# Training Large Event Models

> Pretrain or fine-tune point-in-time LLMs on NOSIBLE WORLD — a dated, multilingual, replayable archive of 100M+ events from the open web that fixes both dated-fact hallucination and benchmark contamination, and supports clean contamination-resistant evals by replaying to any past `as_of` date.

**URL:** https://nosible.com/training-large-event-models

## Hero — Train or fine-tune point-in-time LLMs

For teams training and fine-tuning frontier models.

**Train or fine-tune point-in-time LLMs.**

Static-corpus models hallucinate dated facts and saturate on benchmarks that leaked into their pretraining.

NOSIBLE WORLD is a dated, multilingual, replayable archive that fixes both.

CTAs:
- **Start Trial** → [trial hub](https://nosible.com/start-trial)
- **See the research** → [#evidence](https://nosible.com/training-large-event-models#evidence)

## §01 — Static corpus vs point-in-time: Static corpora age. The world does not.

A frontier model trained on text that ends in October still answers questions asked in March. It either hallucinates a dated fact, or recites a benchmark answer that leaked into its training corpus. Dated retrieval fixes both.

### Static · contaminated
- Context: public eval · pre-2024 snapshot
- **Question:** Which 1968 novel introduced the term "replicant"?
- **Model answer:** Do Androids Dream of Electric Sheep?
- **// match in pretraining corpus:** The question and the answer both appear verbatim in the pretraining set, so the model is reciting from memory rather than reasoning from evidence.

### NOSIBLE · point-in-time
- Context: `as_of = 2026-04-15`
- **Question:** As of 2026-04-15, what fraction of Humanity's Last Exam can the top open model solve?
- **Model answer:** Resolves to events published before 2026-04-15. Cites each source by publication minute.
- **✓ fact dated to publication minute:** The answer resolves to a record published after the eval snapshot. Replay the prompt at any past `as_of` date and the answer changes accordingly.

## §02 — Temporal reasoning & contamination (2024–2026): Recent papers converge on the same fix.

Frontier LLMs lag humans on temporal reasoning, and static benchmarks now leak into pretraining. The fix is consistent across these papers: pin the training cutoff and score on events that resolve after it.

### SealQA: Raising the Bar for Reasoning in Search-Augmented Language Models
- Authors: Pham, Nguyen, Zunjare, Chen, Tseng & Vu
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2506.01062
- Value: Direct FreshQA successor where frontier LLMs score near zero on questions about post-cutoff events.

### Time-R1: Towards Comprehensive Temporal Reasoning in LLMs
- Authors: Liu, Han, Yu, Li & You
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2505.13508
- Value: Three-stage RL curriculum trains a 3B model that beats DeepSeek-R1 on future event prediction beyond knowledge cutoff.

### Chronologically Consistent Large Language Models
- Authors: He, Lv, Manela & Wu
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2502.21206
- Value: Trains ChronoBERT and ChronoGPT only on text predating each cutoff, proving strict temporal separation preserves NLP benchmarks.

### Instruction Tuning Chronologically Consistent Language Models
- Authors: He, Lv, Manela & Wu
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2510.11677
- Value: Instruction-tunes a chronologically consistent model family with fixed open weights, the SFT step most point-in-time papers skip.

### TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining
- Authors: Li, Armandpour, Mirzadeh et al.
- Year / journal: 2025 · ACL 2025 Oral
- URL: https://arxiv.org/abs/2504.02107
- Value: 114 Common Crawl dumps as a time-stratified pretraining benchmark, comparing meta-schedules and replay ratios for continual learning.

### TARDIS: Mitigating Temporal Misalignment via Representation Steering
- Authors: Tan, Frati, Rao, Zhao & Suarez
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2503.18693
- Value: Unsupervised representation editing shifts activations toward a target time period with no weight updates.

### New News: System-2 Fine-tuning for Robust Integration of New Knowledge
- Authors: Park, Zhang & Tanaka
- Year / journal: 2025 · arXiv
- URL: https://arxiv.org/abs/2505.01812
- Value: Documents the fine-tune versus in-context gap when models try to internalize fresh events. Self-QA pushes news into weights.

### Look-Ahead-Bench: A Standardized Benchmark of Look-ahead Bias in Point-in-Time LLMs for Finance
- Authors: Benhenda
- Year / journal: 2026 · arXiv
- URL: https://arxiv.org/abs/2601.13770
- Value: Finance-grade benchmark for point-in-time LLMs, measuring alpha decay across regimes to separate prediction from memorization.

## §03 — NOSIBLE WORLD · the dated corpus: Every event dated to the minute. Every claim replayable to that minute.

**Field note:** "If your training corpus ends in October, your model lives in October. The world does not."

One hundred million events mined from the open web, each one carrying a verified first-publication timestamp, persistent actor identifiers, full source evidence, and labels from seven independent ontologies.

### Corpus stats

| Stat | Value | Label |
|------|-------|-------|
| Events | 100M+ | Events |
| Sources | 300K+ | Sources |
| Languages | 95 | Languages |
| Point-in-time | 30 years | Point-in-time |

### Post-cutoff cases · 2024 to 2026

Six real, dated, post-cutoff events. Each is a question a 2024 model cannot ground without retrieval, and a NOSIBLE record that resolves to a verified publication timestamp.

| Risk | Place | Date | Event |
|------|-------|------|-------|
| Regulation | Brussels | 2024·08 | EU AI Act enters into force, imposing dataset and copyright disclosure on foundation models. |
| Capability | San Francisco | 2024·12 | OpenAI announces o3 with frontier gains on ARC-AGI, resetting the reasoning benchmark frontier. |
| AI release | Hangzhou | 2025·01 | DeepSeek-R1 open-weights release reprices US AI majors on a single Monday. |
| Copyright | N.D. Cal. | 2025·08 | Bartz v. Anthropic settles for $1.5B over roughly 500K pirated training-corpus titles. |
| Discovery | SDNY | 2025·11 | NYT v. OpenAI: court orders production of 20M ChatGPT logs to plaintiffs. |
| Benchmark | HLE | 2026·04 | Humanity's Last Exam climbs from 10% to 46% in twelve months; static evals saturated. |

## §04 — Why dates matter

Static corpora start aging the day they ship, and the models trained on them inherit the date. NOSIBLE WORLD is the fix: an open-web archive with every event dated to the publication minute, replayable to any past `as_of`.

**Pretrain on the open web, dated to the minute.**

## §05 — What you build: Datasets from one ledger.

Each one wires into your existing training and evaluation stack.

### §01 — Point-in-time pretraining slice
A chronologically ordered token stream where every document carries a verified first-publication timestamp. Replay it byte-for-byte at any past `as_of` date your eval requires.

### §02 — Contamination-resistant eval set
Pin a training cutoff, score against events that resolve after it. Forward-window questions grow with the ledger. Compatible with the ForecastBench and AntiLeak-Bench protocols.

### §03 — Fine-tuning data with timestamps
Instruction-response pairs where every cited fact carries its publication time, source, and language. The model is trained to refuse when the evidence post-dates its corpus.

## §06 — Get started

NOSIBLE WORLD: a dated, replayable corpus for training and evaluating point-in-time LLMs.

CTA:
- **Start Trial** → [trial hub](https://nosible.com/start-trial)

## Related
- [Home](https://nosible.com)
- [NOSIBLE Search API](https://nosible.com/search-api)
- [Research](https://nosible.com/blog)
- [Start Trial](https://nosible.com/start-trial)