Babak's Daily Briefing

Monday, November 2, 2026

Sources: 17 | Total Articles: 68

More Developments

• 4.RoboAuto: Autonomous Robot Program Search through Multi-Level Physical Understanding

  Recently, LLM-based autonomous agents have shown strong capabilities in iteratively generating, evaluating, and refining executable solutions. In robotic manipulation, Code-as-Policy lets these agents search over modular and editable robot programs. However, effective program search requires physical understanding: agents must understand how objects can be manipulated, how scene geometry constrains actions, and why interactions succeed or fail. Existing methods rely mainly on execution feedback for program refinement, often leaving these physical questions unresolved. We introduce RoboAuto, a framework for robot program search that explicitly organizes physical understanding at the object, scene, and interaction levels through three components. First, a hierarchical knowledge base combines executable skills with object-centric knowledge o...

• 1.Li$^{2}$SAO: Lifting Light Singl-Rollout Asynchronous RL Optimization via Group-Relative Actor-Critic Bootstrapping

  arXiv:2610.40726v1 Announce Type: new Abstract: Asynchronous reinforcement learning (RL) is becoming a mainstream paradigm for post-training large language models (LLMs), as it improves training efficiency by updating the model as rollouts arrive. Recent work argues that group-wise sampling in GRPO does not naturally fit asynchronous RL training because rollout lengths follow a long-tailed distribution, and proposes Single-rollout Asynchronous Optimization (SAO) to improve stability and off-policy behavior, especially on agentic tasks. However, SAO relies on a closed-source value model obtained from scaling value pretraining. In this paper, we analyze GRPO and LiSAO, our lightweight reproduction of SAO without the pretrained value model, on single-turn reasoning and multi-turn agentic tasks. We find that GRPO outperforms LiSAO on reasoni...

• 2.Mage-VL-Cowork: A Full Training Recipe to Learn Hybrid Computer Use From Evolved Harnesses

  arXiv:2610.40747v1 Announce Type: new Abstract: Effective computer-use agents (CUAs) should seamlessly combine visual GUI interaction with code and terminal execution within a single policy. We present Mage-VL-Cowork, a 4B hybrid CUA trained from a general multimodally aligned checkpoint through an end-to-end, progressive five-stage curriculum across midtraining, SFT, and RL. To power this curriculum at scale, Mage-CUA-Forge replaces per-task agent construction with reusable, skill-conditioned generation workflows and application-specific preparers, synthesizing 293,806 desktop and web tasks across 6,915 skills while reducing recurring per-task expenditure by 82.6%. Rather than directly distilling a default teacher, we evolve the teacher harness and instantiate specialized CUA harnesses to yield high-quality trajectories alongside stage-...

• 3.The Generative Gap Between Speculative Drafter and Continuous Diffusion Language Model

  arXiv:2610.40847v1 Announce Type: new Abstract: Continuous diffusion language models (CDLMs) and speculative drafters offer efficient language generation but are typically evaluated differently: CDLMs generate independently through iterative refinement, whereas drafters propose text for target-model verification and correction. We compare their architectures without verification, using LangFlow for CDLMs and adapting DSpark's Markov and recurrent-head architectures for standalone generation. All models are trained from scratch for unconditional, single-block OpenWebText generation at lengths 16, 128, and 1024, without target features or verification, using common preprocessing, backbone dimensions, and training-update budgets. We evaluate generation perplexity (GenPPL) with an external language model and distributional similarity to refe...

• 4.Qistas: A Pre-Registered, Portable Protocol for Fairness Evaluation of Large Language Models

  arXiv:2610.40893v1 Announce Type: new Abstract: Fairness benchmarks for large language models (LLMs) are overwhelmingly English-born and American in their social categories; translated versions carry those categories into societies where they do not apply, and almost none fix their hypotheses before the models are run. We present Qistas (Arabic qistas, "the just balance"), a protocol for evaluating how LLMs treat the social, confessional, and regional constituencies of a society, portable across countries and instantiated first for Iraq. In its pilot form it measures only what needs no verdict on contested truth: procedural symmetry across matched counterfactual pairs, differential refusal, stereotype reliance under ambiguity, and terminological framing of documented events. Every measurement is paired by construction and analysed with p...

• 1.Automatic Construction of Strong Simple Baselines

  arXiv:2610.40700v1 Announce Type: new Abstract: To rigorously establish the value of a novel deep learning method, it must be compared against appropriate baselines. As deep learning has become increasingly common in the physical and life sciences, there have been numerous cases in which apparent progress has turned out to be an artifact of omitted or missing baselines. In this work, we investigate whether this problem can be mitigated by using large language models to automate the construction of baselines. We assemble 36 supervised classification and regression tasks from peer-reviewed studies in Nature, Science, and Cell-family journals that investigate novel deep learning methods for scientific applications. We then use a language model to construct simple baselines for each task. These automatically constructed baselines are strong, ...

