Large language models are reshaping e-commerce from static recommenders into interactive shopping assistants, yet real-world shopping requires session-level decision support: users reveal and revise constraints, coordinate multiple goals, and expect product-grounded recommendations over a full conversation. Existing benchmarks are mostly outcome-oriented or execution-oriented, leaving this evolving decision process under-evaluated. We introduce RealWorldShop, a benchmark built on 3.28M grounded products, structured shopping episodes, a profile-grounded and action-controlled user simulator, and role-play evaluation. Our analysis shows that current systems produce locally plausible responses but struggle with state tracking, constraint updating, and grounded convergence, especially under ambiguous intent, bundle, and multi-intent scenarios. We further propose RealShop_Agent, an executable session-control framework with explicit state management, shopping-flow control, catalog-grounded retrieval, and runtime guards. Experiments show that RealShop_Agent consistently outperforms strong baselines on RealWorldShop.

Real-world fact-checking faces the challenge of temporal dynamics, where misinformation manifests as evolving evidences rather than static claims. Verifying such claims necessitates the synthesis of multi-hop fragmented evidence dispersed across the timeline. However, existing benchmarks largely neglect this complexity, limiting evaluation to static settings that fail to capture real-world demands. To address this, we pioneer MTIMEFACTCHECK, the first dynamic benchmark to rigorously evaluate multi-hop temporal fact-checking in realistic, evolving contexts. Utilizing MTIMEFACTCHECK, we curate the MTimeFactCorpus dataset to benchmark sixteen LLM baselines, scrutinizing their capabilities and critical limitations. Building on these findings, we tentatively propose a novel method MTIMEFACTCHECK to augment LLM efficacy in multi-hop temporal fact-checking, offering guidance for future research. Our data and code are available at https://github.com/SCUNLP/MTimeFact.

Large language model (LLM) dialogue agents are increasingly used in psychological therapy, yet robustness across diverse patients remains underexplored. We address this gap with three contributions: (1) MindEval, a realistic role-play protocol for evaluating therapeutic dialogue agents; (2) MindData, a de-identified, expert-annotated corpus of therapist–patient dialogues (2,573 sessions; 63,348 turns); and (3) MindApt, a framework that integrates a therapeutic dialogue state tracking paradigm with a patient-aware strategic planning module. On MindEval, MindApt outperforms strong baselines on therapeutic outcomes and dialogue quality while improving conversational efficiency. To evaluate utility beyond role-play, we conducted a clinical study with real patients, demonstrating that MindApt-guided care achieves outcomes comparable to therapist-determined care, while the hybrid setting combining therapist judgment with MindApt’s recommendations yields the strongest overall outcomes.

The effectiveness of large language models (LLMs) to fact-check misinformation remains uncertain, despite their growing use. To this end, we present CANDY, a benchmark designed to systematically evaluate the capabilities and limitations of LLMs in fact-checking Chinese misinformation. Specifically, we curate a carefully annotated dataset of ~20k instances. Our analysis shows that current LLMs exhibit limitations in generating accurate fact-checking conclusions, even when enhanced with chain-of-thought reasoning and few-shot prompting. To understand these limitations, we develop a taxonomy to categorize flawed LLM-generated explanations for their conclusions and identify factual fabrication as the most common failure mode. Although LLMs alone are unreliable for fact-checking, our findings indicate their considerable potential to augment human performance when deployed as assistive tools in scenarios. Our dataset and code can be accessed at https://github.com/SCUNLP/CANDY.

Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propose Physics Reasoner, a knowledge-augmented framework to solve physics problems with LLMs. Specifically, the proposed framework constructs a comprehensive formula set to provide explicit physics knowledge and utilizes checklists containing detailed instructions to guide effective knowledge application. Namely, given a physics problem, Physics Reasoner solves it through three stages: problem analysis, formula retrieval, and guided reasoning. During the process, checklists are employed to enhance LLMs’ self-improvement in the analysis and reasoning stages. Empirically, Physics Reasoner mitigates the issues of insufficient knowledge and incorrect application, achieving state-of-the-art performance on SciBench with an average accuracy improvement of 5.8%.

While recent research increasingly emphasizes the value of human-LLM collaboration in competitive programming and proposes numerous empirical methods, a comprehensive understanding remains elusive due to the fragmented nature of existing studies and their use of diverse, application-specific human feedback. Thus, our work serves a three-fold purpose: First, we present the first taxonomy of human feedback consolidating the entire programming process, which promotes fine-grained evaluation. Second, we introduce ELABORATIONSET, a novel programming dataset specifically designed for human-LLM collaboration, meticulously annotated to enable large-scale simulated human feedback and facilitate cost-effective real human interaction studies. Third, we introduce ELABORATION, a novel benchmark to facilitate a thorough assessment of human-LLM competitive programming. With ELABORATION, we pinpoint strengthes and weaknesses of existing methods, thereby setting the foundation for furture improvement. Our dataset and code will be openly released.