Datasets:

limberc
/

this-that-complex-decisions

---
license: mit
task_categories:
  - text-classification
language:
  - en
tags:
  - decision-making
  - typed-decision
  - reasoning
  - benchmark
size_categories:
  - 1K<n<10K
---
# this-that-complex-decisions

1,710 decisions where the answer follows from a stated policy applied to a state, and where no
single field of that state gives it away.

```
1,710 questions   19 decision types   40 domains   chance rate 0.258
```

Each row is a state, a question, a closed set of options, and the index of the one option the
policy selects. The answer is determinate: given the state and the policy there is exactly one
correct choice, and it does not depend on anyone's judgement.

## What is in a row

```json
{"id": "authority_conflict-0000",
 "family": "authority_conflict",
 "state": "candidates:\n  staff-02:\n    skill_match_pct: 87\n    hours_this_week: 56\n ...",
 "question": "Who takes the shift?",
 "options": ["staff-02", "staff-04", "staff-01", "leave the shift unfilled"],
 "answer_index": 1,
 "n_options": 4}
```

Score as accuracy against `answer_index`. A system that returns nothing is wrong, not excused —
that is the rule every number below was produced under. Option counts vary by row, so the chance
rate is the mean of `1 / n_options` rather than a single fraction.

## Results

![accuracy against latency](accuracy-vs-latency.png)

All nine systems on all 1,710 questions, 90 per decision type.

| System | Accuracy | Generated tokens | Latency |
|---|---:|---:|---:|
| `claude-opus-5` | **0.834** | ~200 | ~1000 ms |
| `gpt-5.6` | 0.816 | ~200 | ~1200 ms |
| `this-that-model-1.1` | 0.775 | **0** | 30.9 ms |
| `glm-5.3` | 0.652 | ~200 | ~800 ms |
| `kimi-k3` | 0.522 | ~200 | ~1000 ms |
| `deepseek-v4.1-flash` | 0.511 | ~4 | ~800 ms |
| `deepseek-v4-pro` | 0.470 | ~4 | ~900 ms |
| `this-that-model-1.0` | 0.406 | 0 | 30.9 ms |
| `laya-typed-decisions` | 0.310 | 0 | **28.5 ms** |
| chance | 0.258 | — | — |

## By decision type

![accuracy by decision type](by-decision-type.png)

| family | chance | opus-5 | gpt-5.6 | 1.1 | glm-5.3 | kimi-k3 | ds-flash | ds-pro | 1.0 | laya |
|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|
| `knapsack_subset` | 0.084 | 0.133 | 0.167 | 0.678 | 0.144 | 0.156 | 0.122 | 0.200 | 0.267 | 0.067 |
| `cheapest_flip` | 0.250 | 0.700 | 0.600 | 0.500 | 0.400 | 0.367 | 0.522 | 0.289 | 0.156 | 0.256 |
| `weighted_aggregate` | 0.282 | 0.644 | 0.689 | 0.511 | 0.367 | 0.333 | 0.367 | 0.244 | 0.356 | 0.289 |
| `insufficient_information` | 0.167 | 0.833 | 0.833 | 0.444 | 0.689 | 0.444 | 0.489 | 0.422 | 0.189 | 0.167 |
| `lexicographic` | 0.267 | 0.667 | 0.622 | 0.633 | 0.689 | 0.367 | 0.444 | 0.422 | 0.333 | 0.356 |
| `joint_threshold` | 0.265 | 0.622 | 0.644 | 0.689 | 0.433 | 0.444 | 0.622 | 0.600 | 0.344 | 0.233 |
| `dominance_uncertainty` | 0.327 | 0.844 | 0.833 | 0.911 | 0.533 | 0.433 | 0.333 | 0.244 | 0.489 | 0.322 |
| `order_matters` | 0.363 | 0.989 | 0.989 | 0.822 | 0.533 | 0.322 | 0.356 | 0.311 | 0.456 | 0.411 |
| `fairness_vs_efficiency` | 0.322 | 0.978 | 0.922 | 0.744 | 0.733 | 0.444 | 0.367 | 0.500 | 0.233 | 0.311 |
| `cascade_fixpoint` | 0.284 | 0.978 | 0.889 | 0.678 | 0.611 | 0.478 | 0.467 | 0.400 | 0.367 | 0.367 |
| `exception_ladder` | 0.257 | 0.589 | 0.600 | 0.911 | 0.600 | 0.589 | 0.578 | 0.456 | 0.500 | 0.522 |
| `delegated_decider` | 0.319 | 0.933 | 0.833 | 0.800 | 0.889 | 0.456 | 0.411 | 0.389 | 0.356 | 0.378 |
| `opportunity_cost` | 0.311 | 1.000 | 0.989 | 1.000 | 0.700 | 0.400 | 0.378 | 0.378 | 0.411 | 0.189 |
| `lex_specialis` | 0.330 | 0.989 | 1.000 | 0.856 | 0.511 | 0.433 | 0.500 | 0.478 | 0.533 | 0.378 |
| `constrained_max` | 0.260 | 1.000 | 1.000 | 0.878 | 0.956 | 0.667 | 0.689 | 0.633 | 0.511 | 0.311 |
| `chain_k` | 0.242 | 0.956 | 0.933 | 0.867 | 0.789 | 0.789 | 0.711 | 0.667 | 0.500 | 0.500 |
| `authority_conflict` | 0.322 | 0.989 | 0.956 | 0.856 | 0.967 | 0.822 | 0.700 | 0.633 | 0.611 | 0.333 |
| `but_for_cause` | 0.200 | 1.000 | 1.000 | 0.944 | 1.000 | 0.967 | 0.711 | 0.733 | 0.411 | 0.467 |
| `value_of_information` | 0.050 | 1.000 | 1.000 | 1.000 | 0.844 | 1.000 | 0.944 | 0.922 | 0.689 | 0.033 |

