9 citations · 15 across the 31 of their papers we have counts for
40 papers
Understanding KV Residency in Production-Scale Agentic LLM Workloads
Mayank Arya, Agrim Bari, Anjaly Parayil +4
Agentic large language model (LLM) applications turn one user request into a sequence of model and tool calls over a growing context. Retaining this context's key-value (KV) state…
FOCUS: Training-Free Decision-Preserving Context Compression for LLM Agents
Shantanu Dixit, Anson Bastos, Xuchao Zhang +2
LLM agents accumulate interaction histories that grow linearly with task length, causing quadratic inference cost scaling and performance degradation from attention dilution. Exist…
PSD: Pseudo Self-Distillation of Memory Representation Capabilities for LLM Agents
Pirzada Suhail, Menglin Xia, Xuchao Zhang +3
Memory systems are becoming a core component of LLM agents, but constructing and maintaining memory remains expensive because it relies on repeated calls to large proprietary langu…
Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?
Zijian Luo, Runzhi He, Pengfei Gao +6
Modern software systems rely heavily on third-party dependencies, but upgrading those dependencies remains a costly maintenance activity. Dependency upgrades do not always preserve…
AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces
Sungho Park, Wonjoong Kim, Rongyuan Tan +10
LLM agents remain unreliable on long-horizon tasks, where small local failures can compound over extended interactions and lead to overall task failure. Although external harnesses…
LoopsBench: From Harness Engineering to Loop Engineering in Coding Agent Evaluation
Han Li, Zhemin Fang, Rili Feng +8
Coding agent infrastructure is shifting from harness engineering toward loop engineering as coding agents are deployed for sustained long-horizon software development. Existing ben…
Change2Task: From Repository Changes to Executable Coding Agent Tasks and Environments
Haomin Qi, Xingliang Wang, Xuanqi Gao +9
Scaling coding agents requires a continuing supply of executable data for training, benchmarking, and continuous evaluation. Each task must couple a realistic software state with a…
DepRepair: LLM-Based Source-Code Repair for Dependency Breaking Changes
Shenghao Yang, Bo Lu, Yaochen Liu +5
Modern software projects depend on numerous third-party libraries, whose updates often introduce breaking changes. Adapting consumer code to such changes remains labor-intensive an…
AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation
Chenyu Zhao, Shenglin Zhang, Wenwei Gu +5
Large language model (LLM) agents are increasingly used for multi-step, stateful tool-use tasks, yet production reliability remains limited. Unlike static software repair, agent re…
Debugging the Debuggers: Failure-Anchored Structured Recovery for Software Engineering Agents
Chenyu Zhao, Shenglin Zhang, Yihang Lin +7
Software engineering agents are increasingly deployed in evaluable engineering environments, yet post-failure recovery remains costly, manual, and ad hoc. Existing systems expose t…
EvidenT: An Evidence-Preserving Framework for Iterative System-Level Package Repair
Chenyu Zhao, Minghua Ma, Shenglin Zhang +5
Frequent toolchain updates and growing ISA diversity have made system-level software package repair increasingly important. Diagnosing and repairing build failures remains challeng…
Revisiting Transformer Layer Parameterization Through Causal Energy Minimization
Jin Xu, Camille Couturier, Victor Rühle +2
Transformer blocks typically combine multi-head attention (MHA) for token mixing with gated MLPs for token-wise feature transformation, yet many choices in their parameterization r…
WebXSkill: Skill Learning for Autonomous Web Agents
Zhaoyang Wang, Qianhui Wu, Xuchao Zhang +12
Autonomous web agents powered by large language models (LLMs) remain brittle on long-horizon browser workflows. A key bottleneck is a grounding gap in existing skill formulations:…
AutoAdapt: An Automated Domain Adaptation Framework for LLMs
Sidharth Sinha, Anson Bastos, Xuchao Zhang +3
Large language models (LLMs) excel in open domains but struggle in specialized settings with limited data and evolving knowledge. Existing domain adaptation practices rely heavily…
Budget-Aware Agentic Routing via Boundary-Guided Training
Caiqi Zhang, Menglin Xia, Xuchao Zhang +5
As large language models (LLMs) evolve into autonomous agents that execute long-horizon workflows, invoking a high-capability model at every step becomes economically unsustainable…
Memora: A Harmonic Memory Representation Balancing Abstraction and Specificity
Menglin Xia, Xuchao Zhang, Shantanu Dixit +6
Agent memory systems must accommodate continuously growing information while supporting efficient, context-aware retrieval for downstream tasks. Abstraction is essential for scalin…
Continuous Benchmark Generation for Evaluating Enterprise-scale LLM Agents
Divyanshu Saxena, Rishikesh Maurya, Xiaoxuan Ou +7
The rapid adoption of AI agents across domains has made systematic evaluation crucial for ensuring their usefulness and successful production deployment. Evaluation of AI agents ty…
Simulating Environments with Reasoning Models for Agent Training
Yuetai Li, Huseyin A Inan, Xiang Yue +6
LLM agents excel in compact environments requiring deep reasoning but remain brittle when operating in broader, more complex contexts that demand robustness across diverse tools an…
SynthAgent: Adapting Web Agents with Synthetic Supervision
Zhaoyang Wang, Yiming Liang, Xuchao Zhang +9
Web agents struggle to adapt to new websites due to the scarcity of environment specific tasks and demonstrations. Recent works have explored synthetic data generation to address t…
Can Language Models Go Beyond Coding? Assessing the Capability of Language Models to Build Real-World Systems
Chenyu Zhao, Shenglin Zhang, Zeshun Huang +8
Large language models (LLMs) have shown growing potential in software engineering, yet few benchmarks evaluate their ability to repair software during migration across instruction…
Cost-Aware Retrieval-Augmentation Reasoning Models with Adaptive Retrieval Depth
Helia Hashemi, Victor Rühle, Saravan Rajmohan
Reasoning models have gained significant attention due to their strong performance, particularly when enhanced with retrieval augmentation. However, these models often incur high c…
LEGOMem: Modular Procedural Memory for Multi-agent LLM Systems for Workflow Automation
Dongge Han, Camille Couturier, Daniel Madrigal Diaz +3
We introduce LEGOMem, a modular procedural memory framework for multi-agent large language model (LLM) systems in workflow automation. LEGOMem decomposes past task trajectories int…
Attention Enhanced Entity Recommendation for Intelligent Monitoring in Cloud Systems
Fiza Husain, Anson Bastos, Anjaly Parayil +4
In this paper, we present DiRecGNN, an attention-enhanced entity recommendation framework for monitoring cloud services at Microsoft. We provide insights on the usefulness of this…
StepFly: Agentic Troubleshooting Guide Automation for Incident Diagnosis
Jiayi Mao, Liqun Li, Yanjie Gao +9
Effective incident management in large-scale IT systems relies on troubleshooting guides (TSGs), but their manual execution is slow and error-prone. While recent advances in LLMs o…