Papers by Jingyao Chen
E2EDev: Benchmarking Large Language Models in End-to-End Software Development Task (2026.acl-long)
Copied to clipboard
| Challenge: | Existing E2ESD benchmarks are limited by coarse-grained requirement specifications and unreliable evaluation protocols. |
| Approach: | They propose a benchmark to assess whether generated software meets user needs . they use a fine-grained set of user requirements and a fully automated testing pipeline . |
| Outcome: | E2EDev is a benchmark to assess whether generated software meets user needs through mimicking real user interactions. |
Investigating Inference-time Scaling for Chain of Multi-modal Thought: A Preliminary Study (2025.findings-acl)
Copied to clipboard
| Challenge: | Inference-time scaling of chain-of-thought (CoT) has been demonstrated as a promising approach for addressing multi-modal reasoning tasks. |
| Approach: | They propose to integrate visual and textual modalities within the reasoning process . they adopt a consistency-enhanced verifier to ensure effective guidance for both methods across different thought paradigms. |
| Outcome: | The proposed method outperforms text-only reasoning on 10 tasks spanning diverse domains and requires higher token consumption for processing richer visual inputs. |
nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow (2025.acl-long)
Copied to clipboard
| Challenge: | *Natural Language to Visualization (NL2Vis) seeks to transform natural-language descriptions into visual representations of given tables. |
| Approach: | They propose a collaborative agent workflow for NL2Vis that incorporates three agents . the model is called **nvAgent** and comprises a processor agent for database processing and context filtering, a composer agent for planning visualization generation and a validator agent for code translation and output verification. |
| Outcome: | The proposed workflow surpasses state-of-the-art models on the VisEval benchmark. |