7 citations · 25 across the 49 of their papers we have counts for
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cs.AI2026
Compare to Correct: Pairwise-Guided Logit Adjustment for LLM Test-Time Scaling
Cong Hua, Qianqian Xu, Zitai Wang +4
Sampling-based test-time scaling enhances LLM reasoning by aggregating multiple reasoning paths for the same query. Existing methods use answer frequencies or path confidence to co…
cs.AI2026
Narrative Paradoxes in LLM Inference: Shaping Reasoning Trajectories toward Misalignment
Haoming Yang, Ke Ma, Yangbangyan Jiang +6
Explicit reasoning in large language models (LLMs) is often associated with reliability, yet it can still pose risks when a model infers harmful goals under a benign context. We id…
cs.AI2024★ 5 cited
Sequential Manipulation Against Rank Aggregation: Theory and Algorithm
Ke Ma, Qianqian Xu, Jinshan Zeng +4
Rank aggregation with pairwise comparisons is widely encountered in sociology, politics, economics, psychology, sports, etc . Given the enormous social impact and the consequent in…