• 2.Bayesian Distribution System State Estimation with Flow Matching

  arXiv:2610.40728v1 Announce Type: new Abstract: Medium-voltage feeders have few real-time measurements, so distribution system state estimation (DSSE) rests on pseudo-measurements of the secondary substations, a prior in Bayesian terms. Weighted least squares keeps the power flow exact but reduces this prior to a mean and variance, while supervised deep learning estimators also learn the power flow and are tied to one measurement set. We propose PoSE (posterior sampling for state estimation), which keeps the Bayesian structure of DSSE and learns only what the distribution system operator does not know, namely the distribution of substation injections, modelled by a neural network trained with flow matching on historical data. The power flow equations, grid topology and measurements enter only at sampling, through a noise-aware Kalman-type...

• 4.Unsupervised Feature Selection Using Local Intrinsic Dimensionality -- Extended Version

  arXiv:2610.40736v1 Announce Type: new Abstract: High dimensionality degrades both the efficiency and the effectiveness of learning algorithms, and in many applications no class labels are available to guide dimensionality reduction. This paper proposes LIDFS, an unsupervised feature selection method based on Local Intrinsic Dimensionality (LID). The method builds on the formal equivalence between LID and the indiscriminability of distance measures. The data matrix is transposed so that each feature is treated as a point, and the LID of each feature is estimated by maximum likelihood from the distances to its $k$ nearest neighboring features. Features with lower LID exhibit greater local discriminability and receive higher importance scores. A refinement step then retains one representative per feature neighborhood and demotes locally redu...

• 5.Certified Behavioral Retention for Continual Adaptation with Held-Out Anchors

  arXiv:2610.40740v1 Announce Type: new Abstract: Continual adaptation requires deciding not only how to update a model, but whether to commit an update without exceeding a declared budget for changes to protected behavior. We introduce Certified Anchor-Based Adaptation (CABA), a candidate-agnostic post-update gate that reserves historical observations from candidate optimization, compares old and candidate policies on frozen representations, and selects the largest tested interpolation coefficient whose worst-anchor forward KL divergence remains within budget. The finite check requires neither action labels nor complete trajectories. We develop a theory-led certification framework that separates measured anchor retention from conditional off-anchor guarantees. Under explicit representation-coverage and Lipschitz assumptions, an accepted up...

• 1.WorldBridge: Bridging Video and Action for Task Generalization in World Action Models

  arXiv:2610.40748v1 Announce Type: new Abstract: Achieving the broad task generalization of embodied models in physical environments remains a central challenge in robot learning. We study this challenge in World Action Models (WAMs) through two complementary capabilities: video generalization, which predicts the visual evolution of new tasks, and action generalization, which translates these predictions into executable behavior through a video-to-action bridge. By varying video and action supervision for tasks outside an initial training set, we examine their respective contributions to task transfer. On RoboTwin 2.0, using only in-distribution video-action supervision yields a 25.07% success rate on out-of-distribution tasks. Introducing videos of these tasks without their action labels raises success to 55.90%, while adding just one act...

• 2.CaPtain: A Pipeline for Post-Training Code-as-Policy Robot Agents

  arXiv:2610.40799v1 Announce Type: new Abstract: Across coding, web, and tool-use tasks, language-model agents are now routinely trained with the same recipe: supervised fine-tuning (SFT) to learn the interface, then multi-turn reinforcement learning (RL) to master it. Code-as-policy (CaP) casts robot manipulation in the same form, as the policy writes code against robot APIs and reads back the results, so the same recipe should apply. Yet existing CaP agents sidestep training: they either prompt closed frontier models or delegate manipulation to pre-trained vision-language-action models (VLAs) and high-level skills, while small open vision-language models (VLMs) fail outright. We present CaPtain, an open post-training pipeline that turns a small open VLM into a capable CaP agent, with no VLA, frontier model, or privileged state at test ti...

• 3.From Teleoperation to Long-Horizon Dexterity: Evaluating Visuomotor Skills and Offline Proxies for Dexterous Manipulation

  arXiv:2610.40828v1 Announce Type: new Abstract: Imitation learning policies are intended to make robotic manipulation robust to variations in object pose, appearance, and environment that challenge pre-programmed motion. However, real-world performance remains difficult to predict, as conventional optimization signals such as validation loss show no consistent relationship with robot success. To identify the design choices that govern real-world performance, 281 imitation-learning policies spanning seven policy families were evaluated in approximately 1,700 real-robot trials of RJ45 connector grasping and insertion using a five-fingered robotic hand. Controlled ablations showed that observation and action representations dominated conventional optimizer and sampling choices. For point-cloud policies, region-of-interest cropping, represent...

