6 papers
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…
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…
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…