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RESEARCH · 1 SOURCE · The Decoder

arXiv caps submissions at two per person per month starting Oct 1, 2026

arXiv will limit submissions to two papers per person per calendar month beginning Oct 1, 2026 (the existing cap of three active submissions remains). The rule — which counts rejected papers toward the limit unless deleted — responds to a surge in submissions (40,363 in Sept 2026) partly driven by AI tools and is intended to ease overload on volunteer moderators handling many low-quality, micro-result, and AI-written papers.

8.0

RESEARCH · 1 SOURCE · arXiv cs.AI

SciSlopBench and SciSlopHarness (arXiv:2610.00531v1): benchmarking and mitigating ‘scientific slop’ in AI-generated papers

This arXiv paper introduces SciSlopBench, a dataset of 390 AI-generated papers paired with matched human papers, and six measures across Structure, Argument, and Artifacts to benchmark “scientific slop” — failures where individually plausible sections are connected by broken scientific reasoning. The authors show their measures identify the AI paper in pairs with 85.9% accuracy (versus 68.7% for Binoculars), correlate higher slop with lower ICLR ratings and rejections from 2017–2025, and propose SciSlopHarness, a harness-level LLM revision framework that reduces the remaining AI–human gap by 63% over the strongest revision baseline without needing human reference targets.

7.0

RESEARCH · 1 SOURCE · arXiv cs.AI

ScientistTwo (arXiv:2609.19644v1): autonomous multi-agent framework for end-to-end scientific discovery

A new arXiv preprint (arXiv:2609.19644v1) introduces ScientistTwo, a fully autonomous multi‑agent framework that—given an initial research problem—claims to establish baselines, formulate hypotheses, run experiments and ablations, and validate results via a closed‑loop simulated peer‑review engine without human intervention. The paper reports benchmarking against work from top ML venues (ICLR, ICML, NeurIPS) and asserts that ScientistTwo autonomously produces expert‑level, publishable papers and executable code that outperform human state‑of‑the‑art models and receive higher average ratings from automated reviewers (claims as stated by the authors in the preprint).

8.0