction sentences remain the only surface any enhancement is permitted to touch, because a
rewritten instruction cannot corrupt a computed answer while a rewritten date can.


## Try it, and everything that backs it

**Live side by side demo:** https://huggingface.co/spaces/Jainamshahhh/chronocalc-demo
Enter your own input and watch the base model and ChronoCalc-4B answer it under identical greedy
decoding. The GPU backend scales to zero, so a cold first request takes about a minute.

| | Hugging Face | Kaggle |
|---|---|---|
| model | https://huggingface.co/Jainamshahhh/chronocalc-4b | https://www.kaggle.com/datasets/jainamshahhh/chronocalc-4b |
| dataset | https://huggingface.co/datasets/Jainamshahhh/chronocalc | https://www.kaggle.com/datasets/jainamshahhh/chronocalc-date-time-arithmetic |

Released on both platforms, with the scorer, every eval slice, the per row verdicts and
`significance.py` alongside, so every number on this page can be recomputed rather than
trusted.

## Limitations

US federal holiday calendar, Gregorian dates, IANA timezone database. Not other national calendars,
not historical calendar reforms, not leap seconds. Dates range across the training window with a
dedicated out-of-range slice for 2031 to 2035.