Tootfinder

Algorithmic Collective Action with Multiple Collectives
Claudio Battiloro, Pietro Greiner, Bret Nestor, Oumaima Amezgar, Francesca Dominici
https://arxiv.org/abs/2508.19149 http…

Algorithmic Collective Action with Multiple Collectives
As learning systems increasingly influence everyday decisions, user-side steering via Algorithmic Collective Action (ACA)-coordinated changes to shared data-offers a complement to regulator-side policy and firm-side model design. Although real-world actions have been traditionally decentralized and fragmented into multiple collectives despite sharing overarching objectives-with each collective differing in size, strategy, and actionable goals, most of the ACA literature focused on single collec…

Galois theory by calculator
Thomas W. Mattman, Robertson-Figaniak, Zoe Steele
https://arxiv.org/abs/2508.18595 https://arxiv.org/pdf/2508.18595

Galois theory by calculator
We present an algorithm to determine the Galois group of an irreducible monic polynomial $f(x) \in \mathbb{Z}[x]$ of degree at most five. Following work of Conrad, Dummit, and Stauduhar this comes down to answering two questions: Is a given integer a square? and Does a given polynomial have an integral root? Since these are both easily addressed with a calculator, our algorithm amounts to Galois theory by calculator. For example, we have an implementation at Desmos.com. In an appendix we presen…

Anchored Langevin Algorithms
Mert Gurbuzbalaban, Hoang M. Nguyen, Xicheng Zhang, Lingjiong Zhu
https://arxiv.org/abs/2509.19455 https://arxiv.org/pdf/2509.…

Anchored Langevin Algorithms
Standard first-order Langevin algorithms such as the unadjusted Langevin algorithm (ULA) are obtained by discretizing the Langevin diffusion and are widely used for sampling in machine learning because they scale to high dimensions and large datasets. However, they face two key limitations: (i) they require differentiable log-densities, excluding targets with non-differentiable components; and (ii) they generally fail to sample heavy-tailed targets. We propose anchored Langevin dynamics, a unif…

Singleton algorithms for the Constraint Satisfaction Problem
Dmitriy Zhuk
https://arxiv.org/abs/2509.18434 https://arxiv.org/pdf/2509.18434

Singleton algorithms for the Constraint Satisfaction Problem
A natural strengthening of an algorithm for the (promise) constraint satisfaction problem is its singleton version: we first fix a constraint to some tuple from the constraint relation, then run the algorithm, and remove the tuple from the constraint if the answer is negative. We characterize the power of the singleton versions of standard universal algorithms for the (promise) CSP over a fixed template in terms of the existence of a minion homomorphism. Using the Hales-Jewett theorem, we show …

"But but we also thought animals were pure machines and didn't have sentience!!!!"
This is like a set of nested dolls of logical fallacies:
0. (Preamble) Being wrong before doesn't mean other people are wrong right now.
1. Just because you were wrong about animals not being machines doesn't mean machines aren't being machines.
2. You're actually not talking about machines, but about algorithms (who happen to be running on a machine, but could just as well be calculated manually by a human).
3. LLM models themselves aren't even an algorithm—they're literally just a large number of large numbers.
So what you're really asking is "Can many llarge numbers be concious and sentient?"
This answers itself.

Lebesgue-type estimates for greedy algorithms in quasi-Banach spaces
Miguel Berasategui, Pablo M. Bern\'a, Hung Viet Chu, Andrea Garc\'ia
https://arxiv.org/abs/2508.16893

Lebesgue-type estimates for greedy algorithms in quasi-Banach spaces
We continue the study of Lebesgue-type parameters for various greedy algorithms in quasi-Banach spaces. First, we introduce a parameter that can be used with the quasi-greedy parameter to obtain the exact growth of the Lebesgue parameter for strong partially greedy bases. Second, we establish a new upper bound for the Lebesgue parameter for semi-greedy bases using the quasi-greedy and the squeeze symmetry parameters. Finally, we answer several open questions regarding the optimal power in vario…

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Kangyu Wu, Jiaqi Qiao, Ya Zhang
https://arxiv.org/abs/2509.15984

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (…

Efficient Linearizability Monitoring
Parosh Aziz Abdulla, Samuel Grahn, Bengt Jonsson, Shankaranarayanan Krishna, Om Swostik Mishra
https://arxiv.org/abs/2509.17795 https://

Efficient Linearizability Monitoring
This paper revisits the fundamental problem of monitoring the linearizability of concurrent stacks, queues, sets, and multisets. Given a history of a library implementing one of these abstract data types, the monitoring problem is to answer whether the given history is linearizable. For stacks, queues, and (multi)sets, we present monitoring algorithms with complexities $\mathcal{O}(n^2)$, $\mathcal{O}(n\; log\, n)$, and $\mathcal{O}{(n)}$, respectively, where $n$ is the number of operations in …

End-to-end RL Improves Dexterous Grasping Policies
Ritvik Singh, Karl Van Wyk, Pieter Abbeel, Jitendra Malik, Nathan Ratliff, Ankur Handa
https://arxiv.org/abs/2509.16434 https:…

End-to-end RL Improves Dexterous Grasping Policies
This work explores techniques to scale up image-based end-to-end learning for dexterous grasping with an arm + hand system. Unlike state-based RL, vision-based RL is much more memory inefficient, resulting in relatively low batch sizes, which is not amenable for algorithms like PPO. Nevertheless, it is still an attractive method as unlike the more commonly used techniques which distill state-based policies into vision networks, end-to-end RL can allow for emergent active vision behaviors. We id…

Verbalized Algorithms
Supriya Lall, Christian Farrell, Hari Pathanjaly, Marko Pavic, Sarvesh Chezhian, Masataro Asai
https://arxiv.org/abs/2509.08150 https://

Verbalized Algorithms
Instead of querying LLMs in a one-shot manner and hoping to get the right answer for a reasoning task, we propose a paradigm we call \emph{verbalized algorithms} (VAs), which leverage classical algorithms with established theoretical understanding. VAs decompose a task into simple elementary operations on natural language strings that they should be able to answer reliably, and limit the scope of LLMs to only those simple tasks. For example, for sorting a series of natural language strings, \em…