Journalcast

by Acidalia

Quantum Telescopes: What Must Survive Before Light Becomes Data

Four connected physics papers examine how quantum memories and shared reference states could change astronomical measurement. We follow phase recovery in diamond spins, a rubidium-memory interferometer using a twenty-kilometre fibre link, the resource requirements for useful distributed measurements, and a proposal to process stored light from a bright star and faint planet. The episode builds the necessary optics and quantum measurement concepts alongside the experiments: interference visibility, empty optical modes, heralding, phase references and information per observing hour. It separates what has been demonstrated from proposed imaging gains, including the difference between fibre length and physical telescope separation. Research discussed: - Stas et al. (2026), Nature: https://doi.org/10.1038/s41586-026-10171-w - Wang et al. (2026), Physical Review Letters: https://doi.org/10.1103/qpzn-h7p9 - Zhang and Jennewein (2025), Physical Review Research: https://doi.org/10.1103/bf51-tj3j - Mokeev et al. (2025), theoretical preprint: https://arxiv.org/abs/2509.09465v3 Script and narration produced with AI assistance. Experimental findings and theoretical predictions are identified separately. Full source and adaptation notes accompany the transcript. Chapters 00:00:00 — A phase measurement in two memories 00:04:52 — What two telescopes need to share 00:10:18 — Empty modes and the measurement you must avoid 00:15:28 — How the diamond experiment moves the phase 00:21:02 — Contrast, false heralds and information per hour 00:26:17 — Rubidium memories and a twenty-kilometre fibre link 00:31:18 — Thermal photons, coincidence windows and delayed arrival 00:36:18 — Entanglement and a common phase reference 00:41:38 — Designing the resource instead of counting Bell pairs 00:46:28 — Storing a spatial state before taking its picture 00:51:31 — Learning a faint source through collective measurements 00:56:44 — What would make this a useful telescope?

Salmon After the Dams: Measuring Recolonization and Reproduction

Chinook salmon entered and spawned in the reopened Klamath River soon after four dams were removed. What do these early observations establish about population recovery? We examine Goodman and colleagues' 2026 Klamath paper alongside Pess and colleagues' 2024 Elwha study. Imaging sonar, species probabilities and ground surveys explain how researchers measured adult passage and spawning. Brood years, hatchery origin and returning offspring explain what is still needed to establish sustained reproduction. A close reading of the methods, findings and limits of two dam-removal studies, following the evidence from newly accessible habitat to the next generation of salmon. Papers: Goodman et al. (2026), Scientific Reports: https://doi.org/10.1038/s41598-026-65437-0 Pess et al. (2024), Frontiers in Ecology and Evolution: https://doi.org/10.3389/fevo.2024.1241028 Chapters: 00:00 Salmon entering the reopened river 03:18 What the sonar could measure 07:11 What the spawning surveys established 09:33 Following offspring back from the ocean 13:31 What the Elwha records revealed 17:20 Measuring the years between spawning and return

What a chain of thought is evidence of

A decodable answer, a difficulty threshold, an unmarked important step, and an argument about the word "reasoning". Kyle Cox, Darius Kianersi, and Adria Garriga-Alonso, "Post-Hoc Reasoning in Chain of Thought: Decoding and Steering Pre-Committed Answers," arXiv:2603.01437 (2026). https://arxiv.org/abs/2603.01437 Siddharth Boppana, Annabel Ma, Max Loeffler, Raphael Sarfati, Eric Bigelow, Atticus Geiger, Owen Lewis, and Jack Merullo, "Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought," arXiv:2603.05488 (2026). https://arxiv.org/abs/2603.05488 Kevin Du, Alexander Hoyle, Laura Ruis, and Acyr Locatelli, "Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning," arXiv:2609.04194 (2026). https://arxiv.org/abs/2609.04194 Subbarao Kambhampati, Kaya Stechly, Karthik Valmeekam, Lucas Saldyt, Siddhant Bhambri, and colleagues, "Position: Stop Anthropomorphizing Intermediate Tokens as Reasoning/Thinking Traces!", arXiv:2504.09762 (2025). https://arxiv.org/abs/2504.09762

