Datasets:

AgentsSci
/

EMNLP_Cost-Aware-Protocol-Routing

This release contains no upstream problem text, gold answers, reference solutions, or answer options (see docs/license_audit.md). Every table keys on stable identifiers instead. This document explains how to attach the text yourself from the upstream sources, under those sources' own licenses.

The two identifier columns

Every per-problem table carries both:

problem_id — the canonical, cross-solver identifier. Use this to join the same problem across the gpt-oss-120b and Gemma-4-31B-it settings.
source_problem_id — the identifier exactly as it appeared in that setting's own run artifacts. This is what you match against upstream.

They differ because the two solver campaigns were exported under different naming schemes. For OmniMath and LAB-Bench the two solvers used different source_problem_id namespaces for the very same problems:

Benchmark	gpt-oss-120b `source_problem_id`	Gemma-4-31B-it `source_problem_id`	canonical `problem_id`
omnimath2	`omni2_1`	`omni2:t01:1`	`omni2_...`
labbench	`labbench_cloningscenarios_000001`	`lab-bench:CloningScenarios:00540e26-...`	`labbench_...`
jeebench	`jeebench:JEE Adv 2016 Paper 1:1`	same	same
scibench	`scibench:atkins:e1.1(a)(a)`	same	same

Both solvers cover an identical canonical problem set in every paired setting (verified: symmetric difference is empty for all five pairs). Always join on problem_id, never on source_problem_id, when comparing solvers.

data/problems.csv carries both columns for all 6,803 released problems, plus a legacy_tier_id column for OmniMath (omni2:tNN:idx) that matches the Gemma namespace and the primary-split identifiers.

Per-benchmark reconstruction

Omni-MATH-2 (4,181 problems)

Upstream: https://huggingface.co/datasets/martheballon/Omni-MATH-2 (Apache-2.0).

The released slice is a filtered exact-answer subset built locally; it is not a contiguous upstream range, so you cannot slice it by index. Use data/problems.csv (source, difficulty, difficulty_tier, domain) to identify rows, then match against the upstream corpus. The legacy_tier_id column encodes the project's own tier partition: omni2:tNN:idx is the idx-th problem of difficulty tier NN. Tier sizes are in registry/release_manifest.json under coverage.omnimath_tier_counts.

Caveat: reconstructing this slice exactly requires the project's filtering script, which is not part of this dataset release. It is in the code repository at https://github.com/ChihHsuan-Yang/EMNLP_Cost-Aware-Protocol-Routing.

JEEBench (515 problems)

Upstream: https://github.com/dair-iitd/jeebench (MIT).

source_problem_id has the form jeebench:<paper>:<index>, e.g. jeebench:JEE Adv 2016 Paper 1:1. Split on : to recover the upstream paper name and 1-based question index. All 515 public problems are covered.

SciBench (565 problems)

Upstream: https://github.com/mandyyyyii/scibench (MIT).

source_problem_id has the form scibench:<textbook>:<problem_id>, e.g. scibench:atkins:e1.1(a)(a). The textbook segment names the JSON file under dataset/original/. The project's text-only slice holds 574 rows carrying 571 distinct identifiers (three identifiers are reused, see below); 565 of those 571 were executed under all four protocols and are released. The six unexecuted identifiers are all from the thermo textbook: scibench:thermo:1.3, 1.6, 2.13, 6.10, 9.9, and 14.5. Why those six were not run is not recorded in the artifacts this release was built from.

Three SciBench source identifiers are ambiguous upstream — scibench:quan:2.13, scibench:stat:5.8-5, and scibench:stat:Problem 1.1.1 each name two distinct problems. The canonical problem_id disambiguates them with a content-hash suffix (...#<12 hex>); the plain source_problem_id does not. When reconstructing those three, expect two upstream candidates and disambiguate by content.

LAB-Bench (1,542 problems, in two nested slices)

Upstream: https://huggingface.co/datasets/futurehouse/lab-bench (CC-BY-SA-4.0), revision 5c77cec648430f30611808808861eb86f81d5eaa, train split.

Two slices are released, and the smaller is a strict subset of the larger:

llm_strict (741): subsets CloningScenarios (33), ProtocolQA (108), SeqQA (600).
text_no_tool (1,542): the strict slice plus DbQA (520), LitQA2 (199), SuppQA (82).

FigQA (181) and TableQA (244) are excluded upstream-side because they require image assets. There are no row-level exclusions within the included subsets.

The Gemma-namespace source_problem_id embeds the upstream UUID directly: lab-bench:<Subset>:<uuid>. The gpt-oss namespace (labbench_<subset>_<zero-padded index>) does not; use problem_id and data/problems.csv (subset column) to bridge.

Answer-option order. The runs used a deterministically shuffled option order derived from the upstream row id and ideal answer, and the gold label in the runs is the shuffled letter. If you reconstruct from upstream you will get the upstream option order, and your letter labels will not match. This release does not contain the shuffle, so per-option correctness is not reconstructible from this release alone. The per-problem correctness flags here are unaffected — they record whether the protocol's chosen option was the correct one.

Do-not-train request. LAB-Bench ships a canary and an upstream request not to train on it. This release contains no LAB-Bench content, only identifiers. Do not use these identifiers to assemble a training corpus that would violate that request.

What you cannot reconstruct from this release

Raw model generations. No protocol produced text is included. Per-protocol correctness is the finest-grained outcome released.
Baseline final answers. The confidence probe's prompt included the baseline's final answer as untrusted input. That answer text is not in this release, so data/probe_inputs.jsonl carries baseline_final_answer: null. The probe is therefore not re-runnable from this release alone; see docs/schema.md.
Per-protocol token/call/wall-time costs for the nine non-primary settings. Cost accounting is released only through the aggregate tables. The primary OmniMath 423-problem held-out analysis in data/aggregate/ main_routing_heldout.csv reports costs, but the per-problem cost columns behind it are not staged here.