Babak's Daily Briefing

Monday, May 11, 2026

Sources: 20 | Total Articles: 96

• 1.Evaluating 6G PHY Evolution: What the Industry Is Really Trying to Solve

  Summary available at source link.

• 2.Amazon’s Globalstar deal gives Amazon Leo a faster path into D2D

  Amazon’s planned acquisition of Globalstar is about far more than satellites. It gives Amazon Leo a faster path into direct-to-device connectivity, combining spectrum, operational assets, and Apple-facing service continuity in a move that could reshape the hybrid terrestrial-NTN landscape.

• 3.SoftBank’s Physical AI push gives AI-RAN a sharper purpose

  SoftBank is starting to give AI-RAN a more concrete job description: not just running AI workloads near the network, but serving as the real-time infrastructure layer for robots and other physical systems. The company’s recent materials suggest it wants to move the AI-RAN conversation from telecom architecture to real-world machine action.

• 4.South Korea puts 6G inside its national AI push

  South Korea has unveiled a three-year national roadmap aimed at becoming one of the world’s top three AI powers by 2028, with 6G commercialization positioned as part of that broader push.

• 5.b-com’s Open XG Hub targets one of telecom’s biggest gaps: turning experimentation into deployment

  In an interview with Peter Pietrzyk, Managing Director of 6GWorld, Patrick Savell, Head of Connectivity at b-com, said platforms such as Open XG Hub are designed to help bridge one of the industry’s most persistent challenges: moving promising ideas from research environments into deployable network systems. The bigger point is that, as telecom becomes more software-driven and AI-native, the bottleneck is increasingly less about invention and more about validation, integration, and operational readiness.

• 1.CyBiasBench: Benchmarking Bias in LLM Agents for Cyber-Attack Scenarios

  Large language models (LLMs) are increasingly deployed as autonomous agents in offensive cybersecurity. In this paper, we reveal an interesting phenomenon: different agents exhibit distinct attack patterns. Specifically, each agent exhibits an attack-selection bias, disproportionately concentrating its efforts on a narrow subset of attack families regardless of prompt variations. To systematically quantify this behavior, we introduce CyBiasBench, a comprehensive 630-session benchmark that evaluates five agents on three targets and four prompt conditions with ten attack families. We identify explicit bias across agents, with different dominant attack families and varying entropy levels in their attack-family allocation distributions. Such bias is better characterized as a trait of the agents, rather than a factor associated with the attack...

• 2.Agentick: A Unified Benchmark for General Sequential Decision-Making Agents

  AI agent research spans a wide spectrum: from RL agents that learn from scratch to foundation model agents that leverage pre-trained knowledge, yet no unified benchmark enables fair comparison across these approaches. We present Agentick, a benchmark for sequential decision-making agents designed to evaluate RL, LLM, VLM, hybrid, and human agents on common ground and to power research on the fundamental challenges of sequential decision-making. Agentick provides 37 procedurally generated tasks across six capability categories, four difficulty levels, and five observation modalities, all exposed through a single Gymnasium-compatible interface. The benchmark ships with a Coding API, oracle reference policies for all tasks, pre-built SFT datasets, a composable agent harness, and a live leaderboard. An evaluation spanning 27 configurations an...

• 3.A Self-Healing Framework for Reliable LLM-Based Autonomous Agents

  Autonomous agents based on Large Language Models (LLMs) are increasingly being utilized in complex software systems. However, reliability remains a significant challenge due to unpredictable failures such as hallucinations, execution errors, and inconsistent reasoning. This paper proposes a reliability-aware self-healing framework for LLM-based software agents. The framework integrates failure detection, reliability assessment, and automated recovery mechanisms. First, we define a taxonomy of failure types and introduce a quantitative reliability assessment model. Next, we propose a failure detection method that identifies abnormal agent behavior based on execution patterns and output consistency. Finally, we design a self-healing mechanism that dynamically recovers from failures through adaptive replanning and corrective prompting strate...

• 4.Reward Shaping and Action Masking for Compositional Tasks using Behavior Trees and LLMs

  Decomposing complex tasks into a sequence of simpler subtasks can improve learning efficiency for an autonomous agent. Reinforcement learning (RL) can be used to optimize agent policies to complete subtasks, but requires well-defined subtask rewards and benefits from action masking. Recent work uses large language models (LLMs) to automate reward shaping and action masking, however none of them fully address reactivity to subtask failure and modularity to varying objects for compositional tasks. To overcome these challenges, we develop masking reward behavior tree (MRBT), a symbolic structure used as a reactive and modular reward and action mask function. We design an MRBT template and derive logical specifications to construct and verify MRBTs for a sequence of object-interaction subtasks. Further, we develop an automated pipeline that u...

• 5.LLM Ghostbusters: Surgical Hallucination Suppression via Adaptive Unlearning

  Hallucinations, outputs that sound plausible but are factually incorrect, remain an open challenge for deployed LLMs. In code generation, models frequently hallucinate non-existent software packages, recommending imports and installation commands for fictional libraries. This creates a critical supply-chain vulnerability: an attacker can proactively register such packages on public registries with malicious payloads that are subsequently installed and executed by developers or autonomous agents, a class of package confusion attack known as slopsquatting. Once a model is deployed, mitigating this failure mode is difficult: full retraining is costly, and existing approaches either cause severe degradation of model utility or rely on a pre-specified forget-set, an assumption that does not apply to the unbounded space of hallucinations. To ad...

• 1.Domain-level metacognitive monitoring in frontier LLMs: A 33-model atlas

  arXiv:2605.06673v1 Announce Type: new Abstract: Aggregate metacognitive quality scores mask within-model variation across MMLU benchmark domains. We administered 1,500 MMLU items (250 per domain, under an a priori six-domain grouping) to 33 frontier LLMs from eight model families and computed Type-2 AUROC per model-domain cell using verbalized confidence (0-100). Total observations: 47,151. Every model with above-chance aggregate monitoring showed non-trivial domain-level variation. Applied/Professional knowledge was reliably the easiest benchmark domain to monitor (mean AUROC = .742, ranked top-2 in 21 of 33 models); Formal Reasoning and Natural Science were reliably the hardest (one of the two ranked bottom-2 in 27 of 33 models). The three middle domains were statistically indistinguishable (Kendall's W = .164). A subject-level coheren...

• 2.VITA-QinYu: Expressive Spoken Language Model for Role-Playing and Singing

  arXiv:2605.06765v1 Announce Type: new Abstract: Human speech conveys expressiveness beyond linguistic content, including personality, mood, or performance elements, such as a comforting tone or humming a song, which we formalize as role-playing and singing. We present VITA-QinYu, the first expressive end-to-end (E2E) spoken language model (SLM) that goes beyond natural conversation to support both role-playing and singing generation. VITA-QinYu adopts a hybrid speech-text paradigm that extends interleaved text-audio modeling with multi-codebook audio tokens, a design enabling richer paralinguistic representation while preserving a clear separation between modalities to avoid interference. We further develop a comprehensive data generation pipeline to synthesize a total of 15.8K hours of natural conversation, role-playing, and singing dat...

