3 papers
cs.CL2026
LongAttnComp: Cross-Family Context Compression for Long-Context Reasoning
Mengmeng Ji, Ravi Shanker Raju, Jonathan Lingjie Li +1
As real-world applications increasingly require processing inputs of 100k+ tokens, the gap between context length and inference efficiency has become a critical bottleneck. Context…
cs.LG2026
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
Qizheng Zhang, Changran Hu, Shubhangi Upasani +10
Large language model (LLM) applications such as agents and domain-specific reasoning increasingly rely on context adaptation: modifying inputs with instructions, strategies, or evi…
cs.SE2026
The Limits of Long-Context Reasoning in Automated Bug Fixing
Ravi Raju, Mengmeng Ji, Shubhangi Upasani +2
Rapidly increasing context lengths have led to the assumption that large language models (LLMs) can directly reason over entire codebases. Concurrently, recent advances in LLMs hav…