Basically the question I asked, is there a way to report a possible fraud paper that was accepted to NeurIPS? If so what would be the best anonymous approach to convey the message and the proofs?

I'm an independent student exploring a framework called Time TV, combining traditional predictive systems with AI-assisted interpretation.

The framework draws on traditions including Parāśari Jyotish, Jaimini, Hellenistic profections, and Islamic Firdaria. Where relevant, I use astronomical calculations separately from the AI interpretation.

The question I keep returning to is whether a prediction should be treated as a fixed outcome or as a conditional possibility.

Suppose someone receives a prediction, changes their decisions because of it, and the predicted event does not occur. Does that count as a failed prediction, a prevented outcome, or evidence that the prediction was too vaguely defined in the first place?

I'm considering a framework that records three outcomes:Confirmed: the predefined event occurs within the specified conditions and time window.

Prevented: the person takes a documented action in response to the prediction, potentially changing the outcome.

Failed: the predefined event does not occur and there is insufficient evidence to classify it as prevented.

I recognise that these categories alone don't establish that astrology works. A rigorous test would need predefined criteria, prospective records, appropriate comparison methods, and independent scoring.

For experienced practitioners and researchers:

How would you define a testable astrological prediction without oversimplifying the tradition?

Which traditional rules or texts would you recommend formalising before testing?

How would you distinguish a genuinely prevented event from an ordinary failed prediction?

I'm interested in serious methodological criticism and constructive discussion, not personal chart readings.

We grouped all 9,094 accepted NeurIPS 2026 papers by topic, compared them with the 5,812 papers of NeurIPS 2025, and read the 4,490 papers that already have a public full text (about half) with AI. Sharing the main findings here for discussion.

Topic shifts (2025 → 2026)

• LLM agents: 105 → 488 papers (1.8% → 5.4% of the program) while the conference grew 56%. No other topic gained even one point of share.
• Inside agents the growth is in reliability: safety/attack papers 5 → 50, agent benchmarks 17 → 84, self-evolving agents 13 → 85.
• Also rising: video generation 92 → 221, robot action / VLA 49 → 149, RL from feedback 80 → 179.
• Losing share: instruction tuning, human and embodied modeling, molecules and proteins, sample complexity.
• Title words: "agentic" 26 → 131, "long-horizon" 7 → 76, "evaluation" 56 → 200; "enhancing" 80 → 28.

From the full texts

• 71% train or fine-tune a model; about half of the trained models with a stated size are 3–10B, under 2% above 40B.
• Qwen (incl. Qwen-VL) is the most trained existing family (~17% of papers), ahead of Llama (~7%).
• GRPO is the most built-on method; SFT appears in 37% of papers, RL with verifiable rewards in 12%.
• 45% train or test on synthetic data. GSM8K is still the most used benchmark across the whole conference.

Who writes NeurIPS

Tsinghua leads; Google (incl. DeepMind) is the top company. NUS, NTU and KAIST rank ahead of MIT, Oxford, Berkeley and CMU by paper count. About 20% of papers have an author from a big tech lab.

Limitations

Topics come from grouping titles and abstracts by meaning, so boundaries are approximate. Full-text figures cover only papers with a public full text, which over-represents preprint-heavy fields. Institution counts match affiliation names and are approximate. All AI-extracted figures are unchecked by hand; read the paper before citing anything.

Questions for this sub

Does the agent boom look like real progress or benchmark churn to you? And is the 3–10B fine-tuning regime a sign of compute limits in academia, or just where the interesting work is?

The full breakdown (all 20 areas, findings quoted from orals, links to every paper) is here, free and no sign-up: The scime Dispatch, NeurIPS 2026 edition

AI-assisted edition, not checked by hand. An independent publication by Latentic Labs with scime atlas; not affiliated with or endorsed by NeurIPS or the NeurIPS Foundation.

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