3 citations · 5 across the 8 of their papers we have counts for
8 papers
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
Linghua Zhang, Jun Wang, Jingtong Wu +1
Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments…
Towards Generalization of Block Attention via Automatic Segmentation and Block Distillation
Shuaiyi Li, Zhisong Zhang, Yan Wang +5
Block attention, which processes the input as separate blocks that cannot attend to one another, offers significant potential to improve KV cache reuse in long-context scenarios su…
A Decomposition Perspective to Long-context Reasoning for LLMs
Yanling Xiao, Huaibing Xie, Guoliang Zhao +8
Long-context reasoning is essential for complex real-world applications, yet remains a significant challenge for Large Language Models (LLMs). Despite the rapid evolution in long-c…
RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments
Linghua Zhang, Jun Wang, Jingtong Wu +1
Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments…
On the Transformations across Reward Model, Parameter Update, and In-Context Prompt
Deng Cai, Huayang Li, Tingchen Fu +11
Despite the general capabilities of pre-trained large language models (LLMs), they still need further adaptation to better serve practical applications. In this paper, we demonstra…
On the Worst Prompt Performance of Large Language Models
Bowen Cao, Deng Cai, Zhisong Zhang +2
The performance of large language models (LLMs) is acutely sensitive to the phrasing of prompts, which raises significant concerns about their reliability in real-world scenarios.…
A Thorough Examination of Decoding Methods in the Era of LLMs
Chufan Shi, Haoran Yang, Deng Cai +4
Decoding methods play an indispensable role in converting language models from next-token predictors into practical task solvers. Prior research on decoding methods, primarily focu…
Reasons to Reject? Aligning Language Models with Judgments
Weiwen Xu, Deng Cai, Zhisong Zhang +2
As humans, we consistently interact with our peers and receive feedback in the form of natural language. This language feedback allows us to maintain appropriate behavior, and rect…