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20242026
most citedExpanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling

16 citations · 22 across the 12 of their papers we have counts for

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Showing 2026Show all

6 papers · 1 filter

cs.CL2026

LongWoF-Bench: Evaluating EvoMap Genes for Verifiable Long-Workflow Tasks

Xiao Zhang, Qumeng Sun, Jiahao Li +5

Large language models are increasingly expected to execute complex workflows whose success depends on maintaining interdependent constraints and producing artifacts that satisfy st…

cs.AI2026

AutoResearch: Insight In, Hallucination Out

Yiming Ren, Xiang Liu, Bruno +5

Autonomous research systems are increasingly capable of executing long research workflows, yet automation alone does not ensure that the resulting process remains scientifically gr…

cs.SE2026

From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution

Junjie Wang, Yiming Ren, Haoyang Zhang

This beta technical report asks how reusable experience should be represented so that it can function as effective test-time control and as a substrate for iterative evolution. We…

cs.LG2026

Smaller Models are Natural Explorers for Policy-Level Diversity in GRPO

Yiran Xu, Yiming Ren, Zicheng Lin +8

We identify a new dimension for enhancing rollout diversity in Group Relative Policy Optimization (GRPO) for LLMs. While GRPO relies on diverse rollouts, prevailing strategies prim…

cs.CV2026

Mitigating the Reasoning Tax in Vision-Language Fine-Tuning with Input-Adaptive Depth Aggregation

Yiming Ren, Yujiu Yang, Junjie Wang

Supervised fine-tuning (SFT) on visual instruction data often improves perceptual capabilities in vision-language models (VLMs) while degrading reasoning performance, creating a pe…

cs.CL2026

SIN-Bench: Tracing Native Evidence Chains in Long-Context Multimodal Scientific Interleaved Literature

Yiming Ren, Junjie Wang, Yuxin Meng +11

Evaluating whether multimodal large language models truly understand long-form scientific papers remains challenging: answer-only metrics and synthetic "Needle-In-A-Haystack" tests…