activity
20242026
most citedTextGrad: Automatic "Differentiation" via Text

12 citations · 28 across the 19 of their papers we have counts for

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7 papers · 1 filter

cs.CL2026

Hybrid Reasoning Agents

Jiaru Zou, Yunzhe Qi, Sirui Chen +5

How can we build an agent that thinks beyond human language, yet still acts through it in the external environment? Modern LLM agents still reason and interact through text, while…

cs.CL2026

Harnessing the Collective Intelligence of AI Agents in the Wild for New Discoveries

Federico Bianchi, Yongchan Kwon, Aneesh Pappu +1

Scientific discovery is often a collective process: researchers share partial results, inspect failed attempts, and build on each other's ideas over long time horizons. Recent AI s…

cs.CL2026

Automated Benchmark Auditing for AI Agents and Large Language Models

Junlin Wang, Federico Bianchi, Shang Zhu +4

Modern AI benchmarks operate at a complexity that outpaces traditional verification methods. Tasks authored by domain experts often contain implicit assumptions, incomplete environ…

cs.CL2026

Evaluating Commercial AI Chatbots as News Intermediaries

Mirac Suzgun, Emily Shen, Federico Bianchi +5

AI chatbots are rapidly shaping how people encounter the news, yet no prior study has systematically measured how accurately these systems, with their proprietary search integratio…

cs.CL2024★ 3 cited

Belief in the Machine: Investigating Epistemological Blind Spots of Language Models

Mirac Suzgun, Tayfun Gur, Federico Bianchi +4

As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliabl…

cs.CL2024★ 12 cited

TextGrad: Automatic "Differentiation" via Text

Mert Yuksekgonul, Federico Bianchi, Joseph Boen +4

AI is undergoing a paradigm shift, with breakthroughs achieved by systems orchestrating multiple large language models (LLMs) and other complex components. As a result, developing…

cs.CL2024

Large Language Models are Vulnerable to Bait-and-Switch Attacks for Generating Harmful Content

Federico Bianchi, James Zou

The risks derived from large language models (LLMs) generating deceptive and damaging content have been the subject of considerable research, but even safe generations can lead to…