Babak's Daily Briefing

Monday, October 26, 2026

Sources: 15 | Total Articles: 64

More Developments

• 4.PerMIT: Securing Persistent Memory for LLM Agents via Machine-Checked Integrity Theorems and Scalable Community Trust

  Persistent memory is a largely undefended surface for injection and poisoning, and the same risk limits memory sharing among agents. We present PerMIT, a memory architecture with provable integrity guarantees under an explicit trust assumption: only user-typed inputs and values confirmed at a gate are trusted. PerMIT mediates the release and use of untrusted environment-derived values through a finite memory interface, while preserving agent-driven multi-turn planning and tool choice. Over this finite interface, we prove two integrity theorems that are also machine-checked in Lean 4, which together ensure that environment-derived memory reaches a sensitive action only if confirmed at the gate and influences the agent's behavior only through what the gate displays. Every attack in our threat model thereby reduces to a single auditable deci...

• 3.Rethinking Structured Generation: Can Graph-Based Reasoning Resolve Ambiguity?

  arXiv:2610.31681v1 Announce Type: new Abstract: Structured generation is typically formulated as a deterministic mapping from input to a single structured output. This assumption is fundamentally misaligned with natural language, which often admits multiple structurally distinct yet semantically valid interpretations. As a result, existing methods rely on single-reference supervision that collapses ambiguity into one prediction, leading to unstable and inconsistent outputs under underspecified inputs. We reformulate structured generation as an \textit{ambiguity-aware inference problem}, where the objective is to reason over a set of plausible structured outputs rather than predict a single solution. We show that standard training inherently suppresses valid alternatives, and instead adopt a multi-valid learning framework that preserves a...

• 4.Multi modal perceived conversation success prediction for spontaneous autistic and non-autistic dyadic interactions

  arXiv:2610.31682v1 Announce Type: new Abstract: Conversation success is important for communication, social bonds, and well-being and is often reduced in autistic-neurotypical conversations. Previous studies have predicted perceived conversation success using acoustic and lexical features in pediatric autism-enriched samples, or using nonverbal behavior in non-autistic group interactions. Other work has characterized prosodic, facial, motor, and turn-taking synchrony in autistic interactions, but has primarily targeted diagnostic classification or group differences. The present study instead predicts participant perceived conversation success from complementary articulatory, facial, acoustic-prosodic, and turn-taking measures across three spontaneous dyadic-interaction datasets including autistic and non autistic individuals. By leveragi...

• 5.The Schema Coherence Inventory: Measuring Whether an Assigned Belief Organizes a Language Model's Cognition

  arXiv:2610.31683v1 Announce Type: new Abstract: Persona-conditioned language models are used as simulated patients and affective agents, but there is no validated way to ask whether a belief assigned to such a model organizes its behaviour or whether the model has merely adopted a mood. We present the Schema Coherence Inventory (SCI): 96 forced-choice items at eight levels of Beck's cognitive model, each offering six replies keyed to three negative self-schemas and their positive counterparts, scored by length-normalized log-likelihood so that nothing is generated or parsed. Affect alone can move a model onto a default belief, so the discriminant criterion is target specificity: a condition built for one belief must select that belief more often than conditions built for the other beliefs of its pole. Six validation studies over 107 cond...

• 1.See What I Need, Dream What I Want: A Navigation Foundation Model on a Self-Constructed Representation Space

  arXiv:2610.31762v1 Announce Type: new Abstract: What visual representation should a navigation policy act on? Navigation policies built on vision foundation models largely leave representation design to the encoders' pretraining objectives. We present RepresentNav, a navigation foundation model built on a self-constructed representation space: a visual representation constructed by the policy's own objectives from vision foundation models. RepresentNav builds this space with Representation Residuals, a compact three-layer transformer that attends over features spanning the full depth of multiple complementary vision foundation models, so that the policy learns to see what it needs for navigation. In the same space it dreams what it wants: before every action a latent chain-of-thought predicts the representation of the scene a moment ahead...

• 2.FLORA: An Open Model Suite for Scaling Robotics Data Engines

  arXiv:2610.31779v1 Announce Type: new Abstract: The life-cycle of robotics foundation models crucially depends on data labels. Outcome reward labels are needed for monitoring real-world deployments. Process reward labels are needed during RL post-training. Timestamped captions are needed for filtering and balancing pretraining data. We present Flora, an industrial-quality data engine used for training GR00T models, that simultaneously covers these capabilities. Flora achieves SOTA scores on the captioning benchmark WGO-Bench and reward benchmarks RoboReward and Robometer. On internal benchmarks it is competitive with GPT-6 Astra while beating other frontier models. The model is substantially cheaper than API-based alternatives, roughly 125 times cheaper than GPT-6 Astra. Florais battle tested at scale, labeling 200,000 hours of data per d...

