用2.5D分解破解LLM空间构建中的坐标错误，神经符号方法让三维结构理解更可靠
arXiv:2605.07066v2 Announce Type: replace Abstract: Autonomous systems that build structures from natural-language instructions need reliable spatial …
神经符号架构让企业智能体摆脱LLM幻觉与领域漂移，实现合规推理
arXiv:2604.00555v4 Announce Type: replace-cross Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain …
神经符号框架，将一阶逻辑自动转化为自然语言语句，革新语义解析与定理验证
arXiv:2605.18155v1 Announce Type: new Abstract: Translating formal language into natural language is a foundational challenge in NLP, driving various …
零样本对话状态跟踪新突破：有界神经符号代理框架实现高效稳健的NLU推理
arXiv:2605.19077v1 Announce Type: new Abstract: Task-oriented dialogue systems -- handling transactions, reservations, and service requests -- require…
最新研究：LLM在税法推理中存在数据污染风险，别被“假懂”骗了！
arXiv:2605.16052v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have significantly enhanced automated legal reasoning.…
LLM的溯因推理一直是短板，Graph of States用因果图+状态机构建结构化信念状态，把无头苍蝇式的探索变成定向搜索，一举解决证据虚构、上下文漂移等四大痛点。
arXiv:2603.21250v2 Announce Type: replace Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language M…