• 3.IntentGrasp: A Comprehensive Benchmark for Intent Understanding

  arXiv:2605.06832v1 Announce Type: new Abstract: Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source datasets curation, intent label contextualization, and task format unification. IntentGrasp contains a large-scale training set of 262,759 instances and two evaluation sets: an All Set of 12,909 test cases and a more balanced and challenging Gem Set of 470 cases. Extensive evaluations on 20 LLMs across 7 families (including frontier models such as GPT-5.4, Gemini-3.1-Pro, and Claude-Opus-4.7) demo...

• 4.TajPersLexon: A Tajik-Persian Lexical Resource and Hybrid Model for Cross-Script Low-Resource NLP

  arXiv:2605.06886v1 Announce Type: new Abstract: This work introduces TajPersLexon, a curated Tajik--Persian parallel lexical resource of 40,112 word and short-phrase pairs for cross-script lexical retrieval, transliteration, and alignment in low-resource settings. We conduct a comprehensive CPU-only benchmark comparing three methodological families: (i) a lightweight hybrid pipeline, (ii) neural sequence-to-sequence models, and (iii) retrieval methods. Our evaluation establishes that the task is essentially solvable, with neural and retrieval baselines achieving 98-99% top-1 accuracy. Crucially, we demonstrate that while large multilingual sentence transformers fail on this exact lexical matching, our interpretable hybrid model offers a favorable accuracy-efficiency trade-off for practical applications, achieving 96.4% accuracy in an OCR...

• 5.MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes

  arXiv:2605.06897v1 Announce Type: new Abstract: The rise of Internet of Things (IoT) devices in the physical world necessitates voice-based interfaces capable of handling complex user experiences. While modern Large Language Models (LLMs) already demonstrate strong tool-usage capabilities, modeling real-world IoT devices presents a difficult, understudied challenge which combines modeling spatiotemporal constraints with speech inputs, dynamic state tracking, and mixed-initiative interaction patterns. We introduce MIST (the Multimodal Interactive Speech-based Tool-calling Dataset), a synthetic multi-turn, voice-driven code generation task that operates over IoT devices. We find that there is a significant gap between open- and closed-weight multimodal LLMs on MIST, and that even frontier closed-weight LLMs have substantial headroom. We re...

• 1.RateQuant: Optimal Mixed-Precision KV Cache Quantization via Rate-Distortion Theory

  arXiv:2605.06675v1 Announce Type: new Abstract: Large language models cache all previously computed key-value (KV) pairs during generation, and this KV cache grows linearly with sequence length, making it a primary memory bottleneck for serving. Quantizing the KV cache to fewer bits reduces this cost, yet all current quantizers assign the same bit-width to every attention head, ignoring the large variation in head importance. A natural idea is to allocate more bits to important heads and fewer to the rest. We show, however, that such mixed-precision allocation has a hidden pitfall: each quantizer follows a different distortion curve D(b)=alpha*beta^{-b}, and the decay rate beta varies from 3.6 to 5.3 across quantizer designs. Applying one quantizer's distortion model to another inverts the allocation order and makes performance worse than...

• 2.LKV: End-to-End Learning of Head-wise Budgets and Token Selection for LLM KV Cache Eviction

  arXiv:2605.06676v1 Announce Type: new Abstract: Long-context inference in Large Language Models (LLMs) is bottlenecked by the linear growth of Key-Value (KV) cache memory. Existing KV cache compression paradigms are fundamentally limited by heuristics: heuristic budgeting relies on statistical priors rather than task objectives, causing resource misallocation, while heuristic selection relies on coupled query-key interactions or static inductive biases (e.g., attention sinks). To address this limitation, we introduce LKV (Learned KV Eviction), which formulates KV compression as an end-to-end differentiable optimization problem. LKV integrates LKV-H to learn task-optimized global budgets, and LKV-T to derive intrinsic KV importance without materializing attention matrices. This design bypasses heuristic proxies, strictly aligning compressi...

• 3.A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence

  arXiv:2605.06678v1 Announce Type: new Abstract: According to the United Nations Office for Disaster Risk Reduction (2025), the average annual cost of natural catastrophes increased from 70--80 billion USD between 1970 and 2000 to 180--200 billion USD between 2001 and 2020. Reports from organizations such as the IFOA and the WWF highlight the need for the insurance sector to adapt to this rapidly evolving context by developing medium- to long-term strategies that go beyond the one-year horizon of prudential regulations such as Solvency II. This paper introduces an artificial intelligence framework based on Conditional Generative Adversarial Networks (Conditional GANs) to generate future spatio-temporal trajectories of climatic indices. The approach focuses on the Soil Wetness Index (SWI), a key indicator used in France to assess drought se...

• 4.Breaking the Illusion: When Positive Meets Negative in Multimodal Decoding

  arXiv:2605.06679v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are frequently undermined by object hallucination, generating content that contradicts visual reality, due to an over-reliance on linguistic priors. We introduce Positive-and-Negative Decoding (PND), a training-free inference framework that intervenes directly in the decoding process to enforce visual fidelity. PND is motivated by our finding of an attention imbalance in VLMs, where visual features are under-weighted. Our framework introduces a dual-path contrast: a positive path that amplifies visual evidence and a negative path that constructs counterfactuals to penalize prior-dominant generation. By contrasting outputs from both paths during decoding, PND steers generation toward visually grounded results. Experiments on POPE, MME, and CHAIR demonstrate state...

• 5.On the Role of Strain and Vorticity in Numerical Integration Error for Flow Matching

  arXiv:2605.06680v1 Announce Type: new Abstract: Flow matching generates data by integrating a learned velocity field, where the number of integration steps (NFE) directly determines inference cost. We analyze which properties of the velocity field govern integration error by decomposing the velocity Jacobian into its symmetric part S (strain rate) and antisymmetric part Omega (vorticity). We prove that strain and vorticity play different roles: strain controls exponential error amplification through the logarithmic norm, while vorticity contributes only linearly to the local truncation error. We further show that the optimal transport velocity field is irrotational and has zero material derivative, implying second-order Euler accuracy; for exact displacement interpolation, the associated Lagrangian particle dynamics are integrated exactly...

• 1.Modular Lie Algebraic PDE Control of Multibody Flexible Manipulators

  arXiv:2605.06709v1 Announce Type: new Abstract: This paper addresses PDE-based control for flexible multibody robotic systems, presenting a subsystem-based framework for serial manipulators with arbitrary links in 3D space. The approach uses a screw-theoretic Lie-algebraic model where motion, deformation, and forces are expressed as body-fixed twists and wrenches in se(3). By substituting a strain-based deformation PDE into the dynamics, distributed elastic acceleration is eliminated, yielding a model governed by twist acceleration and the deformation field. Subsystem twist trajectories are generated from task-space endpoints via deflection-compensating inverse kinematics, providing real-time correction for tip deformation. A nominal controller for each link ensures exponential decay of twist errors via a Lyapunov function nu_i. An adapti...