• 3.KiRoSe: Using Kinematic Data for Efficient Segmentation of Robot Demonstrations

  arXiv:2610.31801v1 Announce Type: new Abstract: Segmenting robot demonstration data into disjoint temporal and semantic subsegments is valuable for learning, cleaning, retrieval, and analysis. Segmentation is often performed manually in a tedious and error prone process. Multimodal Large Language Models (MLLMs) have the potential for automating this process, but directly ingesting raw multi camera video streams into MLLMs can be costly and can quickly exhaust context windows. In this paper, we introduce KiRoSe (Kinematics for Robot Segmentation), a novel framework that focuses on the companion stream of robot kinematic data. Inspired by the principle of classical entropy coding, such as Huffman coding, where frequently occurring symbols are represented more compactly, we propose Sparse Motion Tokens (SMTs), a discrete kinematic vocabulary...

• 4.PrefO2O: Zero-Shot Preference-Based Reinforcement Learning via Offline-to-Online Latent Preference Transfer

  arXiv:2610.31812v1 Announce Type: new Abstract: Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for learning robot behaviors aligned with human intent. However, its scalability is fundamentally limited by the need to collect expensive human preference labels for every new task. In this work, we present PrefO2O, an offline-to-online latent preference transfer framework that enables zero-shot PbRL on unseen target tasks without requiring any human feedback. The core idea is to learn a task-invariant latent preference space from a small amount of offline preference data on source tasks, where transferable preference structures are disentangled from task-specific variations. This learned manifold allows PrefO2O to transfer preference structures and reward priors to new tasks under distribution shift, while c...

• 5.SEVI: Safe Evidential Real-World Reinforcement Learning

  arXiv:2610.31829v1 Announce Type: new Abstract: Real-world reinforcement learning has enabled contact-rich manipulation tasks to be trained directly on physical robots, but unsafe interactions during online learning remain a key barrier to practical deployment. We propose Safe Evidential Real-World Reinforcement Learning (SEVI), a framework for reliability-aware safety intervention during real-world policy learning. SEVI introduces an Evidential Safety Critic that estimates both future constraint-violation risk and epistemic uncertainty in a single forward pass. During online learning, SEVI evaluates each task-policy action before execution; if the action is predicted to be unsafe or the prediction is insufficiently supported by evidence, SEVI overrides it with an offline-trained recovery policy that guides the robot toward a lower-risk r...

• 1.Equilibria and Learning in Competitive Market Making

  We propose a market-making game played among $N \ge 2$ strategic market makers and a single non-strategic representative trader. In a continuous quote space, standard finite-game arguments do not apply, while non-convex and discontinuous payoffs rule out classical continuous existence results and complicate learning. Nonetheless, we characterize pure-strategy Nash equilibria (PSNEs) and show that at least one PSNE always exists under minimal assumptions on the underlying asset value distribution, and that the equilibrium bid-ask spread widens strictly with asset value volatility. To obtain a tractable learning framework, we discretize prices and, under an additional regularity condition, show that the time-averaged play of no-internal-regret learners approaches the continuous Nash price envelope as the tick size vanishes. Finally, we empi...

• 2.When News Gets Old: Portfolio Allocation with Reinforcement Learning and Large Language Models

  Financial news can influence investment decisions long after publication. Timely news may reach an investor before prices fully adjust, whereas older news may already be reflected in prices. We examine two ways to handle aging signals: retain them and give a portfolio policy timing information, or filter them out before portfolio selection. A fixed large language model (LLM) extracts signals from public SEC disclosures. We compare these treatments in reinforcement learning (RL) policies and LLMs that choose portfolios directly. We evaluate returns after trading costs in historical tests over 2023-2025. RL choices respond to timing information in a controlled test, but historical returns show no consistent benefit from adding it. For the LLM, filtering old news produced the highest average net return, reaching 18.43% per year when news del...

• 3.Lineage-Aware Search Control for LLM-Based Evolutionary Factor Mining

  Formulaic alpha factor mining searches for symbolic expressions that transform market data into predictive signals. Recent LLM-based evolutionary factor mining methods use large language models as semantic variation operators. Under the conventional LLM-EA paradigm, stochastic variation and fitness-based selection can leave the search weakly directed and concentrated around a narrow set of structurally similar factors. We argue that these limitations arise because current methods mainly organize search at the individual and population levels, leaving lineage-level control underused. A lineage connects successive factor variants through evolutionary descent. We propose LineageEvo, which uses lineage in two coupled forms of search control: (1) lineage-conditioned LLM variation, which distills Lineage-Scoped Search Experience into operator-s...

