IJCAI-ECAI 2026 杰出论文奖公布，三篇论文获奖

The 35th International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) distinguished paper awards recognise some of the best papers presented at the conference each year. This year, three articles were named as distinguished papers .

And the winners are…

Abstract: In approval-based budget division, the task is to allocate a divisible resource to the candidates based on the voters’ approval preferences over the candidates. For this setting, Brandl et al. [2021] have shown that no distribution rule can be strategyproof, efficient, and fair at the same time. In this paper, we aim to circumvent this impossibility theorem by focusing on approximate strategyproofness. To this end, we analyze the incentive ratio of distribution rules, which quantifies the maximum multiplicative utility gain of a voter by manipulating. While it turns out that several classical rules have a large incentive ratio, we prove that the Nash product rule (NASH) has an incentive ratio of 2, thereby demonstrating that we can bypass the impossibility of Brandl et al. by relaxing strategyproofness. Moreover, we show that an incentive ratio of 2 is optimal subject to some of the fairness and efficiency properties of NASH, and that the positive result for the Nash product rule even holds when voters may report arbitrary concave utility functions. Finally, we complement our results with an experimental analysis.

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Abstract: In this paper, we introduce novel tuple inconsistency measures that quantify the extent to which individual tuples contribute to violations of denial constraints in inconsistent databases. Then, we formally show that one of the proposed measures fully satisfies a set of well-motivated axiomatic properties. As an application, we lift tuple inconsistency to the level of query answers, obtaining a principled framework for measuring and explaining the inconsistency inherent in query results. We further investigate the computational complexity of tuple and answer inconsistency measures under denial constraints, identifying tractable cases that make their application to real-world data feasible. Finally, our experimental study on varying dataset and inconsistency settings demonstrates that, in comparison to existing methods, the proposed measures yield more fine-grained and informative assessments of inconsistency in databases.

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Abstract: Time series forecasting (TSF) plays a vital role across various domains such as finance, energy, healthcare, and meteorology. Currently, most deep learning based TSF methods typically operate with a fixed lookback window. This approach comes from the high compute and memory costs of long contexts, as well as the standard practice of using sliding windows. This creates a trade-off. Making the window larger reduces the number of training samples, which can harm stability and generalization. However, keeping the window small prevents the model from using long history during inference. We propose an inference-only streaming autoregressive framework that replaces repeated full-context recomputation with a one-time context warmup and incremental decoding, enabling efficient long-history forecasting without retraining. While straightforward caching attentions is brittle for time series due to distribution shifts and noisy or redundant histories, we address these issues with cache-consistent normalization and selective memory under a fixed cache budget. Across diverse benchmarks, our approach substantially reduces inference latency with no or marginal accuracy loss, and often improves performance when longer lookbacks are beneficial.

Read the paper in full here .