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01.
medRxiv (Medicine) 2026-06-24

TSPO PET binding in vivo reflects increased phagocytic microglia at post mortem in people with frontotemporal dementia

Authors:
Vontobel↗D. S↗Lai↗K. O↗Bacioglu↗Nolan↗Maddison↗D. C↗Adamski↗Goddard↗Shapiro↗N. L↗…

Brain inflammation is a key feature of frontotemporal dementia (FTD). TSPO PET is widely used as an in vivo proxy for neuroinflammation, but whether the elevated signal reflects microglial, astrocytic, or vascular pathology is controversial. We paired ante mortem [11C]PK11195 TSPO PET with post mortem neuropathology in 10 individuals with FTD (5 FTLD-tau, 5 FTLD-TDP) and 5 controls, combining CD68 immunohistochemistry across 17 regions, multiplex immunofluorescence pairing TSPO with microglial/macrophagic (IBA1, CD68), astrocytic (GFAP) and endothelial (CD31) markers, and three-dimensional single-cell reconstruction. CD68 burden was elevated in FTD, concentrated in white matter, and correlated with regional TSPO PET binding across pathologies ({beta} = 8.40, P < 0.001). Only the CD68-TSPO co-localised fraction tracked the PET signal, with no TSPO upregulation per-cell. The elevated TSPO PET signal in FTD likely reflects an increased burden of lysosome-enriched CD68+ microglia, supporting TSPO PET as a microglial-burden biomarker in both FTLD-tau and FTLD-TDP.

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02.
arXiv (quant-ph) 2026-06-24

Resource theory of interactive quantum instruments

Authors:
Chung-Yun Hsieh↗Armin Tavakoli↗Huan-Yu Ku↗Paul Skrzypczyk↗

arXiv:2603.27676v2 Announce Type: replace Abstract: Quantum instruments describe both the classical outcome and the updated quantum state in a measurement process. To do this in a non-trivial way, instruments must have the capability to interact coherently with the state that they measure. Here, we develop a resource theory for instruments. We consider a relevant quantifier of the separation between interactive and non-interactive instruments and show that it admits three distinct operational interpretations in terms of quantum information tasks. These concern (i) the preservation of maximally entangled states after a local measurement, (ii) the average ability to preserve random states after measurement, and (iii) the ability to recover the classical information generated from measuring half of a maximally entangled state. We also introduce a natural set of allowed operations and show that the third task fully characterises the resource content of instruments. Our general framework reproduces as special cases established resource theories for channels and measurements.

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03.
arXiv (CS.CV) 2026-06-16

Chroma-gated, differentiable OKLCH interpolation: Continuous Oklab fallback for color-cast reduction

Authors:
Naoyuki Uchida↗

OKLCH – the cylindrical (lightness, chroma, hue) form of Ottosson's Oklab color space – is the interpolation space recommended by CSS Color 4 for gradients and color-mix(), and it is now broadly deployed. Its polar parameterization, however, casts color near the neutral axis in two ways: (1) an inter-hue detour between two chromatic endpoints that sweeps through an unintended hue (blue to yellow visibly passing through green), and (2) an off-line bow when one endpoint is achromatic. Existing remedies are uniformly two-valued – a threshold switch that fires only at an achromatic endpoint – so they address only (2); on chromatic pairs every one of them reduces to raw OKLCH, leaving the (1) inter-hue cast untreated. We introduce Continuous Oklab fallback (COFb), a one-parameter, differentiable chroma gate $w(C)=C^n/(C^n+\sigma^n)$ that continuously blends the OKLCH path toward the linear Oklab path as chroma falls. A single gate reduces the (1) cast that the two-valued family leaves untreated and unifies the handling of (1) and (2) without any endpoint test. We characterize a cast-hue trade-off frontier, adopt a default ($n=1$, the rational Michaelis-Menten form; $\sigma\approx0.19$ for a typical sRGB palette, from a normalization-independent cast-half criterion), and verify the gate's properties symbolically. At the default, COFb halves the inter-hue path detour (mean lateral deviation -49.5%, chroma-weighted hue excursion -35.5%). We also state the method's limits: on (2) alone the two-valued switch remains better, and like any Cartesian blend COFb does not preserve chroma. In deployment, COFb runs entirely in plain Oklab (a,b) to sRGB, so it serves as a fallback that delivers the same cast-reduced gradients where modern CSS color interpolation (color-mix(in oklch) and the like) is unavailable – older engines, image and video pipelines, or GPU shaders.