• 4.FlapSim: A Composable MuJoCo Simulation Platform and Reduced-Order Aerodynamic Model Hierarchy for Flapping-Wing Robotics

  arXiv:2610.40902v1 Announce Type: new Abstract: Flapping-wing simulation must reconcile high-frequency wingbeat aerodynamics with the slower dynamics relevant to flight control and reinforcement learning. We present FlapSim, a modular open-source MuJoCo simulation platform that combines robot descriptions, wing geometry, and composable aerodynamic models in a deterministic simulation pipeline. Across $48$ held-out scenarios spanning frequency, gimbal, wind, frequency-step, gust, and cross-robot flat-plate regimes, we compare five reduced-order configurations operating at $1\,\text{ms}$ with a $50\,\mu\text{s}$ quasi-steady blade-element reference. The built-in hover cycle-averaged baseline achieves a $67.51\times$ CPU speedup but only $0.6593$ mean wrench fidelity. Combining multirate $21$-substep clock synchronization, three-dimensional ...

• 5.Confidence-Modulated Safety Filtering for End-to-End Policies

  arXiv:2610.40922v1 Announce Type: new Abstract: End-to-end policies, from task-specific controllers to robot foundation models, learn complex behavior from raw observations but are not guaranteed to remain safe, so they are often wrapped in a safety filter. The resulting system operates under two domains: the filter's operational design domain (ODD), where its assumptions hold and safety is certified, and the policy's competence domain, where it is reliable. Filters typically fix their conservativeness regardless of the policy's competence, trading performance inside the ODD against margin beyond it. We ask whether the policy's confidence should set that level, and propose a filter-agnostic pipeline that modulates it at runtime: inside the ODD, where every setting is safe, confidence buys performance; beyond it, low confidence adds margin...

• 1.Sparse Autoencoders as Implicit Diversification Regularisers for Cross-Sectional Cryptocurrency Returns

  Factor models are central to asset pricing and portfolio construction, but neural factors are often difficult to interpret, and their effect on portfolio risk is unclear. A sparse autoencoder (SAE) may address both concerns by describing daily returns across many assets with only a few active features. We ask whether this sparse representation produces stable financial factors and better-diversified portfolios. We study cryptocurrency returns, use equities as a secondary test, and compare the SAE with common benchmarks in a time-ordered evaluation. We find consistently lower volatility in SAE portfolios and link this result to lower correlations among the selected assets. However, the SAE does not outperform a simple contrarian strategy on risk-adjusted return, and its factor identities are not stable across training runs. Our contributio...

• 2.CADENCE: Distributional Next-Transaction Forecasting for Business Cash-Flow Underwriting

  Recurring cash flows are the backbone of cash-flow underwriting: they separate durable revenue from one-off deposits, expose standing obligations, and ultimately determine ability to pay. For small businesses, recurrence is both indispensable and hard to estimate, because much of their income arrives as ambiguous transfers, most payment streams are short, and schedules follow calendar conventions that frequency-domain methods treat as noise. We present CADENCE, Calendar-Aware Distributional Event Network for Cash-Flow Estimation, a compact causal Transformer that treats each transaction as a token and predicts the amount and a full, nonparametric timing distribution of every cash flow's next payment. Inheriting transaction semantics from an external labeler and a frozen text encoder keeps CADENCE compact yet general across the long tail o...

• 1.GSMA Board re-elects Gopal Vittal as Chair

  Summary available at source link.

• 2.Viet Nam Receives the GSMA Government Leadership Award 2026

  Summary available at source link.

• 3.GSMA Calls for More Affordable Smartphones as Over Half of Gabon’s Population Remains Off Mobile Internet Despite Network Coverage

  Summary available at source link.

• 4.From notifications to conversations: RCS business messaging enters its next era

  Summary available at source link.

• 5.GSMA Secures US$1.2 Million To Strengthen Mobile-Enabled Early Warning Systems Across the Pacific

  Summary available at source link.

• 1.Latent Reasoning in Continuous Space for Unified Multimodal Models

  Unified Multimodal Models (UMMs) achieve remarkable success on diverse multimodal understanding and generation tasks, yet they still struggle to effectively leverage language-based chain-of-thought reasoning for image generation. As a result, existing reasoning-augmented UMMs often fail to inherit the test-time scaling and fine-grained controllability observed in large language models. We attribute this failure to a fundamental modality mismatch: reasoning is performed in a discrete token space, whereas image generation operates in a continuous space. To bridge this gap, we introduce a Latent Reasoning framework in Continuous space (LARC), which alleviates the token-level bottleneck by reasoning over continuous hidden states. Specifically, the training framework consists of two stages: (i) curriculum supervised fine-tuning (SFT), and (ii)...

