activity
20162026
most citedOn the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)

44 citations · 120 across the 46 of their papers we have counts for

collaborators

52 papers

cs.LG2026

Activation-Conditioned Self-Distillation

Zhexi Lu, Subhajit Chaudhury, Tejaswini Pedapati +2

On-policy self-distillation uses a model as its own teacher to provide dense supervision for reasoning, often through reference-solution conditioning. Providing privileged informat…

cs.AI2026

Mitigating Misalignment Contagion by Steering with Implicit Traits

Maria Chang, Ronny Luss, Miao Liu +3

Language models (LMs) are increasingly used in high-stakes, multi-agent settings, where following instructions and maintaining value alignment are critical. Most alignment research…

cs.LG2026

ZoomR: Memory Efficient Reasoning through Multi-Granularity Key Value Retrieval

David H. Yang, Yuxuan Zhu, Mohammad Mohammadi Amiri +4

Large language models (LLMs) have shown great performance on complex reasoning tasks but often require generating long intermediate thoughts before reaching a final answer. During…

cs.AI2026

The Unlearning Mirage: A Dynamic Framework for Evaluating LLM Unlearning

Raj Sanjay Shah, Jing Huang, Keerthiram Murugesan +2

Unlearning in Large Language Models (LLMs) aims to enhance safety, mitigate biases, and comply with legal mandates, such as the right to be forgotten. However, existing unlearning…

cs.CR2026

AgentSCOPE: Evaluating Contextual Privacy Across Agentic Workflows

Ivoline C. Ngong, Keerthiram Murugesan, Swanand Kadhe +3

Agentic systems are increasingly acting on users' behalf, accessing calendars, email, and personal files to complete everyday tasks. Privacy evaluation for these systems has focuse…

cs.DL2026

LongDA: Benchmarking LLM Agents for Long-Document Data Analysis

Yiyang Li, Zheyuan Zhang, Tianyi Ma +4

We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume we…