• 2.An Aerial Manipulator for Perception-Driven Flower Targeting Toward Contactless Pollination in Vertical Farming

  arXiv:2605.06759v1 Announce Type: new Abstract: The decline of natural pollinators has created a major challenge for crop production in controlled indoor agriculture, particularly in vertical farming environments where natural insect pollination is absent. This motivates the development of robotic systems capable of performing precise flower targeting tasks while minimizing physical interference with delicate floral structures. This paper presents an aerial manipulator platform for perception driven flower detection, localization, and approach in vertical farming environments. The proposed system integrates onboard RGBD based perception, model predictive path integral (MPPI) based unmanned aerial vehicle (UAV) control on a PX4 platform, and a lightweight 2DoF manipulator for precise end effector positioning. The platform is evaluated in b...

• 3.Bi3: A Biplatform, Bicultural, Biperson Dataset for Social Robot Navigation

  arXiv:2605.06863v1 Announce Type: new Abstract: We contribute Bi3, a dataset of social robot navigation among groups of people in a constrained lab space. Compared to prior data collection efforts for social robot navigation, our dataset is unique in that it features: an original experiment design giving rise to close navigation encounters between two humans and a robot; five different navigation algorithms; two different robot platforms; a diverse participant pool of 74 people recruited from two sites in the USA and France; multimodal data streams including 10.5 hours of human and robot ground-truth motion tracks, RGB video, and user impressions over robot performance. Our analysis of the collected dataset through metrics like interaction density and human velocity suggests that Bi3 represents a benchmark of unique diversity and modeling...

• 4.Traffic Scenario Orchestration from Language via Constraint Satisfaction

  arXiv:2605.06966v1 Announce Type: new Abstract: Autonomous vehicles (AVs) require extensive testing in simulation, but test case generation for driving scenarios is laborious. The desired scenarios are often out-of-distribution and have precise requirements on interactions with the AV policy under test. Manually programming scenarios allows for precise controllability but is difficult to scale. On the other hand, statistical models can leverage compute and data, but struggle with precise controllability when out-of-distribution. We cast scenario orchestration as a constraint-solving problem and present a language-in, simulation-out scenario orchestrator for closed-loop testing AVs. Our approach leverages foundation model reasoning to translate general, natural language descriptions into a set of constraints as a scenario representation. T...

• 5.AirBender: Adaptive Transportation of Bendable Objects Using Dual UAVs

  arXiv:2605.07003v1 Announce Type: new Abstract: The interaction of robots with bendable objects in midair presents significant challenges in control, often resulting in performance degradation and potential crashes, especially for aerial robots due to their limited actuation capabilities and constant need to remain airborne. This paper presents an adaptive controller that enables two aerial vehicles to collaboratively follow a trajectory while transporting a bendable object without relying on explicit elasticity models. Our method allows on-the-fly adaptation to the object's unknown deformable properties, ensuring stability and performance in trajectory-tracking tasks. We use Lyapunov analysis to demonstrate that our adaptive controller is asymptotically stable. Our method is evaluated through hardware experiments in various scenarios, de...

• 1.A Geometry-Aware Residual Correction of Hagan's SABR Implied Volatility Formula

  This paper proposes a hybrid methodology to improve the approximation of SABR (Stochastic Alpha Beta Rho) implied volatility by combining analytical structure with machine learning. The approach augments the neural-network input representation with geometric features derived from the stochastic differential equations of the SABR model. Unlike approaches that fully replace analytical formulas with black-box models, the proposed framework preserves the analytical backbone of the model. The hybridization operates along two complementary dimensions. First, geometry-aware variables reflecting intrinsic properties of the SABR dynamics are used as structured inputs to the network. Second, the neural network is trained to learn the residual error relative to Hagan's closed-form approximation rather than implied volatility directly. The resulting ...

• 2.SNAPO: Smooth Neural Adjoint Policy Optimization for Optimal Control via Differentiable Simulation

  Many real-world problems require sequential decisions under uncertainty: when to inject or withdraw gas from storage, how to rebalance a pension portfolio each month, what temperature profile to run through a pharmaceutical reactor chain. Dynamic programming solves small instances exactly but scales exponentially in state dimensions. Black-box reinforcement learning handles high-dimensional states but trains slowly and produces no sensitivities. We introduce SNAPO (Smooth Neural Adjoint Policy Optimization), a framework that embeds a neural policy inside a known, differentiable simulator, replaces hard constraints with smooth approximations, and computes exact gradients of the objective with respect to all policy parameters and all inputs in a single adjoint pass. We demonstrate SNAPO on three domains: natural gas storage (training in und...

• 3.Neural-Actuarial Longevity Forecasting: Anchoring LSTMs for Explainable Risk Management

  Traditional multi-population models, such as the Li-Lee framework, rely on the assumption of mean-reverting country-specific deviations. However, recent data from high-longevity clusters suggest a systemic break in this paradigm. We identify a stationarity paradox where mortality residuals in countries like Sweden and West Germany exhibit persistent unit roots, leading to a systematic mispricing of longevity risk in linear models. To address these non-linearities, we propose Hybrid-Lift, a neural-actuarial framework that combines Hierarchical LSTM networks with a Mean-Bias Correction (MBC) anchoring mechanism. Positioned as a governance-friendly model challenger rather than a replacement of classical approaches, the framework exhibits selective superiority on out-of-sample validation (2012-2020): it outperforms Li-Lee by 17.40% in Sweden ...

• 4.INEUS: Iterative Neural Solver for High-Dimensional PIDEs

  In this paper, we introduce INEUS, a meshfree iterative neural solver for partial integro-differential equations (PIDEs). The method replaces the explicit evaluation of nonlocal jump integrals with single-jump sampling and reformulates PIDE solving as a sequence of recursive regression problems. Like Physics-Informed Neural Networks (PINNs), INEUS learns global solutions over the entire space-time domain, yet it offers a more efficient treatment of nonlocal terms and avoids the computationally expensive differentiation of full PIDE residuals. These features make INEUS particularly well suited for high-dimensional PDEs and PIDEs. Supported by a contraction-based convergence proof for linear PIDEs, our numerical experiments show that INEUS delivers accurate and scalable solutions for various high-dimensional linear and nonlinear examples.

• 5.Multi-Dimensional Behavioral Evaluation of Agentic Stock Prediction Systems Using LLM Judges with Closed-Loop Reinforcement Learning Feedback

  Agentic stock prediction systems make sequences of interdependent decisions (regime detection, pathway routing, reinforcement learning control) whose individual quality is hidden by aggregate metrics such as mean absolute percentage error (MAPE) or directional accuracy. We present a behavioral evaluation framework that addresses this gap. Behavioral traces logged at every autonomous decision point are grouped into five-day episodes and scored along six domain-specific dimensions (regime detection, routing, adaptation, risk calibration, strategy coherence, error recovery) by an ensemble of three large language model (LLM) judges (GPT 5.4, Claude 4.6 Opus, Gemini 3.1 Pro). Perturbation-based validation on 420 episodes yields targeted score drops of $-1.6$ to $-2.4$ on intended dimensions versus an average of $-0.32$ on the remaining five, w...

• 1.GSMA Calls for Urgent Action to Protect Connectivity Resilience Across Africa

  Summary available at source link.

• 2.€475 billion required for Europe to complete its 5G journey and regain digital leadership, new GSMA study finds

  Summary available at source link.