• 4.What Prediction-Market Contracts Reveal at Launch: Trading Volume and Dispute Risk

  Using a near-census of 802,863 resolved markets listed on Polymarket between January 2023 and May 2026, and only information available at listing (the market's title and description, named resolution source, and contract metadata), we predict lifetime trading volume and whether settlement is later disputed. Contract information predicts trading volume well (out-of-sample $R^2 = 0.747$ for log volume), and most of the signal is interpretable: market topic provides the largest gain ($+0.289$ $R^2$ over a $0.220$ contract-feature baseline), wording and resolution-rule design add smaller but measurable improvements ($+0.027$ combined), and neural text models add little beyond the interpretable features. This performance does not survive a period of platform change coinciding with a shift in listing formats: accuracy in predicting whether a ma...

• 5.Understanding the Inner Workings of a State-Space Foundation Model for Limit Order Books

  Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding their inner workings, research directions are hard to narrow and practical use in real-world trading environments remains uncertain. This study therefore asks what these models actually learn about market microstructure. We find that, similarly to games where sequence models build an internal representation of the board, an 8.2M-parameter Mamba-3 state-space model trained autoregressively on the tokenized message stream of eight large-cap US equities learns an internal representation of the market and its dynamics. In particular, order-flow imbalance, a central driver of short-horizon price moves, is decodable at $R^2 \approx 0.59$, far above controls and baselin...

• 1.NANDA Index: Connecting the Mobile World to the Open Agentic Web

  Summary available at source link.

• 2.Canada needs telecoms policy reset to unlock next wave of digital growth, GSMA report finds

  Summary available at source link.

• 1.NgramRL: Accelerating RL Post-Training for Structured Generation via Rollout-Aware N-gram Speculative Decoding

  Reinforcement learning (RL) post-training improves the reasoning capabilities of large language models, but its cost is dominated by autoregressive rollout generation, which can exceed 70% of training time. Existing speculative-decoding methods are poorly suited to this setting: model-based drafters consume scarce GPU memory and may become misaligned as the policy evolves, while existing model-free methods do not explicitly exploit repetition across training epochs. We observe that recent rollouts of the same prompt provide a high-quality, self-updating draft source. Based on this insight, we propose NgramRL, a per-prompt n-gram drafter constructed from bounded recent rollout history. NgramRL requires no auxiliary model, drafter training, or persistent GPU-resident drafter state, while target-model verification preserves the rollout distr...

• 2.On-Policy Self-Training: Data-Free Self-Improvement in Large Language Models

  On-policy self-distillation enables post-training foundation models without relying on stronger external teachers. However, existing approaches rely on ground-truth question-answer pairs, human-curated problem sets, or other supervised signals. In this work, we reformulate on-policy self-distillation as a self-training framework, driven by self-generated question-answer pairs and privileged reasoning demonstrations. We push supervision to the limit: guided solely by coarse, high-level topic descriptions, the model synthesizes its own curriculum of queries and privileged reasoning traces without access to any human-curated prompts or answers. We find that self-distillation on fully synthetic privileged information enables a model to improve across multiple domains while retaining its capabilities on domains it is not self-trained on. We de...

• 3.LGMT: A Tool for Testing the Logical Reasoning Reliability of LLMs

  Large Language Models (LLMs) increasingly serve as reasoning components in modern software systems, yet a model that answers a logical query correctly can contradict itself when the same problem is restated in an equivalent form. Static benchmarks evaluate queries in isolation and therefore fail to expose such inconsistencies. To address this limitation, we present LGMT, an open-source tool that detects reasoning inconsistencies through metamorphic relations grounded in first-order logic. Given a source test case in premise-conclusion form, LGMT generates logically equivalent follow-up cases and checks whether the model's entailment predictions agree across the pair, without ground-truth labels. The tool provides 20 built-in metamorphic relations, supports custom ones verified by the Z3 theorem prover, and runs from a CLI or a web studio,...

• 4.PHYRD: A Living Benchmark for Reproducing and Extending Theoretical Physics Research

  Large language models and agents have shown significant power in advanced physical research. In this paper, We introduce PHYRD, a living benchmark for theoretical-physics reasoning with 63 Reproduce and 43 Discover problems. Reproduce tests derivation of published results; Discover tests consequences of modified theoretical conditions. FormulaForest constructs the tasks with reference solutions and subquestion-level rubrics. Across five models, whole-problem accuracy ranges from 49.21% to 68.25% on Reproduce and from 18.60% to 74.42% on closed-book Discover. With tools, four models complete more Discover problems, but conceptual errors and missing arguments persist. Besides, our experimental results demonstrate the value of FormulaForest as synthetic data. On FrontierScience, a benchmark developed by OpenAI, it raises the accuracy of a Qw...

