activity
20132026
most citedPretrained Language Models for Sequential Sentence Classification

117 citations · 269 across the 39 of their papers we have counts for

collaborators
Showing cs.AIShow all

15 papers · 1 filter

cs.AI2026

Harmony Harness: Multi-Objective Self-Evolution of Agent Harnesses

Weichen Yu, Lesly Miculicich, Bhavana Dalvi Mishra +5

Recent work on self-evolving LLM agents treats the agent harness as an optimization target that can be iteratively improved through execution feedback. Existing approaches, however…

cs.AI2026

Harness^2: Recursive Agent Harnessing for an Open World

Ruiyao Xu, Yanfei Chen, Zhongying CuiZhu +6

LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a promising approach to automatic…

cs.AI2026

VEPA: Verified Evolving Formal Policy for Lifelong LLM Agent Guardrails

Duy Nguyen, Lesly Miculicich, Bhavana Dalvi Mishra +5

LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can create serious safety and security risks. Runtime guardrail…

cs.AI2026

AIM: Agentic Idea Management for Automated Research

Hyeong Kyu Choi, Bhavana Dalvi Mishra, Jiefeng Chen +7

Frontier LLMs are increasingly used to automate scientific research through iterative search. We distinguish idea-driven search from solution-driven search and identify three core…

cs.AI2026

EnvHarness: Awakening Static Worlds for Agent Learning

Chengsong Huang, Zifeng Wang, Rujun Han +14

LLM agents learn by interacting with environments, yet these environments are hand-built and static: blind to an agent's weaknesses, and quickly left behind as it improves. While r…

cs.AI2026

ScientistOne: Towards Human-Level Autonomous Research via Chain-of-Evidence

Rui Meng, Bhavana Dalvi Mishra, Jiefeng Chen +10

Autonomous research agents produce competitive solutions and professional-looking manuscripts, yet their outputs contain verifiability failures undetectable by surface-level evalua…

cs.AI2026

SkillOS: Learning Skill Curation for Self-Evolving Agents

Siru Ouyang, Jun Yan, Yanfei Chen +13

LLM-based agents are increasingly deployed to handle streaming tasks, yet they often remain one-off problem solvers that fail to learn from past interactions. Reusable skills disti…

cs.AI2026

MARS: Modular Agent with Reflective Search for Automated AI Research

Jiefeng Chen, Bhavana Dalvi Mishra, Jaehyun Nam +3

A critical bottleneck in automating AI research is the execution of complex machine learning engineering (MLE) tasks. MLE differs from general software engineering due to computati…

cs.AI2025★ 1 cited

AstaBench: Rigorous Benchmarking of AI Agents with a Scientific Research Suite

Jonathan Bragg, Mike D'Arcy, Nishant Balepur +36

AI agents hold the potential to revolutionize scientific productivity by automating literature reviews, replicating experiments, analyzing data, and even proposing new directions o…

cs.AI2025

HARPA: A Testability-Driven, Literature-Grounded Framework for Research Ideation

Rosni Vasu, Peter Jansen, Pao Siangliulue +4

While there has been a surge of interest in automated scientific discovery (ASD), especially with the emergence of LLMs, it remains challenging for tools to generate hypotheses tha…

cs.AI2025

HypER: Literature-grounded Hypothesis Generation and Distillation with Provenance

Rosni Vasu, Chandrayee Basu, Bhavana Dalvi Mishra +3

Large Language models have demonstrated promising performance in research ideation across scientific domains. Hypothesis development, the process of generating a highly specific de…

cs.AI2025★ 2 cited

CodeScientist: End-to-End Semi-Automated Scientific Discovery with Code-based Experimentation

Peter Jansen, Oyvind Tafjord, Marissa Radensky +6

Despite the surge of interest in autonomous scientific discovery (ASD) of software artifacts (e.g., improved ML algorithms), current ASD systems face two key limitations: (1) they…

cs.AI2024★ 4 cited

DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents

Peter Jansen, Marc-Alexandre Côté, Tushar Khot +5

Automated scientific discovery promises to accelerate progress across scientific domains. However, developing and evaluating an AI agent's capacity for end-to-end scientific reason…

cs.AI2022★ 1 cited

Entailer: Answering Questions with Faithful and Truthful Chains of Reasoning

Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark

Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would allo…

cs.AI2018★ 8 cited

Reasoning about Actions and State Changes by Injecting Commonsense Knowledge

Niket Tandon, Bhavana Dalvi Mishra, Joel Grus +3

Comprehending procedural text, e.g., a paragraph describing photosynthesis, requires modeling actions and the state changes they produce, so that questions about entities at differ…