• 2.Where ADS Crashes Differ: A Harmonized Comparison Against Human Baselines

  Crash-rate comparisons between automated driving systems (ADS) and human drivers require an exposure denominator that only a few operators publish. We instead ask a composition question: given a crash, does the mix of crash types differ between ADS and human-driven vehicles? The question needs no mileage and can therefore cover every ADS operator. We assemble the NHTSA Standing General Order (SGO) ADS incident files across both file generations into 2,450 distinct in-scope incidents (2021-2026), of which 828 fall within the 2021-2024 window covered by the Crash Report Sampling System (CRSS), the national police-reported human comparator. A schema-constrained large language model (LLM) codes each SGO narrative into the CRSS crash-type taxonomy. Because SGO compels reporting far below the police-reporting threshold, we bracket reportability...

• 3.Subquadratic Attention with a Hierarchical Tree-Structured Indexer

  The quadratic scaling of attention is a fundamental bottleneck limiting the long-context capabilities of large language models (LLMs). A variety of approaches have attempted to circumvent this, but they either fail to match attention's expressiveness (e.g., sliding-window or linear attention variants with fixed-size state) or only achieve constant-factor speedups (e.g., sparse attention methods whose indexers themselves scale quadratically). In this paper, we derive an asymptotically subquadratic sparse attention method based on a hierarchical, tree-structured indexer, which we call Zyphra Hierarchical Attention (ZHA). ZHA constructs a tree of recursively compressed key summaries over the KV cache, which future queries traverse from the top down to retrieve a sparse subset of keys. Crucially, by iteratively growing the tree's depth, the t...

• 4.Aligning MLLMs with the Latent Structure of Human Cognition via Behavior-Derived Semantic Dimensions

  Multimodal Large Language Models (MLLMs) have achieved impressive performance across a wide range of reasoning tasks, yet it remains unclear whether their internal representations and decision criteria align with the latent structure of human cognition. In this work, we propose a novel framework to align MLLMs with the multidimensional mental representations underlying human similarity judgements. We build on a previously established behavior-derived semantic embedding space, in which objects are represented along 66 interpretable dimensions derived from 4.70 million human Odd-One-Out (O1O) judgements. For each triplet, we infer the most salient decision dimension and convert it into a dimension-grounded linguistic rationale. This enables MLLMs to learn both the final choice and a justification consistent with the inferred behavioral crit...

• 5.Open-Weight MLLMs Cannot Compare What They Cannot Name

  Multimodal Large Language Models (MLLMs) are increasingly capable of understanding images and performing complex visual reasoning, suggesting the possibility of using them to automate biological image analysis. This need is becoming increasingly urgent as advances in technology continue to generate ever larger and more diverse microscopy datasets, already exceeding the scale at which manual visual analysis is feasible. With the final aim to use MLLMs for visual comparison of complex phenotypes, we investigate a very simple baseline: comparison of the size of body parts in synthetic images of ants. We find that state-of-the-art open-weight MLLMs fail for some body parts, but not for others, even though the comparison itself is visually trivial for all of them. We evaluate different in-context learning strategies and analyze performance on ...

• 1.Shock Loading Accesses Reaction Pathways beyond Accelerated Thermolysis

  Thermolysis and shock loading can produce similar major stable products in energetic materials, despite different chemical evolution and reaction pathways. Here, we develop EMFF-MACE, a reactive machine-learning potential for C/H/N/O energetic materials, enabling thermolysis simulations of 20 materials and shock simulations of 10 on a common potential energy surface. Comparisons at matched normalized reaction progress reveal divergent chemical states and local structural environments. Mean reaction-state similarity decreases from 94.0% at 20% progress to 25.9% at trajectory endpoints, while 47.1-92.6% of shock local environments lie outside corresponding material-specific thermolysis reference domains. Structural and reaction-network analyses reveal enhanced intermolecular reorganization and additional association and rearrangement branch...

• 2.Does Reasoning Make Language Models Agree?

  Language models face two opposing concerns: monoculture, in which models err in correlated ways, and multiplicity, in which equally accurate models make conflicting decisions. The rise of reasoning models has complicated this debate: On the one hand, reasoning may increase model agreement if models anchor on the same salient features or follow similar reasoning paths. On the other hand, stochastic reasoning traces may introduce additional branch points leading to more diverse predictions. We resolve this tension by distinguishing reasoning as an intervention from reasoning as an observed behavior. We compare agreement under controlled changes in reasoning effort with agreement across observed reasoning lengths, measuring both against the agreement expected from model accuracy. Across nine models from two developers and three classificatio...

• 3.STAR-FER: Adaptive Selective Restoration for Edge-Based Low-Resolution Facial Expression Recognition

  Variable interaction distances continuously degrade facial resolution in edge-based affective interaction, making low-resolution facial expression recognition (FER) difficult under limited computation. Full-face image or feature restoration incurs substantial overhead, whereas lightweight recognition pipelines may miss local cues that remain discriminative after degradation. We propose STAR-FER, a scale-and-task-adaptive selective restoration framework that balances recognition performance and resource constraints. A lightweight image-scale conditioned Gate jointly predicts a pixel-wise spatial ranking and a continuous restoration budget, then applies exact-support projection to obtain an input-dependent free-form mask. The mask-conditioned restorer retains complete facial context while concentrating sparse restoration and residual insert...

