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20232026
most citedTool Learning in the Wild: Empowering Language Models as Automatic Tool Agents

6 citations · 14 across the 21 of their papers we have counts for

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8 papers · 1 filter

cs.AI2026

Agora: Enhancing LLM Agent Reasoning Via Auction-Based Task Allocation

Kaiji Zhou, Aleš Leonardis, Yue Feng

Enhancing the reasoning capabilities of large language model (LLM) agents requires effective orchestration of diverse expert models and tools. However, existing frameworks typicall…

cs.CL2026

RouteRec: Strict Evaluation of Recommender-Agent Selection and Aggregation

Kaiji Zhou, Vladimir Kalmykov, Yue Feng

Recommender systems increasingly face a choice among heterogeneous agents -- collaborative filters, sequential models, content-based retrievers, and LLM-based rerankers -- yet no s…

cs.CL2026

From Passive Generation to Investigation: A Proactive Scientific Peer Review Agent

Haishuo Fang, Yue Feng, Iryna Gurevych

Large language models (LLMs) have shown promise in automating scientific peer review. However, existing approaches often struggle to generate in-depth reviews supported by concrete…

cs.CL2026

When to Think Deeply: Inhibitory Deliberation for LLM Reasoning

Zhixuan He, Yue Feng

Reasoning Large Language Models can improve problem-solving performance through deliberative inference, but invoking slow reasoning for every input is computationally expensive and…

cs.SE2026

Certified Program Synthesis with a Multi-Modal Verifier

Yueyang Feng, Dipesh Kafle, Vladimir Gladshtein +5

Certified program synthesis (aka vericoding) is the process of automatically generating a program, its formal specification, and a machine-checkable proof of their alignment from a…

cs.CL2026

From Query to Counsel: Structured Reasoning with a Multi-Agent Framework and Dataset for Legal Consultation

Mingfei Lu, Yi Zhang, Mengjia Wu +1

Legal consultation question answering (Legal CQA) presents unique challenges compared to traditional legal QA tasks, including the scarcity of high-quality training data, complex t…