3 papers
cs.AI2026
Know Thy Reasoner: Not All Language Models Explore Alike
Moulik Choraria, Argyrios Gerogiannis, Anirban Das +4
Compute scaling for LLM reasoning trades off exploring solution approaches (\emph{breadth}) against refining promising ones (\emph{depth}), yet why a given trade-off works, and why…
cs.CL2026
ContextWeaver: Selective and Dependency-Structured Memory Construction for LLM Agents
Yating Wu, Yuhao Zhang, Sayan Ghosh +4
Large language model (LLM) agents often struggle in long-context interactions. As the agent accumulates more interaction history, context management approaches such as sliding wind…
cs.LG2024
Efficient Model-Agnostic Multi-Group Equivariant Networks
Razan Baltaji, Sourya Basu, Lav R. Varshney
Constructing model-agnostic group equivariant networks, such as equitune (Basu et al., 2023b) and its generalizations (Kim et al., 2023), can be computationally expensive for large…