Wednesday, 4 November 2026: 4 new papers, 3 cross-lists and 6 replacements in Physics and Society (physics.soc-ph), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Roger Federer won about 80% of his professional matches with only 54% of the points. Hierarchical scoring amplifies small advantages on points into large advantages on matches, but also allows a player to win with fewer points than the opponent. Using Monte Carlo simulations and exact Markov chain calculations, we show that in tennis, badminton, volleyball and table tennis this amplification is set almost entirely by the number of points per match. We then let a player win ordinary points with probability p1 and crucial points, those closing a game, set or match, with probability p2. In every sport a narrow region of the (p1, p2) plane exists where a player wins most matches with fewer than half of the points, because crucial points carry more influence than their share of points. Of ATP matches played in 1991-2025, 4.7% were won with fewer points, 16% more than independent points predict (5% including the serve), a constant excess. Pressure performance measured in other seasons predicts match outcomes beyond the points won, and Fabio Fognini won 51% of his matches with 49% of the points, in what we call the Fognini paradox.
De-siloing can create groupthink; silos can prevent knowledge sharing and reduce research efficiency. The same tension appears in research systems built with large language model (LLM) agents, yet their organization is usually fixed by design. I propose treating laboratory structure and information sharing as experimental variables. I introduce Labtune, an open framework for bounded research workflows with user-selected laboratory organization, communication policy, model configuration and budget. Users can choose among six editable designs, from multidisciplinary silos and transdisciplinary teams to co-leaders, hub-and-spoke structures, flat co-labs and a single scientist. Information sharing can independently vary from none to high detail, with an optional channel for conditional abstractions called wisdom. Sharing is enforced by the controller at phase barriers rather than through unrestricted agent chat, while delegation does not create additional budget or permissions. Development evidence is separated from protected confirmation, and an optional multi-lab layer allows laboratories with different structures to exchange filtered information. Labtune is an idea and system-description paper, not a benchmark result. Its contribution is to make laboratory organization and information sharing configurable and measurable under matched budgets. I do not claim that one design or level of sharing is best; the aim is to create a concrete experimental space in which those differences can be tested.
The transition towards sustainable membrane manufacturing is challenged by its reliance on fossil-based polymers, organic solvents, and generation of large volumes of contaminated water. We combined process simulation, life cycle assessment, and Monte Carlo simulation to identify hotspots in the manufacturing of ultrafiltration hollow-fiber membranes for pharmaceutical applications and evaluate strategies to mitigate impact and reduce waste generation. Process models informed by industry input compared fossil- and bio-based membranes on a cradle-to-gate basis per square meter of membrane produced. Key environmental hotspots include polymer and bore solution preparation, driven by the contribution of polymer and solvent, accounting for 30-80% of total impacts across the categories investigated, and wastewater dilution, driven by the environmental burden of solvent-contaminated wastewater management. Under the modeled conditions, although polymer substitution can reduce environmental burdens, cellulose-based membranes show higher impacts than polysulfone membranes in at least 85% of simulations within each impact category, due to chemical-intensive cellulose modification and regeneration. In contrast, solvent recycling can mitigate solvent- and wastewater-related burdens when combined with reduced water demand, yielding climate change benefits at water demands below approximately 36 L per m2 under the modeled recovery configuration and highlighting its potential to reduce the environmental footprint of membrane manufacturing.
We analyze Fractional Orbital Bombardment Systems (FOBS) as a means of delivering nuclear weapons by placing warheads into partial orbits around the Earth, and show that they do not represent a game-changing advance in technology or capability. We consider potential uses of such a weapon, including evading missile defenses and delivering attacks with short warning time. FOBS could underfly current exoatmospheric defenses, but the required payload reduction may not justify its use if other countermeasures are deemed effective. If a FOBS attack could be successfully disguised as a satellite launch, the warning time could be short enough to raise fears of a decapitating strike. The development of such a capability could lead countries to shorten decision-making timelines in a crisis, which would be destabilizing. The US can take steps to ensure that very-short-warning attacks would not succeed, thereby enhancing deterrence during a crisis.
Large language model (LLM) agents are increasingly used as synthetic participants in opinion-dynamics experiments, often assuming that an agent's behaviour is fully determined by its prompt. We test this assumption with a framework in which LLM agents discuss a contested policy issue over repeated rounds while their stance is quantified by a fixed psychometric instrument independent of any dynamics model. Across six LLMs, committed personas are intrinsically stable: in each of the five models tested without peer input, every agent keeps its assigned side for 20 rounds. The observed loss of alignment under discussion is therefore interaction-induced, we call it alignment drift, and model-specific: gpt-4.1 keeps opinion clusters apart, claude-sonnet-5 holds them partially, and gpt-5.6-sol and claude-opus-5 abandon them. Under selective attention, one LLM maintains separated opinion clusters under stance-homophilous attention. Qualitative profiles persist at doubled population and horizon and on a second issue. Such heterogeneity makes the LLM choice a first-class validity variable for social simulation.
Capacity expansion models often dramatically simplify the physics of how power flows through a transmission network in order to preserve computational tractability. However, with the rapid growth of electrification, large loads, renewables, and storage, increased realism in planning future grids is imperative. We extend the open-source capacity expansion model GenX with AC optimal power flow physics, and analyze generation, storage, and transmission investment decisions for 20-zone, hourly models of Germany and Texas. We compare our results against those using two common approximations, the transport model and DC optimal power flow. We find that total costs and emissions largely agree across all formulations. However, we find substantial differences in outputs related to transmission, generation siting, storage, and reactive-power support between the approximate formulations and the true AC physics formulation. We identify where AC physics changes planning decisions and discuss how to potentially capture these effects without the full computational cost.
Generative AI can improve immediate performance but may erode the human skills required for future work. We study a model in which individuals tend to imitate successful peers, while their independent skills grow with practice and erode with reliance on AI. Because each agent's skill reflects their history, non-users and AI users develop different skill distributions, which we derive exactly. Skill erosion can produce bistability and hysteresis between a low-adoption, high-skill state and a high-adoption, low-skill state. When agents rarely change behaviour at random, the two transitions are asymmetric. A single AI user spreads through a population of experts only if AI-assisted work earns more than expert work, whatever the timescale of skill adaptation. A single non-user, by contrast, can lead an AI-dependent population back to independent work only if they regain enough skill before returning to AI, which becomes easier the more persistent behaviours are. As AI improves, adoption can jump abruptly, and a sufficiently long increase in its benefit, such as a period of low prices, can leave a population relying on AI after the benefit has returned to its initial value. Skill erosion thus creates a history-dependent switching cost that can make widespread AI adoption difficult to reverse.