Tootfinder

Sources: Kalshi is raising $100M led by Paradigm at a $1B valuation; Polymarket is completing a $200M investment led by Founders Fund at a ~$1B valuation (Kate Clark/Bloomberg)
https://www.bloomberg.com/news/articles/20

World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Junhao Shi, Zhaoye Fei, Siyin Wang, Qipeng Guo, Jingjing Gong, Xipeng QIu
https://arxiv.org/abs/2506.21230

World-aware Planning Narratives Enhance Large Vision-Language Model Planner
Large Vision-Language Models (LVLMs) show promise for embodied planning tasks but struggle with complex scenarios involving unfamiliar environments and multi-step goals. Current approaches rely on environment-agnostic imitation learning that disconnects instructions from environmental contexts, causing models to struggle with context-sensitive instructions and rely on supplementary cues rather than visual reasoning during long-horizon interactions. In this work, we propose World-Aware Planning …

Communication-Efficient Publication of Sparse Vectors under Differential Privacy
Quentin Hillebrand, Vorapong Suppakitpaisarn, Tetsuo Shibuya
https://arxiv.org/abs/2506.20234

Communication-Efficient Publication of Sparse Vectors under Differential Privacy
In this work, we propose a differentially private algorithm for publishing matrices aggregated from sparse vectors. These matrices include social network adjacency matrices, user-item interaction matrices in recommendation systems, and single nucleotide polymorphisms (SNPs) in DNA data. Traditionally, differential privacy in vector collection relies on randomized response, but this approach incurs high communication costs. Specifically, for a matrix with $N$ users, $n$ columns, and $m$ nonzero …

STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner
Zhou Tianxing, Wang Zhirui, Ao Haojia, Chen Guangyan, Xing Boyang, Cheng Jingwen, Yang Yi, Yue Yufeng
https://arxiv.org/abs/2506.21030

STEP Planner: Constructing cross-hierarchical subgoal tree as an embodied long-horizon task planner
The ability to perform reliable long-horizon task planning is crucial for deploying robots in real-world environments. However, directly employing Large Language Models (LLMs) as action sequence generators often results in low success rates due to their limited reasoning ability for long-horizon embodied tasks. In the STEP framework, we construct a subgoal tree through a pair of closed-loop models: a subgoal decomposition model and a leaf node termination model. Within this framework, we develo…

@… In my mind, there are librarians, accountants/bookkeepers, civil servants, small shop owners, conveyancers and other low-stakes solicitors, farmers, town planners… most of the people I have met who fit these job descriptions tend to work slowly, methodically, and well.
I actually think most programmers and other software professionals work slowly…

Solving Infinite Families of Dual Conformal Integrals and Periods
Song He, Xuhang Jiang
https://arxiv.org/abs/2506.20095 https://arxi…

Solving Infinite Families of Dual Conformal Integrals and Periods
We compute infinite families of all-loop planar, dual conformal invariant (DCI) integrals, which contribute to four-point Coulomb-branch amplitudes and correlators in ${\cal N}=4$ supersymmetric Yang-Mills theory, by solving ``boxing" differential equations via package HyperlogProcedures; this amounts to an ``inverse-boxing" operation/integration recursively acting on lower-loop cases (with the box integral as the starting point), and the resulting single-valued harmonic polylogarithmic functio…

Kalshi raised $185M led by Paradigm at a $2B valuation; sources: Polymarket is completing a $200M investment led by Founders Fund at a ~$1B pre-money valuation (Kate Clark/Bloomberg)
https://www.bloomberg.com/news/articles/20…

Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
Malte Probst, Raphael Wenzel, Tim Puphal, Monica Dasi, Nico A. Steinhardt, Sango Matsuzaki, Misa Komuro
https://arxiv.org/abs/2506.20320

Finding the Easy Way Through -- the Probabilistic Gap Planner for Social Robot Navigation
In Social Robot Navigation, autonomous agents need to resolve many sequential interactions with other agents. State-of-the art planners can efficiently resolve the next, imminent interaction cooperatively and do not focus on longer planning horizons. This makes it hard to maneuver scenarios where the agent needs to select a good strategy to find gaps or channels in the crowd. We propose to decompose trajectory planning into two separate steps: Conflict avoidance for finding good, macroscopic tr…

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
Wang Bill Zhu, Miaosen Chai, Ishika Singh, Robin Jia, Jesse Thomason
https://arxiv.org/abs/2506.20097

PSALM-V: Automating Symbolic Planning in Interactive Visual Environments with Large Language Models
We propose PSALM-V, the first autonomous neuro-symbolic learning system able to induce symbolic action semantics (i.e., pre- and post-conditions) in visual environments through interaction. PSALM-V bootstraps reliable symbolic planning without expert action definitions, using LLMs to generate heuristic plans and candidate symbolic semantics. Previous work has explored using large language models to generate action semantics for Planning Domain Definition Language (PDDL)-based symbolic planners.…

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Elia Trevisan, Khaled A. Mustafa, Godert Notten, Xinwei Wang, Javier Alonso-Mora
https://arxiv.org/abs/2506.21205

Dynamic Risk-Aware MPPI for Mobile Robots in Crowds via Efficient Monte Carlo Approximations
Deploying mobile robots safely among humans requires the motion planner to account for the uncertainty in the other agents' predicted trajectories. This remains challenging in traditional approaches, especially with arbitrarily shaped predictions and real-time constraints. To address these challenges, we propose a Dynamic Risk-Aware Model Predictive Path Integral control (DRA-MPPI), a motion planner that incorporates uncertain future motions modelled with potentially non-Gaussian stochastic pre…