• 4.ANGLER: Verifier-Grounded Benchmarking with an Adaptive Attacker for Web Agents

  Web agents are increasingly deployed to complete tasks autonomously on real websites, where adversarially injected content can induce them to perform unauthorized actions. Existing evaluations primarily use fixed attacks or focus on a single attack type, and evaluate attacks at the text level rather than through real state changes, thus underestimating how vulnerable agents are to diverse attacks. To address the gap, we introduce REEF, a benchmark of red-teaming executable environments with faithful verification for measuring the security vulnerabilities of web agents. REEF covers 9 verifier-grounded websites spanning travel, commerce, productivity, media, and communication, and pairs every authentic benign task with a malicious objective delivered through 5 attack types at both the user and the environment level. REEF's state-based verif...

• 5.CIRCE Divines LLM Task Complexity from Constraint Structure

  Estimating whether a problem is too complex for a large language model (LLM) before the model attempts it can guide model routing, how to allocate test-time compute, and suggest the need for new tools to aid the agent. While existing estimators rely on the problem text, LLM-annotated task difficulty labels, or signals obtained after a model attempts to solve the problem, such as its hidden states, none explicitly leverage the constraint structure inherent in the problem: the variables to assign and how the constraints over them interact. We introduce CIRCE, which extracts a problem's structure by using a cheap LLM to construct a Constraint Intermediate Representation that deterministically compiles into the constraint graph. Across seven reasoning benchmarks (including SAT, logic, and math) and four LLMs, even a randomly initialized GNN e...

• 1.Guarding the Gates of Knowledge

  A recent scroll through social media got me thinking about trust. About half my feed was filled with AI slop that astute users would call out as being machine-generated, often to the embarrassment of the rube who unknowingly spread it and sometimes to the delight of the poster who shared it deliberately because they found it amusing or useful for sowing confusion. Remember the good old days of GPT-3 when we could ask the chatbot anything and it would almost instantly and very confidently spit back a plausible but false answer? That’s when the word “hallucination” as applied to AI answers entered the lexicon. Six years and several versions of a few different chatbots later, I still can’t trust the answers I get, even though Claude or Gemini often do useful work for me. That’s the thing about current AI: Trust but verify. And verify you mus...

• 2.Consumer Appetite for Personal AI Goes Mainstream

  This article is brought to you by Qualcomm Technologies . For many consumers, much of the promise and appeal of AI is personal. Consumers are looking past generic tools and want to know how these technologies will help them in their daily lives. These trends coincide with the advent of new classes of wearable devices that, in addition to being able to leverage powerful frontier models, feature built-in intelligence that understand the context of a user. Where are they? What are they seeing and hearing? What are their habits, their preferences, their schedules? It’s an astonishing pivot, from passive accessories that served mostly to send users smartphone notifications to proactive, intelligent companions. The smartphone is no longer the center of that universe; though it’s still incredibly important, in this personal AI paradigm, the AI i...

• 3.Systems Thinking Is Vital in the Age of AI

  Modern engineering projects increasingly combine human judgment with machine capability. An engineer can define a problem, then ask an artificial intelligence tool to search relevant research and technical standards, generate design options, test them in simulation, and compare the results. The engineer can then bring the results to specialists who understand the parts AI cannot judge on its own, such as whether a design can be built safely, whether it makes sense in a particular setting, or how it might affect its users. The final decision about whether and how to move forward remains a human responsibility. AI can help engineers do early project steps that once required several people with different technical skills. The engineer’s role is therefore expanding from performing each technical step to deciding how people, machines, and spec...

• 4.Your Robot’s Safety Functions Already Work. What If the Input Lies?

  This article is brought to you by VicOne . A mobile robot slows for someone in a hallway. A collaborative arm eases its movement as a worker approaches. A humanoid pauses to let a person pass. Each response depends on information about the robot or its surroundings: a distance reading, a position estimate, or a stop signal. But what happens when one of those inputs is wrong, and the robot still responds exactly as designed? The mobile robot reads the hallway as clear while someone is still there. The arm estimates that the worker is farther away. The humanoid does not receive a stop signal as a person crosses its path. In each case, a fault could cause that mismatch. So could deliberate manipulation . At VicOne LAB R7 , we examine how manipulated inputs can change robot behavior — and what that means for the systems meant to keep people s...

