Large language model agents can invoke tools fluently, but enterprise workflows demand more than selecting the right tools: actions must strictly comply with organizational policies, tool feedback often conceals hidden side effects under partial observability, and long-horizon tasks require persistent state tracking ac...
Scientific discovery often begins when scattered clues call for a new way of describing the world. Such abductive exploration can require constructing the representation in which an explanation is stated, when the world is epistemically open, and composing evidence scattered across contexts, when it is structurally int...
Wei-Han Li, Tianshi ZHENG, Yang-Qiu Song et al.· 0 citations
Real-world decision-making often involves uncertainty expressed in linguistic rather than numerical terms, and Prospect Theory (PT) provides a classic framework for modeling human behavior under such uncertainty. Although recent studies have developed frameworks to estimate PT parameters for Large Language Models (LLMs...
Rui Wang, Qi-Han Lin, Jiayu Liu et al.· 2 citations
A cross-modal grounding study shows that text-level VCI ordering largely survives faithful rendering and blind image-level preference judgment, supporting Ekphrasis as a measure of visual ideation beyond prose quality.
Hongyu Luo, Hexi Wang, Hui-Hao Jing et al.· 0 citations
AgentIdeaBench is introduced, a multidisciplinary benchmark that evaluates scientific ideation under two matched settings, static observation and active exploration, and Scientific World Modeling is explored, a generation-time loop that refines a draft hypothesis through structured thought experiments.
Yunxiang Mo, Tianshi ZHENG, Yi-Sen Gao et al.· 0 citations
Consistent gains over DVD on LVBench, Video-MME, EgoSchema, and LongVideoBench suggest that option-aware evidence acquisition transfers beyond MMR-V, and proposes PACE (Progressive Acquisition of Critical Evidence), a factor-guided framework for long-video evidence acquisition.
Bai-Xuan Xu, Yinyui Xu, Tianshi ZHENG et al.· 1 citation
Results indicate that MultivationBench presents a significant challenge: all tested models struggle to maintain consistent motivation reasoning across sequential contexts, revealing a critical disconnect between static recognition capabilities and the dynamic reasoning essential for human-like social understanding.
This survey focuses on co-evolution in agentic systems, a multi-component form of self-evolution in which multiple agents and their environment impose adaptive pressure on one another.
Qing Zong, Jiayu Liu, Junhao Shen et al.· 0 citations
This work proposes InferenceDynamics, a flexible and scalable multi-dimensional routing framework by modeling the capability and knowledge of models, and demonstrates its effectiveness and generalizability in group-level routing using modern benchmarks including MMLU-Pro, GPQA, BigGen-Bench, and LiveBench.
Haochen Shi, Tianshi ZHENG, Weiqi Wang et al.· Annual Meeting of the Associ...· 0 citations
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