8 citations · 19 across the 9 of their papers we have counts for
6 papers · 1 filter
Cortex: Consolidating Context into Parametric Memory
Jintian Zhang, Feng He, Xinnian Liang +5
Context can improve large language model performance, but these gains may be lost after context removal. Inspired by biological systems consolidation, we formulate Memory Consolida…
Harness Learning Enables Generalizable Test-Time Adaptation
Alvin Zhang, Xuecheng Liu, Zixuan Wang +6
A language-model agent is jointly defined by its model and its harness, the executable program that organizes model calls, tool use, and information flow. Because different tasks c…
Message Passing Enables Efficient Reasoning
Xuecheng Liu, Daman Arora, Gokul Swamy +1
While inference-time scaling has improved the reasoning abilities of large language models (LLMs), the need to generate long chains-of-thought (CoTs) is a computational bottleneck.…
GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
Daman Arora, Anush Kini, Sayak Ray Chowdhury +3
Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-bas…
Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models
Daman Arora, Subbarao Kambhampati
There have been wide spread claims in the literature about the emergent reasoning capabilities of Pretrained Large Language Models. However, recent studies, have found that their a…
Have LLMs Advanced Enough? A Challenging Problem Solving Benchmark For Large Language Models
Daman Arora, Himanshu Gaurav Singh, Mausam
The performance of large language models (LLMs) on existing reasoning benchmarks has significantly improved over the past years. In response, we present JEEBench, a considerably mo…