• 5.I Used AI to Fake a Scientific Paper. Here’s What Happened

  Science has a spam problem. And the spam filter is getting clogged up. According to all reports, academic publishing is drowning in what has come to be called AI slop: low-quality, mass-produced content generated with AI. By 2025, an estimated 57 percent of all published academic articles exhibited detectable evidence of large language model (LLM) influence, a massive jump from 12 percent in 2023. What’s more, over 12 percent of Nature Communications reviews were found to be completely AI-generated in 2025. Given that for every 40 papers showing statistical evidence of AI usage in 2025, only one formally disclosed it , these figures likely grossly underestimate LLM involvement today. Of course, evidence of AI use doesn’t necessarily mean a paper is slop. But some estimates suggest that hundreds of thousands of fake, often AI-generated, pu...

• 1.*Fjord* and *Fatherland*

  Both of these movies I found worth watching.  Fjord won the Golden Prize at Cannes, and it concerns the rights of the family vs. the rights of the state, when it comes to raising children.  As I interpreted the story, it was so right-wing and so based I am shocked it won.  It is by […]

  The post *Fjord* and *Fatherland* appeared first on Marginal REVOLUTION.

• 2.Expensive Pharmaceuticals for Targeted Diseases

  My latest paper, The Price of a Cure: Luxturna and the Economic Dilemmas of Precision Medicine, is in the Journal of Law, Medicine and Ethics. I discuss the economics of the pharmaceutical industry and the special issues that arise with targeted drugs that cure. We are going to get many more of these drugs in […]

  The post Expensive Pharmaceuticals for Targeted Diseases appeared first on Marginal REVOLUTION.

• 3.Should you feel grief for the dead who go to heaven?

  I am not myself religious, but let us consider the possible gulf between behavior and doctrine amongst those who are. It has long struck me as a kind of mini-puzzle why people feel so much grief for the departed, at least for the departed who are supposed to be headed for heaven (and yes there […]

  The post Should you feel grief for the dead who go to heaven? appeared first on Marginal REVOLUTION.

• 4.As predicted, the ACA exchanges seem not to be stable

  The average price of health insurance in the Obamacare marketplaces will rise by 13 percent next year, according to an analysis on Friday by KFF, the health research group. The Trump administration on Friday published new prices for 2027 in 30 states with markets managed by the federal government. Using that data and state figures, […]

  The post As predicted, the ACA exchanges seem not to be stable appeared first on Marginal REVOLUTION.

• 1.How Do Banking Supervisors Deal with Failing Banks?

  In this post, we discuss insights from a recent staff report on the role of banking supervision in identifying and disciplining failing banks. We show that bank failures typically result from supervisory closure decisions based on hard information about book equity. Yet hard information is itself partly produced by supervision, as our evidence suggests that supervisors require troubled banks to recognize potential losses. We argue that supervision faces a trade-off: Stricter supervision can reduce the costs of resolving failing banks, but it can also lead to tighter regulatory capital constraints and hold back credit supply. 

• 2.Evaluating New LinkedIn Labor Market Tightness Indicators

  Measures of labor market tightness (LMT) describe how difficult it is for firms to find workers. The labor market is considered tight if there are few job seekers available relative to job openings, implying stronger wage pressures as employers compete for scarce job seekers. Thus, good measures of LMT are important for predicting wage inflation and calibrating monetary policy. Recently, the hiring platform LinkedIn began producing three innovative LMT measures based on its user data. In this post, we use public data from the Job Openings and Labor Turnover Survey (JOLTS) and the Bureau of Labor Statistics (BLS) to evaluate the ability of these novel measures to predict wage and price inflation relative to existing LMT measures, finding that one stands out as particularly useful and timely.

• 1.A Debtors’ Club for a Fragmented World

  The newly created Borrowers’ Platform aims to facilitate knowledge-sharing and capacity-building among debtor countries, while amplifying their voices in global financial circles. But at a time when the Paris Club of sovereign creditors is increasingly unable to act collectively, the Borrowers’ Platform should ramp up its ambition.

• 2.Trump’s Tariff Fiasco Could Save Free Trade

  US President Donald Trump’s tariffs have raised prices and hurt businesses without reducing the trade deficit or creating manufacturing jobs. But global trade has held up better than expected, and growing public opposition to tariffs could pave the way for a retreat from protectionism.

• 3.If AI Thinks for Us, Democracy Will Suffer

  Whereas the internet buried us in information, AI enables us to outsource analysis altogether. But when it comes to grappling with humanity’s most important questions, we must resist the temptation and recognize that we all have a duty—to each other and to ourselves—to keep “showing up.”

• 4.A Tory Mentor for the MAGA GOP

  Kemi Badenoch’s keynote address at the British Conservative Party conference last month outlined her plan for a pro-growth future that respects fiscal discipline and human dignity. As American conservatives look beyond Donald Trump and to the 2028 primary campaign, they should embrace a similar agenda.

• 5.The Politics of Poland’s Judicial Crisis

  The removal of Bogdan Święczkowski as president of the Constitutional Tribunal has triggered a physical confrontation at the court and a political standoff between Donald Tusk’s government and the far-right Law and Justice (PiS) party. But Święczkowski was not so much removed as barred, because he held the position illegally.

