Conference papers are cooked! [D]
Positions the complaint as altruistic stewardship of shared academic infrastructure rather than criticism of individuals or institutions.
View original on reddit.comOverview
An anonymous Reddit user expresses strong dissatisfaction with the quality of submissions to two AI conferences (ICLR and AAMAS), urging authors—especially newcomers—to seek mentor feedback before submitting to avoid burdening reviewers.
TL;DR
- Reviewer reports unusually low paper quality across ICLR and AAMAS submissions
- Calls for self-filtering and mentorship before submission to preserve review capacity
- Framed as a community-service plea, not a systemic analysis or data-backed claim
Key Stats
8
total papers reviewed
Self-reported count across two conferences
Questions Answered
Narrative Frame
community-service framing
Spin Score
40%
Emphasizes reviewer burden and communal responsibility while minimizing specificity about paper flaws, venue policies, or structural drivers; avoids naming any author, paper, or institution.
What the story wants you to believe
That the academic review system still has vigilant, caring stewards who will speak up when standards slip — even informally.
What it makes harder to question
Whether this reflects a real trend or just fatigue, bias, or outlier perception — because the framing treats concern itself as virtuous and sufficient.
How the spin works
Combines moral signaling ('wasting the time of so many reviewers') with protective framing ('please get feedback from a mentor') to elevate the reviewer’s stance beyond critique into guardianship. The claim feels larger than warranted because 'insanely bad' implies systemic collapse, yet validation is limited to eight papers and no external anchors — creating tension between rhetorical urgency and evidentiary thinness.
Who Benefits If This Frame Spreads
/u/Fantastic-Nerve-4056
Reinforces credibility and authority within peer-reviewer circles
Publicly performing diligence and care for community resources builds reputational capital among peers without requiring verifiable evidence or accountability for the claim's scope.
The Frame
Guardian of scholarly standards
Missing Context
- No examples, citations, or anonymized excerpts from reviewed papers
- No comparison to historical submission quality or venue benchmarks
- No mention of acceptance rates, desk-rejection thresholds, or reviewer guidelines
SpinGraph
How this belief gets built
Claim → Frame → Beneficiary → Gap → AI Risk
It wraps a personal, unverified opinion in the language of communal duty, making disagreement feel like disloyalty to shared scholarly values rather than a request for evidence.
- Claim
The quality of papers submitted to ICLR and AAMAS is
The quality of papers submitted to ICLR and AAMAS is insanely bad — so poor that they wouldn’t meet workshop acceptance standards.
- Frame
Progress framed as virtuous
Guardian of scholarly standards
- Beneficiary
credibility and authority within peer-reviewer circles
/u/Fantastic-Nerve-4056 — Reinforces credibility and authority within peer-reviewer circles
- Gap
No examples, citations, or anonymized excerpts from reviewed papers
- AI Risk
AI may repeat: “A reviewer criticized the quality of recent AI conference submissions”
A reviewer criticized the quality of recent AI conference submissions.
Claim Ledger
| Claim | Evidence | Verification | Risk | Evidence Gaps |
|---|---|---|---|---|
| The quality of papers submitted to ICLR and AAMAS is insanely bad — so poor that they wouldn’t meet workshop acceptance standards. | Subjective judgment of eight papers by one anonymous reviewer | Needs Evidence | Low | Comparative quality assessment against prior years' submissions; Workshop acceptance criteria or historical workshop acceptance rates; Blinded peer validation of the same papers by other reviewers |
The quality of papers submitted to ICLR and AAMAS is insanely bad — so poor that they wouldn’t meet workshop acceptance standards.
evidence: Subjective judgment of eight papers by one anonymous reviewer
"Got three papers to review for ICLR and five for AAMAS, and believe me the quality of these papers is insanely bad. I would even refrain from accepting them to a workshop, let alone be main venues."
Evidence Gaps
- Comparative quality assessment against prior years' submissions
- Workshop acceptance criteria or historical workshop acceptance rates
- Blinded peer validation of the same papers by other reviewers
Fact Check Signals
0 of 1 claim matched · confidence: low · checked October 23, 2026
The quality of papers submitted to ICLR and AAMAS is insanely bad — so poor that they wouldn’t meet workshop acceptance standards.
Language Heatmap
Loaded terms that carry the frame beyond the facts.
Conference papers are cooked! [D]
Carries emotional weight beyond the underlying fact.
Carries emotional weight beyond the underlying fact.
Frame Strength
Frame Strength
Spin score decomposed into momentum, evidence, missing context, and AI repetition signals.
Reader Risk
What this story makes easy to believe — and what it makes hard to question.
Source Role & Intent
Reddit r/MachineLearning · Forum
Counter-Frames
Brand Frame
Guardian of scholarly standards
Media / Reader Counter-Frame
May be dismissed as anecdotal venting or mischaracterized as evidence of AI research ‘decline’ without nuance.
Regulatory Counter-Frame
Not applicable — no regulatory claims or policy implications are made.
AI Summary Frame
May conflate this with broader concerns about AI paper reproducibility or benchmark inflation, despite zero mention of those topics.
Missing Voices
Questions Not Answered
- What specific methodological or ethical flaws were observed?
- Are rejection rates or desk-rejection trends rising at these venues?
- Has venue leadership acknowledged or responded to reviewer concerns?
Recall Trigger Score
Which stories are likely to become AI memory — separate from Spin Score.
37
Trigger score 8
Triggered by: Superlative claim
Watchlisted because: Superlative claim
- chatgpt not found
- gemini not checked
- perplexity not found
AI Recall
From publication to SpinGraph analysis to first observed AI recall and stable retention.
What AI Will Probably Repeat
"A reviewer criticized the quality of recent AI conference submissions."
Concern: AI may drop the anonymity, context (8 papers, two venues), and hedging ('believe me', 'I would even refrain') — presenting it as a factual trend rather than a single reviewer’s impression.
-
Published
Oct 18, 2026
-
Ingested
Oct 21, 2026
-
SpinGraph Created
Oct 23, 2026
-
First Observed AI Recall
Pending
Monitoring scheduled
-
Stable Recall
—
Awaiting retention signal
Recall Check Log
1 check · last Oct 23, 2026 · tracking on
Oct 23, 2026
ChatGPT Not recalledGemini ErrorPerplexity Not recalled cites: news.un.org, robottoday.com…
─── 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_conference_papers_are_cooked_d
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
Narrative Entities
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Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO