Babak's Daily Briefing

Monday, November 9, 2026

Sources: 16 | Total Articles: 64

More Developments

• 2.FinEvolveKG: Controlled and Attributable Prompt Evolution for Financial Knowledge Graph Construction

  arXiv:2611.08643v1 Announce Type: new Abstract: Financial institutions construct knowledge graphs (KGs) from shared SEC 10-Ks to serve risk analysis, portfolio management, and investment research, whose analytical needs differ. Extraction must adapt without fragmenting shared grounding, typing, and temporal rules. Hand-authored persona prompts and fine-tuning add cost. Weight-free prompt optimization offers an alternative, but unconstrained evolution of a monolithic prompt can rewrite shared rules alongside persona guidance, obscuring changes and complicating review. We introduce FinEvolveKG, a KG-construction harness separating reusable general and persona-policy blocks and mechanically confining GEPA, a reflective prompt optimizer, to the designated block. During persona adaptation, the approved general block remains byte-stable and ev...

• 3.Compression of Emotion in Language Model Representations

  arXiv:2611.08683v1 Announce Type: new Abstract: Large language models (LLMs) have been evaluated on their ability to identify emotions expressed in text and produce fluent emotional language. However, output predictions alone do not reveal how fine-grained emotion distinctions are organized internally. We study seven LLMs on GoEmotions, an English Reddit dataset annotated with 27 fine-grained emotion categories plus neutral. We compare model predictions and emotion-prompted final-token representations with the 28 GoEmotions categories and with coarser emotion-family and sentiment groupings. We find a consistent pattern of $\textit{overcompression}$ relative to the fine-grained GoEmotions taxonomy. Under the tested KMeans protocol, when we cluster their hidden representations without using emotion labels, the clearest split is into two ...

• 4.Contextuality and Contradictions: Exploring Real-World Identifications of AI-Generated Writing

  arXiv:2611.08704v1 Announce Type: new Abstract: As AI-generated content spreads into nearly every domain of human interaction, practices and discourses of AI detection, both formal and informal, have become an everyday part of how people interact with text and with each other. In this paper, we explore the real-world practices and discourses of AI identification that have emerged in response to a shifting discursive landscape where the human provenance of a text can no longer be taken for granted. We pair qualitative coding with computational analysis of lexical change, grounded in linguistic anthropological perspectives on language and communication, to investigate AI identifications among English Wikipedia editors and students. We find that these identifications draw on a diversity of evidentiary types, from discrete textual features a...

• 5.RL-ATTACK: A Generalized Black-Box Adversarial Attack on Language Models via Reinforcement Learning

  arXiv:2611.08711v1 Announce Type: new Abstract: Large language models (LLMs) exhibit vulnerabilities to adversarial perturbations that can induce unintended responses. These vulnerabilities can be identified via intentional attacks and subsequently used to improve model safety. In black-box settings where gradients are inaccessible, existing methods tend to guess adversarial transformations using external knowledge, ignoring the information provided by the target model. To mitigate this, we propose a trainable black-box framework based on reinforcement learning, which learns from the target model to generate tailored adversarial transformations. We demonstrate that our approach achieves lower post-attack accuracy, greater query efficiency and better semantic preservation on both natural language inference and generative Q&A tasks. On nat...

• 1.Q-OIL: Optimistic Exploration in Learning from Human Interventions

  arXiv:2611.08576v1 Announce Type: new Abstract: As robots are deployed in the real world, they will inevitably make mistakes. Human interventions provide valuable feedback for improving their behavior, enabling them to adapt online to new scenarios. However, studying human-in-the-loop robot learning is difficult: real-world experiments are slow and expensive, so most algorithm development relies on simulated human models. We show that common human modeling assumptions do not match how real people intervene in practice. Through analyzing intervention data from human participants, we find that humans tend to intervene when agent progress stalls, and that their corrective actions are often suboptimal. Motivated by these findings, we introduce a progress-based model of human intervention that achieves higher rank correlation with real-user al...

• 2.Meituan-Robotics-0: A Vision-Language-Action Foundation Model for Desktop Manipulation

  arXiv:2611.08577v1 Announce Type: new Abstract: We present Meituan-Robotics-0 (MR0), a vision-language-action foundation model and embodied-agent system for stationary-base desktop manipulation. MR0 combines 147 million vision-language samples with 15,060 hours of curated robot trajectories spanning 12 embodiments. A three-stage training recipe aligns multimodal representations with robot behavior through discrete action-token pre-training, transfers these priors to continuous cross-embodiment control through flow-matching mid-training, and adapts the policy through real-world post-training. We introduce Rollback, which pairs replaced policy segments with human corrections to provide positive and negative supervision for CFG-DAgger without value learning or advantage labeling. A unified end-effector interface supports heterogeneous embodi...

• 4.ForPC: Force-Aware Whole-Body Control for Precise Humanoid Cart Pushing

  arXiv:2611.08664v1 Announce Type: new Abstract: Humanoid robots are expected to take over strenuous material handling in manufacturing, logistics, and healthcare, where operating existing wheeled carts would let them move heavy loads without modifying the workplace. However, humanoid cart pushing remains challenging because the bimanual grasp closes a force-coupled loop in which the contact wrenches that drive the cart also disturb the robot's balance, and payload changes alter the coupled dynamics. We propose ForPC, a force-aware whole-body control framework that regulates cart velocity through two-stage reinforcement learning. A locomotion prior is first trained under varied hand loads and upper-body postures to provide balance support. A force latent and a state latent are then inferred from onboard observations, without external force...

• 5.National and State-Level Approaches to Automated Vehicle Certification: A Comparative Framework for State Operational Governance

  arXiv:2611.08681v1 Announce Type: new Abstract: Automated vehicle (AV) deployment in the United States is expanding without a unified federal certification framework, leading to substantial variation in how states authorize and oversee AV testing and deployment. This paper examines how U.S. states regulate Society of Automotive Engineers (SAE) Level 4-5 automated driving systems (ADS), identifies common certification approaches, compares them with international practices, and evaluates the implications for North Carolina's future regulatory strategy. Publicly available state statutes, administrative regulations, agency guidance, and federal policy documents were reviewed. Ten states, selected to capture the range of regulatory approaches, were analyzed using a structured framework covering operational scope, governance and oversight, fede...

• 1.Towards Robust Reinforcement Learning Market Making: Lessons from Loss Function Experimentation

  Market makers play a vital role in ensuring efficient trade within financial markets. Recent literature has delved into training market-making systems using reinforcement learning. This paper delves into the impact of various loss function formulations on risk management behavior within reinforcement-based market making. Through empirical evaluation, we contrast profit-maximizing strategies with risk-averse approaches, finding that penalties for inventory and cash management foster more resilient and adaptable market-making strategies. Our results underscore the significance of striking a balance between profit maximization and risk management in algorithmic trading, providing actionable insights for refining strategies in practical settings.

• 2.Does a Trading Policy Need to Know What It Trades? Microstructure Invariance and Zero-Shot Transfer of Execution Policies

  Execution algorithms are calibrated asset by asset, because the queues, trade rates and bid-ask spreads they face differ by orders of magnitude across assets. For reinforcement learning this is zero-shot generalisation across contexts of very different scales, and microstructure invariance proposes a normalisation fixed by theory: measured in each asset's own units of time, size and price, markets look alike. In an exact order-by-order replay of public Nasdaq data, we train policies once, on 954 stocks in 2019, to choose between waiting in the queue and crossing the spread, and evaluate them unchanged on 1,964 unseen stocks in 2020-21, 2,629 stocks in 2025-26, crypto pairs and a second exchange. First, a policy that sees a stock only through its recent trade rate, trade size, spread and price increment (tick) costs within 0.015 spreads pe...

• 3.Neural Realized Measures: Forecast-Supervised Intraday Aggregation for Volatility Forecasting

  High-frequency volatility forecasting typically separates measurement from prediction: intraday observations are first compressed into a daily realized measure using a fixed aggregation rule, and the resulting statistic is then passed to a forecasting model. This conventional pipeline therefore treats measure construction as forecast-agnostic preprocessing. We ask whether the aggregation stage itself can instead be learned from the downstream forecasting objective while preserving the statistical structure of established realized measures. We propose Neural Realized Measures (NRM), a primitive-preserving, forecast-supervised aggregation framework that retains the original measure-specific intraday contributions but replaces uniform aggregation with positive, mean-normalized context-dependent weights. Uniform weights exactly recover the co...

• 4.Credibility Clustering for Experience Rating: An Automated Approach to Risk Group Allocation in Group Life Insurance

  In insurance, experience rating plays a crucial role in mitigating adverse selection by adjusting premiums according to historical claims experience. A key step in this process is the classification of insured collectives into homogeneous risk groups. Within group life insurance, this classification traditionally relies on manual decisions and arbitrary thresholds, which may lead to inefficiencies and inconsistent outcomes. This paper proposes a data-driven methodology for automating risk group allocation based on a combination of the multidimensional Bühlmann-Straub model and k-means clustering -- referred to as credibility clustering. By incorporating credibility weights, the method accounts for the varying statistical significance of experience data and ensuresthat risk classifications are both actuarially sound and practically impleme...

• 5.Hard to Fool: A Bounded LLM Research Loop for Quantitative Finance, and What Two Dozen Campaigns Found

  Agentic research loops that let a language model propose a change, run a fixed experiment and keep whatever improves the metric work well in machine learning. In trading research the same loop is a machine for producing false discoveries: a backtest that "improves the metric" is, by default, overfit, look-ahead, a multiple-comparisons artifact, cost-free, or market drift wearing a strategy. We describe Autonomous Quant Researcher, an open-source loop built on the opposite premise: the model proposes, and only trusted code decides. Proposals are strict declarative JSON over a fixed vocabulary, so there is no model-written code to sandbox. Evaluation runs on immutable chronological stores split into discovery, validation and a final holdout that can be opened exactly once, after a durable receipt is written. Acceptance is a conjunction of a...

• 1.Khalifa University, GSMA and AMD Collaborate to Bring Radio Frequency Foundation Models to Telecom Operators

  Summary available at source link.

• 2.Enterprises target 207% return on digital transformation spending, but cost and tech integration complexity threaten ambitions

  Summary available at source link.

• 3.MENA Enterprises Plan Major Digital Investment, But Policy Barriers Delay Projects for One in Nine

  Summary available at source link.

• 4.GSMA MWC26 Doha opens with call to align on global industry standards and close MENA’s connectivity gap

  Summary available at source link.

• 5.6G could add $1.24 trillion to global economy by 2040 – but spectrum shortages risk losing $520 billion of benefits, GSMA finds

  Summary available at source link.

• 1.CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse

  Large language models reinvent the wheel all the time. They conflate task-solving with the execution of the solutions, which forces them to solve every problem from scratch. We show that once a task has been reasoned through, the application of its solution to a new problem instance can be done very efficiently and with little involvement from language models. We introduce agentic reasoning caching, a method for constructing robust CPU-executable agentic reasoning caches (ARCs) from LLM reasoning traces. ARCs show on-par performance with frontier LLMs across a range of difficult mathematical and scientific tasks, are fully explainable, continually trainable, robust to input perturbations, and 16-291x cheaper to invoke than the source model.

• 2.GroundWork: Bridging Human and LLM Reasoning Through a Structured Argumentation Model

  Large language models (LLMs) are increasingly integrated into workflows that require reasoning over text documents, yet interaction remains largely conversational. This leaves argumentative structure implicit or embedded in text, making it difficult to maintain, compare, and revise arguments as analyses evolve. We present GroundWork, a mixed-initiative system that makes argument structure a shared, editable state for humans and LLMs. GroundWork operationalizes the Toulmin model of argumentation as a typed graph with explicit operations for human and LLM edits. It exposes this structure through coordinated corpus views and an editable workbench for constructing, comparing, testing, and tracing source-grounded arguments. We evaluate GroundWork through an expert study with four participants spanning consulting, product development, law, and ...

• 3.Assessing the Impact of Retrieval-Augmented Generation on Algorithmic Problem-Solving

  Competitive programming problems pose distinctive challenges for Large Language Models, as they demand not only proficient code generation but also advanced algorithmic reasoning. This study presents a Retrieval-Augmented Generation approach specifically designed to enhance the capacity of Large Language Models to address such problems. Our approach integrates an embedding-based retrieval mechanism and a similarity search algorithm to identify analogous problems within a knowledge database, employing a dynamically adjusted threshold to mitigate retrieval noise. We conducted an evaluation using the CodeContests dataset, focusing on the test split problems that exhibit similarity to instances from the training split. The experimental framework considered three conditions: (i) problems provided solely with their original descriptions, (ii) p...

• 4.Are These Programmers Ripe Enough to Eat? Evaluating How LLMs Handle Lexical Knowledge Conflicts in Translation

  External lexical resources can help large language models (LLMs) translate texts in low-resource languages and specialized domains, but these resources may conflict with models' prior lexical knowledge. To isolate this conflict, we construct a controlled Mandarin-to-English translation task in which familiar source words are assigned new meanings within and across semantic categories. We treat these prompt-supplied meanings as contextual knowledge (CK) and systematically evaluate the effects of word frequency, sentence context, and prompt framing on the translated outputs. Across nine models, CK reliance is greater for less frequent source words and in sentence contexts that support the supplied meanings. Prompt framing can reduce CK reliance, but seven of nine models favor CK even when explicitly instructed to ignore the supplied meaning...

• 5.CID: Measuring Cognitive Work Through Semantic State Change

  Traditional information theory provides powerful tools for analyzing statistical properties of information but offers limited insight into meaning and semantic transformation. It has never been possible to quantify the meaning of an arbitrary piece of information. Large language models represent information as vectors embedded within high-dimensional semantic spaces, enabling meaning to be analyzed geometrically. This paper defines semantic state as the vector representation of information within a model's semantic space and defines cognitive work as the transformation between semantic states, movement through semantic space. To quantify this transformation, we introduce the Contextual Information Differential (CID), a covariance-aware metric based on Mahalanobis distance measuring semantic displacement relative to the structure of the em...

• 1.MemoryDojo: Benchmarking Poisoning Through LLM Agent Memory

  LLM agents benefit from using memory systems to improve performance on long-horizon, multi-session tasks. However, memory introduces a critical attack surface. Malicious instructions encountered in one session may be stored and propagated through memory before being retrieved and acted upon in later sessions. Because memory systems differ in how they process and retrieve information, the downstream effects of poisoning can vary significantly across systems. To quantify the effects of poisoning across different memory architectures, we introduce MemoryDojo, a benchmark for tracing the lifecycle of poisoned information through agent memory and measuring its influence on subsequent agent behavior. MemoryDojo is easily extensible, consists of 155 multi-session long-horizon trajectories covering four domains, and supports five different memory...

• 2.Open-world Reconstruction-based Classifier with Concept Drift Adaptation

  Data stream processing has become central to modern machine learning applications, with concept drifts and novel class appearances posing the primary challenges faced by recognition methods. Adapting to concept drifts while also being able to detect novel classes and distinguish groups of unknown observations can be seen as a step towards effective method employment in an open-world setting. This work proposes a classification method that can detect and learn the distributions of new, unknown classes, as well as adapt to concept drifts once the distribution of known classes shifts. The proposed method uses the autoencoders' reconstruction errors and the KDE that models their distribution. The autoencoders incrementally adapt to the changing distributions of known classes via weight fine-tuning, resulting in continuous adjustment to evolvi...

• 3.Pdb or Not to Be: Comparing Debugging Interfaces With Pass$_{X}$@$k$

  Interactive debuggers are indispensable to human software development, yet coding agents overwhelmingly rely on edit-execute-observe loops, using common bash tools, test scripts, and print statements to debug via repeated shell commands. Stepping through a debugger provides live access to runtime state, which may enable different problem-solving capabilities. However, prior work typically introduces the debugger as an additional tool or through a new subagent, making its individual contribution difficult to isolate from the associated scaffold changes. To address this, we compare a debugger-only agent against a bash-only agent on all 500 SWE-bench Verified instances, holding the rest of the scaffold fixed. We evaluate both architectures on two models, GPT-OSS-120B and Nemotron-3-Ultra-550B, with three independent runs per architecture $\t...

• 4.Failure Prediction and Classification in Vision-Language-Action Models Via Calibrated Latent Probing

  Vision-Language-Action models (VLAs) enable general-purpose embodied intelligence, but their performance is brittle under distribution shift, where unanticipated failures can damage equipment or endanger people. Deploying VLAs in real-world systems therefore requires a runtime monitor that is timely, calibrated, and informative while being efficient enough to run on the edge. Existing methods fail to fulfill these requirements. We present VLA-Tug, a lightweight probe over the latent layers of a VLA backbone that improves early failure detection and bounds the missed-detection and false-alarm rates, with minimal inference overhead. We show that supervising on the pre-failure segment of a trajectory increases early failure detection at matched false-positive rates by up to 30% over the strongest learned baseline. Under shift to unseen tasks...

• 5.Learning a patient world model for health system simulation in intensive care

  Designing healthcare strategies requires anticipating their clinical and operational consequences before implementation, a task that often relies on health system simulation. Such a simulation necessitates modeling how patient states evolve and generate care demands. However, conventional approaches typically tailor compact state transitions to a single policy question and omit history-dependent dynamics, thus requiring the redefinition of states and transitions when there is a new question that entails different clinical information. We propose to learn a patient world model from longitudinal EHRs using a neural network to provide a shared clinical representation of patient trajectories that can serve simulation questions across scenarios. Taking ICU operations as an example, we train the model on MIMIC-IV, freeze it, and couple its time...

• 1.AI Agents Are Testing the Limits of Zero-Trust Security

  This article is brought to you by Queen’s University Belfast . Several teams around the globe are leveraging AI by creating a task force of agents managed by an orchestrator and creating applications to support risk assessment, client profiling, match making, and information yield with a slight overview of decision making. One of the important components for these agents is to ensure that the logs are managed and historic relevance is not being used to avoid skewing towards same decision making. Thus, the system is expected to keep checkpoints in place where agents are prompted to delete such historic relevance. However, despite such methodology in place, a future prompt could make the agents bring forward the information that was supposed to be deleted and can be tricked into revealing more information, which is a classic case of context...

• 2.AI Text Watermarking May Not Be Able to Meet the E.U.’s New Rules

  The European Union now requires AI-generated text to be watermarked so that it can be identified as such. But tests on OpenAI’s new textGrain watermark, released in October and developed to comply with the new E.U. law , show that these watermarks often do not survive additional editing of the text or when it’s under 150 words. With no clear legal benchmark for how well watermarks should work, it’s uncertain whether textGrain will meet Europe’s transparency rules, which could cost the company significantly and make it difficult to detect AI-generated text as models grow more sophisticated. AI models calculate the probabilities of different possible next “tokens” (the next word or word fragment it generates) based on the text already written, then select one and repeat that process until the response is complete. After the prompt “today th...

• 3.How a Kenyan Startup Is Rethinking AI for Small Businesses

  The first time Mutembei Kariuki thought about how AI models could work in Kenya, his home country, was in 2019. He was attending a conference at the University of Tokyo on the future of AI, and kept hearing about how this technology was going to transform the world. “But nobody mentioned Africa at all,” he says. Even then—before AI giants like OpenAI and Anthropic had launched their chatbots to the public—Kariuki could see significant obstacles to AI adoption in Africa. Internet access was slow and unequal, computing resources for training models were scarce, and there was little high-quality, structured training data. Most people, meanwhile, reached the internet through outdated, low-memory cellphones rather than computers or high-end phones. Together with cofounders Jude Mwenda and Stephanie Njerenga , Kariuki created Fastagger , a star...

• 1.End the State Veto on Power Lines

  It’s one of the peculiarities of American regulatory history that a natural gas pipeline can cross state lines with a single federal permit, but oil and electricity must run a gauntlet of state approvals. When a power line crosses one state to reach another, for example, the intervening state often imposes exorbitant tolls or, worse, […]

  The post End the State Veto on Power Lines appeared first on Marginal REVOLUTION.

• 2.The economics of honeypots

  Of course as an economist I am always wondering what the equilibrium is.  And, in this case, what is the scarce factor? It seems that in the case under current consideration, the returns to honeypotting are very high for somebody.  Maybe for China, maybe for the shorts, or maybe for parts of the Deep State […]

  The post The economics of honeypots appeared first on Marginal REVOLUTION.

• 3.Seb Krier, Ricardian?

  Here is the initial post. In the comments Michael Wiebe notes: “So progress per R&D dollar fell by 63%, even while progress per year doubled.”  File under: David Ricardo, underrated.

  The post Seb Krier, Ricardian? appeared first on Marginal REVOLUTION.

• 4.The Paraguayan bank story

  Despite its weaknesses, Paraguay’s young democracy was still able to deliver under stress. Its free, boisterous press was the first to sound the alarm about Peña’s shenanigans. The Central Bank’s supervision chief did his job: he examined ueno’s numbers and recommended blocking the sale of its bad loans to a related company (Peña had appointed […]

  The post The Paraguayan bank story appeared first on Marginal REVOLUTION.

• 1.Has U.S. Monetary Policy Really Improved? A 150‑Year Perspective

  Has U.S. monetary policy actually improved over time? Or was the apparent success of the modern Federal Reserve mostly the result of more favorable economic conditions and better luck? In this post, we summarize our paper,^* which develops a framework for comparing monetary policy across different economic environments. Our approach asks how close policy came to the best response given the economic conditions and shocks policymakers faced. Applying this framework to 150 years of U.S. monetary history, we find that meaningful improvements in monetary policy occurred only in the last thirty years. Most of the gains reflect, among other developments, substantially better responses to financial shocks and ...

• 2.The U.S. Oil Sector’s Role in Restraining Prices

  The start of the Middle East conflict in early 2026 caused an unprecedented drop in global petroleum supply. In response, oil prices jumped—but not to unprecedentedly high levels. The U.S. oil sector was a key factor in moderating the shock. For one, its steady increases in drilling helped create a large oversupply in the global market before the conflict, making it less of a challenge to offset the drop in Middle East oil output. In addition, U.S. commercial and government inventories were rapidly drawn down, filling half the gap between global supply and demand in the second quarter by some estimates. Inventories, though, are a limited resource and U.S. stockpiles stopped falling in July. As a consequence, the continued shortfall in global production has been managed by running down inventories in the rest of the world.

• 3.How Market Position Shapes Credit Card Interest Rates

  Why is it that two borrowers who look much alike can be offered very different rates when applying for a credit card? In a recent paper, we address this question by examining how the rate offered changes when the lender’s funding cost falls. Data on 14 million newly issued cards show that when a lender's funding costs decline, the lender passes less of that decline on to the offer rate when it holds a large market share. That is, two borrowers with similar credit scores and incomes borrowing from the same lender can see noticeably different interest rates on the credit card they are applying for.

• 1.Why Trump Crowned the Man Who Cost Him Congress

  By appointing Stephen Miller to be his chief of staff, US President Donald Trump has revealed what he cannot give up after decisive midterm losses. The moral depravity of one of the most widely reviled members of his administration is the citadel he will defend until the end.

• 2.Why Politicians Love Stimulus Checks

  Stimulus checks sent to households promise to supercharge the economy, but much of the money simply sits in people's bank accounts. They are popular with voters but do little to address the underlying causes of economic downturns and leave future generations to foot the bill.

• 3.Who’s Winning (and Who’s Losing) the Ocean? — Marie-May Jeremie

  Summary available at source link.

• 4.The Midterms Won’t Change Much for US Allies

  Even if the Democrats’ midterm victory marks the beginning of the end of the Trump administration’s “America First” approach to foreign affairs, it does not augur the return of a consistent, reliable US foreign policy. America's friends and allies around the world will have to continue to hedge their bets.

• 5.Revolts of the Young

  Young protesters have taken to the streets in France, India, Serbia, and several other countries, rising up against corrupt mainstream parties and economic inequality. But their efforts to improve liberal democracy will likely be overshadowed by the far right’s far more organized efforts to seize power.

• 1.American Tower says satellites still need terrestrial infrastructure

  Steve Vondran, chief executive officer of American Tower, recently told investors that the company views satellite and terrestrial wireless infrastructure as complementary rather than competing infrastructure models In sum –…

• 2.SK Telecom targets December launch for 5G SA as mobile business recovers

  For 6G, SK Telecom said it is participating in technology research and development and standardization activities, while monitoring major technology developments and global trends In sum – what to know…

• 3.Deutsche Telekom maps out its next wireless moves

  The U.S. segment generated €20.5 billion in revenue for Deutsche Telekom in Q3, up 9.1% year-on-year, while revenue in Europe increased 4.2% to €3.3 billion In sum – what to…

• 4.Seamless, not identical: what NTN-native 6G will feel like

  One service experience by the early 2030s — and what still won’t be identical In sum – what to know: Most direct-to-device services live today share a structural limitation: the radio was standardized,…

• 5.Neutral host networks and the economics of shared infrastructure (Reader Forum)

  Neutral host networks could cut the cost and complexity of indoor mobile coverage by replacing duplicated operator infrastructure with shared systems. But commercial alignment, investment models and regulatory differences remain…

• 1.DeepRT-Cal: Learning to Calibrate Ray-Traced Twin Channel Statistics with Low-Fidelity Environmental Geometry

  Sixth-generation (6G) wireless systems increasingly rely on digital twin channels (DTCs) to represent and interact with physical radio environments. Ray tracing (RT) is naturally suited to DTC construction because it provides deterministic and physically interpretable channel predictions. However, high-fidelity three-dimensional environment models are expensive to obtain, whereas geometric inaccuracies in low-fidelity models can alter the detected multipath components (MPCs) and consequently distort received-power and delay-domain twin channel statistics. Building on the DeepRT ray-tracing framework, this paper proposes DeepRT-Cal, a geometry-aware cross-fidelity calibration framework that predicts high-fidelity twin channel statistics from low-fidelity geometry and RT features.The network encodes global link descriptors, masked low-fidel...

• 2.VLA Handover: Failure-Prediction-Based Switching from On-Robot to Server VLA Models

  Large vision-language-action (VLA) models generalize well but are difficult to run in real time on a robot, and small VLAs that run there fail more often. Remote inference over beyond-5G and 6G networks lets a robot use a large VLA on a server, but querying the server for every action chunk limits the number of robots per server. We propose VLA handover: the robot runs a small VLA (S-VLA) and switches to a large VLA (L-VLA) on a server only when the S-VLA is likely to fail. We formulate the switching as minimizing the request cost under a required success probability and, since this problem cannot be solved directly, propose a heuristic framework that retrains neither VLA. In simulation with SmolVLA and $π_{0.5}$ on three of the four LIBERO suites, VLA handover raised the success rate by 11-22 percentage points over the S-VLA alone and se...

• 3.Age of Information for Power-Saving Devices with DRX Mechanism

  Reducing the power consumption of User Equipment (UE) has always been a crucial issue in communication systems. As a result, Discontinuous reception (DRX) is introduced in today's New Radio (NR) networks as a power-saving strategy. In addition, the variety of low latency applications in 6G makes the issue of maintaining data freshness increasingly important. This paper aims to study the relationship between freshness and power-saving. To capture the information freshness, we use Age of Information (AoI) as the performance metric. To our best knowledge, unlike previous studies focusing on power saving and delay trade-offs, this is the first study considering the impact of DRX on AoI. We solve the closed-form solution of the peak average AoI with the DRX mechanism. Moreover, we propose a parameters adjustment strategy. Based on the simulati...

• 4.Stochastic High-Resolution Parameter-Based Channel Modeling for Sub-Terahertz Outdoor Device-to-Device Propagation at 145 GHz

  Since device-to-device (D2D) systems operating in the sub-THz regime are of interest for a variety of 6G applications, modeling their propagation channels is therefore essential. This paper presents high-resolution parameter estimation (HRPE) of specular multipath components (MPCs) as well as diffuse multipath components (DMCs) from an extensive measurement campaign. The extracted MPCs and DMCs are then clustered, and statistical models for both intra- and inter-cluster statistics are established. Among other insights, we find a large number (typically 10-50) of clusters; both the powers and densities of MPCs within each cluster show significant variations. DMC occurs both when associated with discrete MPCs and standalone. The paper provides a complete model that can generate double-directional impulse responses, and a MATLAB program impl...

• 5.Stochastic AI Model Placement for AI-and-RAN Coexistence in 6G Networks

  This paper considers AI-service placement over geographically distributed RAN sites equipped with GPU resources partitioned into fixed, isolated multi-instance GPU (MIG) profiles. We propose C$^2$AIR, a chance-constrained optimization model that maximizes expected AI requests served while protecting uncertain RAN workloads and enforcing probabilistic capacity reliability requirements for admitted AI services. C$^2$AIR allows a service to run at multiple sites and accounts for discrete profile, memory, and GPU-slice constraints. We evaluate C$^2$AIR on a four-site deployment calibrated from NVIDIA Aerial RAN capacity figures and MLPerf Inference results, and on seven larger scenarios with up to 15 sites and 40 AI services, computing every violation and overload probability exactly from the demand distributions. C$^2$AIR keeps the RAN viola...

• 1.The Logic of Factor Portfolios: Boolean Rules, Interactions, and Trading Costs

  Investors turn signals into holdings through decision rules: buy the top decile, hold stocks with high momentum and low volatility, exclude stocks that fail a screen. Many such rules are Boolean functions of threshold statements about signal ranks, and writing them this way shows both what a rule holds and how often it trades. A Walsh decomposition splits the return of any rule into exposures to individual signals and to interactions among signals; an intersection and a union of the same two screens, for example, have opposite interaction exposures. The same coefficients, together with the persistence of the underlying statements, determine turnover. Interactions are less persistent than their components, so rules that load on higher-order interactions trade more, and thresholds discard information, so at equal risk threshold implementati...

• 2.The End-of-Day Reversal After 2019: A Pre-Registered Test on S&P 500 Stocks Excluding Heavily Traded Names

  Baltussen, Da and Soebhag (2025) document an end-of-day reversal in U.S. stocks through December 2019: intraday losers outperform intraday winners over the last half hour of trading. We test it on later data, on point-in-time S&P 500 members excluding about 80 heavily traded stocks we had examined in earlier, unpublished work. Under a private pre-registration, the 2016-2019 data had to reproduce the published sign before any statistic linking the sort to the outcome was computed on later primary-sample data. Measured to the last trade before the closing auction, the dollar-volume-weighted reversal is 3.13 bps per day (t = 7.5) in 2016-2019 and 3.05 bps (t = 3.0) in 2023-2026, with a one-sided 95% bound on the decline of 61%. The result depends on the window: the reversal is indistinguishable from zero in 2020-2022, 2023 carries much o...

• 4.A general technique for explicit quantile representations

  This work proves existence of contours independent of probability level for exact quantile integrals of strictly positive real-analytic densities. Explicit constructions cover five broad distribution families and yield a finite-interval representation of Black-Scholes implied volatility using forward functions.

• 5.Price-Volume Correlations Are Always Zero

  We argue that the use of the empirical time series of values, volumes, and prices of consecutive trades made during the averaging interval to calculate the price-volume correlation in a way that follows the definition of correlation always gives zero correlation. We show that the usual expression for the empirical calculation of the correlation misleads the researchers and doesn't fulfill the definition of price-volume correlation. The mistake has its origin in the dependence of volume weighted average price on the mean values and volumes of trades during the averaging interval but not on time series of prices only. The true sense of the declared positive or negative results of the empirical calculations of price-volume correlations made by numerous researchers in reality is determined by the difference between the volume weighted average...

• 4.Characterizing AI Service Traffic for 6G: An Open Testbed and Initial Results from 3GPP SA4

  The design of sixth generation (6G) mobile systems requires measurements of artificial intelligence (AI) service traffic under controlled network conditions. The 3rd Generation Partnership Project (3GPP) Service and System Aspects Working Group 4 (SA4) documents such measurements in draft Technical Report 26.870. In this paper, we describe the open testbed used for the study and analyze the results recorded in version 0.6.2 and its companion workbook. The testbed combines network emulation, packet capture, application measurements, and scenario execution against hosted and self-hosted models. The reported uplink to downlink volume ratios span more than six orders of magnitude. A personal assistant agent has a ratio of 0.007 at its local control interface and 0.50 on its wide-area egress. Deterministic agent signaling follows the configure...

• 5.Energy-efficient End-to-End Communication System with Spiking Neural Networks

  2030 is expected to witness the first commercial 6G systems. However, traditional modular communication architectures cannot meet the latency, optimisation, and integration requirements demanded by various sub-millisecond, data-intensive emerging 6G applications. End-to-end (E2E) communication addresses these limitations by replacing traditional processing blocks with neural networks. Nevertheless, conventional artificial neural networks (ANNs) in E2E systems use dense computation. This results in substantial energy consumption and computational resource requirements, which may not be feasible in resource constrained devices. This paper addresses the aforementioned challenge by developing a spiking neural network (SNN) encoder that targets lower encoder inference energy while maintaining communication reliability. In particular, the SNN e...