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04.
Nature (Science) 2026-06-09

People are turning to AI chatbots to plug gaps in health information

Authors:
Moritz Gerstung↗

A systematic assessment of health-related queries to a chatbot powered by artificial intelligence highlights shortfalls in health-care provision and the responsibilities of AI companies. A systematic assessment of health-related queries to a chatbot powered by artificial intelligence highlights shortfalls in health-care provision and the responsibilities of AI companies.

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05.
arXiv (CS.CV) 2026-06-18

Simple Domain Generalization Methods are Strong Baselines for Open Domain Generalization

Authors:
Masashi Noguchi↗Shinichi Shirakawa↗

In real-world applications, a machine learning model is required to handle an open-set recognition (OSR), where unknown classes appear during the inference, in addition to a domain shift, where the data distribution differs between the training and inference phases. Domain generalization (DG) aims to handle the domain shift situation where the target domain of the inference phase is inaccessible during the model training. Open domain generalization (ODG) considers DG and OSR. Domain-augmented meta-learning (DAML) is a method targeting ODG; however, it has a complicated learning process. By contrast, although various DG methods have been proposed, they have not been evaluated in ODG situations. In this study, we comprehensively evaluate the existing DG methods in ODG and show that the two simple DG methods, CORrelation ALignment (CORAL) and maximum mean discrepancy (MMD), are competitive with DAML in several cases. In addition, we propose simple extensions of CORAL and MMD by introducing the techniques used in DAML, such as ensemble learning and Dirichlet mixup data augmentation. The experimental evaluation demonstrates that the extended CORAL and MMD can perform comparably to DAML with lower computational costs. This suggests that the simple DG methods and their simple extensions are strong baselines for ODG.

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06.
medRxiv (Medicine) 2026-06-22

Reform of the intermediate level of the health system in the Democratic Republic of the Congo: Adaptations and limits in the stabilization of the personnel of the Provincial Health Division: A cohort study.

Authors:
Mushagalusa↗C. R↗Mayeri↗D. G↗Gaylord↗A. N↗Chimanuka↗C. M↗Mwene-Batu↗P. L↗Albert↗M. T↗…

Background: Human resources are one of the pillars of health systems. Since the World Health Organization's report on human resources issues, several countries have integrated this component into the various reforms aimed at strengthening their health systems. This study aims to explore the effects of reforming the intermediate level of a health system operating in a fragile state context. Methodology Our study was conducted in the Democratic Republic of Congo (DRC). It was a cohort study of the staff of the 14 Provincial Health Divisions (PHD) out of the 26 existing in the DRC. We established a database of the staff of these 14 PHD from 2016, just after the implementation of the intermediate level reform and the allocation of this staff by the Ministry of Health. We did a recall in 2021, in each of these PHD to survey this staff through a structured questionnaire and supplemented by the files of the agents available in each PHD. Sociodemographic, economic and academic variables were collected and analyzed. Data were entered into an Excel 2016 database and processed with SPSS software version 25. The chi-square test was used for comparison of proportions with a statistical significance level of p < 0.05. Risk ratios ratios (RR) and their 95% confidence intervals were calculated as measures of association. The error threshold was set at 5%. Results A total of 657 agents with an average age of 45.2 years had been identified in 2016 at the start of the survey and in 2021, 118 or 18% of them were no longer part of the PHD agents. Among the causes of absence noted: 48% of agents placed on leave, 16% promoted to other functions within the health system, 16% desertion and dismissal and 11% cases of death. 19.8% of absentees are executives, 19.5% men against 10.3% women; 22.3% of absentees in unstable provinces against 16.6% in stable ones. The factors associated with the absence of agents in the PHD remain the reaching of retirement age [RR (95% CI) = 5.5 (1.2-24.9) ]and male agents [RR (95% CI) = 3.2 (1.3-7.9)]. Among the agents who remained, 92% kept their initial position, 6% were subject to an internal permutation accompanied by a promotion. The factors associated with the stability of human resources at the level of the Provincial Health Division are: female gender, manager with experience or seniority > 5 years, Age > 35 years, Stable province, Presence of a partner bonus. Conclusion Even in a crisis and fragile context, health system reform is possible. It is possible to organize staff recruitment through a selection process independent of the political authorities of the Ministry of Health and supported by the technical services of the Ministry and partners . Experience and the presence of a financial bonus are motivating factors for staff stability. The involvement of Technical and Financial Support Partners in the recruitment process helped the Ministry of Health to minimize political influence in the recruitment of middle-level executives.

