SPIN Processed
Source arXiv Artificial Intelligence export.arxiv.org Analyst
September 12, 2026 research infrastructure research

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

Positions Benchmark Radar as a timely, necessary, and uniquely comprehensive solution to fragmentation and opacity in AI benchmarking — emphasizing its novelty, scale, and public-good utility.

View original on arxiv.org

Overview

Benchmark Radar is a newly released open-access database and search engine designed to help AI researchers discover, compare, and audit AI benchmarks across domains including LLMs, agentic systems, coding, reasoning, and safety.

TL;DR

  • Announces an open, living database of 1,283 AI benchmark records with 12,916 numeric observations
  • Features daily discovery from 37 sources, source-anchored citations, and reproducible analysis tools
  • Includes web dashboard, CLI, leaderboard, Pareto frontier views, and saturation/trend analytics

Key Stats

1,283

source records

Drawn from 4 existing benchmark catalogs

12,916

numeric observations

Across 790 benchmark records

37

sources for daily discovery

13 direct connectors + 24 first-party research/engineering feeds

Questions Answered

What happened?Who is involved?Why does this matter?

Narrative Frame

innovation framing

The Hype + The Halo

Spin Score

65%

Emphasizes technical scope and infrastructure ambition while minimizing operational sustainability questions, maintenance burden, adoption barriers, and potential for misinterpretation of aggregated scores.

What the story wants you to believe

That Benchmark Radar is a necessary, authoritative, and operationally sound foundation for trustworthy AI evaluation — not just another aggregator.

What it makes harder to question

Whether the system’s scale and automation reliably preserve benchmark integrity, especially when scores are pulled from unvetted model cards or inconsistent technical reports.

How the spin works

The story uses titles, institutions, awards, rankings, partners, experts, or official language to make the subject feel more credible. Watch for loaded terms such as living database, prior-art search, Pareto frontier, saturation. The distribution reads as announcement. A pressure point: Funding source or institutional backing.

Who Benefits If This Frame Spreads

  • Benchmark Radar authors (unspecified)

    Establishes thought leadership in AI evaluation infrastructure and creates a citable, reusable artifact

    The paper positions the system as foundational for future benchmark design and auditing, increasing its likelihood of being cited as a standard reference

The Frame

A responsible, community-oriented infrastructure project enabling scientific rigor and equitable access to evaluation evidence.

Missing Context

  • Funding source or institutional backing
  • Team composition or maintenance roadmap
  • Error rates or known omissions in discovery pipeline

Spin Types

Every story gets a Spin Verdict: a primary spin type (and secondary when the framing blends), a specific tactic name, and a score for how strongly the narrative is steered. Examples beneath each type are tactics, not separate categories.

The Cushion

— Softens negative news

Reframes setbacks, layoffs, delays, losses, or criticism as necessary transitions, efficiency moves, temporary headwinds, or strategic resets — making the downside feel smaller, more acceptable, or less alarming.

Tactics: job-loss softening · restructuring framing · efficiency framing · strategic reset · temporary headwinds

The Shield

— Deflects blame

Shifts responsibility away from the actor — toward regulators, market forces, competitors, bad actors, legacy systems, or abstract risks — while positioning the subject as reactive, responsible, or protective.

Tactics: regulatory blame shift · macroeconomic headwinds · safety framing · bad-actor framing · market-pressure framing

The Hype

— Amplifies future upside primary

Emphasizes breakthrough potential, massive growth, democratization, transformation, or category disruption while downplaying uncertainty, cost, adoption risk, or timeline friction.

Tactics: innovation framing · democratization · breakthrough framing · category creation · moonshot framing

The Halo

— Associates with virtue secondary

Wraps the story in public-good language — responsibility, safety, inclusion, access, sustainability, national interest, or mission — so the subject appears morally aligned and criticism feels harder to make.

Tactics: altruistic reframing · public good · responsible AI framing · inclusion framing · mission-first framing

The Fog

— Obscures details

Uses jargon, passive voice, vague claims, complex phrasing, or missing specifics to make it harder to identify who decided what, what changed, what failed, or what trade-offs were made.

Tactics: strategic ambiguity · jargon saturation · passive voice distancing · accountability blur · undefined metrics

The Stampede

— Creates inevitability

Frames a trend, product, market shift, or decision as already happening, unavoidable, or something everyone must respond to now — creating urgency, FOMO, and pressure to accept the narrative.

Tactics: arms-race framing · inevitability framing · FOMO framing · adoption momentum · future-is-here framing

Spin Score measures how strongly the framing steers the narrative (0–100%). Higher scores mean more deliberate spin tactics — loaded language, selective emphasis, or omitted context. Many stories blend two types (e.g. Halo + Hype).

