12 citations · 17 across the 14 of their papers we have counts for
16 papers
Many Brains, One Geometry: A Shared Visual-Semantic Space for Cross-Dataset fMRI Decoding
Moein Khajehnejad, Michelangelo Tronti, Forough Habibollahi +3
Visual decoding from fMRI is typically siloed by participant and experiment, obscuring whether heterogeneous neural measurements can be organized within a common computational geom…
Self-Organizing Agent Teams Learn to Reason Together
Aneesh Pappu, Mirac Suzgun, Yongchan Kwon +5
Collective intelligence depends not only on what team members know, but also on how they organize their work. When the structure of a solution is unknown, useful roles and division…
string2string Studio: An Interactive, In-Browser Platform for String-to-String Algorithms
Mirac Suzgun, James Zou, Stuart M. Shieber +1
We present string2string Studio, an interactive in-browser platform for string-to-string analysis across natural language processing, computational biology, and the digital humanit…
Voice "Cloning" is Style Transfer
Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds +3
Artificially generated speech is increasingly embedded in everyday life. Voice cloning in particular enables applications where identity preservation is important, such as completi…
AutoResearchClaw: Self-Reinforcing Autonomous Research with Human-AI Collaboration
Jiaqi Liu, Shi Qiu, Mairui Li +33
Automating scientific discovery requires more than generating papers from ideas. Real research is iterative: hypotheses are challenged from multiple perspectives, experiments fail…
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…
Scientific reasoning does not reliably translate into scientific forecasting in frontier AI
Sean Wu, Pan Lu, Yupeng Chen +7
AI systems are increasingly used to support forward-looking scientific judgment, but it remains unclear whether they can form reliable expectations about future scientific advances…
"Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most
Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi +1
Despite speech recognition systems achieving low word error rates on standard benchmarks, they often fail on short, high-stakes utterances in real-world deployments. Here, we study…
Learning to Discover at Test Time
Mert Yuksekgonul, Daniel Koceja, Xinhao Li +8
How can we use AI to discover a new state of the art for a scientific problem? Prior work in test-time scaling, such as AlphaEvolve, performs search by prompting a frozen LLM. We p…
Test-Time Optimization of Physical Query Plans with LLMs
Mehmet Hamza Erol, Xiangpeng Hao, Federico Bianchi +3
Traditional query optimization relies on cost-based optimizers that estimate execution cost (e.g., runtime, memory, and I/O) using predefined heuristics and statistical models. Imp…
To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis
Federico Bianchi, Yongchan Kwon, Zachary Izzo +2
How many mistakes do published AI papers contain? Peer-reviewed publications form the foundation upon which new research and knowledge are built. Errors that persist in the literat…
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
Yongchan Kwon, Shang Zhu, Federico Bianchi +2
The ability of large language models (LLMs) to follow user instructions is central to their reliability, safety, and usefulness. While prior studies assess instruction adherence in…
Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory
Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi +2
Despite their impressive performance on complex tasks, current language models (LMs) typically operate in a vacuum: Each input query is processed separately, without retaining insi…
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…
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…
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…