activity
20232026
most citedMenatQA: A New Dataset for Testing the Temporal Comprehension and Reasoning Abilities of Large Language Models

4 citations · 16 across the 48 of their papers we have counts for

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Showing 2026 · cs.CLShow all

10 papers · 2 filters

cs.CL2026

Constitutional Midtraining: Content Presence Drives Alignment Gains

Desiree Cho, Cameron Tice, Bernie Hogan +4

Post-training alignment is often shallow, eroding under fine-tuning. It remains untested as to whether constitutional midtraining interventions can produce durable alignment when c…

cs.CL2026

LongCrafter: Towards Diverse Long-Context Understanding via Evidence-Graph-Guided Instruction Synthesis

Chenhao Yuan, Yinhao Xu, Shuwen Xu +8

Synthesizing long-context supervised fine-tuning (SFT) data is a scalable way to enhance the long-context understanding of large language models (LLMs), yet existing approaches sha…

cs.CL2026

MemTools: A Unified Research Framework for Interoperable Agent Memory

Chengfeng Zhao, Jinhui Chen, Sirui Liang +4

While memory systems are essential for agent architectures, pervasive architectural fragmentation restricts systematic research. Existing implementations typically couple different…

cs.CL2026

Neural Procedural Memory: Empowering LLM Agents with Implicit Activation Steering

Chengfeng Zhao, Yuqiao Tan, Shizhu He +3

While Large Language Models (LLMs) excel as static solvers, transforming them into autonomous agents remains challenging. This transition requires continuous environmental interact…

cs.CL2026

Empowering GUI Agents via Autonomous Experience Exploration and Hindsight Experience Utilization for Task Planning

Tianyi Men, Zhuoran Jin, Pengfei Cao +3

Multimodal web agents can assist humans in operating repetitive GUI tasks, where effective task planning is essential for decomposing complex tasks into executable actions. While s…

cs.CL2026

Why Multi-Step Tool-Use Reinforcement Learning Collapses and How Supervisory Signals Fix It

Yupu Hao, Zhuoran Jin, Huanxuan Liao +2

Tool use enables large language models (LLMs) to perform complex tasks, and recent agentic reinforcement learning (RL) methods show promise for enhancing model capabilities. Howeve…

cs.CL2026

Look Light, Think Heavy: What Multimodal Chain-of-Thought Reasoning Can and Cannot Do

Zhuoran Jin, Kejian Zhu, Hongbang Yuan +5

Chain-of-Thought (CoT) has become a standard method for improving reasoning capabilities in large language models (LLMs) by eliciting step-by-step thinking, but its effectiveness i…

cs.CL2026

SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models

Shiqiang Cai, Nianhong Niu, Shizhu He +2

Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A high-quality scientific taxon…

cs.CL2026

GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum

Shuwen Xu, Yao Xu, Jiaxiang Liu +4

Agentic knowledge graph question answering (KGQA) requires an agent to iteratively interact with knowledge graphs (KGs), posing challenges in both training data scarcity and reason…

cs.CL2026

WideSeek: Advancing Wide Research via Multi-Agent Scaling

Ziyang Huang, Haolin Ren, Xiaowei Yuan +6

Search intelligence is evolving from Deep Research to Wide Research, a paradigm essential for retrieving and synthesizing comprehensive information under complex constraints in par…