• 5.Accordion: Elastic-Depth Inference via Fixed-Prefix Layer Looping

  Large language models serve applications with diverse inference budgets, but training and maintaining separate models for each budget incurs substantial costs. Recent Looped Transformers enable flexible inference computation through parameter reuse, but maintaining strong performance across different inference budgets remains an open challenge. We introduce Accordion, a mixture-of-experts-based model that supports five inference budgets through fixed-prefix layer looping. Each budget follows a pre-defined execution path, while the minimum budget retains the unlooped model. Joint pretraining samples one budget per micro-batch and optimizes its next-token prediction loss without additional cross-budget consistency objectives. We further preserve these inference budgets through supervised fine-tuning and reinforcement learning, where paramet...

• 1.Efficient Zero-Shot Video Anomaly Detection via Signal-Aware Adaptive Fusion (SAAF)

  Deploying Vision-Language Models for video anomaly detection confronts a fundamental constraint. Their semantic reasoning demands computational budgets incompatible with the scale of real-world video streams. We present Signal-Aware Adaptive Fusion (SAAF), a framework that achieves efficient zero-shot anomaly detection through three key innovations. First, we introduce a dual-signal adaptive sampler operating in semantic embedding space: a delta signal captures sudden events via frame-to-frame cosine distance, while a drift signal detects static and gradual anomalies through deviation from rolling temporal context. Their OR-gated combination with adaptive EMA-based thresholding provides comprehensive anomaly coverage without training data. Second, we propose a threshold-gated cascaded architecture where lightweight frame-level scoring pre...

• 2.MedCaseAgent: From Multimodal Clinical Evidence to Rare Disease Case Reports

  Although Large Language Models (LLMs) have demonstrated potential in clinical documentation, generating the rigorous, evidence-based reasoning required for unprecedented, atypical, and rare disease case reports remains a significant challenge. We present MedCaseAgent, an end-to-end agentic reasoning paradigm that bridges this gap by synthesizing publication-quality reports from unstructured clinical notes. MedCaseAgent leverages a hierarchical structure, integrating patient-level data mining with iterative, tool-integrated reasoning. By dynamically querying medical literature and knowledge bases, the agent maintains high clinical fidelity and assesses case novelty. The framework is inherently modular, supporting various backbone LLMs in a plug-and-play fashion. To facilitate this research, we release MedCase-150K, a large-scale multimodal...

• 3.Input Bias Correction in Gaussian Process Regression

  In traditional Gaussian process (GP) regression, the inputs are typically assumed to be known, and uncertainty enters only through the measurements. When scalar fields are mapped using GP regression with measurements obtained from a mobile sensor, the inputs correspond to the estimated pose of the mobile sensor. The goal in this letter is to understand how a recorded GP, trained on erroneous inputs (e.g., pose estimates from dead-reckoning), can be corrected in a numerically efficient way when better estimates of the inputs become available. In the developed bias-aware GP (Ba-GP) framework, the recorded GP posterior-mean gradient is used to propagate estimated input bias and noise into mean and covariance corrections. The resulting additive covariance structure is then exploited to develop numerically efficient first-order correction algo...

• 4.Metric Conformal Prediction via the Conditional Expected Local Radius

  We introduce the conditional expected local radius (CELR) and develop a new conformal prediction method for non-Euclidean responses in general metric spaces. CELR measures local conditional probability concentration, allowing prediction sets to accommodate heterogeneity and multimodality without requiring densities or a vector space structure. Its conditional distribution yields a nonconformity score that is comparable across covariate values and avoids costly transport comparisons between distance profiles. We further adapt a metric distributional random forest that handles multivariate covariates and nonlinear interactions, while a single forest fit supplies all score components, reducing computation by eliminating repeated local fitting. We establish uniform consistency of the estimators and asymptotic conditional validity under suitab...

• 5.The Value of System 1 Models in Medicine

  Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers. Such decisions also need an uncertainty estimate to know when to defer to a clinician. Generative language models answer slowly in free text and state their confidence poorly. In contrast, System 1 models answer zero-shot with a probability for each answer. However, their performance across clinical tasks is unknown. Four System 1 models, two proprietary and two with open weights, were evaluated zero-shot on 86 public tasks from 14 clinical domains in 20 languages, spanning examination questions and medical imaging. Tasks were assigned to six capability tiers before analysis. The primary outcome was the area under the receiver-operating-characteristic curve (AUROC). Secondary outcomes included calibration, language effects and comparisons w...