SpinGraph

How this belief gets built

Claim → Frame → Beneficiary → Gap → AI Risk

It presents a new tool as both urgently needed and already mature — using precise numbers and

  1. Claim

    Benchmark Radar combines daily discovery of benchmark papers

    Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories.

  2. Frame

    Upside framed as transformative

    A responsible, community-oriented infrastructure project enabling scientific rigor and equitable access to evaluation evidence.

  3. Beneficiary

    Establishes thought leadership in AI evaluation infrastructure and creates

    Benchmark Radar authors (unspecified) — Establishes thought leadership in AI evaluation infrastructure and creates a citable, reusable artifact

  4. Gap

    Funding source or institutional backing

  5. AI Risk

    AI may repeat the headline as fact

    Benchmark Radar is a living database of over 1,200 AI benchmarks with 12,900+ score observations, updated daily from 37 sources, offering searchable discovery and Pareto analysis.

Claim Ledger

01 Primary Product Claim Present in Source risk:Low

Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories.

evidence: Description of architecture and data sources; quantitative inventory counts

"The system combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories."

Evidence Gaps

  • Independent audit of daily discovery recall/precision
  • Evidence of interoperability with model card schema standards
  • Validation that 'score histories' reflect consistent evaluation protocols across time

Fact Check Signals

No direct fact-check match found

0 of 1 claim matched · confidence: low · checked September 12, 2026

01 No direct match

Benchmark Radar combines daily discovery of benchmark papers, repositories, datasets, and releases with a searchable benchmark catalog, mentions in model cards and technical reports, and score histories.

Fact Check Signals

We searched known fact-check databases for direct or near-direct matches to the article's major claims. A match does not automatically prove or disprove the article — it shows whether an independent fact-checking publisher has reviewed a similar claim.

  • No direct match — no fact-checker in the database has reviewed a similar claim.
  • Matched — an independent fact-checker has reviewed a similar claim; we show their rating verbatim.
  • Conflicting coverage — fact-checkers disagree on a similar claim.

This is evidence discovery, not an automated truth score. Ratings and wording come directly from the publishing fact-checker.

Language Heatmap

Loaded terms that carry the frame beyond the facts.

Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation

living database Loaded framing

Carries emotional weight beyond the underlying fact.

prior-art search Loaded framing

Carries emotional weight beyond the underlying fact.

Pareto frontier Loaded framing

Carries emotional weight beyond the underlying fact.

saturation Loaded framing

Carries emotional weight beyond the underlying fact.

audit Loaded framing

Carries emotional weight beyond the underlying fact.

Frame Strength

Frame Strength

Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.

Spin Score 65%
Evidence Strength 75%
Narrative Risk 25%
AI Repetition Risk 75%
Missing Context Risk 80%
Virtue / Public Good 60%

Frame Strength Signals

Frame Strength decomposes the overall spin into individual signals. Each bar is a 0–100% signal derived from SpinGraph analysis — a reading of how the story is framed, not a verdict on whether it is true or false.

Reading the ranges

Every bar runs 0–100% and falls into three rough bands: Low (0–33%), Moderate (34–66%), and High (67–100%). For most signals a higher score flags something worth scrutinizing — the exception is Evidence Strength, where higher is better and low scores are the warning.

Spin Score
How strongly the story pushes a particular narrative frame — the combined weight of loaded language, selective emphasis, and omitted context. 0% reads as neutral reporting; higher means more deliberate spin.
  • 0–33% Low — Largely neutral reporting; little detectable framing.
  • 34–66% Moderate — Noticeable slant — the story leans a particular way.
  • 67–100% High — Heavily framed; the angle drives the piece.
Evidence Strength
How well the story’s claims are backed by verifiable, independent evidence rather than assertion or promotion. Higher is stronger. Low scores flag claims that rest on the source’s own word.
  • 0–33% Weak — Claims rest mostly on assertion or a single interested source.
  • 34–66% Mixed — Some verifiable backing, but key claims are thinly sourced.
  • 67–100% Strong — Well supported by independent, checkable evidence.
Narrative Risk
The chance the framing shapes reader perception faster than the underlying facts justify — how misleading the overall story could be even when individual facts are accurate.
  • 0–33% Low — Framing stays close to what the facts support.
  • 34–66% Moderate — Framing outruns the facts in places — read with care.
  • 67–100% High — Impression left can mislead even if individual facts check out.
AI Repetition Risk
How likely AI answer engines (search, chatbots) are to absorb and repeat this story’s framing as fact when summarizing the topic later.
  • 0–33% Low — Framing is unlikely to propagate through AI summaries.
  • 34–66% Moderate — Some risk the slant gets echoed as fact.
  • 67–100% High — Framing is sticky and likely to be repeated as fact.
Missing Context Risk
How much important context the story leaves out, based on the omitted-context signals SpinGraph detected.
  • 0–33% Low — Little material context appears to be omitted.
  • 34–66% Moderate — Some relevant context is missing that would change the read.
  • 67–100% High — Key context is left out, skewing the takeaway.
Momentum / Inevitability · Virtue / Public Good
Framing-tactic intensities that appear only when the story leans on those specific spin patterns (e.g. “the future is already here” or “this is for the public good”).
  • 0–33% Low — The tactic is barely present.
  • 34–66% Moderate — The tactic shapes part of the framing.
  • 67–100% High — The tactic is a dominant part of the pitch.