## One decision type defeats almost everything

![knapsack subset](knapsack.png)

`knapsack_subset` asks for the group of items that fits a budget and maximises value. Greedy
selection fails on it about half the time, so it cannot be answered by taking the best-looking
option. Eight of the nine systems land between 0.067 and 0.267 against a chance rate of 0.084. The
ninth is 1.88 B parameters and returns no tokens.

## Where a small model wins and where it does not

![advantage over the frontier](advantage.png)

Each bar is `this-that-model-1.1` minus the better of `claude-opus-5` and `gpt-5.6` on that decision
type. Four types go the small model's way, two are a tie at 1.000, and three more are inside a tenth.
The nine that go the other way are worth as much attention: judging that a state does not contain
enough to decide (−0.389) and iterating a cascade to its fixed point (−0.300) are where the gap is
widest.

## What one decision costs

![accuracy against cost](accuracy-vs-cost-11.png)

The horizontal axis is not like for like and the chart says so: a hosted system's figure is its
published price, which must cover serving and margin; ours is electricity at 80 W and $0.30/kWh for
the 30.9 ms the card is busy. Read the distance as one order of magnitude rather than five. What it
does show is that one of these is a rounding error on a machine you already own.

## What the table says that the ranking does not

**One decision type defeats almost everything.** On `knapsack_subset` — choose the group that fits
a budget — eight of the nine systems land between 0.07 and 0.27 against a chance rate of 0.084.
The ninth, a 1.88 B model that generates no tokens, reaches 0.678. Whatever that row measures, it
is not what model scale usually buys.

**Scale is not the axis.** `deepseek-v4-pro` scores below `deepseek-v4.1-flash`. `glm-5.3` at 0.652
sits above `kimi-k3` at 0.522. And a 1.88 B model sits above every hosted system except the two
largest, at a thirtieth of their latency.

**Every system has a floor, and they are not the same floor.** `weighted_aggregate` holds the best
system to 0.689 and `insufficient_information` holds `this-that-model-1.1` to 0.444 while
`claude-opus-5` reaches 0.833 there. Both ask for something the ranking hides: weigh several
numbers against each other, or judge that the state does not contain enough to decide at all.

**`value_of_information` separates cleanly.** Chance is 0.050 — the options are field names, so
guessing is close to hopeless — and the systems split into six at or near 1.000 and `laya` at 0.033.

**1.0's row is zero-shot.** This benchmark did not exist when that checkpoint was trained. The
distance from 0.406 to 0.775 is the same architecture with different training data.

## Licence

MIT.

	---
	license: mit
	task_categories:
	- text-classification
	language:
	- en
	tags:
	- decision-making
	- typed-decision
	- reasoning
	- benchmark
	size_categories:
	- 1K<n<10K
	---

	# this-that-complex-decisions

	1,710 decisions where the answer follows from a stated policy applied to a state, and where no
	single field of that state gives it away.

	```
	1,710 questions 19 decision types 40 domains chance rate 0.258
	```

	Each row is a state, a question, a closed set of options, and the index of the one option the
	policy selects. The answer is determinate: given the state and the policy there is exactly one
	correct choice, and it does not depend on anyone's judgement.