• 3.GSMA Announces Senior Leadership Changes Across Events and Industry Services Portfolio

  Summary available at source link.

• 4.Zain launches landmark ‘Regulatory Academy’ with GSMA Advance to empower its Policy and Regulatory professionals

  Summary available at source link.

• 5.Pleias and GSMA Launch ‘CommonLingua’,  an Open Source Language Identification Model supporting 61 African Languages

  Summary available at source link.

• 1.LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling

  Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS strategies are largely hand-crafted: researchers manually design reasoning patterns and tune heuristics by intuition, leaving much of the computation-allocation space unexplored. We propose an environment-driven framework, AutoTTS, that changes what researchers design: from individual TTS heuristics to environments where TTS strategies can be discovered automatically. The key to AutoTTS lies in environment construction: the discovery environment must make the control space tractable and provide cheap, frequent feedback for TTS search. As a concrete instantiation, we formulate width--depth TTS as controller synthesis over pre-collected reasoning trajectories and...

• 2.Abductive Reasoning with Probabilistic Commonsense

  Recent efforts to improve the reasoning abilities of Large Language Models (LLMs) have focused on integrating formal logic solvers within neurosymbolic frameworks. A key challenge is that formal solvers lack commonsense world knowledge, preventing them from making reasoning steps that humans find obvious. Prior methods address this by using LLMs to supply missing commonsense assumptions, but these approaches implicitly assume universal agreement on such commonsense facts. In reality, commonsense beliefs vary across individuals. We propose a probabilistic framework for abductive commonsense reasoning that explicitly models this variation, aiming to determine whether most people would judge a statement as true or false. We introduce Probabilistic Abductive CommonSense (PACS), a novel algorithm that uses an LLM and a formal solver to sample ...

• 3.SphereVAD: Training-Free Video Anomaly Detection via Geodesic Inference on the Unit Hypersphere

  Video anomaly detection (VAD) aims to automatically identify events that deviate from normal patterns in untrimmed surveillance videos. Existing methods universally depend on large-scale annotations or task-specific training procedures, severely limiting their rapid deployment to novel scenes. We observe that intermediate-layer features of pre-trained multimodal large language models (MLLMs) already encode rich anomaly semantics, yet existing approaches rely on the language output pathway and fail to exploit the geometric discriminability latent in these representations. Based on this finding, we propose SphereVAD, a fully training-free, zero-shot VAD framework that recasts anomaly discrimination as von Mises-Fisher (vMF) likelihood-ratio geodesic inference on the unit hypersphere, unleashing latent discriminability through principled geo...

• 4.Similar Pattern Annotation via Retrieval Knowledge for LLM-Based Test Code Fault Localization

  Software failures remain a major challenge in modern software development, and identifying the code elements responsible for failures is a time-consuming debugging task. While extensive research has focused on fault localization in the system under test (SUT), failures can also originate from faulty system test scripts. This problem, known as Test Code Fault Localization (TCFL), has received significantly less attention despite its importance in continuous integration (CI) environments where large test suites are executed frequently. TCFL is particularly challenging because it typically operates under black-box conditions, relies on limited diagnostic signals such as error messages and partial logs, and involves large system-level test scripts that expand the fault localization search space. In this paper, we propose SPARK, a framework th...

• 5.KL for a KL: On-Policy Distillation with Control Variate Baseline

  On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OPD remains unstable in practice due to the high gradient variance of its single-sample Monte Carlo estimator, and recipes for stable training are still immature. We propose vOPD (On-Policy Distillation with a control variate baseline), which casts OPD as policy-gradient RL and stabilizes it by introducing a control variate baseline-canonically a value function -- from the RL literature. We show that the OPD value function admits a closed form as the per-token negative reverse KL divergence between the student and the teacher, available directly from the already-computed forward pass with no additional critic or inference. Existing stabilization methods either compute the full token-level reve...

• 1.UniPool: A Globally Shared Expert Pool for Mixture-of-Experts

  Modern Mixture-of-Experts (MoE) architectures allocate expert capacity through a rigid per-layer rule: each transformer layer owns a separate expert set. This convention couples depth scaling with linear expert-parameter growth and assumes that every layer needs isolated expert capacity. However, recent analyses and our routing probe challenge this allocation rule: replacing a deeper layer's learned top-k router with uniform random routing drops downstream accuracy by only 1.0-1.6 points across multiple production MoE models. Motivated by this redundancy, we propose UniPool, an MoE architecture that treats expert capacity as a global architectural budget by replacing per-layer expert ownership with a single shared pool accessed by independent per-layer routers. To enable stable and balanced training under sharing, we introduce a pool-leve...

• 2.BAMI: Training-Free Bias Mitigation in GUI Grounding

  GUI grounding is a critical capability for enabling GUI agents to execute tasks such as clicking and dragging. However, in complex scenarios like the ScreenSpot-Pro benchmark, existing models often suffer from suboptimal performance. Utilizing the proposed \textbf{Masked Prediction Distribution (MPD)} attribution method, we identify that the primary sources of errors are twofold: high image resolution (leading to precision bias) and intricate interface elements (resulting in ambiguity bias). To address these challenges, we introduce \textbf{Bias-Aware Manipulation Inference (BAMI)}, which incorporates two key manipulations, coarse-to-fine focus and candidate selection, to effectively mitigate these biases. Our extensive experimental results demonstrate that BAMI significantly enhances the accuracy of various GUI grounding models in a trai...

• 3.Why Global LLM Leaderboards Are Misleading: Small Portfolios for Heterogeneous Supervised ML

  Ranking LLMs via pairwise human feedback underpins current leaderboards for open-ended tasks, such as creative writing and problem-solving. We analyze ~89K comparisons in 116 languages from 52 LLMs from Arena, and show that the best-fit global Bradley-Terry (BT) ranking is misleading. Nearly 2/3 of the decisive votes cancel out, and even the top 50 models according to the global BT ranking are statistically indistinguishable (pairwise win probabilities are at most 0.53 within the top 50 models). We trace this failure to strong, structured heterogeneity of opinions across language, task, and time. Moreover, we find an important characteristic - *language* plays a key role. Grouping by language (and families) increases the agreement of votes massively, resulting in two orders of magnitude higher spread in the ELO scores (i.e., very consiste...

• 4.DARTS: Targeting Prognostic Covariates in Budget-Constrained Sequential Experiments

  Randomized controlled trials typically assume that prognostic covariates are known and available at no cost. In practice, obtaining high-dimensional pretreatment data is costly, forcing a trade-off between covariate-adaptive precision and a measurement budget. We introduce Dynamic Adaptive Rerandomization via Thompson Sampling (DARTS), which treats covariate acquisition as a sequential optimization problem embedded within a design-based causal inference task. A budgeted combinatorial Thompson sampler learns which covariates are most prognostic across successive batches; selected covariates then drive rerandomization and regression adjustment to reduce batch-level average treatment effect variance. Our primary theoretical contribution is a decoupling result: adaptive covariate selection based on past batches preserves batch-level randomiza...

