17 citations · 33 across the 40 of their papers we have counts for
44 papers
Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models
Aditya Thimmaiah, Lara Marinov, Jayanth Srinivasa +3
Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constraint. MDLMs generate outputs b…
Retrofitting Code Using LLMs to Support Exceptional Behavior
Linghan Zhong, Jiyang Zhang, Jayanth Srinivasa +2
Exception Related Code (ERC), which includes throw statements, conditions (if statements) that guard those throw statements, and try/catch blocks, is an essential component of soft…
CROCODIL: Cross-Model Code Editing with LLMs
Linghan Zhong, Aditya Thimmaiah, Jayanth Srinivasa +2
Large language models (LLMs) have become ubiquitous tools for code generation and editing. However, development teams often use multiple LLM assistants. Different developers may pr…
Predicting Program Exit Code with LLMs and Programming Language Semantics
Lara Marinov, Aditya Thimmaiah, Jayanth Srinivasa +2
Large language models (LLMs) have shown proficiency in various software engineering tasks, such as code generation and translation. However, a key limitation in their performance m…
CAST: Critique-Aware Supervision for Training Reliable Long-Horizon Tool-Calling Agents
Amir Saeidi, Zehua Zhang, Rishitosh Singh +6
Large language model (LLM) agents are increasingly deployed in long-horizon, interactive, and stateful environments. In these settings, a single wrong action, such as refunding the…
Survive or Collapse: The Asymmetric Roles of Data Gating and Reward Grounding in Self-Play RL
Sophia Xiao Pu, Zhaotian Weng, Chengzhi Liu +4
Self-play reinforcement learning trains language models on their own generated tasks, co-evolving a proposer and solver without human labels. Recent systems report strong reasoning…
TIER: Trajectory-Invariant Execution Rewards for Multi-Step Tool Composition
Anay Kulkarni, ChiaEn Lu, Dheeraj Mekala +3
Tool use enables large language models to solve complex tasks through sequences of API calls, yet existing reinforcement learning approaches fail to scale to multi-step composition…
EnactToM: An Evolving Benchmark for Functional Theory of Mind in Embodied Agents
Gurusha Juneja, Dylan Lu, Saaket Agashe +7
Theory of Mind (ToM), the ability to track others epistemic state, makes humans efficient collaborators. AI agents need the same capacity in multi agent settings, yet existing benc…
Predicting Plasticity in Deep Continual Learning: A Theoretical Perspective
Jiuqi Wang, Jayanth Srinivasa, Claire Chen +3
Deep continual learning requires models to adapt to new tasks without retraining from scratch. However, neural networks can lose their ability to adapt to new tasks after training…
FAMA: Failure-Aware Meta-Agentic Framework for Open-Source LLMs in Interactive Tool Use Environments
Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay +4
Large Language Models are being increasingly deployed as the decision-making core of autonomous agents capable of effecting change in external environments. Yet, in conversational…
Context Bootstrapped Reinforcement Learning
Saaket Agashe, Jayanth Srinivasa, Gaowen Liu +2
Reinforcement Learning from Verifiable Rewards (RLVR) suffers from exploration inefficiency, where models struggle to generate successful rollouts, resulting in minimal learning si…
AgentDS Technical Report: Benchmarking the Future of Human-AI Collaboration in Domain-Specific Data Science
An Luo, Jin Du, Xun Xian +12
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) and artificial…
Nalar: Workflow-Aware Management of Agentic Applications
Marco Laju, Donghyun Son, Saurabh Agarwal +4
LLM-driven agentic applications automate complex, multi-step tasks, but serving them efficiently remains difficult due to heterogeneous components, dynamic model-driven control flo…
Software-Defined Agentic Serving
Saurabh Agarwal, Marco Laju, Jayanth Srinivasa +2
As multi-agent LLM pipelines grow in complexity, existing serving paradigms fail to adapt to the dynamic serving conditions. We argue that agentic serving systems should be program…
Can Agentic AI Match the Performance of Human Data Scientists?
An Luo, Jin Du, Fangqiao Tian +9
Data science plays a critical role in transforming complex data into actionable insights across numerous domains. Recent developments in large language models (LLMs) have significa…
Dora: QoE-Aware Hybrid Parallelism for Distributed Edge AI
Jianli Jin, Ziyang Lin, Qianli Dong +5
With the proliferation of edge AI applications, satisfying user quality of experience (QoE) requirements, such as model inference latency, has become a first class objective, as th…
LLMs Lean on Priors, Not Programming Language Semantics
Aditya Thimmaiah, Jiyang Zhang, Jayanth Srinivasa +2
Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pretraining. Program execution provides…
How Can Input Reformulation Improve Tool Usage Accuracy in a Complex Dynamic Environment? A Study on -bench
Venkatesh Mishra, Amir Saeidi, Satyam Raj +5
Recent advances in reasoning and planning capabilities of large language models (LLMs) have enabled their potential as autonomous agents capable of tool use in dynamic environments…
Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures
Changrong Wu, Yiyao Yu, Myungjin Lee +4
Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or proc…
EXP-Bench: Can AI Conduct AI Research Experiments?
Patrick Tser Jern Kon, Jiachen Liu, Xinyi Zhu +10
Automating AI research holds immense potential for accelerating scientific progress, yet current AI agents struggle with the complexities of rigorous, end-to-end experimentation. W…
AssistedDS: Benchmarking How External Domain Knowledge Assists LLMs in Automated Data Science
An Luo, Xun Xian, Jin Du +12
Large language models (LLMs) have advanced the automation of data science workflows. Yet it remains unclear whether they can critically leverage external domain knowledge as human…
Bidirectional LMs are Better Knowledge Memorizers? A Benchmark for Real-world Knowledge Injection
Yuwei Zhang, Wenhao Yu, Shangbin Feng +5
Despite significant advances in large language models (LLMs), their knowledge memorization capabilities remain underexplored, due to the lack of standardized and high-quality test…
An Outlook on the Opportunities and Challenges of Multi-Agent AI Systems
Fangqiao Tian, An Luo, Jin Du +12
A multi-agent AI system (MAS) is composed of multiple autonomous agents that interact, exchange information, and make decisions based on internal generative models. Recent advances…
SafeKey: Amplifying Aha-Moment Insights for Safety Reasoning
Kaiwen Zhou, Xuandong Zhao, Gaowen Liu +4
Large Reasoning Models (LRMs) introduce a new generation paradigm of explicitly reasoning before answering, leading to remarkable improvements in complex tasks. However, they pose…