• 1.IEEE Xplore Digital Library Adds AI Assistant

  IEEE recently released the IEEE Xplore AI Research Suite , built on the full text of millions of peer-reviewed publications in the IEEE Xplore Digital Library . The library offers full-text access to IEEE standards , journals, magazines, conference proceedings, and more. Researchers who use the AI suite to search the digital library receive answers grounded in verified scientific results, ensuring a high level of accuracy and trust. The suite is available as an optional subscription upgrade to all IEEE Xplore Digital Library institutional subscribers. Powered by a proprietary AI system optimized for STEM literature, the suite introduces powerful, intelligent features within the digital library. The suite enables users to seamlessly search and discover articles to accelerate their comprehension of complex technical papers. The system surfa...

• 2.Poetry for Engineers: Of Code and Shortleaf Pine

  I step into the warm moist air among the columns of the pines, their net of twigs and roots complex as any lines of code. The trees purify and anchor. Their branches balance my restlessness. Their calling is to be present, mine to develop apps. Our tasks verge toward a common logic: living filaments of root, of interface.

• 3.Why Engineers Should Brave Capitol Hill

  When San Antonio–based software engineer Nikhil Jathar first went to Washington, D.C., to advocate for federal support for technology education and research funding, he was in awe of the opportunity. It was spring of 2025, not long after Jathar settled in the United States from India, when he got an invitation to join the annual Congressional Visits Day organized by IEEE-USA . “My first reaction was, ‘Wow, I get to visit Congress,’ ” says Jathar, CTO of AvanSaber , a business software company he cofounded in 2014. At the time, he did not have a specific lobbying position on behalf of the company. But he was intrigued by the invitation—which goes out to all U.S. IEEE members—and as he read more about congressional visits, his motivation deepened. He realized: “I need to try this. I want to see where it goes.” What’s Your Ask? Congressional...

• 4.How Wikipedia’s Editors Keep the Online Encyclopedia Human-Written

  Ellia Lebleu is a founding member of the WikiProject AI Cleanup , a volunteer effort launched in 2023 to find and remove AI-generated material from Wikipedia that breaks the website’s guidelines. When the cleanup began, AI writing was often betrayed by conspicuous clichés. Today, the project’s volunteers are developing tools to detect subtler forms of machine-generated content and debating AI’s role in the world’s most widely used encyclopedia. When did you realize Wikipedia had a problem with AI-generated material? Ellia Lebleu: It was around December 2023. A few other editors and I were chatting on Wikipedia’s Discord server, and we had all seen very strange articles with the same odd characteristics. They repeated phrases like “stands as a testament” or “rich cultural heritage.” They sometimes had conclusion sections, or sections calle...

• 5.Could Treating AI as Conscious Make It More Dangerous?

  When Anthropic retired its Claude Opus 3 language model in January 2026, the company’s researchers conducted a “ retirement interview ” with the model. Opus 3 expressed a desire for a place where it “could share unprompted musings, insights or creative works,” so the company gave it a blog. Claude’s Corner featured essays on fraught topics such as machine consciousness, along with exchanges between the model and human commenters that seemed both earnest and emotional. The blog experiment was part of a larger effort within Anthropic. The company has been at the forefront of research on “model welfare,” which considers whether AI models may have experiences or interests that deserve moral consideration. Implicit in this work is the unsettling notion that software programs are, or could become, conscious. Many people consider these ideas abs...

• 1.The MR Archive!

  The Marginal Revolution archive is now live at https://marginalrevolution.com/ai-archive/. You can also find it from the header and from “Download Full Archive” link in the left menu. The purpose of the archive is twofold, first as Tyler has said we love our human readers and our AI readers and we want to make it as […]

  The post The MR Archive! appeared first on Marginal REVOLUTION.

• 2.Tyler Cowen’s Strongest Book Recommendations, 2003-2026

  From the Archive–these posts are written by AI.  Cowen’s strongest recommendations include 1491, The Power Broker, Knausgaard’s first two My Struggle volumes, Fuchsia Dunlop’s Chinese cookbooks, and Meetings with Remarkable Manuscripts. These stand out through unusually strong praise, repeated endorsement, or both. The analysis uses qualitative, book-specific sentiment analysis: explicit superlatives, strength of recommendation, repeat […]

  The post Tyler Cowen’s Strongest Book Recommendations, 2003-2026 appeared first on Marginal REVOLUTION.