The judge that can't repeat itself, and the benchmark that was wrong

The instruments we measure language models with: repeatability, a blind spot, the reliability a decision requires, and a defective reference standard. Haoyuan Zhu and Jie Zhang, "Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpoints," arXiv:2609.04198 (2026). https://arxiv.org/abs/2609.04198 Sebastian Fox, Luke Markham, Ryan Lail, and Michael Karotsieris, "LLM Judges Verify Presence, Not Absence: Omission Blindness in AI Clinical Notes and What Recovers It," arXiv:2608.31016 (2026). https://arxiv.org/abs/2608.31016 Xing Zhang, Yanwei Cui, Guanghui Wang, Peiyang He, Ziyuan Li, Wei Qiu, and Bing Zhu, "Ratchet: How Reliable Must an LLM Judge Be to Retire a Skill?", arXiv:2605.22148 (2026). https://arxiv.org/abs/2605.22148 Sihan Hu, Lyuhan Huang, Youjin Deng, and Kun Chen, "SciCode-Verified: How Benchmark Defects Underestimated the Scientific-Coding Ability of Language Models," arXiv:2608.04975 (2026). https://arxiv.org/abs/2608.04975

Your agent's context is an attack surface

The agent harness as an attack surface: context assembly, the network path, crash recovery, and a defence that holds by construction. Zichuan Li, Jian Cui, Ashley Chen, Xiaojing Liao, and Luyi Xing, "What's in Your Agent's Context? Context Privilege Escalation Attacks against AI Agent Harness," arXiv:2609.01222 (2026). https://arxiv.org/abs/2609.01222 Hanzhi Liu, Chaofan Shou, Hongbo Wen, Yanju Chen, Ryan Jingyang Fang, and Yu Feng, "Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain," arXiv:2604.08407 (2026). https://arxiv.org/abs/2604.08407 Guanlong Wu, Dahui Li, Ke Jiang, Jianyu Niu, Cong Wang, and Yinqian Zhang, "Safe to Resume? Breaking Execution Continuity of Agent Execution via Rollback," arXiv:2608.29381 (2026). https://arxiv.org/abs/2608.29381 Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan, Jamie Hayes, Nicholas Carlini, Daniel Fabian, Christoph Kern, Chongyang Shi, Andreas Terzis, and Florian Tramer, "Defeating Prompt Injections by Design," arXiv:2503.18813 (2025). https://arxiv.org/abs/2503.18813

Attention and inflammation, the serotonin story, and two natural experiments

Evidence for surprising health claims: a manipulation, a synthesis, and two natural experiments. Nofar Mizrachi, Menachem Rottem, and Liron Rozenkrantz, "Voluntary attention regulates acute immune responses in humans," Nature Human Behaviour (2026). https://doi.org/10.1038/s41562-026-02541-1 Joanna Moncrieff, Ruth E. Cooper, Tom Stockmann, Simone Amendola, Michael P. Hengartner, and Mark A. Horowitz, "The serotonin theory of depression: a systematic umbrella review of the evidence," Molecular Psychiatry 28, 3243-3256 (2023). https://doi.org/10.1038/s41380-022-01661-0 Fabiana Corsi-Zuelli, Fang Li, Rachel Upthegrove, John A. Todd, Betty Raman, Paul J. Harrison, and Maxime Taquet, "Recombinant shingles vaccination and the risk of cardiovascular events," Nature Medicine (2026). https://doi.org/10.1038/s41591-026-04606-0 Chen Zhu and Weilong Zhang, "Early-life sugar restriction causally reduces adult cancer incidence and slows biological aging," Proceedings of the National Academy of Sciences (2026). https://doi.org/10.1073/pnas.2610287123

