Tootfinder

Coming up from $-\infty$ for KPZ via stochastic control
Nicolas Perkowski, Carlos Villanueva Mariz
https://arxiv.org/abs/2508.18140 https://arxiv.org/pdf/2…

Coming up from $-\infty$ for KPZ via stochastic control
We derive a lower bound, independent of the initial condition, for the solution of the KPZ equation on the torus through its representation as the value function of a (conditional) stochastic control problem. With the same techniques, we also prove a bound for its oscillation, again independent of initial conditions, from which a Harnack type inequality for the rough heat equation (on the torus) can be obtained.

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Lucas de Lara (IECL)
https://arxiv.org/abs/2507.16370

Canonical Representations of Markovian Structural Causal Models: A Framework for Counterfactual Reasoning
Counterfactual reasoning aims at answering contrary-to-fact questions like ''Would have Alice recovered had she taken aspirin?'' and corresponds to the most fine-grained layer of causation. Critically, while many counterfactual statements cannot be falsified -- even by randomized experiments -- they underpin fundamental concepts like individual-wise fairness. Therefore, providing models to formalize and implement counterfactual beliefs remains a fundamental scientific problem. In the Markovian …

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
Qifeng Hu, Shamsulhaq Basir, Inanc Senocak
https://arxiv.org/abs/2508.15695

Conditionally adaptive augmented Lagrangian method for physics-informed learning of forward and inverse problems using artificial neural networks
We present several advances to the physics and equality constrained artificial neural networks (PECANN) framework that substantially improve its capability to learn solutions of canonical partial differential equations (PDEs). First, we generalize the augmented Lagrangian method (ALM) to support multiple independent penalty parameters, enabling simultaneous enforcement of heterogeneous constraints. Second, we reformulate pointwise constraint enforcement and Lagrange multipliers as expectations …

Remote Channel Synthesis
Yassine Hamdi, Deniz G\"und\"uz
https://arxiv.org/abs/2507.15757 https://arxiv.org/pdf/2507.15757

Remote Channel Synthesis
We consider the problem of synthesizing a memoryless channel between an unobserved source and a remote terminal. An encoder has access to a partial or noisy version $Z^n = (Z_1, \ldots, Z_n)$ of a remote source sequence $X^n = (X_1, \ldots, X_n),$ with $(X_i,Z_i)$ independent and identically distributed with joint distribution $q_{X,Z}.$ The encoder communicates through a noiseless link to a decoder which aims to produce an output $Y^n$ coordinated with the remote source; that is, the total var…

ProfiLLM: An LLM-Based Framework for Implicit Profiling of Chatbot Users
Shahaf David, Yair Meidan, Ido Hersko, Daniel Varnovitzky, Dudu Mimran, Yuval Elovici, Asaf Shabtai
https://arxiv.org/abs/2506.13980

ProfiLLM: An LLM-Based Framework for Implicit Profiling of Chatbot Users
Despite significant advancements in conversational AI, large language model (LLM)-powered chatbots often struggle with personalizing their responses according to individual user characteristics, such as technical expertise, learning style, and communication preferences. This lack of personalization is particularly problematic in specialized knowledge-intense domains like IT/cybersecurity (ITSec), where user knowledge levels vary widely. Existing approaches for chatbot personalization primarily …

After all, they called Joe Biden, the former Senator from CapitalOne and a devout Catholic, a socialist and communist.
One of the differences between the Right and Left in the USA is that the Right is dedicated to the idea of political tribal solidarity and social binaries, while the Left puts an overriding emphasis on its own diversity, that of the Right, and that of society.
It is part of how they manage to maintain themselves in power.

I am slowly but surely enjoying Reiner Keller's "Sociology of knowledge approach to discourse". I highly appreciate the level of care towards what is identified/extracted and taken for real. However, as with any other sociological framework, the position of the analyst remains problematic.
Even if one masters the analytical practice, communicating it remains problematic, as language must be used to do so. Traditionally, sociologists attempt to find a new form of language, which often becomes increasingly abstract and incomprehensible.
I wonder what the alternative would be: expressing analytical insights in every possible language or discursive logic (which is practically impossible), or at least in those connected to the analysis. Language translation or 'elif' could be examples of this. However, I also consider 'speaking in your terms' when communicating with individuals from different backgrounds.
#ADSK #Sociology #DiscourseAnalysis

Should we teach vibe coding? Here's why not.
2/2
To address the bigger question I started with ("should we teach AI-"assisted" coding?"), my answer is: "No, except enough to show students directly what its pitfalls are." We have little enough time as it is to cover the core knowledge that they'll need, which has become more urgent now that they're going to be expected to clean up AI bugs and they'll have less time to develop an understanding of the problems they're supposed to be solving. The skill of prompt engineering & other skills of working with AI are relatively easy to pick up on your own, given a decent not-even-mathematical understanding of how a neutral network works, which is something we should be giving to all students, not just our majors.
Reasonable learning objectives for CS majors might include explaining what types of bugs an AI "assistant" is most likely to introduce, explaining the difference between software engineering and writing code, explaining why using an AI "assistant" is likely to violate open-source licenses, listing at lest three independent ethical objections to contemporary LLMs and explaining the evidence for/reasoning behind them, explaining why we should expect AI "assistants" to be better at generating code from scratch than at fixing bugs in existing code (and why they'll confidently "claim" to have fixed problems they haven't), and even fixing bugs in AI generated code (without AI "assistance").
If we lived in a world where the underlying environmental, labor, and data commons issues with AI weren't as bad, or if we could find and use systems that effectively mitigate these issues (there's lots of piecemeal progress on several of these) then we should probably start teaching an elective on coding with an assistant to students who have mastered programming basics, but such a class should probably spend a good chunk of time on non-assisted debugging.
#AI #LLMs #VibeCoding

Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
Abigail Copiaco, Christian Ritz, Yassine Himeur, Valsamma Eapen, Ammar Albanna, Wathiq Mansoor
https://arxiv.org/abs/2506.09065

Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis
The prevalence of Autism Spectrum Disorder (ASD) has surged rapidly over the past decade, posing significant challenges in communication, behavior, and focus for affected individuals. Current diagnostic techniques, though effective, are time-intensive, leading to high social and economic costs. This work introduces an AI-powered assistive technology designed to streamline ASD diagnosis and management, enhancing convenience for individuals with ASD and efficiency for caregivers and therapists. T…

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
Yehia Abd Alrahman, Nir Piterman
https://arxiv.org/abs/2505.21672 …

On Reconfigurable Bisimulation, with an Application to the Distributed Synthesis Problem
We consider the problem of distributing a centralised transition system to a set of asynchronous agents recognising the same language. Existing solutions are either manual or involve a huge explosion in the number of states from the centralised system. The difficulty arises from the need to keep a rigid communication scheme, specifying a fixed mapping from events to those who can participate in them. Thus, individual agents need to memorise seen events and their order to dynamically compare the…