cs.CL（2026-02-27）

📊 共 13 篇论文 | 🔗 1 篇有代码

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支柱九：具身大模型 (Embodied Foundation Models) (12 🔗1) 支柱二：RL算法与架构 (RL & Architecture) (1)

🔬 支柱九：具身大模型 (Embodied Foundation Models) (12 篇)

#	题目	一句话要点	标签	🔗	⭐
1	ArgLLM-App: An Interactive System for Argumentative Reasoning with Large Language Models	提出ArgLLM-App，一个基于论证推理的大语言模型交互系统，用于二元决策任务。	large language model
2	TRIZ-RAGNER: A Retrieval-Augmented Large Language Model for TRIZ-Aware Named Entity Recognition in Patent-Based Contradiction Mining	提出TRIZ-RAGNER框架，用于专利中TRIZ感知的命名实体识别，提升矛盾挖掘效果。	large language model
3	The Astonishing Ability of Large Language Models to Parse Jabberwockified Language	大型语言模型展现出惊人的解析乱语能力，揭示语言结构的奥秘	large language model
4	Divide and Conquer: Accelerating Diffusion-Based Large Language Models via Adaptive Parallel Decoding	提出DiCo：通过自适应并行解码加速扩散模型大语言模型的推理速度。	large language model
5	From Static Benchmarks to Dynamic Protocol: Agent-Centric Text Anomaly Detection for Evaluating LLM Reasoning	提出Agent-Centric动态协议，用于评估LLM在文本异常检测中的推理能力。	large language model
6	Do LLMs Benefit From Their Own Words?	研究表明，大型语言模型在多轮对话中可能并不总是受益于自身历史回复，并提出选择性上下文过滤方法。	large language model
7	Controllable Reasoning Models Are Private Thinkers	提出可控推理模型以提升AI Agent的隐私保护能力	instruction following	✅
8	Task-Centric Acceleration of Small-Language Models	TASC：面向任务的小语言模型加速框架，通过自适应序列压缩提升效率。	large language model
9	ARGUS: Seeing the Influence of Narrative Features on Persuasion in Argumentative Texts	ARGUS框架：探究叙事特征对论辩文本说服力的影响	large language model
10	LLM-Driven Multi-Turn Task-Oriented Dialogue Synthesis for Realistic Reasoning	提出LLM驱动的多轮任务型对话合成框架，用于评估LLM的真实推理能力	large language model
11	BRIDGE the Gap: Mitigating Bias Amplification in Automated Scoring of English Language Learners via Inter-group Data Augmentation	提出BRIDGE框架，通过跨组数据增强缓解英语学习者自动评分中的偏差放大问题	large language model
12	A Novel Hierarchical Multi-Agent System for Payments Using LLMs	提出HMASP，首个基于LLM的多智能体支付系统，实现端到端支付工作流。	large language model

🔬 支柱二：RL算法与架构 (RL & Architecture) (1 篇)

#	题目	一句话要点	标签	🔗	⭐
13	Preference Packing: Efficient Preference Optimization for Large Language Models	提出Preference Packing，提升LLM偏好优化训练的资源效率	DPO direct preference optimization large language model

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