65 citations · 208 across the 57 of their papers we have counts for
9 papers · 1 filter
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…
SepGen: Multi-Stem Audio-Video Separation and Generation in a Single Model
Aviad Dahan, Rajaei Khatib, Yonatan Bitton +3
A 4D audio-visual scene comprises a video, the dynamic geometry it depicts, and the sound sources that populate it, each with its own position and trajectory. Rendering such a scen…
HumanoidToolBench: Benchmarking Humanoid Tool Use from Selection to Mobile Execution
Kyochul Jang, Seohyeon Park, Ohchul Kwon +9
As robotic hardware and learning methods advance, humanoids need tools to perform tasks beyond their inherent physical limits. Successful tool use requires selecting a suitable too…
Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs
Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona +2
Metacognition is a critical component of intelligence that describes the ability to monitor and regulate one's own cognitive processes. Yet LLMs exhibit systemic deficiencies in ke…
Cross-Lingual Exploration for Parametric Knowledge
Elisha Diskind, Itamar Trainin, Uri Shaham +3
Parametric knowledge in Large Language Models is not equally accessible across languages. As a result, standard inference techniques often struggle to surface localized facts, lead…
Real-Time Execution with Autoregressive Policies
Sangkyu Lee, Seohyeon Park, Tackgeun You +4
Real-time execution, enabled by asynchronous inference that ensures both smooth action trajectories and fast reactivity, is critical for realistic deployments of large-scale Vision…
Seeing Isn't Knowing: Do VLMs Know When Not to Answer Spatial Questions (and Why)?
Yue Zhang, Zun Wang, Han Lin +3
Spatial reasoning benchmarks typically evaluate whether vision-language models can derive the correct answer from a visual observation. Yet in real 3D environments, the observation…
Location Not Found: Exposing Implicit Local and Global Biases in Multilingual LLMs
Guy Mor-Lan, Omer Goldman, Matan Eyal +6
Multilingual large language models (LLMs) have minimized the fluency gap between languages. This advancement, however, exposes models to the risk of biased behavior, as knowledge a…
Latent Reasoning with Supervised Thinking States
Ido Amos, Avi Caciularu, Mor Geva +4
Reasoning with a chain-of-thought (CoT) enables Large Language Models (LLMs) to solve complex tasks but incurs significant inference costs due to the generation of long rationales.…