2 citations · 2 across the 7 of their papers we have counts for
7 papers
The Red Queen Gödel Machine: Co-Evolving Agents and Their Evaluators
Alex Iacob, Andrej Jovanović, William F. Shen +12
Self-improving agents are state-of-the-art on agentic coding benchmarks, yet their search methods assume a stationary evaluation criterion. This ignores a central feature of evolut…
From Recognition to Understanding: Unlocking Cognitive Time Series Reasoning with LLMs
Xin Qiu, Junlong Tong, Yao Zhang +3
Time series analysis has recently been coupled with Large Language Models (LLMs) to leverage their reasoning and world knowledge capabilities, yet gains remain limited. We attribut…
Beyond Uniform Tokens: Adaptive Compression for Time Series Language Models
Jialin Gan, Xin Qiu, Guangzhe Chen +1
Large language models (LLMs) have enabled time series (TS) analysis by jointly modeling numerical observations and textual context through a shared token interface. However, TS tok…
Rethinking the Role of LLMs in Time Series Forecasting
Xin Qiu, Junlong Tong, Yirong Sun +3
Large language models (LLMs) have been introduced to time series forecasting (TSF) to incorporate contextual knowledge beyond numerical signals. However, existing studies question…
ViCA: Efficient Multimodal LLMs with Vision-Only Cross-Attention
Wenjie Liu, Hao Wu, Xin Qiu +6
Modern multimodal large language models (MLLMs) adopt a unified self-attention design that processes visual and textual tokens at every Transformer layer, incurring substantial com…
SonicBench: Dissecting the Physical Perception Bottleneck in Large Audio Language Models
Yirong Sun, Yanjun Chen, Xin Qiu +8
Large Audio Language Models (LALMs) excel at semantic and paralinguistic tasks, yet their ability to perceive the fundamental physical attributes of audio such as pitch, loudness,…
The Few Govern the Many:Unveiling Few-Layer Dominance for Time Series Models
Xin Qiu, Junlong Tong, Yirong Sun +2
Large-scale models are at the forefront of time series (TS) forecasting, dominated by two paradigms: fine-tuning text-based Large Language Models (LLM4TS) and training Time Series…