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20212026
most citedLarge Language Models are not Fair Evaluators

33 citations · 77 across the 29 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

PaperBanana-Interact: Scientific Diagram Refinement with Multi-Turn Human Feedback

Xueqing Wu, Ashwin Balasubramanian, Bingxuan Li +7

Recent efforts have aimed to automate scientific diagram generation from paper content (Lin et al., 2026; Zhu et al., 2026a). However, fully satisfying an author's visual and commu…

cs.CL2026

PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems

Hanglong Lv, Dawei Zhu, Lei Li +11

Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally complete, single-turn queries. O…

cs.CL2026

DFlare: Scaling Up Draft Capacity for Block Diffusion Speculative Decoding

Jiebin Zhang, Zhenghan Yu, Song Liu +9

Block diffusion speculative decoding accelerates LLM inference by predicting all tokens within a block simultaneously for the target model to verify in parallel. Predicting an enti…

cs.CL2026

Learning to Draft: Adaptive Speculative Decoding with Reinforcement Learning

Jiebin Zhang, Zhenghan Yu, Liang Wang +8

Speculative decoding accelerates large language model (LLM) inference by using a small draft model to generate candidate tokens for a larger target model to verify. The efficacy of…

cs.CL2026★ 1 cited

PaperBanana: Automating Academic Illustration for AI Scientists

Dawei Zhu, Rui Meng, Yale Song +4

Despite rapid advances in autonomous AI scientists powered by language models, generating publication-ready illustrations remains a labor-intensive bottleneck in the research workf…