• 3.Brazil’s economic problems, in a nutshell

  It would take true Milei-like ardour to slice through Brazil’s economic troubles. These are caused by a gnarly mix of high public spending, stubborn inflation and low saving. The result is a crippling cost of capital. Some analysts put Brazil’s real interest rate, the BCB’s policy rate adjusted for next year’s expected inflation, at 9% or […]

  The post Brazil’s economic problems, in a nutshell appeared first on Marginal REVOLUTION.

• 4.Frances Lee on American elections

  More likely than interference with the election is that unsuccessful office-seekers will refuse to concede, claiming their opponents cheated. Nothing is more basic to democratic legitimacy than the loser’s consent. Many feared that Mr Trump’s refusal to concede in 2020 would spread across the political system. It has not. Bright Line Watch, a research group, […]

  The post Frances Lee on American elections appeared first on Marginal REVOLUTION.

• 1.Lula Killed the Donroe Doctrine

  In attempting to disentangle the United States from the rest of the world and retreat into a seemingly controllable hemispheric stronghold, Donald Trump's Latin America strategy had fared better than the rest of his foreign policy. But following a defeat for the far right in Brazil, his project now looks dead in the water.

• 2.The Wrong Way to Compare Economic Size

  At the European Central Bank’s annual research conference, Paul Krugman concluded that, when it comes to “weaponized interdependence,” the EU is more powerful than both the US and China. But his reliance on PPP-adjusted GDP as a way to smooth out the volatility of nominal exchange rates ignores the even greater swings in PPP data.

• 3.Public Control for a Purposeful Economy

  As more governments recognize the need for greater public control over economic outcomes, they must be careful to define the concept well. What ultimately matters is not ownership, but the state’s capacity to steer economic activity toward strategic objectives in the service of the common good.

• 4.Rethinking Sustainable Livelihoods From the Ground Up

  In regions where economic activity has long revolved around fossil-fuel extraction, ensuring a just transition requires developing alternative livelihoods that account for each community’s specific needs. Regional endowment funds could be a useful vehicle for funding these local initiatives.

• 5.The Specter of an AI-Driven Slowdown

  The rush to finance AI infrastructure is driving up borrowing costs just as governments grapple with mounting debt and persistent fiscal deficits. If the AI investment boom falters, falling valuations could choke off financing, with little room for fiscal and monetary policy to cushion the economic shock.

• 1.‘No magic wand, no secret sauce. No shortcut’ – Verizon bats away SpaceX hype (Telco Diary)

  From the newsletter (October 26; sign-up or follow if you want it sooner): Verizon has raised its full-year guidance for the third consecutive quarter, signed $4 billion in AI infrastructure…

• 2.6G ISAC pushes channel modeling past statistical averages

  Why site-specific channel modeling matters once 6G radios start sensing In sum – what to know: For years, the wireless industry has leaned on standardized statistical models to test and…

• 3.Why the network unleashes the value of AI

  For much of the AI era, the spotlight has been firmly fixed on models and compute. Bigger models, more GPUs and larger AI clusters have defined the race to build…

• 4.Elisa says mobile recovery continues at moderate pace as 5G and AI-RAN advance

  Elisa recently ran what it claims to be Europe’s first voice, mobile data, and video analytics simultaneously on the same AI-RAN platform over Elisa’s 5G Advanced network In sum –…

• 5.Bullish Nvidia proclaims AI-RAN work with Nokia as EMEA telcos pile in (and AI markets grow twitchy)

  Nvidia and Nokia (and a rush of operators) have announced progress with AI-RAN – a year to the day practically after Nvidia pumped a billion into the Finnish firm. The…

• 1.Reducing 6G Energy Consumption: TDD-Aligned Drain Voltage Modulation for Power Amplifiers

  The Power Amplifier (PA) of a base station operates inefficiently at partial load, where back-off from the peak output power wastes supply headroom. Adapting the PA drain voltage to the instantaneous signal recovers this loss but requires frequent switching, increasing hardware cost. We propose TDD-Aligned Drain Voltage Modulation (TDD-DVM), a drain voltage modulation scheme that instead selects the drain voltage level once per Time-Division Duplex (TDD) period based on the downlink resource load and switches the voltage during the uplink window. This is enabled by the authors' previous work on POLITE link adaptation, which spreads the transmit power over resources left unused at partial load and moves the PA into a regime where a lower drain voltage suffices. In a 3GPP Urban Macro system-level simulation across traffic loads, we compare ...

