activity
20232026
most citedOneBit: Towards Extremely Low-bit Large Language Models

8 citations · 27 across the 33 of their papers we have counts for

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11 papers · 1 filter

cs.CL2026

CheckRLM: Effective Knowledge-Thought Coherence Checking in Retrieval-Augmented Reasoning

Dingling Xu, Ruobing Wang, Qingfei Zhao +8

Reasoning Language Models (RLMs) have significantly improved performance on complex tasks by extending the reasoning chain. However, these chains are prone to containing factual er…

cs.IR2026

Memory Shot for Long-Term Dialogue

Chunyi Peng, Haidong Xin, Xuanshuo Sheng +7

Large Language Models (LLMs) have demonstrated strong capabilities in general conversation, instruction following, and complex reasoning. However, in long-term dialogue settings, t…

cs.IR2026

ReAlign: Optimizing the Visual Document Retriever with Reasoning-Guided Fine-Grained Alignment

Hao Yang, Yifan Ji, Zhipeng Xu +6

Visual document retrieval aims to retrieve a set of document pages relevant to a query from visually rich collections. Existing methods often employ Vision-Language Models (VLMs) t…

cs.AI2026

AgentCPM-Report: Interleaving Drafting and Deepening for Open-Ended Deep Research

Yishan Li, Wentong Chen, Yukun Yan +12

Generating deep research reports requires large-scale information acquisition and the synthesis of insight-driven analysis, posing a significant challenge for current language mode…

cs.AI2026

Lang2Act: Fine-Grained Visual Reasoning through Self-Emergent Linguistic Toolchains

Yuqi Xiong, Chunyi Peng, Zhipeng Xu +6

Visual Retrieval-Augmented Generation (VRAG) enhances Vision-Language Models (VLMs) by incorporating external visual documents to address a given query. Existing VRAG frameworks us…

cs.CL2026

MetaMem: Evolving Meta-Memory for Knowledge Utilization through Self-Reflective Symbolic Optimization

Haidong Xin, Xinze Li, Zhenghao Liu +6

Existing memory systems enable Large Language Models (LLMs) to support long-horizon human-LLM interactions by persisting historical interactions beyond limited context windows. How…

cs.SE2026

Teaching LLMs to Learn Tool Trialing and Execution through Environment Interaction

Xingjie Gao, Pengcheng Huang, Zhenghao Liu +6

Equipping Large Language Models (LLMs) with external tools enables them to solve complex real-world problems. However, the robustness of existing methods remains a critical challen…

cs.CL2026

Long-Chain Reasoning Distillation via Adaptive Prefix Alignment

Zhenghao Liu, Zhuoyang Wu, Xinze Li +6

Large Language Models (LLMs) have demonstrated remarkable reasoning capabilities, particularly in solving complex mathematical problems. Recent studies show that distilling long re…

cs.LG2026

Revealing the Attention Floating Mechanism in Masked Diffusion Models

Xin Dai, Pengcheng Huang, Zhenghao Liu +6

Masked diffusion models (MDMs), which leverage bidirectional attention and a denoising process, are narrowing the performance gap with autoregressive models (ARMs). However, their…

cs.CL2026

Finding What Matters: Anchoring Context Knowledge with Evolving Indices for Iterative Retrieval

Mingyan Wu, Zhenghao Liu, Xinze Li +7

Retrieval-Augmented Generation (RAG) has become a dominant paradigm for mitigating hallucinations in Large Language Models (LLMs) by incorporating external knowledge. However, exis…

cs.CL2026

SEEK: Steering LLM Reasoning for RAG via Internal Reasoning Sketches

Xinze Li, Yuqing Lan, Zhenghao Liu +7

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by incorporating external knowledge into the generation process. Benefiting from the reasoning capabiliti…