arXiv:2606. 08346v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has become a dominant paradigm for improving the reasoning capabilities of large language models (LLMs).
By Ayush Singh, Umang Goyal, Ankur Dahiya
SiST‑GNN introduces a simultaneous spatial‑temporal message‑passing framework for dynamic graph neural networks, fusing per‑node temporal embeddings with spatial aggregation in a single operation. By maintaining a recurrent hidden state per node and treating it as a cross‑time edge, the model jointly reasons over topology and evolution. Experiments on link‑prediction and node‑classification benchmarks show significant improvements over prior methods, achieving up to 158% gains in live‑update link prediction and outperforming discrete‑time baselines by 7–23% in dynamic node classification.
By Shubhajit Roy, Anirban Dasgupta
arXiv:2608.23493v1 Announce Type: new
Abstract: Self-reflection is a powerful mechanism for credit assignment in human learning, converting sparse outcome feedback into actionable guidance. However,...
By Jialong Liu, Yuling Shi, Ning Yang, Xiaodong Gu, Zuchao Li
arXiv:2608.29588v1 Announce Type: new
Abstract: Reasoning over text-attributed graphs (TAGs) requires large language models (LLMs) to combine a node's text with evidence distributed across its neighb...
By Yilun Liu, Boyu Luo, Yanran Tang, Ruihong Qiu, Zi Huang
The paper investigates why misinformation spreads more quickly on engagement‑based platforms by dissecting the recommendation algorithm of X. It identifies an engagement fungibility mechanism that rewards instant reactions (likes, retweets) over thoughtful engagement (replies, quotes), allowing misinformation—which tends to attract instant reactions—to receive more recommendations. The authors validate this mechanism through a simulation on the USC X 2024 election corpus, showing that adjusting metric weights has little effect, while requiring thoughtful engagement before amplification can significantly reduce the credibility exposure gap without harming mainstream content or engagement.
By Pan Li, Shuang Gao
ConsensusBench is a new dataset that supplies rule‑based process‑level signals for large language model reasoning. It identifies key intermediate conclusions—called Consensus Nodes—by filtering correct trajectories and clustering semantically equivalent statements. By incorporating a process reward derived from these nodes into GRPO‑style reinforcement learning, the authors create ConsensusPR, which reduces reward sparsity and improves performance on benchmarks such as AIME, GSM8K, and MATH‑500.
By Shi-Qi Yan, Chao-Hong Tan, Qian Chen, Wen Wang, Xiangang Li, Zhen-Hua Ling