Searching the Internet for Challenging Benchmarks at Scale

Daniel Deutsch; Mara Finkelstein; Markus Freitag; Parker Riley; Vil\'em Zouhar; Wenda Xu

arxiv: 2509.26619 · v2 · submitted 2025-09-30 · 💻 cs.CL · cs.AI

Searching the Internet for Challenging Benchmarks at Scale

Wenda Xu , Vil\'em Zouhar , Parker Riley , Mara Finkelstein , Markus Freitag , Daniel Deutsch This is my paper

Pith reviewed 2026-05-18 11:37 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords automatic benchmark constructionmulti-armed banditepsilon-greedy searchmachine translation evaluationquestion answeringbenchmark saturationinternet topic search

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The pith

An epsilon-greedy bandit search over web topics finds the hardest benchmarks after exploring only 6 percent of the space.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Static benchmarks quickly saturate as models improve, leaving little room to expose genuine weaknesses. The paper models the internet as a space of topics and casts the search for difficult ones as a multi-armed bandit problem whose difficulty signal comes from costly sample-and-evaluate queries. An epsilon-greedy policy locates the most challenging topics while examining just 6 percent of the space, delivering a 100-fold cost reduction relative to exhaustive search. The resulting difficulty rankings hold up across separate quality metrics, languages, and models on both machine translation and knowledge-question-answering tasks.

Core claim

The authors formalize benchmark construction as a multi-armed bandit over internet topics and show that an epsilon-greedy strategy identifies the most challenging topics while exploring only 6 percent of the search space, yielding a 100 times cost reduction over exhaustive evaluation. Validation on machine translation and knowledge question answering confirms that discovered difficulty is robust across independent metrics, languages, and models.

What carries the argument

Epsilon-greedy policy on a multi-armed bandit in which each arm is an internet topic and the observed reward is a difficulty score from sample-and-evaluate queries.

If this is right

Challenging benchmarks can be regenerated automatically without repeated expert curation.
Model evaluation cost drops dramatically while still surfacing genuine weaknesses.
The same search procedure applies to any domain whose topics can be sampled from the open web.
Dynamic benchmark construction becomes feasible as models continue to improve.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

A living benchmark could be maintained by periodically re-running the bandit search to refresh hard topics as models advance.
The approach might be adapted to locate difficult examples inside a fixed corpus rather than across the entire web.
Incorporating model-specific difficulty signals could produce evaluation sets tailored to individual systems.

Load-bearing premise

The difficulty ranking obtained from limited sample evaluations on one metric and model stays consistent when measured with other metrics, languages, or models.

What would settle it

If topics the method labels as hard produce error rates no higher than easy topics when re-evaluated on a fresh model or metric, the central claim is falsified.

read the original abstract

Many static benchmarks are beginning to saturate: as models rapidly improve, they achieve near-perfect scores on fixed test sets, leaving little headroom to expose genuine model weaknesses -- and even expert-curated challenge sets quickly saturate after hillclimbing. We present a fully automatic framework that searches the Internet at scale to construct challenging benchmarks without human curation. The key insight is to model the Internet as a vast space of topics and formalize the search as a multi-armed bandit problem, where each topic's difficulty is revealed only through expensive sample-and-evaluate queries. Our epsilon-greedy strategy identifies the most challenging topics while exploring only 6% of the search space -- a 100 times cost reduction over exhaustive evaluation. We validate on machine translation and knowledge question answering, confirming that discovered difficulty is robust across independent metrics (GEMBA-SQA and MetricX), languages, and models.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The abstract outlines a bandit search over internet topics to build harder benchmarks, but the 6% exploration and 100x savings claims cannot be checked without the methods section.

read the letter

The paper's core move is to treat internet topics as arms in a multi-armed bandit and use epsilon-greedy to spend evaluations only on promising hard ones. It reports finding challenging material for machine translation and knowledge QA after touching just 6% of the space, which would be a real efficiency win if it holds. The framing addresses a clear problem: static benchmarks lose signal once models improve, and manual challenge sets saturate fast too. Turning the search into an automated, scalable process without expert curation is the practical part that could matter for evaluation work. The claim that difficulty ranks stay consistent across GEMBA-SQA, MetricX, languages, and models is the kind of check that belongs in this kind of paper. The obvious limitation is that we only have the abstract. Nothing is shown about how the topic space is partitioned, how many samples are drawn per arm, what epsilon value is used, or how the 6% and 100x numbers were actually computed. Without those pieces it is hard to tell whether the savings come from the bandit or from easy-to-rank topics and low-variance estimates. The stress-test note is accurate on this point. Researchers who maintain or extend benchmarks in MT and QA would be the natural audience. If the full paper supplies the missing implementation details and shows the statistical support, the work is worth a serious look. I would send it to peer review once the complete version is available.