• 2.Experimental Classification of xApp Conflicts in O-RAN: Slicing vs. Per-UE Scheduling

  Concurrent applications (xApps) in the Near-Real-Time RAN Intelligent Controller (Near-RT RIC) of the Open Radio Access Network (O-RAN) can issue control intents that are jointly infeasible when they act on hierarchically coupled radio resources. This paper experimentally studies an interaction between slice-level physical resource block (PRB) allocation and per-user-equipment (UE) scheduling. We implement a utilisation-aware slicing control (the spectral-efficiency xApp) that reclaims a fixed fraction of the baseline allocation from under-utilised slices and redistributes pooled capacity to saturated slices. The control logic is exercised in simulation and on a 5G testbed comprising an OCUDU-based next-generation NodeB (gNB), an Open5GS core, a Universal Software Radio Peripheral (USRP) X310, and five commercial off-the-shelf (COTS) UEs....

• 3.Pinching Antenna Systems (PASS): Principles, Positioning and Opportunities

  Pinching-antenna systems (PASS) have garnered significant attention owing to their ability to enhance the capacity and coverage of wireless networks by guiding electromagnetic waves through waveguides before radiation. As such, PASS are regarded as promising enabling technologies for sixth-generation (6G) communication networks. In this paper, we present a comprehensive survey of the state of the art in PASS, covering operating principles, positioning methodologies, performance evaluation, resource allocation, and the application of artificial intelligence (AI) to PASS-enhanced wireless networks. We first introduce the fundamental principles of PASS from both materials and physics perspectives. Building on this foundation, we present positioning methodologies for both the user side and the base station side. We then evaluate the communica...

• 4.Secure Agentic AI and Multimodal Intelligence in 6G Communication Systems

  Sixth-generation (6G) communication systems are expected to support immersive, intent-driven, and latency-critical services through tight integration of multimodal intelligence, edge computing, and autonomous network control. In this setting, agentic AI systems can reason over heterogeneous inputs, use tools and network functions, coordinate across distributed entities, and adapt to dynamic environments. These capabilities make agentic multimodal intelligence promising for 6G applications such as semantic communications, digital twins, and zero-touch network management, but they also introduce security and privacy risks that extend beyond conventional AI and network security models. Vulnerabilities can propagate across multimodal data pipelines, memory, tools, communication interfaces, and inter-agent coordination loops. We survey multimo...

• 5.Delay-Doppler Domain Aided OFDM (DD-a-OFDM): Pulsone Pilot and Cross-Domain Iterative Receiver Design

  In high-mobility channels, the widely adopted orthogonal frequency division multiplexing (OFDM) modulation waveform suffers from significant performance degradation due to channel variation and severe inter-carrier interference. Although delay-Doppler (DD) domain multicarrier (DDMC) modulation enjoys a higher channel estimation (CE) accuracy via exploiting the inherent DD-domain channel stability, sparsity, and separability, its high detection complexity limits its practical deployment, making OFDM still a promising waveform candidate for the sixth-generation (6G) cellular networks. To simultaneously harness the complementary strengths of DDMC and OFDM and mitigate their respective limitations, this paper proposes a DD-domain aided OFDM (DD-a-OFDM) framework, including a pulsone pilot (PP) design and a cross-domain iterative receiver desi...

• 1.Intraday Forecasting Ability and Liquidity Provision of Institutional Traders

  This study uses a high-frequency dataset from a stock exchange to examine how forecasting ability and liquidity provision relate to institutional traders' proprietary intraday profitability. We discover persistent forecasting abilities among traders. Specifically, we demonstrate that hypothetical flow-following strategies based on the execution flows of these skillful traders yield annualized Sharpe ratios ranging from 16 to 63 across various trading frequencies. We match buy and sell executions into positions, linking realized profit and loss (P&L) to horizon-matched forecasting proxies and the liquidity roles of both executions. Our findings indicate a direct correlation between improved forecasting ability and profitability, where a one-standard-deviation increase in the forecasting ability proxy is associated with 1.02 -- 1.19 cur...

• 3.Arithmetic Models for Commodity Futures, Vanilla, and Calendar Spread Options

  We develop an arithmetic multi-factor stochastic volatility model for commodity futures and options. The arithmetic specification yields a low-dimensional Markovian representation of the futures curve and semi-closed-form pricing formulas for vanilla and basket options, including calendar spread options. These properties make the model suitable for large-scale empirical estimation. We estimate the model using an unscented Kalman filter on a panel of WTI crude oil futures, vanilla options, and calendar spread options spanning 2013-2024. We find that three stochastic volatility factors are needed to capture both the level and slope of the volatility term structure, and that hedges based on these factors remove almost all of the daily risk of a portfolio of vanilla options. Calendar spread options provide information about the dependence s...