• 5.Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models

  Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena they face -- knowledge boundaries, capability ceilings, compositional shifts, and open-ended task variation -- differ in kind from the settings that have shaped prior OOD research, and are further complicated because the pretraining and post-training distributions of modern FMs are often only partially observed. Our position is that OOD for foundation models is a structurally distinct problem that cannot be solved within the prevailing model-centric paradigm, and that agentic systems constitute the missing paradigm required to address it. We defend this claim through four steps. First, we give a stage-aware formalization of OOD that accommodates partially observed ...

• 1.Startup Wants to Run AI Inference From Space

  The rapid advancement of large language models is fueling a global data center boom and driving a surge in energy demand. But the electricity required to power data centers is straining the grid, pushing infrastructure operators to search for alternative sources of power. Some are even looking beyond Earth. One company that’s looking to the stars for energy is Orbital Inc. In mid-April, the Los Angeles–based startup emerged from stealth and announced plans to build space data centers. Backed by Andreessen Horowitz (A16z), Orbital is designing infrastructure for AI inference, where trained models generate outputs. Much like other companies advocating for space-based data centers, Orbital is banking on the “ free ” energy generated by the sun to power compute for workloads such as chatbots and agents, sidestepping terrestrial energy constra...

• 2.AI Is Starting to Build Better AI

  The field of artificial intelligence was built on the premise that machines might someday improve themselves. In 1966, the English mathematician I. J. Good wrote that “an ultraintelligent machine could design even better machines; there would then unquestionably be an ‘intelligence explosion,’ and the intelligence of man would be left far behind.” AI researchers have long seen recursive self-improvement, or RSI, as something to both desire and fear. Today, advances in AI are raising the question of whether parts of that process are already underway. RSI means many things to many people. Some use the idea as a bogeyman to scare up regulation, while others brandish it in marketing. For some, it means a fully autonomous loop, while for others it’s nearly any use of tech to build tech. Safest to say it’s a spectrum. At its strictest, research...

• 3.Chatbots Need Guardrails to Prevent Delusions and Psychosis

  Millions of people worldwide are turning to chatbots like ChatGPT or Claude, and a proliferating class of specialized AI companionship apps for friendship, therapy, or even romance. While some users report psychological benefits from these simulated relationships, research has also shown the relationships can reinforce or amplify delusions, particularly among users already vulnerable to psychosis. AIs have been linked to multiple suicides, including the death of a Florida teenager who had a months-long relationship with a chatbot made by a company called Character.AI. Mental-health experts and computer scientists have warned that chatbot mental health counselors violate accepted mental health standards. As the technology’s ability to mimic human speech and emotions advances, researchers and clinicians are pushing for mandatory guardrails ...

• 4.Ten Technology Enablers Shaping the Future of 6G Wireless

  A guide to ten technological components — from THz communications and AI/ML to reconfigurable intelligent surfaces — poised to define 6G wireless networks. What Attendees will Learn Which frequencies 6G will use — Understand why THz bands (above 100 GHz) and the7–24 GHz range are under consideration, what challenges CMOS technology faces at sub-THz frequencies, and how new semiconductor approaches aim to close the output-power gap for future link budgets. How AI/ML and joint communications and sensing reshape the air interface — how auto encoder-based end-to-end learning can replace traditional signal-processing blocks, and how a single waveform may serve both data transmission and radar-like environmental sensing. What reconfigurable intelligent surfaces and photonics bring to the radio environment— Explore how programmable metamaterial ...

• 5.Do We Really Need Smarter AI to Cure Cancer?

  By some estimates, more than a trillion dollars have already been invested in artificial intelligence. But large tech companies , including Meta and OpenAI, are still not content with today’s AI; they say they’ve set their sights on powerful, versatile AI that by some measure would match or even exceed human performance. A remarkable amount of resources is being poured into developing artificial general intelligence (AGI) or even more capable artificial super intelligence (ASI). Excitement around the potential of such a technology is often accompanied by casual claims of some remarkable capabilities. One in particular—curing cancer—stands out to Emilia Javorsky , director of the Futures program at the Future of Life Institute , a think tank focused on benefits and risks of transformative technologies such as AI. In March, Javorsky publish...

• 1.How Leaders Can Move Past Personal Obstacles

  Brian Stauffer/theispot.com Imagine you’re Gabrielle, a senior leader at a fast-growing tech company. Two of your top performers are also your biggest headaches, and they’re making everyone miserable — most of all, you. One is technically brilliant but undermines colleagues’ ideas with sly sarcasm and strategic inaction. The other is a creative powerhouse but belittles […]

• 2.Why Businesses Should Experiment With Quantum Computing Now

  Matt Chinworth/theispot.com Executives tracking the latest news about quantum computing might conclude that with technical milestones still to be reached, the prudent approach is to watch and wait before investing. But that overlooks what other, bolder companies recognize: Quantum computing is an enabling technology, and user organizations have a critical role to play in shaping […]

• 3.Calibrate AI Use to the Decision at Hand

  PPaint/Ikon Images On a rainy Tuesday in London, the leadership team of a consumer goods company reviewed two business decisions: “Where should we open our next five stores?” and “Should we pivot the brand toward wellness?” Generative AI had been used to support the decision-making process for addressing both questions. The team ended up with […]

• 4.Behind the AI in the Newsroom: The Washington Post’s Vineet Khosla

  In this episode of Me, Myself, and AI, host Sam Ransbotham speaks with Vineet Khosla, CTO of The Washington Post, about how AI is reshaping the way news is produced, delivered, and consumed. Vineet argues that journalism itself isn’t broken — but the formats people use to consume news are rapidly evolving, especially as audiences […]

• 5.The Innovation Advantage GenAI Can’t Give You

  Eliot Wyatt/Ikon Images For most of modern business times, competitive advantage belonged to whoever had the best ideas. Better ideas meant better products, which meant more customers, which meant more revenue and profit. The entire innovation industry — consultancies, design firms, brainstorming retreats fueled by sticky notes and gallons of La Croix — was built […]

• 1.Building Opportunity: The Intergenerational Effects of Chilean School Construction -- by Adrienne Lucas, Patrick McEwan

  In 1965–1966, Chile built and staffed thousands of new primary classrooms in supply-constrained communities. Using a quasi-experimental design and large census samples, we show that childhood exposure to school construction substantially improved the schooling and labor market outcomes of adults and closed a persistent female disadvantage in school attainment. Women’s exposure to the policy had large intergenerational spillovers on their children’s on-time grade progression and completed schooling. The marginal value of public funds is 13, including direct effects on adults and intergenerational spillovers.

• 2.Health Inequalities Among Danish Retirees 2004-2022 -- by Paul Bingley, Nabanita Datta Gupta, Malene Kallestrup-Lamb, Alexander O.K. Marin

  Using Danish SHARE data from 2004–2022, we examine income gradients in health among retirees ages 60–79 across functional, diagnosed, comprehensive, mental, and cognitive domains. Higher-income retirees are healthier across all dimensions, but the evolution of inequality differs across measures. Functional and comprehensive health gaps narrow over time because lower-income retirees improve, whereas mental health gaps remain large and persistent. Diagnosed and cognitive health show smaller, less stable gradients. Overall, health inequality at older ages is substantial but not uniform: physical health disparities compress, while mental health disparities show no sign of convergence.

