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20232026
most citedChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?

16 citations · 40 across the 24 of their papers we have counts for

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Showing 2025Show all

13 papers · 1 filter

cs.SE2025

SweRank+: Multilingual, Multi-Turn Code Ranking for Software Issue Localization

Revanth Gangi Reddy, Ye Liu, Wenting Zhao +7

Maintaining large-scale, multilingual codebases hinges on accurately localizing issues, which requires mapping natural-language error descriptions to the relevant functions that ne…

cs.CL2025

Foundational Automatic Evaluators: Scaling Multi-Task Generative Evaluator Training for Reasoning-Centric Domains

Austin Xu, Xuan-Phi Nguyen, Yilun Zhou +3

Finetuning specialized generative evaluators has emerged as a popular paradigm to meet the increasing demand for scalable evaluation during both training and test-time. However, re…

cs.CL2025

Synthesizing Agentic Data for Web Agents with Progressive Difficulty Enhancement Mechanisms

Shrey Pandit, Xuan-Phi Nguyen, Yifei Ming +4

Web-based 'deep research' agents aim to solve complex question - answering tasks through long-horizon interactions with online tools. These tasks remain challenging, as the underly…

cs.AI2025

Hard2Verify: A Step-Level Verification Benchmark for Open-Ended Frontier Math

Shrey Pandit, Austin Xu, Xuan-Phi Nguyen +3

Large language model (LLM)-based reasoning systems have recently achieved gold medal-level performance in the IMO 2025 competition, writing mathematical proofs where, to receive fu…

cs.AI2025

LiveResearchBench: A Live Benchmark for User-Centric Deep Research in the Wild

Jiayu Wang, Yifei Ming, Riya Dulepet +7

Deep research -- producing comprehensive, citation-grounded reports by searching and synthesizing information from hundreds of live web sources -- marks an important frontier for a…

cs.AI2025

SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents

Xuan-Phi Nguyen, Shrey Pandit, Revanth Gangi Reddy +4

Equipping large language models (LLMs) with complex, interleaved reasoning and tool-use capabilities has become a key focus in agentic AI research, especially with recent advances…

cs.CL2025

J4R: Learning to Judge with Equivalent Initial State Group Relative Policy Optimization

Austin Xu, Yilun Zhou, Xuan-Phi Nguyen +2

To keep pace with the increasing pace of large language models (LLM) development, model output evaluation has transitioned away from time-consuming human evaluation to automatic ev…

cs.AI2025

Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning

Jiayu Wang, Yifei Ming, Zixuan Ke +4

Reinforcement learning (RL) has become the dominant paradigm for improving the performance of language models on complex reasoning tasks. Despite the substantial empirical gains de…

cs.CL2025

Evaluating Judges as Evaluators: The JETTS Benchmark of LLM-as-Judges as Test-Time Scaling Evaluators

Yilun Zhou, Austin Xu, Peifeng Wang +2

Scaling test-time computation, or affording a generator large language model (LLM) extra compute during inference, typically employs the help of external non-generative evaluators…

cs.CL2025

MAS-ZERO: Designing Multi-Agent Systems with Zero Supervision

Zixuan Ke, Austin Xu, Yifei Ming +4

Multi-agent systems (MAS) leveraging the impressive capabilities of Large Language Models (LLMs) hold significant potential for tackling complex tasks. However, most current MAS de…

cs.SE2025★ 1 cited

SweRank: Software Issue Localization with Code Ranking

Revanth Gangi Reddy, Tarun Suresh, JaeHyeok Doo +7

Software issue localization, the task of identifying the precise code locations (files, classes, or functions) relevant to a natural language issue description (e.g., bug report, f…

cs.AI2025★ 3 cited

A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic Systems

Zixuan Ke, Fangkai Jiao, Yifei Ming +9

Reasoning is a fundamental cognitive process that enables logical inference, problem-solving, and decision-making. With the rapid advancement of large language models (LLMs), reaso…

cs.CL2025

Demystifying Domain-adaptive Post-training for Financial LLMs

Zixuan Ke, Yifei Ming, Xuan-Phi Nguyen +2

Domain-adaptive post-training of large language models (LLMs) has emerged as a promising approach for specialized domains such as medicine and finance. However, significant challen…