GIFT‑Eval‑CTX is a new benchmark for contextual forecasting that tests whether models can use verified future information to produce accurate time‑series predictions. It builds on GIFT‑Eval and contains 10,000 instances across 20 datasets, each annotated with macro‑level and micro‑level effects derived from actual future events. The benchmark evaluates traditional TSFMs, advanced LLMs, and agentic systems, showing that structured effects can substantially lower MASE for models like GPT‑5.6 Sol and Claude Opus 4.8, with MoiraiAgent achieving the best overall performance.
By Xu Liu, Hanshu Yan, Taha Aksu, Quang Pham, Zhongzheng Qiao, Jun Hao Liew, Silvio Savarese, Doyen Sahoo, Junnan Li
arXiv:2512. 23847v2 Announce Type: replace-cross Abstract: We develop a statistical procedure to detect lookahead bias in economic forecasts generated by large language models (LLMs).
By Zhenyu Gao, Wenxi Jiang, Yutong Yan
AlphaBet is a reinforcement learning framework that trains a single large language model to search for evidence, reason about it, and report probabilities for unresolved events across multiple pre-resolution dates. By scoring each dated forecast with a proper scoring rule, AlphaBet uses one resolved event to supervise decisions under varying information states, decomposing forecasting regret into evidence-acquisition and probability-reporting gaps. Experiments on GLM-4.5-Air show that AlphaBet improves Brier scores on Forecast-Dojo and FutureX, attributing gains to multi-date training and better information acquisition and probability reporting.
By Liqin Ye, Rongzhi Zhang, Xin Liu, Zheng Li, Yuan He, Qingyu Yin, Priyanka Nigam, Chao Zhang
The paper investigates when forecasting agents should employ different behaviors—retrieval, reasoning, deferring to market priors, or using historical analogs—on binary forecasting tasks. It finds that the optimal mechanism depends on the data source, with structured analogs excelling for some processes and market or conservative baselines for others. The authors propose ReliabilityRoute, a rule‑based system that steers agent behavior using reliability features, achieving competitive performance across multiple LLM versions while highlighting that more reasoning is not always better.
By Yufeng Wang
arXiv:2609.36914v1 Announce Type: new
Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning,...
By Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang
arXiv:2609.13840v1 Announce Type: new
Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters ar...
By Joo Ern Chin, Shih-Fen Cheng, Aldy Gunawan
The paper demonstrates that the outcome of a forecasting leaderboard is largely determined by the evaluator’s design choices rather than the models themselves. By fixing the data, horizon, and period, the authors varied three key evaluation decisions—unit of analysis, error pooling, and scoring metric—and showed that each can reverse or eliminate the apparent superiority of any forecasting method. The study also evaluates the practical impact of these choices on a deployed system, revealing that the selection rule captures a significant portion of the potential performance gain, and confirms the findings on an external public dataset.
By Md Rezwanul Islam, Wael Mohammed
E-Commerce Bench is an open‑source benchmark that simulates a year‑long e‑commerce operation, requiring LLM agents to manage multiple online stores, negotiate with suppliers, optimize sales, fulfill orders, handle returns, and manage cash flow. The environment uses real product and supplier data, a calendar of promotions and shocks, and deterministic customer and negotiation models to enable reproducible evaluation. The study evaluates 18 state‑of‑the‑art models across seven metrics, finding no single model dominates, with GPT‑5.6 Sol achieving the highest year‑end assets but lagging in fraud avoidance and operational efficiency.
By Wei Fan, Xinjie Shen, Xudong Guo, Jianhong Tu, Yang Su, Yinger Zhang, Lianghao Deng, Fengyu Wang, Baohua Dong, Yangqiu Song, Dayiheng Liu
arXiv:2606. 15917v1 Announce Type: new Abstract: We use Group Relative Policy Optimization (GRPO), a recently devised sample and memory efficient reinforcement learning method, to finetune pretrained LLMs in the range of 1.
By Amit Arnold Levy
The paper introduces a counterfactual interpretability layer for multi‑series WaveNet forecasters used in sales forecasting. It decomposes each forecast into contributions that exactly sum to the predicted value, avoiding allocation artifacts seen with SHAP‑style methods. The authors evaluate faithfulness via deletion/insertion tests, showing significant gaps that confirm the attributions reflect genuine model behavior, and they analyze where attribution is informative and where it is not.
By Glib Kechyn
arXiv:2607. 08046v1 Announce Type: cross Abstract: Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast.
By Rapha\"el Sarfati, Pratyush Ranjan Tiwari, Siddharth Boppana, Christopher J. Earls, Srikar Varadaraj, Eric Ho
The paper introduces an agentic forecasting environment built on 2,100+ resolved Polymarket questions, where a language model (Qwen3.5-35B-A3B) learns to gather evidence during rollout via web search, page reading, and financial time series, all filtered to avoid post‑cutoff leaks. Training with single‑epoch GRPO and a Brier‑score reward improves calibration by 30‑40% and reduces search attempts, while the trained policy outperforms four frontier models in evidence‑based forecasting, achieving lower soft‑Brier scores at roughly 5% of the inference cost. The authors release the environment, dataset, and per‑rollout records as a reusable harness for temporal forecasting agents.
By Yusuf Afifi, Artur Kiulian, Anton Polishko, Mykola Khandoga, Hamudi Naanaa, Alina Krasnobrizha