• 3.Interest Rate Caps, Competition, and Strategic Borrowing: Evidence from Kenya -- by Aroon Narayanan, Tavneet Suri, Prashant Bharadwaj

  We study Kenya’s 2016 interest-rate regulation, which capped bank lending rates but left one digital platform, called M-Shwari, exempt on the lending side while imposing a deposit-rate floor across all lenders in the market. Using borrower-level administrative data, survey data, and an RD around the implementation date, we show three main results. First, lending on the exempt platform rose, but with the safest borrowers substituting away toward cheaper capped credit. Second, riskier borrowers increase their savings to build up their credit limits. Third, on the supply side, M-Shwari raises the limits for the safest borrowers in an attempt to retain them. We build and estimate a simple model of screening and credit limit-setting to interpret these reallocations and compute welfare. The observed carve-out for M-Shwari preserves access for h...

• 4.On the Negative Consequences of Low-Wage Offshoring for Innovation -- by Wulong Gu, Alla Lileeva, Daniel Trefler

  Conventional wisdom holds that offshoring intermediates to China stimulates innovation. This is not entirely compelling. On the one hand, (a) offshoring lowers marginal costs and expands sales, thereby increasing the returns to innovation, especially for large firms. On the other hand, (b) offshoring low-quality intermediates reduces the costs of older-generation products, thereby reducing the returns to innovating into newer generations. We examine these two opposing forces over 2002-2011 for 6,024 Canadian firms. Our empirical strategy regresses measures of innovation, such as R&D, on imports of intermediate inputs. To address endogeneity, we construct a model-consistent shift-share instrument whose shocks are the often-dramatic improvements in the quality of HS6 Chinese intermediate inputs. We find that greater offshoring reduced R&D s...

• 5.The Postpandemic U.S. Immigration Surge: New Facts and Inflationary Implications -- by Anton Cheremukhin, Sewon Hur, Ronald R. Mau, Karel Mertens, Alexander W. Richter, Xiaoqing Zhou

  The U.S. experienced an extraordinary surge in immigration from 2021 to 2024, which triggered widespread discussions about its macroeconomic impact, particularly on inflation. To determine the impact of the immigration surge, we first document the salient features of these new immigrants: they are primarily low-skilled relative to the existing workforce and more likely to be hand-to-mouth consumers. We then incorporate these features into a heterogeneous agent model with capital-skill complementarity. We find that the supply- and demand-side effects of the immigration surge roughly cancel out, causing a negligible response of inflation.

• 1.Will Mounting Supply Chain Strains Hamstring the AI Investment Boom?

  The conflict in the Middle East has precipitated a global supply shock—the third in six years following the pandemic in 2020 and Russia’s invasion of Ukraine in 2022. The current shock raises the specter of spillovers to the U.S. through both prices and physical shortages of goods. A critical conduit for spillovers through these channels is via Asian supply chains, especially from middle- to lower-middle income countries in southeast Asia, which are key suppliers for goods needed for the AI infrastructure build-out in the U.S. These countries are also heavily reliant on Middle East energy imports. This post examines key factors related to these Asian supply chain vulnerabilities.

• 2.Stress and Strain from NBFIs to Banks

  Do the recent stresses in the NBFI space—notably the bankruptcies of Tricolor and First Brands, and the decision of Blue Owl Capital Corp II (OBDC II) to end its redemption program and return capital through a wind-down of the fund—create distress for banks? The general sentiment is that the recent stresses are unlikely to amount to systemic concerns, although it does not mean there might not be “some stress and strain” for banks and that policymakers are “watching carefully” for exposure across banks. In a series of previous posts, we showed that shoc...

• 3.Same Shock, Different Roads? A K‑Shaped Pattern at the Pump

  In March 2026, energy prices surged to a four-year high, driven by the Iranian closure of the Strait of Hormuz amid the ongoing conflict in the Middle East. In this Liberty Street Economics post, we use the new consumer spending module of the Economic Heterogeneity Indicators to analyze recent changes in nominal and real gas consumption across different income groups. We find that households had very different experiences with gasoline spending: in March, high-income households increased nominal spending the most and kept real consumption essentially unchanged, while low-income households decreased real consumption of gasoline but still saw sharply increased nominal spending because of the rise in gas pric...

• 4.In What Ways Has U.S. Trade with China Changed?

  Over the past year, U.S. trade policy with China has undergone enormous changes, but with surprisingly little effect on overall trade balances. In fact, the U.S.’s twelve-month trade deficit, while highly volatile due to import front-running early in the year, ended 2025 at $1.2 trillion, almost unchanged from 2024. At the same time, China’s trade surplus with the world actually increased from $1 trillion to $1.2 trillion. However, when looking at changes between individual countries, one sees large shifts in bilateral balances. In this post, we will focus on changing trade flows between the U.S., China, and southeast Asia.

• 5.Explaining the K‑Shaped Economy: What’s Behind the Divide?

  In our companion post, we used a new module of our Economic Heterogeneity Indicators (EHIs) to shed light on how recent retail spending growth has been driven by high-income households. This fact is consistent with the popular press’s idea of a “K-shaped economy” in which higher-income households experience faster growth in spending than lower-income households. In this post, we dive deeper into the reasons behind this divergence by analyzing for which goods this trend holds true and ask whether it can be explained by changes in wages, inflation, or wealth. We find that, since 2023, wealth has increased the most for high...

• 1.Who Will Solve the AI Productivity Puzzle?

  Generative AI tools have been widely available for several years now, but productivity growth has little sustained improvement, because firms have yet to convert task-level time savings into measurable economic output. Is the most effective solution to overhaul existing companies—or to bypass them altogether?

• 2.Gold’s Grim Message

  Central banks’ purchases and repatriation of gold are on the rise, and both should be viewed as a symptom of deglobalization. They signal the advent of a more geopolitically fragmented world in which cross-border transactions of all kinds are poised to become more difficult and costly.

• 3.Europe Is Losing the Energy-Security Battle to China

  While tensions around the Strait of Hormuz are rattling global oil markets, energy security is no longer defined solely by access to fossil fuels. What China has understood, and Europe has not, is that security now depends on electricity systems that can deliver low-cost power at scale and support heavy industry.

• 4.Europe Needs a Civic Revolution

  The question for Europe is no longer whether it should become a global power, but whether it can do so democratically and in time to shape, rather than endure, the new world order that is now emerging. Fortunately, European citizens are increasingly aware of what is at stake.

• 5.A Latin American Alternative to Bukele?

  Though few have noticed, Mexico has reduced its daily homicide count by 41% over the past 18 months, owing to President Claudia Sheinbaum’s pioneering new strategy for confronting organized crime. Such a sharp reduction is an historic achievement for Mexico – and puts Salvadoran President Nayib Bukele's thuggish methods to shame.

