9 papers
Carnot: Interpretable, Interactive, and Optimized Execution of Deep Research Queries
Matthew Russo, Yash Agarwal, Tianyu Li +5
Enterprises increasingly seek to query data lakes using natural language via AI-driven tools like semantic operators or deep research agents. However, the latter operates as an opa…
OBLIQ-Bench: Exposing Overlooked Bottlenecks in Modern Retrievers with Latent and Implicit Queries
Diane Tchuindjo, Devavrat Shah, Omar Khattab
Retrieval benchmarks are increasingly saturating, but we argue that efficient search is far from a solved problem. We identify a class of queries we call oblique, which seek docume…
Reasoning-Intensive Regression
Diane Tchuindjo, Omar Khattab
AI researchers and practitioners increasingly apply large language models (LLMs) to what we call reasoning-intensive regression (RiR), i.e., deducing subtle numerical scores from t…
Vector Policy Optimization: Training for Diversity Improves Test-Time Search
Ryan Bahlous-Boldi, Isha Puri, Idan Shenfeld +6
Language models must now generalize out of the box to novel environments and work inside inference-scaling search procedures, such as AlphaEvolve, that select rollouts with a varie…
PEEK: Context Map as an Orientation Cache for Long-Context LLM Agents
Zhuohan Gu, Qizheng Zhang, Omar Khattab +1
Large language model (LLM) agents increasingly operate over long and recurring external contexts, like document corpora and code repositories. Across invocations, existing approach…
Recursive Language Models
Alex L. Zhang, Tim Kraska, Omar Khattab
We study allowing large language models (LLMs) to process arbitrarily long prompts through the lens of inference-time scaling. We propose Recursive Language Models (RLMs), a genera…