20 citations · 28 across the 8 of their papers we have counts for
10 papers
Seeing the Forest for the Trees: Emergent Agreement from Individual Conversations
Adi Simhi, Yaniv Nikankin, Jonathan Herzig +3
As LLM agents interact in the wild and shape each other's decisions, understanding how they converge toward agreement becomes important. We study this in multi-turn conversations w…
Reasoning Models Know What's Important, and Encode It in Their Activations
Yaniv Nikankin, Martin Tutek, Tomer Ashuach +2
Language models often solve complex tasks by generating long reasoning chains, consisting of many steps with varying importance. While some steps are crucial for generating the fin…
Induction Meets Biology: Mechanisms of Repeat Detection in Protein Language Models
Gal Pomerants, Yaniv Nikankin, Anja Reusch +3
Protein sequences are abundant in repeating segments, both as exact copies and as approximate segments with mutations. These repeats are important for protein structure and functio…
BlackboxNLP-2025 MIB Shared Task: Improving Circuit Faithfulness via Better Edge Selection
Yaniv Nikankin, Dana Arad, Itay Itzhak +4
One of the main challenges in mechanistic interpretability is circuit discovery, determining which parts of a model perform a given task. We build on the Mechanistic Interpretabili…
Global PIQA: Evaluating Commonsense Reasoning Across 100+ Languages and Cultures
Tyler A. Chang, Catherine Arnett, Abdelrahman Sadallah +377
To date, there exist almost no culturally-specific evaluation benchmarks for large language models (LLMs) that cover a large number of languages and cultures. In this paper, we pre…
Same Task, Different Circuits: Disentangling Modality-Specific Mechanisms in VLMs
Yaniv Nikankin, Dana Arad, Yossi Gandelsman +1
Vision-Language models (VLMs) show impressive abilities to answer questions on visual inputs (e.g., counting objects in an image), yet demonstrate higher accuracies when performing…
MIB: A Mechanistic Interpretability Benchmark
Aaron Mueller, Atticus Geiger, Sarah Wiegreffe +20
How can we know whether new mechanistic interpretability methods achieve real improvements? In pursuit of lasting evaluation standards, we propose MIB, a Mechanistic Interpretabili…
Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics
Yaniv Nikankin, Anja Reusch, Aaron Mueller +1
Do large language models (LLMs) solve reasoning tasks by learning robust generalizable algorithms, or do they memorize training data? To investigate this question, we use arithmeti…
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses
Gon Buzaglo, Niv Haim, Gilad Yehudai +4
Memorization of training data is an active research area, yet our understanding of the inner workings of neural networks is still in its infancy. Recently, Haim et al. (2022) propo…
SinFusion: Training Diffusion Models on a Single Image or Video
Yaniv Nikankin, Niv Haim, Michal Irani
Diffusion models exhibited tremendous progress in image and video generation, exceeding GANs in quality and diversity. However, they are usually trained on very large datasets and…