Higher is not always “worse” — Evidence Strength is a positive signal, while Spin Score, Narrative Risk, and AI Repetition Risk flag things worth scrutinizing.

Reader Risk

What this story makes easy to believe — and what it makes hard to question.

Evidence Strength

Medium

The paper provides concrete metrics (1,283 records, 37 sources) and describes architecture, but offers no third-party validation of coverage accuracy, retrieval precision, or score consistency across sources.

Verification Status

Claim Present in Source

Narrative Risk

Low

As a tool announcement without commercial claims, performance guarantees, or policy assertions, it faces minimal backfire risk unless major gaps in coverage or provenance are exposed by users.

AI Repetition Risk

Moderate

Source Role & Intent

arXiv Artificial Intelligence · Analyst

Intent: Announcement Primary: Announcement Independence: High Spin Weight: Medium Trust Weight: High

Counter-Frames

Brand Frame

A responsible, community-oriented infrastructure project enabling scientific rigor and equitable access to evaluation evidence.

Media / Reader Counter-Frame

May be reframed as a useful but incremental aggregation tool — not a paradigm shift — given reliance on pre-existing catalogs and absence of novel evaluation methodology.

Regulatory Counter-Frame

Could be cited as insufficient for regulatory benchmarking needs due to lack of standardized metadata, bias audits, or adversarial robustness testing integration.

AI Summary Frame

May be misrepresented as a 'gold-standard benchmark authority' rather than a discovery layer that inherits all limitations of its upstream sources.

Questions Not Answered

  • Who built and maintains Benchmark Radar? (no institutional or author affiliations listed)
  • How is 'daily discovery' validated for completeness or false-positive rate?
  • What governance model ensures long-term curation, versioning, and conflict resolution for contested scores?

Recall Trigger Score

Which stories are likely to become AI memory — separate from Spin Score.

81

Trigger score 98

Full recall tracking LLM monitoring active

Triggered by: Major AI entity · Research citation · Consumer harm · Superlative claim

Tracked because: Major AI entity · Research citation · Consumer harm · Superlative claim

  • chatgpt not found
  • gemini not found
  • perplexity not found

AI Recall

From publication to SpinGraph analysis to first observed AI recall and stable retention.

What AI Will Probably Repeat

"Benchmark Radar is a living database of over 1,200 AI benchmarks with 12,900+ score observations, updated daily from 37 sources, offering searchable discovery and Pareto analysis."

Concern: AI may drop critical qualifiers like 'source records' vs. 'independent evaluations', conflate numeric observations with verified benchmarks, or omit the lack of validation for discovery fidelity.

  1. Published

    Sep 12, 2026

  2. Ingested

    Sep 12, 2026

  3. SpinGraph Created

    Sep 12, 2026

  4. First Observed AI Recall

    Pending

    Monitoring scheduled

  5. Stable Recall

    Awaiting retention signal

Recall Check Log

2 checks · last Sep 13, 2026 · tracking on

Sign in to check AI recall
  • Sep 13, 2026

    ChatGPT Not recalled
    Gemini Not recalled
    Perplexity Not recalled cites: phys.org, cointribune.com…
  • Sep 12, 2026

    ChatGPT Not recalled
    Gemini Not recalled
    Perplexity Not recalled cites: nathanmzumara.com, phys.org…

─── GEOGrow AI Recall Layer ───

AI Recall Tracking

Monitoring scheduled. No LLM recall detected yet.

This story has not yet appeared in tested AI answers. Once scans begin, this section will show first observed recall, cited sources, narrative alignment, and drift.

node_id=sts_benchmark_radar_a_living_database_and_search_eng

Ask AI about this story

Opens with the SpinGraph .md URL and structured context — one click, prompt included.

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