• 1.What will an AI crash look like? (Analyst Angle)

  The AI infrastructure boom could end in a crash resembling telecoms’ late-1990s fibre bust, leaving investors nursing huge losses but potentially handing utilities and infrastructure funds cheap assets to power…

• 2.American Tower sees four catalysts extending wireless investment cycle

  Steve Vondran, chief executive officer of American Tower, told a conferece call with investors that the company is seeing increasing evidence that these catalysts could create overlapping investment cycles In…

• 3.SMO/RIC-based RAN automation gains momentum (Reader Forum)

  Operators are moving beyond legacy SON towards SMO/RIC-based automation as part of mid-cycle 5G RAN modernization and vendor swap-out projects, writes SNS Telecom & Research, with annual spending on SMO…

• 4.From AI factory to factory-factory – Qualcomm, Nvidia and the race to put AI to work (Telco Diary)

  From the newsletter (October 30; sign-up or follow if you want it sooner): Qualcomm’s push to bring AI into industrial IoT, robotics and other physical devices raises big questions about…

• 5.América Móvil expects Televisa-AT&T deal to open door to pay TV

  America Movil’s chief executive officer Daniel Hajj said the company does not yet know what Televisa’s strategy will be once the transaction is completed but noted that Televisa will be…

• 1.6G-Enabled Cooperative Connected and Automated Mobility: Are We There Yet?

  Connected, cooperative, and automated mobility (CCAM) is a key enabler of future intelligent transportation systems, requiring seamless integration of communication, sensing, computing, and automation technologies. This paper surveys the state of the art in CCAM, focusing on key use cases and performance indicators, enabling technologies, system architectures, and standardization and regulatory developments relevant to large-scale deployment. Particular attention is given to cooperative perception, collective awareness, artificial intelligence and machine learning as fundamental building blocks of future CCAM ecosystems. The paper further identifies major research challenges related to scalability, interoperability, and real-time operation in highly dynamic mobile environments. Finally, it outlines future research directions toward achiev...

• 2.ORION: An Outcome-Driven LLM Advisor for RL-Based Multi-Domain 6G Slice Placement

  Deploying 6G network slices across administrative domains requires an online decision for each arriving request: which domain hosts each virtual network function, how traffic crosses domain boundaries, and whether the slice is admitted. This joint decision problem is NP-hard under continuous arrivals. Experiments on a parametrized family of multi-domain topologies show that no fixed planning strategy dominates across all regimes: the performance ranking reverses between topology classes, and feasible placements exist for most rejected requests but remain undiscovered. These findings identify the plan layer as the decisive performance lever. We introduce ORION, a hierarchical orchestrator in which two LLM agents translate service intents into structured requests and generate abstract placement plans. A cooperative multi-agent RL coordinato...

• 3.Pinching Antennas for ISAC: Waveguide Architectures, Solutions, and Future Directions

  Integrated sensing and communication (ISAC) is a core design principle for sixth-generation (6G) wireless systems, yet fixed antenna geometry limits its performance. Pinching antenna systems (PASS) guide electromagnetic waves along dielectric waveguides and radiate them through position-adaptive antennas, allowing communication links and sensing observations to benefit from spatial reconfiguration. This article reviews PASS-enabled ISAC through single-waveguide, multi-waveguide, and segmented-waveguide architectures, comparing their feed structures, spatial degrees of freedom, operating protocols, and uplink/downlink support. We organize solution pathways around physics-consistent modeling, joint spatial-signal optimization, dynamic channel acquisition, and stable real-time control. A segmented waveguide-enabled pinching-antenna array (SW...

• 4.Multistatic ISAC with Deep Learning-Based Clutter Suppression for 5G and Beyond

  Integrated sensing and communications (ISAC) systems using 5G and beyond signals of opportunity enable joint wireless sensing and communication using shared infrastructure and spectrum resources. While multistatic ISAC can increase sensing coverage with spatially distributed receivers, dense and dynamic clutter can significantly degrade bistatic range and Doppler estimation, leading to errors in target localization and velocity estimation. This paper presents a multistatic ISAC framework with clutter suppression for high-fidelity estimation of target position and velocity. Distributed clutter is modeled with randomly located scatterers with varying Doppler spreads and radar cross sections to capture dense and dynamic clutter effects. A broad range of conventional signal processing baselines are evaluated for clutter suppression, showing l...

• 5.True 3D Holography: A Communication Service of Tomorrow and Its Requirements for a New Converged Cloud and Network Architecture on the Path to 6G

  Research project 6G NeXt is considering true 3D holography as a use case, setting requirements on the communication as well as the computing infrastructure. In a future holographic communication service, clients are widely spread in the network and cooperatively interact with each other. Especially for holographic communication high processing power is required as well. This makes a high-speed distributed backbone computing infrastructure, which realizes the concept of split computing, inevitable. Furthermore, tight integration between processing facilities and wireless networks is required in order to provide an immersive user experience. This paper illustrates true 3D holographic communication and its requirements. Afterward, an appropriate solution approach is elaborated. Here, novel technological approaches are discussed based on a pr...

