Tootfinder

Panthers bring back Renfrow, place Coker on IR https://www.espn.com/nfl/story/_/id/46112946/panthers-bring-back-hunter-renfrow-place-jalen-coker-ir

Cowboys could get secondary reinforcements as injured CB is close to return https://www.si.com/nfl/cowboys/news/dallas-cowboys-could-get-secondary-reinforcements-injured-cb-close-return

Fast jede zweite #Kommune in Deutschland arbeitet an einem #Wärmeplan.
Laut Bundesinstitut für Bau-, Stadt- und Raumforschung haben rund 5.000 Gemeinden begonnen, ihre #Wärmeversorgung

Wärme: Fast jede zweite Kommune startet Wärmeplanung
Knapp die Hälfte aller Gemeinden in Deutschland hat laut Bundesinstitut für Bau-, Stadt- und Raumforschung (BBSR) mit einer kommunalen Wärmeplanung begonnen.

"Dass Deutschland ein eigenes Ministerium für Raumfahrt bekommt, hatte die CSU noch vor wenigen Monaten als großen Coup gefeiert. Nicht nur träumt Parteichef Markus Söder, ein bekennender Weltallfan, von deutschen Astronauten auf dem Mond, vielleicht sogar auf dem Mars."
der will doch nur die marsmännchen zu leberkäs verarbeiten um sie zu essen

Oxford-based Ultromics, which has developed the first FDA-cleared, Medicare-reimbursed AI tools for cardiology diagnostics, raised a $55M Series C (Tamara Djurickovic/Tech.eu)
https://tech.eu/2025/07/31/ultromics-raises-55m-series-c-to-sca…

Ultromics raises $55M Series C to scale AI-driven heart failure detection
Oxford-based Ultromics, a pioneer in AI-driven cardiology diagnostics solutions, has raised $55 million through its Series C funding round.  Ultromics has developed the first FDA-cleared, Medicare-reimbursed AI technology designed to help clinicians detect Heart Failure with Preserved Ejection Fraction (HFpEF) and cardiac amyloidosis, two of the most difficult-to-diagnose forms of heart failure. Built on years of clinical research and analysis of hundreds of thousands of echocardiograms, the …

SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
Haoming Wen, Yushi Bai, Juanzi Li, Jie Tang
https://arxiv.org/abs/2509.25176 https://

SIRI: Scaling Iterative Reinforcement Learning with Interleaved Compression
We introduce SIRI, Scaling Iterative Reinforcement Learning with Interleaved Compression, a simple yet effective RL approach for Large Reasoning Models (LRMs) that enables more efficient and accurate reasoning. Existing studies have observed repetitive thinking patterns in LRMs, and attempts to reduce them often come at the cost of performance. In this paper, we show that this trade-off can be overcome through a training regime that iteratively alternates between compressing and expanding the r…

From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Jie He, Victor Gutierrez Basulto, Jeff Z. Pan
https://arxiv.org/abs/2507.22716

From Sufficiency to Reflection: Reinforcement-Guided Thinking Quality in Retrieval-Augmented Reasoning for LLMs
Reinforcement learning-based retrieval-augmented generation (RAG) methods enhance the reasoning abilities of large language models (LLMs). However, most rely only on final-answer rewards, overlooking intermediate reasoning quality. This paper analyzes existing RAG reasoning models and identifies three main failure patterns: (1) information insufficiency, meaning the model fails to retrieve adequate support; (2) faulty reasoning, where logical or content-level flaws appear despite sufficient inf…

Automated Discovery of Gadgets in Quantum Circuits for Efficient Reinforcement Learning
Oleg M. Yevtushenko, Florian Marquardt
https://arxiv.org/abs/2509.24666 https://

Automated Discovery of Gadgets in Quantum Circuits for Efficient Reinforcement Learning
Reinforcement learning (RL) has proven itself as a powerful tool for the discovery of quantum circuits and quantum protocols. We have recently shown that including composite quantum gates -- referred to as ``gadgets'' -- in the action space of RL agents substantially enhances the RL performance in the context of quantum error correction. However, up to now the gadgets themselves had to be constructed manually. In this paper, we suggest an algorithm for the automated discovery of new gadgets and…

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Daniel Palenicek, Florian Vogt, Joe Watson, Ingmar Posner, Jan Peters
https://arxiv.org/abs/2509.25174

XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning
Sample efficiency is a central property of effective deep reinforcement learning algorithms. Recent work has improved this through added complexity, such as larger models, exotic network architectures, and more complex algorithms, which are typically motivated purely by empirical performance. We take a more principled approach by focusing on the optimization landscape of the critic network. Using the eigenspectrum and condition number of the critic's Hessian, we systematically investigate the i…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Molly Wang
https://arxiv.org/abs/2507.22174 https://arxiv.org/pdf/25…

Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
Reinforcement Learning (RL) has become a well-established approach for optimizing packet routing in communication networks. Standard RL algorithms typically are based on the Markov Decision Process (MDP), which assumes that the current state of the environment provides all the necessary information for system evolution and decision-making. However, this Markovian assumption is invalid in many practical scenarios, making the MDP and RL frameworks inadequate to produce the optimal solutions. Addi…