把推理规则内化进语言模型，让知识图谱推理更忠实可靠，值得关注的前沿方法。
arXiv:2608.17443v1 Announce Type: new Abstract: Knowledge Graph Reasoning (KGR) aims to discover latent facts by leveraging the structural evidence av…
当大模型遇上神经符号验证，自主系统可信度迎来新解法，一篇值得深读的前沿方法。
arXiv:2604.02478v2 Announce Type: replace Abstract: Deep learning models excel at detecting anomaly patterns in normal data. However, they do not prov…
用神经符号验证给LLM推理做可解释诊断，直击黑箱痛点，值得关注。
arXiv:2608.08786v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as data-driven reasoners, yet their chains-of-thought …
把神经符号推理融入RAG，让问答系统不仅能答，还能说清为什么。
arXiv:2608.06292v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) improves question answering by grounding large language models (L…
结合神经符号与自适应修复，精准定位并修补代码漏洞，AI安全新突破！
arXiv:2604.17184v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) can generate plausible code patches, but plausibility is not en…
教大模型用Prolog做逻辑推理，提升复杂任务准确率，AI工具融合新思路。
arXiv:2512.07407v3 Announce Type: replace Abstract: Language models frequently produce plausible yet incorrect reasoning traces that are difficult to …
神经符号AI破解法律AI黑箱，打造可信、可解释的智能模型。
arXiv:2606.15646v1 Announce Type: new Abstract: Large Language Models (LLMs) have transformed natural language processing, but their lack of interpret…
混合神经符号门控与压缩思维链精炼，让小模型也能精准捕捉社交文本中的讽刺信号。
arXiv:2606.16845v1 Announce Type: cross Abstract: Large Language Models (LLMs) natively default to literal semantic interpretations, making zero-shot …
从LLM蒸馏出可解释的ASP规则，为神经符号VQA注入结构化推理能力。
arXiv:2606.03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integra…
融合神经符号可解释性与3D多模态大模型，突破空间推理的闭集局限。
arXiv:2606.01215v1 Announce Type: cross Abstract: Current 3D spatial reasoning methods face a fundamental trade-off: neuro-symbolic 3D (NS3D) concept …
最新研究发现，现代大语言模型与人类脑电图在情感效价轴上存在共享的饱和度规律，揭示AI与大脑情感表征的深层统一性。
arXiv:2606.00129v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as powerful representation learners whose internal features …
用神经符号方法验证大模型输出，保障金融医疗等数据敏感领域的安全可靠。
arXiv:2605.26942v1 Announce Type: new Abstract: LLMs deployed in high-stakes domains face fundamental reliability challenges: hallucinations, inconsis…
神经符号框架通过操作树实现自动形式化，ICML 2026论文提出分解-结构-修复的创新路线，助力数学推理与AI融合。
arXiv:2604.19000v2 Announce Type: replace-cross Abstract: Statement autoformalization acts as a critical bridge between human mathematics and formal m…
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…