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07.
arXiv (CS.AI) 2026-06-16

Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget

Authors:
Davin Choo↗Yohai Trabelsi↗Fentabil Getnet↗Samson Warkaye Lamma↗Wondesen Nigatu↗Kasahun Sime↗Lisa Matay↗Milind Tambe↗St\'ephane Verguet↗

arXiv:2509.00135v2 Announce Type: replace Abstract: As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.

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08.
arXiv (CS.CL) 2026-06-15

DLawBench: Evaluating LLMs Through Multi-Turn Legal Consultation

Authors:
Li Zhang↗Yuzhen Shi↗Yiran Hu↗Jingwen Zhang↗Wenbo Lv↗Yubo Ma↗Wei Wang↗Rongyao Shi↗Yuanyang Qiu↗Xinran Xu↗Yuemeng Qi↗Linlin Miao↗…

Lawyer-client consultation is a critical starting point for legal services. Effective legal assistance hinges on eliciting sufficient and truthful information from clients in order to devise strategies that best protect their interests. This task requires Large Language Models (LLMs) not only to perform robust legal reasoning, but also to strategically elicit material facts through multi-turn interactions and effectively guide clients with diverse personalities. Yet existing legal benchmarks overlook this interactive capability. To fill this gap, we introduce DLawBench, a diagnostic benchmark for real-world legal consultation. Drawing on realistic client behavior, we characterize lawyer-client interactions into four types: Cooperative, Dependent, Withdrawn, and Adversarial. Using dialogues grounded in real cases, DLawBench evaluates whether LLMs can effectively conduct legal consultation under realistic conditions. DLawBench comprises 461 cases from Chinese and U.S. law, 5,532 paired fact entries, 3,411 inquiry rubrics, and 3,348 issue-resolution rubrics, and evaluates 26 representative LLMs. Systematic experiments show substantial headroom: the best-performing model, GPT-5.5, achieves only 0.562 on consultation-grounded legal reasoning. More importantly, DLawBench exposes both sycophancy in legal consultation and a paradox: models perform worse when clients need guidance most.

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09.
arXiv (CS.AI) 2026-06-16

MiroBench: Benchmarking Realism in Agentic Simulation of Real-world Discussions

Authors:
Yaoning Yu↗Ye Yu↗Haojing Luo↗Haohan Wang↗

arXiv:2606.14715v1 Announce Type: cross Abstract: LLM agents are increasingly used to simulate real world interactions, but it remains unclear whether simulated behaviors preserve the content patterns and interaction dynamics of real human behaviors. Existing evaluations remain fragmented, which makes it difficult to compare systems or measure progress. In this paper, we focus on Reddit discussions as a concrete first step toward evaluating real-world social simulation. Reddit threads provide public, topic-grounded, multi-party interactions where people share experiences, debate, seek advice, express emotion, and collectively respond to products, events, and social issues. These discussions offer an observable window into broader social behavior, making them a useful setting for testing whether LLM agents can reproduce not only fluent text, but also the distributional patterns and interaction dynamics of real online communities. We introduce MiroBench, a benchmark for Reddit discussion simulation built from 4,292 real Reddit threads. MiroBench uses statistical tests to compare generated and real discussions across four major aspects: repetition and semantic uniformity, narrative content, toxicity and aggression, and structural complexity. Experiments across five domains and five models show that current simulators remain distributionally mismatched with real Reddit threads, while a lightweight prompt-based improvement procedure provides only limited gains. MiroBench offers a concrete benchmark for measuring, diagnosing, and improving realism in LLM-based social simulation.

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10.
arXiv (CS.CL) 2026-06-18

Notation Matters: A Benchmark Study of Token-Optimized Formats in Agentic AI Systems

Authors:
Lorenz Kutschka↗Bernhard Geiger↗

Large language models in Agentic AI systems consume tool schemas and execution results and emit tool invocations as structured data. The default language for that exchange, JSON, was designed for application-to-application interchange rather than token efficiency, so its structural elements impose substantial token overhead. Recent work proposes token-optimized alternatives such as TOON (Token-Oriented Object Notation) and TRON (Token Reduced Object Notation) as more compact replacements, but these formats have been evaluated only on isolated comprehension or generation tasks. Whether their token reductions hold inside end-to-end agentic loops therefore remains an open question. We evaluate TOON and TRON on four agentic benchmarks (BFCL, MCPToolBenchPP, MCP-Universe, StableToolBench) and five open-weight LLMs, decoupling input compression from output compression to measure comprehension and generation independently. TRON reduces tokens by up to 27% with accuracy within 14pp of the JSON baseline. TOON achieves up to 18% reduction at a similar 9pp accuracy cost, but additionally cascades on multi-turn parsing failures and collapses parallel tool-call output for most models. The code is available at: https://github.com/lkutschka/notation-matters

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11.
arXiv (CS.AI) 2026-06-16

SorryDB: Can AI Provers Complete Real-World Lean Theorems?

