World models learn internal representations of environment dynamics to predict future states, enabling agents to optimize action plans without physical interactions. However, developing world models that genuinely internalize underlying causal physical laws to explicitly reason about action preconditions and subsequent...
Jianan Wang, Hao-Quan Zhai, Si-Yang Zhang et al.· 0 citations
TabSieve, a select-then-predict framework that makes evidence usage explicit and auditable, and introduces TAB-GRPO, a reinforcement learning recipe that jointly optimizes evidence selection and prediction correctness with separate rewards, and stabilizes mixed regression and classification training via dynamic task-ad...
Yong-Yao Wang, Ziqi Miao, Lu Yang et al.· arXiv.org· 5 citations
GANDR (Grounded ANswer DRafter), a two-agent system in which a Drafter writes an answer in a structured legal-reasoning format and a separate Critic, with the same view as a human verifier, audits each claim against its cited source and emits a per-claim audit trace on every round.
Chen Qian, Yi-Meng Wang, Yu Chen et al.· 0 citations
This work introduces DocCite, to its knowledge the first benchmark for systems that verify and correct page-level citations in document images, and proposes AtomCite, an agentic framework that parses an answer into claims, checks each claim against the image of its cited page, and applies a deterministic repair policy.
Chen Qian, Yi-Meng Wang, Yu Chen et al.· 0 citations
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