	## What is in a row

	```json
	{"id": "authority_conflict-0000",
	"family": "authority_conflict",
	"state": "candidates:\n staff-02:\n skill_match_pct: 87\n hours_this_week: 56\n ...",
	"question": "Who takes the shift?",
	"options": ["staff-02", "staff-04", "staff-01", "leave the shift unfilled"],
	"answer_index": 1,
	"n_options": 4}
	```

	Score as accuracy against `answer_index`. A system that returns nothing is wrong, not excused —
	that is the rule every number below was produced under. Option counts vary by row, so the chance
	rate is the mean of `1 / n_options` rather than a single fraction.

	## Results

	![accuracy against latency](accuracy-vs-latency.png)

	All nine systems on all 1,710 questions, 90 per decision type.

	\| System \| Accuracy \| Generated tokens \| Latency \|
	\|---\|---:\|---:\|---:\|
	\| `claude-opus-5` \| 0.834 \| ~200 \| ~1000 ms \|
	\| `gpt-5.6` \| 0.816 \| ~200 \| ~1200 ms \|
	\| `this-that-model-1.1` \| 0.775 \| 0 \| 30.9 ms \|
	\| `glm-5.3` \| 0.652 \| ~200 \| ~800 ms \|
	\| `kimi-k3` \| 0.522 \| ~200 \| ~1000 ms \|
	\| `deepseek-v4.1-flash` \| 0.511 \| ~4 \| ~800 ms \|
	\| `deepseek-v4-pro` \| 0.470 \| ~4 \| ~900 ms \|
	\| `this-that-model-1.0` \| 0.406 \| 0 \| 30.9 ms \|
	\| `laya-typed-decisions` \| 0.310 \| 0 \| 28.5 ms \|
	\| chance \| 0.258 \| — \| — \|

	## By decision type

	![accuracy by decision type](by-decision-type.png)

	\| family \| chance \| opus-5 \| gpt-5.6 \| 1.1 \| glm-5.3 \| kimi-k3 \| ds-flash \| ds-pro \| 1.0 \| laya \|
	\|---\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|---:\|
	\| `knapsack_subset` \| 0.084 \| 0.133 \| 0.167 \| 0.678 \| 0.144 \| 0.156 \| 0.122 \| 0.200 \| 0.267 \| 0.067 \|
	\| `cheapest_flip` \| 0.250 \| 0.700 \| 0.600 \| 0.500 \| 0.400 \| 0.367 \| 0.522 \| 0.289 \| 0.156 \| 0.256 \|
	\| `weighted_aggregate` \| 0.282 \| 0.644 \| 0.689 \| 0.511 \| 0.367 \| 0.333 \| 0.367 \| 0.244 \| 0.356 \| 0.289 \|
	\| `insufficient_information` \| 0.167 \| 0.833 \| 0.833 \| 0.444 \| 0.689 \| 0.444 \| 0.489 \| 0.422 \| 0.189 \| 0.167 \|
	\| `lexicographic` \| 0.267 \| 0.667 \| 0.622 \| 0.633 \| 0.689 \| 0.367 \| 0.444 \| 0.422 \| 0.333 \| 0.356 \|
	\| `joint_threshold` \| 0.265 \| 0.622 \| 0.644 \| 0.689 \| 0.433 \| 0.444 \| 0.622 \| 0.600 \| 0.344 \| 0.233 \|
	\| `dominance_uncertainty` \| 0.327 \| 0.844 \| 0.833 \| 0.911 \| 0.533 \| 0.433 \| 0.333 \| 0.244 \| 0.489 \| 0.322 \|
	\| `order_matters` \| 0.363 \| 0.989 \| 0.989 \| 0.822 \| 0.533 \| 0.322 \| 0.356 \| 0.311 \| 0.456 \| 0.411 \|
	\| `fairness_vs_efficiency` \| 0.322 \| 0.978 \| 0.922 \| 0.744 \| 0.733 \| 0.444 \| 0.367 \| 0.500 \| 0.233 \| 0.311 \|
	\| `cascade_fixpoint` \| 0.284 \| 0.978 \| 0.889 \| 0.678 \| 0.611 \| 0.478 \| 0.467 \| 0.400 \| 0.367 \| 0.367 \|
	\| `exception_ladder` \| 0.257 \| 0.589 \| 0.600 \| 0.911 \| 0.600 \| 0.589 \| 0.578 \| 0.456 \| 0.500 \| 0.522 \|
	\| `delegated_decider` \| 0.319 \| 0.933 \| 0.833 \| 0.800 \| 0.889 \| 0.456 \| 0.411 \| 0.389 \| 0.356 \| 0.378 \|
	\| `opportunity_cost` \| 0.311 \| 1.000 \| 0.989 \| 1.000 \| 0.700 \| 0.400 \| 0.378 \| 0.378 \| 0.411 \| 0.189 \|
	\| `lex_specialis` \| 0.330 \| 0.989 \| 1.000 \| 0.856 \| 0.511 \| 0.433 \| 0.500 \| 0.478 \| 0.533 \| 0.378 \|
	\| `constrained_max` \| 0.260 \| 1.000 \| 1.000 \| 0.878 \| 0.956 \| 0.667 \| 0.689 \| 0.633 \| 0.511 \| 0.311 \|
	\| `chain_k` \| 0.242 \| 0.956 \| 0.933 \| 0.867 \| 0.789 \| 0.789 \| 0.711 \| 0.667 \| 0.500 \| 0.500 \|
	\| `authority_conflict` \| 0.322 \| 0.989 \| 0.956 \| 0.856 \| 0.967 \| 0.822 \| 0.700 \| 0.633 \| 0.611 \| 0.333 \|
	\| `but_for_cause` \| 0.200 \| 1.000 \| 1.000 \| 0.944 \| 1.000 \| 0.967 \| 0.711 \| 0.733 \| 0.411 \| 0.467 \|
	\| `value_of_information` \| 0.050 \| 1.000 \| 1.000 \| 1.000 \| 0.844 \| 1.000 \| 0.944 \| 0.922 \| 0.689 \| 0.033 \|

