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20242026
most citedAdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning

3 citations · 3 across the 10 of their papers we have counts for

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5 papers · 1 filter

cs.AI2026

LogicTrack: Auditing Reasoning Trajectories of Large Language Models with Formal Logic Solvers

Jingyu Hu, Shu Yang, Weiru Liu +1

Chain-of-Thought (CoT) reasoning has been shown to improve the performance of large language models (LLMs), yet existing optimization methods largely rely on outcome-based feedback…

cs.LG2026★ 3 cited

AdaHAT: Adaptive Hard Attention to the Task in Task-Incremental Learning

Pengxiang Wang, Hongbo Bo, Jun Hong +2

Catastrophic forgetting is a major problem in task-incremental learning, where neural networks tend to overwrite previously learned knowledge when trained on new tasks. A number of…

cs.CL2026

StakeBench: Evaluating Language Understanding Grounded in Market Commitment

Yunhua Pei, Jingyu Hu, Yiwei Shi +3

Existing financial NLP benchmarks often rely on labels supplied by outside observers, measuring how language is perceived rather than what speakers have committed to in the market.…

cs.AI2026

Influencing LLM Multi-Agent Dialogue via Policy-Parameterized Prompts

Hongbo Bo, Jingyu Hu, Weiru Liu

Large Language Models (LLMs) have emerged as a new paradigm for multi-agent systems. However, existing research on the behaviour of LLM-based multi-agents relies on ad hoc prompts…

cs.CY2026

Failing on Bias Mitigation: A Case Study on the Challenges of Fairness in Government Data

Hongbo Bo, Jingyu Hu, Debbie Watson +1

The potential for bias and unfairness in AI-supporting government services raises ethical and legal concerns. Using crime rate prediction with the Bristol City Council data as a ca…