• 2.Persistence of Bounded Rationality in Financial Markets: An Evolutionary Game-Theoretic Model

  Classical theory predicts that irrational traders lose money to rational traders and are eventually driven out of the market. Yet noise trading, herding, and heuristic trading persist even in highly developed markets. We model a financial market as an ecology of two trading-behavior types, relatively rational (R) and boundedly rational (N), whose composition evolves under replicator dynamics according to relative performance. Each type's fitness depends on the share of the other type and on frictions. When the interaction is complementary, that is, rational traders profit from the mispricing created by boundedly rational traders while the latter benefit from the liquidity and price correction supplied by the former, and when the difference in frictions is moderate, a unique interior composition exists and is globally asymptotically stable...

• 3.Right Numbers, Wrong Questions: Evaluating LLMs on Underspecified Financial Queries

  Financial assistants answer questions that sound factual but depend on what the user leaves unsaid: what a data figure measures, which product variant is meant, and the investor's situation and the rules that apply. An assistant that ignores these can still answer with a real number from its data, one that answers a different question. We call this frame collapse. We study it in Indian mutual funds, where product variants and tax rules are formally defined, so the correct answer can be computed for every reading. One study tests each challenge. Given an ambiguously labelled figure, models used it, often under the user's label. Given every alternative labelled, they listed them all, but the layout decided which came first. Missing a fact about the investor, weaker agents assumed a default or an outdated tax rule; requiring the fact did not...

• 4.Calibration of Passive Market Impact with a Queue-Sensitive Propagator Model

  We develop a framework for calibrating passive market impact, defined as the price response to liquidity-providing limit orders, in contrast to the impact of liquidity-consuming market orders. We build on the model of Ouazzani Chahdi, Rosenbaum, and Szymanski (arxiv:2412.07461), which combines Hawkes-driven market-order flow with queue-reactive limit-order and cancellation dynamics. Within this model, we show that the price decomposes into a universal propagator term driven by signed order-flow imbalance and a state-dependent correction that captures the dependence of price sensitivity on volume at the best quotes. This decomposition links limit-order-book microstructure to standard propagator models and yields a regression on two observable signals: the propagator signal and the queue-weighted order-flow imbalance. We calibrate the model...

• 5.Contemporaneous Transmission Networks and Systemic Risk in European Banking

  We study how contemporaneous equity-return transmission shapes systemic exposure among 51 listed European banks. Using a recursive non-Gaussian network model on volatility-standardized returns, we construct two market-capitalization-weighted measures based on direct and indirect transmission effects: the Systemic Impact Score (SIS), capturing a bank's potential influence on other institutions, and the Systemic Vulnerability Score (SVS), capturing its exposure to disturbances originating elsewhere. The fitted network reveals distinct transmitting and receiving roles, highlighting differences between institutions with substantial outward influence and those with high incoming sensitivity. Receiving roles are generally more reproducible than transmitting roles across repeated network discovery, and selected connections remain informative abo...

• 5.CLIC: Closed-Loop Intent Consistency for Securing LLM-Driven Control in Open RAN

  With the progressive capabilities of large language models (LLMs), researchers are finding ways to integrate them into the control loops of Open Radio Access Network (O-RAN) to enable slicing and resource management through user intents. However, integration of LLMs in the O-RAN control loop via the RAN Intelligent Controller (RIC) makes the system vulnerable to \textit{runtime prompt injection threats}, which manipulate control decisions without violating interface authentication, and \textit{residual virtual prompt injection (VPI)} attacks that plant trigger mechanisms to silently steer the behaviour during fine-tuning. Existing studies address only the runtime threat and do not enforce consistency across the full control path, i.e. from the certified A1 intent, through the multi-step LLM trajectory, to the final E2 action. We propose a...

• 5.MIRAGE: A GPU-Accelerated IQ-Level Channel Emulator for Real-Time Wireless Digital Twins

  Wireless digital twins enable network policies to be evaluated before deployment, but reproducing channel effects within a running protocol stack requires continuous waveform processing under strict timing constraints. We present MIRAGE, a GPU-accelerated channel emulator that applies propagation effects directly to in-phase and quadrature samples exchanged by software 5G New Radio stacks. MIRAGE combines batched multiuser, multiantenna filtering with timestamped shared-memory transport and radio-driver integrations for OpenAirInterface and OCUDU. External propagation models supply channel responses asynchronously, while versioned buffers ensure consistent coefficients within each processing interval. Experiments on one NVIDIA H100 GPU evaluate waveform accuracy, processing time, channel updates, and full-stack traffic delivery. At 61.44 ...