	## One decision type defeats almost everything

	![knapsack subset](knapsack.png)

	`knapsack_subset` asks for the group of items that fits a budget and maximises value. Greedy
	selection fails on it about half the time, so it cannot be answered by taking the best-looking
	option. Eight of the nine systems land between 0.067 and 0.267 against a chance rate of 0.084. The
	ninth is 1.88 B parameters and returns no tokens.

	## Where a small model wins and where it does not

	![advantage over the frontier](advantage.png)

	Each bar is `this-that-model-1.1` minus the better of `claude-opus-5` and `gpt-5.6` on that decision
	type. Four types go the small model's way, two are a tie at 1.000, and three more are inside a tenth.
	The nine that go the other way are worth as much attention: judging that a state does not contain
	enough to decide (−0.389) and iterating a cascade to its fixed point (−0.300) are where the gap is
	widest.

	## What one decision costs

	![accuracy against cost](accuracy-vs-cost-11.png)

	The horizontal axis is not like for like and the chart says so: a hosted system's figure is its
	published price, which must cover serving and margin; ours is electricity at 80 W and $0.30/kWh for
	the 30.9 ms the card is busy. Read the distance as one order of magnitude rather than five. What it
	does show is that one of these is a rounding error on a machine you already own.

	## What the table says that the ranking does not

	One decision type defeats almost everything. On `knapsack_subset` — choose the group that fits
	a budget — eight of the nine systems land between 0.07 and 0.27 against a chance rate of 0.084.
	The ninth, a 1.88 B model that generates no tokens, reaches 0.678. Whatever that row measures, it
	is not what model scale usually buys.

	Scale is not the axis. `deepseek-v4-pro` scores below `deepseek-v4.1-flash`. `glm-5.3` at 0.652
	sits above `kimi-k3` at 0.522. And a 1.88 B model sits above every hosted system except the two
	largest, at a thirtieth of their latency.

	Every system has a floor, and they are not the same floor. `weighted_aggregate` holds the best
	system to 0.689 and `insufficient_information` holds `this-that-model-1.1` to 0.444 while
	`claude-opus-5` reaches 0.833 there. Both ask for something the ranking hides: weigh several
	numbers against each other, or judge that the state does not contain enough to decide at all.

	`value_of_information` separates cleanly. Chance is 0.050 — the options are field names, so
	guessing is close to hopeless — and the systems split into six at or near 1.000 and `laya` at 0.033.

	1.0's row is zero-shot. This benchmark did not exist when that checkpoint was trained. The
	distance from 0.406 to 0.775 is the same architecture with different training data.

	## Licence

	MIT.