Over fifty new hallucinations in ICLR 2026 submissions

📝 Discussion Summary (Click to expand)

The discussion revolves around the impact and implications of Generative AI, particularly concerning accuracy, proliferation of errors, and professional responsibility.

Here are the three most prevalent themes:

1. Liability and the Waning Enthusiasm for Unvetted AI

A primary concern is that the legal ramifications, or liability, associated with AI-generated failures—especially in complex, judgment-based fields like medicine and law—will soon temper the current excitement around the technology.

Supporting Quote: User "jqpabc123" stated, "The legal system has a word to describe AI 'slop' --- it is called 'negligence'. And as the remedy starts being applied (aka 'liability'), the enthusiasm for AI will start to wane."

2. Proliferation and Acceleration of Errors (Fabrications/Hallucinations)

There is significant debate over AI's tendency to generate outright false information (often termed "lies," "fabrications," or "hallucinations," with users like "jmount" preferring "fabrications"). A recurring point is that while humans make errors, LLMs act as a force multiplier, generating convincing, fake content (like citations) tirelessly and rapidly.

Supporting Quote: User "the_af" argued, "LLM are a force multiplier of this kind of errors though. It's not easy to hallucinate papers out of whole cloth, but LLMs can easily and confidently do it, quote paragraphs that don't exist, and do it tirelessly and at a pace unmatched by humans."
Supporting Quote (on context): User "pmontra" differentiated, "Fabricated citations are not errors. A pre LLM paper with fabricated citations would demonstrate will to cheat by the author."

3. The Primacy of Human Responsibility Over Tool Quality

A strong counter-theme emphasizes that regardless of how unreliable the tool is, the professional using the AI output bears the ultimate responsibility for accuracy and vetting what they publish or present under their name. Critiques of AI providers are seen as defensive maneuvers that deflect necessary diligence.

Supporting Quote: User "SauntSolaire" asserted, "Yes, that's what it means to be a professional, you take responsibility for the quality of your work."
Supporting Quote: User "theoldgreybeard" concluded, "AI is not the problem, laziness and negligence is. There needs to be serious social consequences to this kind of thing, otherwise we are tacitly endorsing it."

🚀 Project Ideas

Citation Integrity Verification Service (CIVS)

Summary

A SaaS tool designed specifically for academic publishers, reviewers, and researchers to automatically verify the existence, accuracy (title, authors, year), and relevance of citations generated or used within scientific papers.
Solves the immediate pain point identified in the discussion: the difficulty and time required for human reviewers to manually check citations, which leads to accepting AI-generated "fabricated" references or simple human errors.

Details

Key	Value
Target Audience	Academic Publishers, Journal Editors, Peer Reviewers, Researchers
Core Feature	Batch processing of manuscript citation lists against cross-referenced scholarly databases (DOI, PubMed, arXiv, etc.) to confirm existence and flag metadata mismatches (title/author drift). Advanced feature uses LLM analysis to check if the cited summary supports the claim made in the citing text.
Tech Stack	Python (Scrapy/BeautifulSoup for non-API scraping/fallback), Existing Citation APIs (Crossref, Semantic Scholar), PostgreSQL, FastAPI.
Difficulty	Medium
Monetization	Hobby

Notes

Why HN commenters would love it: It directly addresses the "force multiplier" effect of LLMs creating junk citations ("LLMs can easily and confidently do it, quote paragraphs that don't exist," said the_af). It provides the missing automated "CI/compiler error check" mechanism for academic submissions.
Potential for discussion or practical utility: There would be massive discussion on the nuances of relevance checking (whether the cited paper actually supports the claim, as noted by alexcdot and michaelt), making it a perfect high-engagement product.

Negligence Liability Forensics Toolkit (NLFT)

Summary

A consulting service and associated software platform that analyzes AI deployment contexts (MLOps pipelines, data provenance, human validation steps) to establish the chain of care taken by a business when deploying AI in high-stakes domains (medicine, law).
Solves the emerging legal/liability concern: As the legal system catches up, businesses need demonstrable proof they exercised "reasonable level of care" when using AI judgments (e.g., in medical diagnosis).

Details

Key	Value
Target Audience	Regulated Industries (Healthcare, Legal, Finance), Risk & Compliance Officers
Core Feature	Data ingestion pipeline that models the prediction lifecycle, tracing every model decision back to its training data, human oversight points (vetting results), and configuration files. Generates compliance reports suitable for regulatory audits or liability defense ("We followed best practice X, Y, Z").
Tech Stack	Go/Rust for performance in data pipeline, standard ML observability tools (weights & biases, MLflow), Graph Database (Neo4j) for modeling relationships in the decision graph.
Difficulty	High
Monetization	Hobby

Notes

Why HN commenters would love it: It directly engages with the initial premise that "liability" will curb AI enthusiasm (jqpabc123). It builds critical tooling for the non-deterministic use cases of AI ("AI is being applied in lots of real world cases where judgment is required to interpret results").
Potential for discussion or practical utility: This taps into the growing requirement for "AI Ethics & Governance" tooling, moving beyond simple detection to proactive risk mitigation, which appeals to the pragmatic, engineering side of the HN readership.

Stylistic Drift Detector (SDD)

Summary

A lightweight tool to help writers differentiate between their own prose and AI-generated additions, specifically targeting stylistic tells like common LLM punctuation or phrasing (e.g., over-reliance on the triple dash or specific verbose structures).
Solves the immediate, low-stakes social concern: The social friction caused by users appearing to be AI-generated due to stylistic artifacts ("Do I need to stop using em-dashes because various people assume they're an AI flag?" asked ghaff).

Details

Key	Value
Target Audience	Authors, Journalists, Individuals concerned about digital provenance/authenticity
Core Feature	Trains a small, local classifier model (or uses a feature extractor) on a user-provided corpus of known 'human' writing, then analyzes new text to generate a "Style Drift Score" based on deviation from the user's established linguistic fingerprint.
Tech Stack	Python, Hugging Face Transformers (fine-tuning small BERT or RoBERTa models), local execution emphasized for privacy concerns.
Difficulty	Low
Monetization	Hobby

Notes

Why HN commenters would love it: It’s a direct, practical solution to a meme/social observation raised in the thread. It targets the "AI flag" issue rather than the complex fraud detection issues, offering a lower barrier-to-entry tool.
Potential for discussion or practical utility: Appeals to the desire to maintain personal agency over one's digital voice. It can spark debate on whether style is trust or signal, contrasting with the high-stakes fraud discussions.