Task-Oriented Grasping (TOG) has emerged as an important skill for purposeful robotic manipulation. Given the task of using a knife to "slice bread," a robot must choose to grasp from the handle, whereas for "hand[ing] the knife over," it is preferable for the handle to remain unobstructed for safe handoff. Current app...
Manav Kulshrestha, Apoorva Vashisth, Damon Conover et al.· 0 citations
Reinforcement learning with self-generated labels, such as majority vote over a model's own samples, often recovers most of the gain of training with verifiable rewards. This is puzzling from the usual prompt-level view, in which majority vote only returns a model's modal answer and cannot add information about correct...
Realistic traffic simulation and autonomous planning both depend on modeling interactions among heterogeneous road users whose dynamics, constraints, and behaviors differ fundamentally. Existing approaches predominantly learn realistic interaction behavior from large-scale human driving demonstrations. In this work, we...
Wei-Jer Chang, Zhouchonghao Wu, Akshay Rangesh et al.· 0 citations
Negation is a fundamental aspect of language understanding and it is important vision-language models (VLMs) are able to process it effectively. We investigate how VLMs process negation by analysing attention patterns of different models. We draw comparisons between human gaze and attention in models when processing af...
Beth Pearson, Michael Wray, Martha Lewis· 0 citations
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Despite their extensive pretraining knowledge and strong reasoning capabilities, large language models (LLMs) struggle to adaptively gather information in experimental design tasks. We study how to amortize sequential Bayesian experimental design (BED) into an LLM by explicitly training the reasoning that guides action...
Anita Kriz, Antonin Berthon, Nicolás Astorga et al.· 0 citations
Reasoning distillation requires chain-of-thought (CoT) data with correct answers, but obtaining such data efficiently remains challenging. Forward CoT generation (FCG) followed by rejection sampling (RS) produces verified, question-only responses, yet may require many teacher calls for difficult problems. Reverse CoT g...
Zinan Tang, Zhuoshi Pan, Kairui Chen et al.· 0 citations
Wolof is spoken by 23M people, mostly in Senegal, but remains weakly supported by open-weight language models, while API models raise cost, governance, and sovereignty constraints for local deployments. In this paper, we present 0.6B-14B open-weight models adapted to Wolof through instruction tuning, preference alignme...
Gw\'enol\'e Lecorv\'e, Delphine Charlet, Pierre Adam et al.· 0 citations
Image-to-text (I2T) understanding and text-to-image (T2I) generation are dual tasks in unified multimodal models (UMMs). Reconstructive post-training couples these tasks, but existing reconstruction-based methods remain perception-centric and provide limited supervision for reasoning about structure, intent, and transf...
Fei-Ze Wu, Zhi-Yuan Yan, Yuri Hong et al.· 0 citations
Automating organizational work means answering many small typed questions about one piece of state (does this email need a reply, how urgent is it, who owns it) with probabilities good enough to act on without a human. The open typed-decision model we compare against, like a standard cross-encoder, answers each questio...
A single late-layer residual-stream direction is identified: a single late-layer residual-stream direction, the initiative vector, extracted from the model's own conversations without training or annotation, which transfers a speaker's turn-taking style to the model.
Chen Yuxuan, Xiao Yang, Jack Sun et al.· 0 citations
Large language models (LLMs) trained on vast amounts of data have demonstrated impressive performance over a range of tasks, but future performance gains will require increasing amounts of data and compute. Federated Learning (FL) facilitates training on decentralized private data, but client resource limitations can m...
G. Legate, Rahaf Aljundi, Eugene Belilovsky· 0 citations