Authors:
Austin Letson↗Leopoldo Sarra↗Auguste Poiroux↗Oliver Dressler↗Paul Lezeau↗Dhyan Aranha↗Frederick Pu↗Aaron Hill↗Miguel Corredera Hidalgo↗Julian Berman↗George Tsoukalas↗Lenny Taelman↗…

arXiv:2603.02668v2 Announce Type: replace Abstract: We present SorryDB, a dynamically-updating benchmark of open Lean tasks drawn from 78 real world formalization projects on GitHub. Unlike existing static benchmarks, often composed of competition problems, hillclimbing the SorryDB benchmark will yield tools that are aligned to the community needs, more usable by mathematicians, and more capable of understanding complex dependencies. Moreover, by providing a continuously updated stream of tasks, SorryDB mitigates test-set contamination and offers a robust metric for an agent's ability to contribute to novel formal mathematics projects. We evaluate a collection of approaches, including generalist large language models, agentic approaches, and specialized symbolic provers, over a selected snapshot of 1000 tasks from SorryDB. We show that current approaches are complementary: even though an agentic approach based on Gemini Flash is the most performant, it is not strictly better than other off-the-shelf large-language models, specialized provers, or even a curated list of Lean tactics.

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12.
arXiv (CS.CV) 2026-06-17

Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing

Authors:
Chao Wang↗Xuanying Li↗Cheng Dai↗Jinglei Feng↗Yuxiang Luo↗Hao Qin↗Yuqi Ouyang↗

Wireframe parsing aims to recover line segments and their junctions to form a structured geometric representation useful for downstream tasks such as Simultaneous Localization and Mapping (SLAM). Existing methods predict lines and junctions separately and reconcile them post-hoc, causing mismatches and reduced robustness. We present Co-PLNet, a point-line collaborative framework that exchanges spatial cues between the two tasks, where early detections are converted into spatial prompts via a Point-Line Prompt Encoder (PLP-Encoder), which encodes geometric attributes into compact and spatially aligned maps. A Cross-Guidance Line Decoder (CGL-Decoder) then refines predictions with sparse attention conditioned on complementary prompts, enforcing point-line consistency and efficiency. Experiments on Wireframe and YorkUrban show consistent improvements in accuracy and robustness, together with favorable real-time efficiency, demonstrating our effectiveness for structured geometry perception. Our code is available at https://github.com/GalacticHogrider/Co-PLNet.

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13.
arXiv (CS.AI) 2026-06-15

UltraSketchLLM: Sub-1-Bit LLM Compression via Sketch and Hardware-Friendly Operators

Authors:
Sunan Zou↗Xueting Sun↗Ziyun Zhang↗Guojie Luo↗

arXiv:2506.17255v2 Announce Type: replace-cross Abstract: Large language models (LLMs) require larger GPU memory size these days, necessitating efficient and extreme weight compression methods. Existing compression methods are either theoretically limited by 1 bit per weight or face severe performance degradation and inefficiency. To deploy LLMs in resource-constrained scenarios, we introduce UltraSketchLLM, compressing LLMs with data sketch. It reduces peak GPU memory footprint with a high compression rate down to 0.5 bit per weight. Combined with hardware-friendly implementation, UltraSketchLLM keeps tolerable performance degradation and extremely low latency overhead with 14.9x speedup compared to naive sketch solution.

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14.
arXiv (quant-ph) 2026-06-17

Time-spectral control of accidental coincidences in daylight entanglement-based free-space QKD

Authors:
Jiyoung Moon↗Yonggi Jo↗Zaeill Kim↗Yong Sup Ihn↗Nam Hun Park↗

arXiv:2606.17365v1 Announce Type: new Abstract: Daylight entanglement-based free-space quantum key distribution (QKD) is limited by accidental coincidences from receiver-admitted background light. We develop and experimentally validate a receiver-level framework linking receiver bandwidth, accepted temporal width, and background-noise density to Bob singles, sifted-key rate, error rate, and quantum bit error rate (QBER) in telecom-wavelength BBM92 QKD. Indoor sweeps show that useful sifted counts saturate near the source-matched bandwidth, whereas broader bandwidth or higher background mainly increases accidental contamination. Increasing the accepted temporal width leaves Bob singles nearly unchanged but directly raises QBER by enlarging the random-overlap probability. A two-dimensional design map shows that the temporal-window margin contracts rapidly with increasing background-to-signal ratio, while the bandwidth margin remains comparatively broad near source-matched filtering. A 10 m rooftop daylight experiment demonstrates operation in the predicted low-accidental regime, yielding a mean sifted-key rate of 2,811 cps and a mean QBER of 4.43%.

