OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking

• URL: http://arxiv.org/abs/2605.03762v1
• Date: Tue, 05 May 2026 13:50:50 GMT
• Title: OracleProto: A Reproducible Framework for Benchmarking LLM Native Forecasting via Knowledge Cutoff and Temporal Masking
• Abstract summary: OracleProto is a framework for evaluating large language models' native forecasting capability.<n>It reconstructs resolved events into time-bounded forecasting samples by combining model-cutoff-aligned sample admission, tool-level temporal masking, content-level leakage detection, discrete answer normalization, and hierarchical scoring.<n>It provides a unified interface for fair cross-model comparison and a controlled signal source for downstream SFT and RL.
• Score: 17.407881336351597
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models are moving from static text generators toward real-world decision-support systems, where forecasting is a composite capability that links information gathering, evidence integration, situational judgment, and action-oriented decision making. This capability is in broad demand across finance, policy, industry, and scientific research, yet its evaluation remains difficult: live benchmarks evaluate forecasts before answers exist, making them the cleanest way to measure forecasting ability, but they expire once events resolve; retrospective benchmarks are reproducible, but they cannot reliably distinguish genuine forecasting from facts a model may have already learned during pretraining. Prompting models to "pretend not to know" cannot replace a genuine knowledge boundary. We propose OracleProto, a reproducible framework for evaluating LLM native forecasting capability. OracleProto reconstructs resolved events into time-bounded forecasting samples by combining model-cutoff-aligned sample admission, tool-level temporal masking, content-level leakage detection, discrete answer normalization, and hierarchical scoring. Instantiated on a FutureX-Past-derived dataset with six contemporary LLMs, OracleProto distinguishes forecasting quality, sampling stability, and cost efficiency under controlled information boundaries, while reducing residual leakage to the $1\%$ level, an order of magnitude below tool-only temporal filtering. OracleProto turns LLM forecasting from one-off evaluation into an auditable, reusable, and trainable dataset-level capability, providing a unified interface for fair cross-model comparison and a controlled signal source for downstream SFT and RL. Code and data are available at https://github.com/MaYiding/OracleProto and https://huggingface.co/datasets/MaYiding/OracleProto.

Related papers

• LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications [76.41731220830714]
  Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external tools, and iterative prediction.<n>We examine applications in finance, weather, health, energy, and operations, and we summarize the benchmarks and datasets used for evaluation.
  arXiv  Detail & Related papers  (2026-08-24T10:02:39Z)
• Nexus : An Agentic Framework for Time Series Forecasting [72.73790673303154]
  Time series forecasting requires reasoning with unstructured contextual data such as news or events.<n>We introduce Nexus, a multi-agent forecasting framework that decomposes prediction into specialized stages.<n>We show that current-generation LLMs possess substantially stronger intrinsic forecasting ability than previously recognized.
  arXiv  Detail & Related papers  (2026-05-14T05:12:13Z)
• LiveFMBench: Unveiling the Power and Limits of Agentic Workflows in Specification Generation [75.05397479715576]
  Large language models (LLMs) and agents have shown promising progress, but their true capabilities and failure modes remain unclear.<n>We present the first systematic and contamination-aware study of LLM- and agent-based formal specification generation for C programs.
  arXiv  Detail & Related papers  (2026-05-02T11:31:33Z)
• T-LLM: Teaching Large Language Models to Forecast Time Series via Temporal Distillation [7.6933817667680096]
  Time series forecasting plays a critical role in decision-making across many real-world applications.<n>We propose T-LLM, a temporal distillation framework that equips general-purpose language models with time series forecasting capability.
  arXiv  Detail & Related papers  (2026-02-02T10:40:27Z)
• Is More Context Always Better? Examining LLM Reasoning Capability for Time Interval Prediction [15.45305246863211]
  Large Language Models (LLMs) have demonstrated impressive capabilities in reasoning and prediction across different domains.<n>This paper presents a systematic study investigating whether LLMs can predict time intervals between recurring user actions.<n>We benchmark state-of-the-art LLMs in zero-shot settings against both statistical and machine-learning models.
  arXiv  Detail & Related papers  (2026-01-15T07:18:40Z)
• Maximally-Informative Retrieval for State Space Model Generation [59.954191072042526]
  We introduce Retrieval In-Context Optimization (RICO) to minimize model uncertainty for a particular query at test-time.<n>Unlike traditional retrieval-augmented generation (RAG), which relies on externals for document retrieval, our approach leverages direct feedback from the model.<n>We show that standard top-$k$ retrieval with model gradients can approximate our optimization procedure, and provide connections to the leave-one-out loss.
  arXiv  Detail & Related papers  (2025-06-13T18:08:54Z)
• From Text to Time? Rethinking the Effectiveness of the Large Language Model for Time Series Forecasting [22.052783052469344]
  Using pre-trained large language models (LLMs) as the backbone for time series prediction has recently gained significant research interest.<n>We observe that training and testing LLM-based models on small datasets often leads to the Decoder and Decoder becoming overly adapted to the dataset.<n>Extensive experiments reveal that although the LLM backbone demonstrates some promise, its forecasting performance is limited.
  arXiv  Detail & Related papers  (2025-04-09T13:20:09Z)
• Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle [13.192628306219248]
  We propose using future event prediction as a continuous evaluation method to assess Large Language Models' temporal generalization and forecasting abilities.<n>Our benchmark, Daily Oracle, automatically generates question-answer pairs from daily news, challenging LLMs to predict "future" event outcomes.
  arXiv  Detail & Related papers  (2024-11-13T04:20:20Z)
• Context is Key: A Benchmark for Forecasting with Essential Textual Information [87.3175915185287]
  "Context is Key" (CiK) is a forecasting benchmark that pairs numerical data with diverse types of carefully crafted textual context.<n>We evaluate a range of approaches, including statistical models, time series foundation models, and LLM-based forecasters.<n>We propose a simple yet effective LLM prompting method that outperforms all other tested methods on our benchmark.
  arXiv  Detail & Related papers  (2024-10-24T17:56:08Z)
• A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting [45.0261082985087]
  We conduct a comprehensive evaluation of Large Language Models (LLMs) for temporal event forecasting.<n>We find that fine-tuning LLMs with raw texts can significantly improve performance.<n>However, issues such as popularity bias and the long-tail problem persist in LLMs.
  arXiv  Detail & Related papers  (2024-07-16T11:58:54Z)
• Automating Dataset Updates Towards Reliable and Timely Evaluation of Large Language Models [81.27391252152199]
  Large language models (LLMs) have achieved impressive performance across various natural language benchmarks. We propose to automate dataset updating and provide systematic analysis regarding its effectiveness. There are two updating strategies: 1) mimicking strategy to generate similar samples based on original data, and 2) extending strategy that further expands existing samples.
  arXiv  Detail & Related papers  (2024-02-19T07:15:59Z)
• ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks [91.55895047448249]
  This paper presents ReEval, an LLM-based framework using prompt chaining to perturb the original evidence for generating new test cases. We implement ReEval using ChatGPT and evaluate the resulting variants of two popular open-domain QA datasets. Our generated data is human-readable and useful to trigger hallucination in large language models.
  arXiv  Detail & Related papers  (2023-10-19T06:37:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.