• 1.Telenor launches sovereign cloud venture in Norway

  Telenor said the platform will operate from nationally controlled data centers in Norway and will remain separated from commercial global cloud environments In sum – what to know: Sovereign cloud – Telenor is creating a standalone Norwegian cloud company focused…

• 2.Samsung, Qualcomm advance 5G FWA uplink performance

  Sanil Ramachandran, director of technology at Samsung Electronics America, told RCR Wireless News that 5G PC1 technology could strengthen the position of 5G FWA as an alternative to fixed broadband services in both urban and rural markets In sum –…

• 3.AI factories to football matches – Vodafone and BT show Euro model for sovereign telcos

  Vodafone’s sovereign-cloud deal with AWS in Germany and BT’s brand refresh and UEFA deal in the UK, plus lots else, shows how European telcos are repositioning as trusted intermediaries for AI, cloud, cybersecurity, and critical infrastructure. In sum – what…

• 4.Verizon is using digital twins and AI to identify storm damage

  The new Verizon system uses drone-captured 3D models and automated damage analysis In sum – what we know: Verizon is rolling out a new digital twin and AI system ahead of the 2026 hurricane season. The goal is essentially to instantly identify…

• 5.F5 report shows enterprises bringing AI inference in-house

  The 2026 F5 State of Application Strategy report highlights a massive shift toward decentralized AI production In sum – what we know: F5 has released its 2026 State of Application Strategy (SOAS) Report, surveying hundreds of enterprise IT and security leaders…

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• 1.Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

  Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 anten...

• 2.RFNoC-Based FPGA Offloading for Fully Programmable PHY Acceleration

  Hardware acceleration has emerged as a key research topic for supporting computationally intensive signal processing and artificial intelligence applications in 6G research and development studies. This paper presents an RF Network on Chip (RFNoC) based hardware acceleration framework that offloads key physical layer procedures to a field programmable gate array (FPGA). The proposed design accelerates procedures, including low density parity check codes (LDPC) encoding and decoding, rate matching and unmatching, interleaving and deinterleaving, scrambling and descrambling, and log likelihood ratio estimation. The accelerator is integrated directly into the OpenAirInterface radio access network software, enabling simultaneous use of the FPGA as driver of the radio front end and a high throughput accelerator. The proposed system is validate...

• 3.Unconsented Sensing: A Sociotechnical Governance Framework for 6G ISAC

  The forthcoming deployment of 6G Integrated Sensing and Communication (ISAC) will transform cellular infrastructure into pervasive, continuous environmental and biometric sensing grids. While current telecom standardization efforts (e.g., 3GPP, ETSI) have formally recognized privacy and trustworthiness as critical pillars for 6G, their proposed mitigations remain overwhelmingly technocentric, relying on cryptographic anonymization and physical layer security. This approach critically underestimates the sociotechnical and legal complexities of the downstream machine learning (ML) models required to interpret raw sensing data, creating a profound collision with existing digital rights legislation. This position paper argues that technical security is insufficient. ISAC trustworthiness must be redefined as mandatory regulatory and sociotechn...

• 4.Movable Subarray-Aided Hybrid Beamforming for Near-Field Multiuser Communications

  Movable antenna (MA)-enabled near-field (NF) communications offer significant potential for 6G, yet existing designs often neglect the practical constraints of hybrid beamforming (HBF) for extremely large-scale MIMO (XL-MIMO). Conversely, MA-aided HBF frequently overlooks the rich NF degrees of freedom (DoFs). This paper proposes a movable subarray (MSA)-aided HBF architecture for NF multiuser systems, which strikes a strategic balance between hardware practicality and spatial flexibility. By coupling MSA movement with HBF, the proposed design simultaneously exploits NF distance-dependent and MSA position-dependent DoFs, enabling highly precise beamfocusing and superior interference mitigation. To alleviate the computational burden, a hybrid planar-spherical wave model is introduced for efficient channel approximation. Furthermore, an alt...

• 5.Toward Quantum-Safe 6G: Experimental Evaluation of Post-Quantum Cryptography Techniques

  6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware desig...

• 1.Corporate transparency and the disposition effect

  The disposition effect describes investors' irrational behavior of selling profitable assets too soon while holding onto losing assets for too long. This study examines the impact of transparency at the firm level on the disposition effect of individual investors who hold that company's stock. Our results show that an increase in corporate transparency significantly reduces the disposition effect. Further analysis reveals that for companies with greater transparency, when the held stock is profitable, investors' confidence in holding it increases, leading to a reduced bias toward selling profitable stocks. When the stock is held at a loss, investors' confidence in holding it weakens, but they often perceive the loss as temporary and maintain confidence in the company's long-term prospects, thus exacerbating the bias toward holding losing ...

• 2.Modeling Dynamic Correlation Matrices with Shrinkage Priors

  Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse posterior uncertainty. In moderate dimensions, an additional difficulty is summarizing the estimated evolving dependence structure for downstream decision-making tasks. We propose a Bayesian approach based on a low-rank factor representation, with latent states evolving under a dynamic shrinkage prior and observation errors following a multivariate factor stochastic volatility model. This specification allows locally adaptive regularization of the estimated correlation structure over time and informative uncertainty quantification. We establish, to our knowledge, a first-of-its-kind posterior contraction result for dynamically regularized Bayesian models, showin...

• 3.Does social media information affect individual investor disposition effect? Evidence from Xueqiu

  The irrational behavior of investors selling profitable assets too early while holding onto losing assets for too long is known as the disposition effect. Due to the development of the Internet, the information environment for individual investors has been greatly improved. As an important source of information for individual investors, whether social media can improve investors' behavioral biases and return to rational expectations is a question worth studying. Based on the post data and actual trading data of the social investment platform Xueqiu.com, this paper studies the impact of social media information on the disposition effect of individual investors. The research results show that social media information can significantly reduce the disposition effect. Furthermore, it is through negative information that social media informatio...

• 4.Structural Limits of OHLCV-Based Intraday Signals in MNQ Futures: A Systematic Falsification Study

  This paper tests whether intraday momentum signals derived from open-high-low-close-volume (OHLCV) data produce a statistically significant trading edge in Micro E-mini Nasdaq 100 futures (MNQ) under realistic execution constraints. Using 947 trading days of five-minute data (2021-2025), fourteen signal families are evaluated, including opening range breakouts, gap strategies, volume signals, cross-session momentum, liquidity grabs, volatility-conditioned classifiers, and news-driven strategies. All signals are assessed using strict institutional criteria: out-of-sample walk-forward validation, minimum T-statistic of 2.0, at least 30 trades, positive net return after a fixed two-point round-trip cost, and multi-year stability. No signal satisfies all criteria simultaneously. The gross edge available to next-bar-open execution is constrain...

• 5.Deepening the Secondary Market: Integrating Trade Credit into Market Clearing with the Cycles Protocol

  Current post-trade clearing systems rely almost exclusively on cash or cash-like collateral, leaving vast reserves of short-term liquidity embedded in trade credit outside formal settlement infrastructures. A key barrier to integrating this liquidity is the near-universal dependence of clearing services on novation, which imposes institutional overhead that restricts accessibility and limits the range of obligations that can be brought into settlement. This paper introduces the Cycles Protocol: a distributed, multilateral clearing mechanism based on double-entry accounting and atomic cycle execution that maximizes balance sheet compression. Unlike novation-based clearing, Cycles does not redistribute counterparty risk; it can thus be applied generally to existing financial networks, without any change in counterparty relations, allowing...