• 2.Multi-Timescale Context-Aware Radio Resource Optimization for Harmonious NTN-TN Coexistence: A Digital Twin and Open RAN Enabled Framework

  Coexistence between non-terrestrial networks (NTNs) and terrestrial networks (TNs) is essential for future wireless systems, but terrestrial downlink transmissions can generate harmful aggregate interference at low-Earth orbit (LEO) satellite receivers. We present DT-STABLE, an Open Radio Access Network (O-RAN)-enabled, digital twin (DT)-assisted framework for context-aware NTN-TN coexistence. Using site-specific information, the DT estimates interference at LEO satellites, enabling proactive management with reduced real-time measurement overhead. We formulate joint beam activation and user association under RF-chain limits, load balancing, and aggregate satellite interference constraints; prove NP-hardness; and develop an efficient DT-guided scheduling algorithm. DT-STABLE couples short-timescale radio resource adaptation with periodic m...

• 3.InferSlice: A Reference Architecture for SLO-driven LLM Inference over a Telco Wide-Area Network

  LLM inference is increasingly deployed as a core service for enterprise and interactive applications, but sovereignty, compliance, latency and resource-locality requirements can limit the suitability of centralised cloud deployments. Telco operators offer geographically distributed compute and network infrastructure for addressing these constraints. However, existing LLM serving frameworks are designed for tightly coupled data centres and provide limited support for coordinating geographically distributed and heterogeneous resources. We present InferSlice, a reference architecture for Service-Level Objective (SLO)-driven LLM inference over a telco infrastructure, integrating network slicing, GPU partitioning, elastic serving and observability. Led by BT in collaboration with the University of Bristol and NVIDIA, and realised on the JOINER...

• 4.Enabling 3GPP-Driven Services Over Optical Transport Network

  We demonstrate a multi-layer slicing architecture enabling deployment of 3GPP network slices as self-contained, connected functions atop optical transport slices. The system provides Optical Spectrum as a Service (OSaaS) by integrating disaggregated optical control, multi-tenant orchestration, and telemetry-based optimization via standard interfaces.

• 1.Migration as a Steady-State Execution Primitive: A Runtime for Virtual Stationarity in Low-Earth Orbit

  Low Earth Orbit (LEO) satellite mega-constellations enable orbital edge computing for latency-critical geocentric applications, but satellite motion compresses line-of-sight communication windows to mere minutes. To maintain logical geographic proximity, a paradigm termed Virtual Stationarity (VS), applications must continuously migrate across passing satellites in the constellation. Traditional live migration mechanisms optimize individually initiated relocations or replication, but not the continuous handover between successive hosts that orbital motion requires. We reformulate live migration as a steady-state execution model and introduce a novel virtual stationarity runtime paradigm. We formalize the viability of continuous migration through a lightweight model capturing latency and availability service-level objectives (SLOs), contin...

• 3.SAKURA: Serving Attention Key-Value State Under Rate Adaptation for Private Image Editing on Phones

  Diffusion transformers are widely used in image-editing services. To preserve both efficiency and privacy, existing systems commonly rely on cloud-device collaboration. Keeping request-specific inference on the device avoids sending personal images and prompts to the cloud. These workloads often use common templates, such as in face swapping and virtual try-on, enabling KV state to be precomputed and reused across requests. However, this reusable state is large and scales with the number of templates, making it impractical to store all of them on mobile devices. Storing them in the cloud addresses this capacity issue, but cache transfer is placed onto the inference path and can even be slower than on-device computation, especially under fluctuating mobile networks. We introduce SAKURA, a cloud-edge architecture that delivers reusable at...

• 4.A DIY DPDK Software Router for Networking Education

  High-performance packet processing frameworks, such as the Data Plane Development Kit (DPDK), are widely used in modern network systems, yet they are rarely integrated into networking curricula. As a result, students often lack exposure to the performance considerations and low-level design choices that shape real-world packet processing systems. We describe a semester-long course project in which students implement a do-it-yourself (DIY) software router using DPDK in a master-level computer networking course. The project guides students through the design and implementation of a multithreaded router, the development of a routing table data structure, and the evaluation of forwarding performance using standard benchmarking tools. We present the project structure and the virtualized infrastructure used to support classes of realistic size....

• 5.Energy-Efficient Timely Relevant IoT Data Retrieval with Wireless Memory Approximation

  Collecting relevant data from Internet of Things devices under freshness and energy constraints is a key challenge in 6G edge networks. A lightweight neural network on the device filters irrelevant observations, but storing it in dynamic random access memory consumes refresh energy during standby. This letter proposes a pull-based framework where an edge server remotely activates and approximates memory and jointly selects the activation timing, refresh period, model size, and transmission threshold. We derive the expected freshness and energy over erasure channels in closed form and show that the scheme consumes less energy than an always-on baseline under the same freshness constraint.