Zheng Wang

Zhaonan Wu, Yanjie Zhao, Zhenpeng Chen et al.

Artifact Evaluation (AE) has become a standard mechanism for linking software engineering papers to their supporting artifacts, but growing submission volume makes purely manual AE increasingly difficult to sustain. Although recent advances in LLM-based agents make AE automation increasingly plausible, current descriptive badge policies define badge semantics but provide no executable verification criteria, leaving both human reviewers and agents without a detailed basis for judgment. To address this gap, we construct ArtifactGuide, a structured scoring rubric grounded in ACM policy, expert-informed calibration, and artifact-based validation, and we design ArtifactCopilot, an agent collecting review evidence following a fixed sequence under ArtifactGuide and deriving the final badge decision from accumulated evidence. We evaluate our framework on 60 real artifacts from recent software engineering conferences using human-adjudicated badges as reference. The results show that ArtifactGuide improves the AE performance of coding agents over official ACM badge-policy prompts, increasing three-run mean exact badge agreement by 10.55 to 28.34 percentage points. Across all evaluated systems and prompting protocols, ArtifactCopilot achieves the highest badge-level agreement at 70.56% and is the only system that completed all repeated runs successfully while producing a review report in every run. A controlled user study with 8 experienced researchers suggests that ArtifactCopilot reports improve reviewer confidence, help reviewers locate evidence, and understand evaluation scope more clearly. Further analysis translates insights from automated AE into practical guidance for designing higher-quality artifacts with clearer review routes, more explicit claim-to-output links, and more concrete reuse paths.

Yiming Dong, Kun Fu, Haoyu Li et al.

Prolonged reinforcement learning with verifiable rewards (RLVR) has been shown to drive continuous improvements in the reasoning capabilities of large language models, but the training is often prone to instabilities, especially in Mixture-of-Experts (MoE) architectures. Training instability severely undermines model capability improvement, yet its underlying causes and mechanisms remain poorly understood. In this work, we introduce a principled framework for understanding RLVR instability through the lens of objective-level hacking. Unlike reward hacking, which arises from exploitable verifiers, objective-level hacking emerges from token-level credit misalignment and is manifested as system-level spurious signals in the optimization objective. Grounded in our framework, together with extensive experiments on a 30B MoE model, we trace the origin and formalize the mechanism behind a key pathological training dynamic in MoE models: the abnormal growth of the training-inference discrepancy, a phenomenon widely associated with instability but previously lacking a mechanistic explanation. These findings provide a concrete and causal account of the training dynamics underlying instabilities in MoE models, offering guidance for the design of stable RLVR algorithms.