arXiv:2606. 08020v1 Announce Type: cross Abstract: Hard-constraint decision systems usually veto infeasible candidates.
By Yifan Wang
ContractRL introduces a contract-constrained sequential repair protocol for structured tool calls, modeling verifier-guided JSON repair as a bounded decision process. The policy observes candidate data, verifier feedback, JSON pointers, repair history, and budget, using a contract-derived action mask to filter invalid operations before a deterministic validator applies changes. Compared to Patch‑SFT and full regeneration, ContractRL achieves higher semantic success (0.9362 vs. 0.9076 and 0.9148) while generating fewer tokens (34.4 vs. 44.9 and 137.2), and policy optimization further improves success rates.
By Miaobo Hu, Shuhao Hu, Xiaobo Guo, Xin Wang, Bokun Wang, Yina Sa, Daren Zha, Jun Xiao
arXiv:2607. 00269v1 Announce Type: new Abstract: LLMs, solvers, and agent teams increasingly generate workflow actions, repairs, and plans, but a generated action may be syntactically valid yet stale, infeasible, conflicting, or destructive of the evidence that triggered a repair.
By Edward Y. Chang, Longling Geng, Emily J. Chang
arXiv:2608. 04552v1 Announce Type: cross Abstract: Black-box language-model reliability is commonly pursued by sampling, prompting, voting, verifying, or iteratively revising individual answers.
By Song Zichen
The paper introduces a protocol for auditing and composing reinforcement‑learning policies using discrete behavioral rules, defining auditability through six testable predicates such as trace integrity and rule coverage. Experiments show that overlapping rule sets do not guarantee behavioral agreement, and that rule‑based fusion often fails to outperform value‑based composition, highlighting limitations in current description layers. The authors provide an evidence‑bounded audit framework and outline future directions for more robust skill composition.
By Liu Hung Ming
arXiv:2607. 18724v1 Announce Type: new Abstract: Text-to-image (T2I) generators often fail to follow their prompts faithfully, producing wrong counts, swapped attributes, ambiguous relations, and illegible text.
By Haoyue Liu, Xiaoyu Ma, Ye Chen, Shuguang Cui, Xiaoying Tang