Composed Image Retrieval (CIR) is an important image retrieval paradigm that enables users to retrieve a target image using a multimodal query that consists of a reference image and modification text. Although research on CIR has made significant progress, prevailing setups still rely simple modification texts that typically cover only a limited range of salient changes, which induces two limitations highly relevant to practical applications, namely Insufficient Entity Coverage and Clause-Entity Misalignment. In order to address these issues and bring CIR closer to real-world use, we construct two instruction-rich multi-modification datasets, M-FashionIQ and M-CIRR. In addition, we propose TEMA, the Text-oriented Entity Mapping Architecture, which is the first CIR framework designed for multi-modification while also accommodating simple modifications. Extensive experiments on four benchmark datasets demonstrate that TEMA’s superiority in both original and multi-modification scenarios, while maintaining an optimal balance between retrieval accuracy and computational efficiency. Our codes and constructed multi-modification dataset (M-FashionIQ and M-CIRR) are available at https://github.com/lee-zixu/ACL26-TEMA/

Large Language Models (LLMs) are widely used for code generation, but their performance degrades on tasks requiring multi-step logical reasoning. In practice, reliability is often improved through multi-sample inference, but its cost grows linearly with the sample size, making it impractical under strict latency constraints. To address this, we propose Reason-Code, an inference-time framework that formulates code generation as a search process guided by execution feedback. It integrates Monte Carlo Tree Search (MCTS) with a lightweight execution sandbox, where candidate programs are evaluated via unit tests. To control inference cost, Reason-Code adopts a conditional budgeting strategy that activates search only when greedy generation fails. Compared with large-sample Best-of-N sampling, Reason-Code is designed to improve reliability without paying the full linear cost of additional sampling under strict latency budgets. Experiments on HumanEval and MBPP show that Reason-Code matches strong sampling baselines (e.g., Best-of-10) with lower token cost and no regression. Additional matched-budget analyses show that execution-guided adaptive inference improves over independent sampling/filtering baselines, while differences between UCB-guided search and simpler iterative repair are limited at low budget.