Over fifty new hallucinations in ICLR 2026 submissions

Legal and ethical framing

• Several comments argue that using LLMs to submit papers with fake citations is straightforward negligence or fraud; once liability attaches (e.g., in law or medicine), many expect AI enthusiasm to cool and even institutional bans.
• Others stress that negligence in law is about failing “reasonable care,” not strict liability; the emotional backlash against AI is seen by some as irrational.

“Hallucination” vs fabrication and pre‑AI baselines

• Many dislike the term “hallucination,” preferring “fabrication,” “lies,” or “confabulation,” emphasizing that humans are still responsible.
• Multiple commenters note citation errors and even fabricated references long predate LLMs; they argue we need a baseline: run the same analysis on pre‑LLM papers and compare error rates.
• Counterpoint: LLMs are a “force multiplier” for both fraud and accidental nonsense—able to churn out plausible but nonexistent papers, quotes, and references at huge scale.

Peer review, tooling, and academic incentives

• 20,000 submissions to one conference are seen as a symptom of publish‑or‑perish culture, conference‑centric CS, and citation metrics being used as KPIs.
• Reviewers say they do not and realistically cannot verify every citation; their job is to assess novelty, soundness, and relevance under tight time and no pay.
• Others argue that if reviewers don’t check citations at all, peer review is a weak quality gate and partly responsible for the mess.
• Several propose automated citation “linters” at submission time, DOIs/bibtex checks, and even LLM‑based tools to flag unsupported claims—though people worry about LLMs hallucinating during checking too.

Responsibility, regulation, and blame

• Big split between “bad tool vs bad craftsman” analogies: some say AI is just a power tool and shoddy outputs indict only the user; others point out that widely deploying an unreliable tool predictably increases slop and externalities.
• Many want strong sanctions: desk rejection plus blacklists, institutional censure, or “walls of shame” for proven fabrications, regardless of whether AI is invoked as an excuse.
• Others emphasize systemic pressures: metric‑driven academia, management mandates to use AI, and vendors overselling capabilities while disclaiming responsibility.

Impact on science and trust; appropriate AI use

• Widespread fear that AI‑generated “slop” (papers, reviews, detectors) will worsen the replication crisis and erode already fragile trust in science.
• Some see LLMs as useful for narrow tasks—finding candidate papers, editing, or fuzz‑testing arguments—but consider using them to write or pad research papers without full human verification as incompatible with serious scholarship.