Papers by Jiahe Jin
Revisiting 3D LLM Benchmarks: Are We Really Testing 3D Capabilities? (2025.findings-acl)
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| Challenge: | Current 3D LLMs are evaluated on Q&A or captioning tasks rather than specific downstream tasks like object detection. |
| Approach: | They propose principles for better assessing genuine 3D understanding by explicitly separating 3D abilities from 1D or 2D aspects when evaluating 3D LLMs. |
| Outcome: | The proposed methods are based on the “2D-Cheating” problem in 3D LLM evaluation, suggesting that they are ineffective . |
Identifying Collective Intelligence Factor in LLM Agent Groups for Generalizable Multi-Agent System Design (2026.findings-acl)
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| Challenge: | Prior studies have focused on designing customized MAS for specific tasks . a critical research question remains: do LLM agent groups exhibit a form of "general intelligence" |
| Approach: | They find a Collective Intelligence factor in human groups that captures their general capability. |
| Outcome: | The proposed model predicts the ACI factor based on the features of LLM agent groups and can improve generalization abilities. |
Beneficial Reasoning Behaviors in Agentic Search and Effective Training Methods to Obtain Them (2026.findings-acl)
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| Challenge: | Agentic search requires large language models to perform multi-step searches to solve complex information needs. |
| Approach: | They propose a training approach that equips agentic search models with reasoning behaviors before reinforcement learning (RL) they compare successful and failed trajectories and propose supervised fine-tuning and standard RL . |
| Outcome: | The proposed approach outperforms direct RL by 37.2% on three web benchmarks and 6.2% on seven multi-hop QA benchmarks. |