activity
20172026
most citedIGLUE: A Benchmark for Transfer Learning across Modalities, Tasks, and Languages

25 citations · 197 across the 57 of their papers we have counts for

collaborators

69 papers

cs.MA2026

Specialize Roles, Mix Deployments: Pushing the Cost-Accuracy Frontier of LLM Agent Teams

Yinsicheng Jiang, Liang Cheng, Yeqi Huang +6

LLM agents are increasingly deployed as multi-role teams, where tasks are divided across specialized roles such as planner, executor, and verifier. In these systems, cost and accur…

cs.CL2026

DashAttention: Differentiable and Adaptive Sparse Hierarchical Attention

Yuxiang Huang, Nuno M. T. Gonçalves, Federico Alvetreti +5

Current hierarchical attention methods, such as NSA and InfLLMv2, select the top-k relevant key-value (KV) blocks based on coarse attention scores and subsequently apply fine-grain…

cs.LG2026

The Cancellation Hypothesis in Critic-Free RL: From Outcome Rewards to Token Credits

Tianhao Cheng, Zeyu Huang, Zihan Qiu +5

A commonly accepted explanation of critic-free RL for LLMs, based on sequence-level rewards, is that it reinforces successful rollouts with a positive advantage while penalizing fa…

cs.LG2026

AdaSplash-2: Faster Differentiable Sparse Attention

Nuno Gonçalves, Hugo Pitorro, Vlad Niculae +4

Sparse attention has been proposed as a way to alleviate the quadratic cost of transformers, a central bottleneck in long-context training. A promising line of work is -entmax a…

cs.LG2026

Adapting Time Series Foundation Models through Data Mixtures

Thomas L. Lee, Edoardo M. Ponti, Amos Storkey

Time series foundation models (TSFMs) have become increasingly popular for zero-shot forecasting. However, for a new time series domain not fully covered by the pretraining set, pe…

cs.CL2026

Is Information Density Uniform when Utterances are Grounded on Perception and Discourse?

Matteo Gay, Coleman Haley, Mario Giulianelli +1

The Uniform Information Density (UID) hypothesis posits that speakers are subject to a communicative pressure to distribute information evenly within utterances, minimising surpris…