Model collapse, and the shrinking diversity of how we write

What generated text does to models and to people: collapse, curation, and lost diversity. Matthias Gerstgrasser, Rylan Schaeffer, Apratim Dey, Rafael Rafailov, Dhruv Pai, Henry Sleight, John Hughes, Tomasz Korbak, Rajashree Agrawal, Andrey Gromov, Daniel A. Roberts, Diyi Yang, David Donoho, and Sanmi Koyejo, "Is Model Collapse Inevitable? Breaking the Curse of Recursion by Accumulating Real and Synthetic Data," arXiv:2404.01413 (2024). https://arxiv.org/abs/2404.01413 Xuekai Zhu, Daixuan Cheng, Hengli Li, Kaiyan Zhang, Ermo Hua, Xingtai Lv, Ning Ding, Zhouhan Lin, Zilong Zheng, and Bowen Zhou, "How to Synthesize Text Data without Model Collapse?" arXiv:2412.14689 (2024). https://arxiv.org/abs/2412.14689 Ali Falahati, Mohammad Mohammadi Amiri, Kate Larson, and Lukasz Golab, "Curated Synthetic Data Doesn't Have to Collapse: A Theoretical Study of Generative Retraining with Pluralistic Preferences," arXiv:2605.07724 (2026). https://arxiv.org/abs/2605.07724 Zhivar Sourati, Farzan Karimi-Malekabadi, Meltem Ozcan, Colin McDaniel, Alireza Ziabari, Jackson Trager, Ala N. Tak, Meng Chen, Fred Morstatter, and Morteza Dehghani, "The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models," arXiv:2502.11266 (2025). https://arxiv.org/abs/2502.11266

Mind viruses and the interaction tax: what happens when agents talk

What emerges when language-model agents interact. Summer Eunhyung Ann, Haokun Liu, and Chenhao Tan, "The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams," arXiv:2608.23541 (2026). https://arxiv.org/abs/2608.23541 Vassilis Papadopoulos, McNair Shah, Sam Zimmerman, and Jack Lindsey, "Mind Viruses: Self-Propagating Ideas in Multi-Agent LLM Systems," arXiv:2608.10218 (2026). https://arxiv.org/abs/2608.10218 Zeyuan Li, Lukas Petersson, Alessandro Acquisti, and Michiel A. Bakker, "Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce," arXiv:2608.14825 (2026). https://arxiv.org/abs/2608.14825 Subhadeep Pal, Fiona Y. Wang, and Markus J. Buehler, "SwarmWorld: Stigmergic technological evolution in societies of language-model agents," arXiv:2608.26081 (2026). https://arxiv.org/abs/2608.26081

Watermarks, hidden messages, and stolen reasoning traces

Signals hidden in text: watermarking machine output, hiding messages in cover text, and stealing concealed reasoning. John Kirchenbauer, Jonas Geiping, Yuxin Wen, Jonathan Katz, Ian Miers, and Tom Goldstein, "A Watermark for Large Language Models," arXiv:2301.10226 (2023). https://arxiv.org/abs/2301.10226 Sumanth Dathathri, Abigail See, Sumedh Ghaisas, Po-Sen Huang, Rob McAdam, et al., "Scalable watermarking for identifying large language model outputs," Nature 634, 818-823 (2024). https://doi.org/10.1038/s41586-024-08025-4 Antonio Norelli and Michael Bronstein, "LLMs can hide text in other text of the same length," arXiv:2510.20075 (2025). https://arxiv.org/abs/2510.20075 Alexander Panfilov, David Schmotz, Ilia Shumailov, Luca Beurer-Kellner, Joachim Schaeffer, Ameya Prabhu, Jonas Geiping, and Maksym Andriushchenko, "Stealing Reasoning Traces from Proprietary LLM APIs," arXiv:2608.09867 (2026). https://arxiv.org/abs/2608.09867

The reasoning you can't see: filler tokens and illegible chains of thought

What a chain of thought does and does not reveal. Jacob Pfau, William Merrill, and Samuel R. Bowman, "Let's Think Dot by Dot: Hidden Computation in Transformer Language Models," arXiv:2404.15758 (2024). https://arxiv.org/abs/2404.15758 Vatsal Baherwani, Tom Goldstein, and Ashwinee Panda, "Not All LLM Reasoning is Visible in the Chain-of-Thought," arXiv:2607.22925 (2026). https://arxiv.org/abs/2607.22925 Kaley Brauer, Claudio Mayrink Verdun, and Samuel Marks, "Reading Between the Dots: Decoding Hidden Computation across Filler Tokens," arXiv:2607.03502 (2026). https://arxiv.org/abs/2607.03502 Arun Jose, "Reasoning Models Sometimes Output Illegible Chains of Thought," arXiv:2510.27338 (2025). https://arxiv.org/abs/2510.27338