• 1.Physics-Inspired Probabilistic Computing for Extremely Large-Scale MIMO Detection in Future 6G Wireless Systems

  Extremely large-scale multiple-input multiple-output (XL-MIMO) architectures are a key enabler of forthcoming 6G wireless communication networks by allowing high data rates through massive spatial multiplexing. Here, we approach these problems with physics-inspired unconventional computing based on Ising machines (IMs). For binary modulation, probabilistic IMs (PIMs) and oscillator-based IMs achieve optimal ML detection with systems up to 2048x2048 antennas with only 100 iterations, matching optimal sphere decoder performance for computationally treatable sizes and outperforming the minimum mean-square error (MMSE) industrial standard. For M-QAM up to 256, a generalized PIM-inspired framework, based on d-dimensional probabilistic variables (p-dits) that directly encode QAM symbols, shows low bit-error-rate across sizes up to 256x256 anten...

• 2.RFNoC-Based FPGA Offloading for Fully Programmable PHY Acceleration

  Hardware acceleration has emerged as a key research topic for supporting computationally intensive signal processing and artificial intelligence applications in 6G research and development studies. This paper presents an RF Network on Chip (RFNoC) based hardware acceleration framework that offloads key physical layer procedures to a field programmable gate array (FPGA). The proposed design accelerates procedures, including low density parity check codes (LDPC) encoding and decoding, rate matching and unmatching, interleaving and deinterleaving, scrambling and descrambling, and log likelihood ratio estimation. The accelerator is integrated directly into the OpenAirInterface radio access network software, enabling simultaneous use of the FPGA as driver of the radio front end and a high throughput accelerator. The proposed system is validate...

• 3.Unconsented Sensing: A Sociotechnical Governance Framework for 6G ISAC

  The forthcoming deployment of 6G Integrated Sensing and Communication (ISAC) will transform cellular infrastructure into pervasive, continuous environmental and biometric sensing grids. While current telecom standardization efforts (e.g., 3GPP, ETSI) have formally recognized privacy and trustworthiness as critical pillars for 6G, their proposed mitigations remain overwhelmingly technocentric, relying on cryptographic anonymization and physical layer security. This approach critically underestimates the sociotechnical and legal complexities of the downstream machine learning (ML) models required to interpret raw sensing data, creating a profound collision with existing digital rights legislation. This position paper argues that technical security is insufficient. ISAC trustworthiness must be redefined as mandatory regulatory and sociotechn...

• 4.Movable Subarray-Aided Hybrid Beamforming for Near-Field Multiuser Communications

  Movable antenna (MA)-enabled near-field (NF) communications offer significant potential for 6G, yet existing designs often neglect the practical constraints of hybrid beamforming (HBF) for extremely large-scale MIMO (XL-MIMO). Conversely, MA-aided HBF frequently overlooks the rich NF degrees of freedom (DoFs). This paper proposes a movable subarray (MSA)-aided HBF architecture for NF multiuser systems, which strikes a strategic balance between hardware practicality and spatial flexibility. By coupling MSA movement with HBF, the proposed design simultaneously exploits NF distance-dependent and MSA position-dependent DoFs, enabling highly precise beamfocusing and superior interference mitigation. To alleviate the computational burden, a hybrid planar-spherical wave model is introduced for efficient channel approximation. Furthermore, an alt...

• 5.Toward Quantum-Safe 6G: Experimental Evaluation of Post-Quantum Cryptography Techniques

  6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware desig...

• 1.Unconsented Sensing: A Sociotechnical Governance Framework for 6G ISAC

  The forthcoming deployment of 6G Integrated Sensing and Communication (ISAC) will transform cellular infrastructure into pervasive, continuous environmental and biometric sensing grids. While current telecom standardization efforts (e.g., 3GPP, ETSI) have formally recognized privacy and trustworthiness as critical pillars for 6G, their proposed mitigations remain overwhelmingly technocentric, relying on cryptographic anonymization and physical layer security. This approach critically underestimates the sociotechnical and legal complexities of the downstream machine learning (ML) models required to interpret raw sensing data, creating a profound collision with existing digital rights legislation. This position paper argues that technical security is insufficient. ISAC trustworthiness must be redefined as mandatory regulatory and sociotechn...

• 2.Toward Quantum-Safe 6G: Experimental Evaluation of Post-Quantum Cryptography Techniques

  6G networks will require quantum-secure cryptography deployed across core infrastructure, edge nodes, resource-constrained IoT devices. Although post-quantum cryptographic (PQC) algorithms have been standardized by NIST, their practical deployability in bandwidth and latency limited wireless systems remains unclear. This paper presents a practical evaluation of NIST selected PQC schemes, including ML-KEM (Kyber), ML-DSA (Dilithium), and Falcon. Benchmarks conducted with OpenSSL and the OQS provider on heterogeneous platforms show that while computational performance is acceptable, ciphertext and signature size expansion significantly impact handshake reliability and bandwidth efficiency, particularly at the network edge. The results highlight key system-level trade-offs and motivate the need for PQC optimization and deployment-aware desig...

• 3.A Disaster-Aware Integrated TN-NTN System-Level Simulator for Resilient 6G Wireless Networks

  Non-terrestrial networks (NTN) have been standardized by the 3rd generation partnership project (3GPP) as a key component of future 6G systems to enhance coverage and resilience. In particular, NTN technologies such as low-earth orbit (LEO) satellites, high-altitude platform stations (HAPS), and unmanned aerial vehicles (UAVs) are expected to support terrestrial networks (TN) during extreme events and disasters. In this paper, we present a lightweight system-level simulator for evaluating post-failure fallback behavior in integrated TN-NTN wireless networks under a partial-failure disaster model. The simulator follows 3GPP Rel-17/18 modeling principles, supports probabilistic terrestrial next-generation node B (gNB) failures, and service migration to NTN. The simulator supports comparative analysis of throughput, packet reception ratio (P...

• 4.Comparative Analysis of Direct-to-Cell (D2C) and 3GPP Non-Terrestrial Networks (NTN) for Global Connectivity

  The quest for ubiquitous mobile coverage has catalyzed two fundamentally distinct architectural paradigms: Direct-to-Cell (D2C) and standardized 3GPP Non-Terrestrial Networks (NTN). D2C, pioneered by SpaceX Starlink and AST SpaceMobile, leverages existing terrestrial spectrum and unmodified consumer handsets to provide emergency connectivity as a market-driven overlay. In contrast, 3GPP NTN, standardized across Releases 17-19, offers a systematic satellite-native framework designed for long-term scalability, high-throughput broadband, and deep integration with terrestrial 5G/6G networks. This paper presents a comprehensive technical comparison of these approaches, analyzing their standardization trajectories, network architectures, physical-layer innovations, security postures, and operational trade-offs. We further examine their implicat...

• 5.Bridging the 6G Gap: Scaling Sustainable ROADM-Based IP-over-WDM via DSCM-Enabled Point-to-Multipoint Designs

  This study compares transponder-based, Point-to-Point, and DSCM-based Point-to-Multipoint (PtMP) access-metro architectures. Findings demonstrate that PtMP IPoWDM significantly optimizes efficiency across diverse geotypes, slashing CAPEX by 92.0% and power by 99.2% compared to the traditional benchmark over a ten-year horizon.