Referee Report

1 major / 1 minor

Summary. The manuscript presents a fully automatic framework for constructing challenging benchmarks by modeling the Internet as a vast space of topics and formalizing the search as a multi-armed bandit problem. An epsilon-greedy strategy is used to identify the most challenging topics for machine translation and knowledge question answering while exploring only 6% of the search space, claimed to yield a 100 times cost reduction over exhaustive evaluation. The discovered topic difficulties are validated as robust across independent metrics (GEMBA-SQA and MetricX), languages, and models.

Significance. If the claims are supported by detailed methods and results, this approach could provide a scalable solution to the saturation of static benchmarks by enabling automatic discovery of genuinely challenging content without human curation. The application of bandit algorithms to efficient exploration of large topic spaces represents a potentially useful technique for dynamic benchmark generation in NLP and related fields.

major comments (1)

[Abstract] Abstract: The central efficiency claim that the epsilon-greedy strategy identifies the most challenging topics while exploring only 6% of the search space (a 100 times cost reduction) is load-bearing for the contribution but is presented without any details on the definition of topic arms, discretization of the internet search space, number of samples per topic, choice of epsilon, variance of difficulty estimates, or the exact computation of the 6% and 100x figures. This prevents assessment of whether the reported savings reflect genuine bandit efficiency or depend on unstated assumptions about topic independence and estimator stability.

minor comments (1)

[Abstract] Abstract: The validation statement mentions robustness across metrics, languages, and models but provides no indication of the specific datasets, models, or statistical tests used, which would aid clarity even at the abstract level.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and for recognizing the potential significance of our approach to dynamic benchmark construction. We address the single major comment below.

read point-by-point responses

Referee: [Abstract] Abstract: The central efficiency claim that the epsilon-greedy strategy identifies the most challenging topics while exploring only 6% of the search space (a 100 times cost reduction) is load-bearing for the contribution but is presented without any details on the definition of topic arms, discretization of the internet search space, number of samples per topic, choice of epsilon, variance of difficulty estimates, or the exact computation of the 6% and 100x figures. This prevents assessment of whether the reported savings reflect genuine bandit efficiency or depend on unstated assumptions about topic independence and estimator stability.

Authors: We agree that the abstract, due to length constraints, omits the methodological specifics needed to fully evaluate the efficiency claims. The full manuscript contains these details in the Methods section. To address the concern, we will revise the abstract to include a concise summary of the topic arm definitions, search space discretization, samples per topic, epsilon value, variance estimation procedure, and the exact derivation of the 6% and 100x figures. This revision will enable readers to assess whether the savings arise from genuine bandit efficiency. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical bandit result validated externally

full rationale

The abstract reports an empirical outcome from running epsilon-greedy on a multi-armed bandit model of internet topics, where the 6% exploration figure and 100x cost reduction are measured results of the search process itself. Validation uses independent external metrics (GEMBA-SQA, MetricX) on machine translation and knowledge QA tasks across languages and models. No equations, fitted parameters, or self-citations appear in the provided text that would make the headline claim reduce to its inputs by construction. The derivation chain is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on modeling the internet as discrete topics whose difficulty can be estimated via model evaluations; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption The Internet can be modeled as a vast space of topics whose difficulty is revealed only through sample-and-evaluate queries.
Stated as the key insight in the abstract.

pith-pipeline@v0.9.0 · 5657 in / 1055 out tokens · 30032 ms · 2026-05-18T11:37:45.823519+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We formalize this task and treat it as a multi-armed bandit problem. ... epsilon-greedy strategy identifies the most challenging topics while exploring only 6% of the search space

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.