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15.
arXiv (CS.AI) 2026-06-15

StreamMemBench: Streaming Evaluation of Agent Memory for Future-Oriented Assistance

Authors:
Guanming Liu↗Yuqi Ren↗Hansu Gu↗Peng Zhang↗Weihang Wang↗Jiahao Liu↗Ning Gu↗Tun Lu↗

arXiv:2606.14571v1 Announce Type: new Abstract: A central role of personal-agent memory is to turn stored information and prior interactions into future-oriented assistance. In daily use, useful cues come from what the agent observes and how the user interacts with the agent, and the agent must carry them forward from the current request to similar future tasks. Existing memory benchmarks usually test dialogue recall or task improvement in isolation, leaving the trajectory from streaming observations to later assistance largely untested. We introduce StreamMemBench, a streaming benchmark that constructs a two-step task sequence around each evidence anchor from EgoLife egocentric streams. The initial task tests evidence use, while the follow-up task tests whether feedback and interaction experience are reused. Four metrics diagnose evidence recall, initial evidence use, feedback incorporation, and follow-up reuse. Experiments with eight memory systems across two backbones show that current systems often fail to use observed evidence or turn feedback into reliable follow-up behavior, even when evidence is stored or feedback is incorporated locally. StreamMemBench is publicly available at https://github.com/landian60/StreamMemBench.

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16.
arXiv (CS.AI) 2026-06-15

AdaTKG: Adaptive Memory for Temporal Knowledge Graph Reasoning

Authors:
Seunghan Lee↗Jun Seo↗Jaehoon Lee↗Sungdong Yoo↗Minjae Kim↗Tae Yoon Lim↗Dongwan Kang↗Hwanil Choi↗SoonYoung Lee↗Wonbin Ahn↗

arXiv:2605.07121v2 Announce Type: replace Abstract: Temporal knowledge graphs (TKGs) represent time-stamped relational facts and support a wide range of reasoning tasks over evolving events. However, existing methods produce entity representations that are static at the entity level, in that each representation is a function of learned parameters only and retains no trace of the interactions in which the entity has participated. In this paper, we depart from this static view and propose that each entity be modeled as an adaptive process whose representation is refined every time the entity participates in a fact. To this end, we propose AdaTKG, which maintains a per-entity memory that is updated with every observed interaction, with the memory accumulating online and predictions improving as more interactions arrive. Specifically, we instantiate the memory update as a learnable exponential moving average governed by a single shared scalar instead of using learnable parameters for each entity, enabling AdaTKG to handle entities unseen during training. Extensive experiments confirm consistent gains over TKG baselines, demonstrating the effectiveness of adaptive memory. Code is available at: https://github.com/seunghan96/AdaTKG

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17.
arXiv (CS.AI) 2026-06-24

Attention in Motion: Secure Platooning via Transformer-based Misbehavior Detection

Authors:
Konstantinos Kalogiannis↗Ahmed Mohamed Hussain↗Hexu Li↗Panos Papadimitratos↗

arXiv:2512.15503v3 Announce Type: replace-cross Abstract: Vehicular platooning promises transformative improvements in transportation efficiency and safety through the coordination of multi-vehicle formations enabled by Vehicle-to-Everything (V2X) communication. However, the distributed nature of platoon coordination creates security vulnerabilities, allowing authenticated vehicles to inject falsified kinematic data, compromise operational stability, and pose a threat to passenger safety. Traditional misbehaviour detection approaches, which rely on plausibility checks and statistical methods, suffer from high False Positive (FP) rates and cannot capture the complex temporal dependencies inherent in multi-vehicle coordination dynamics. We present Attention In Motion (AIMformer), a transformer-based framework specifically tailored for real-time misbehaviour detection in vehicular platoons with edge deployment capabilities. AIMformer leverages multi-head self-attention mechanisms to capture intra-vehicle temporal dynamics, with a spatio-temporal variant that further models inter-vehicle spatial correlations. It incorporates global positional encoding with vehicle-specific temporal offsets to handle join/exit maneuvers. We propose a Precision-Focused Binary Cross-Entropy (PFBCE) loss function that penalizes FPs to meet the requirements of safety-critical vehicular systems. Extensive evaluation across 4 platoon controllers, multiple attack vectors, and diverse mobility scenarios demonstrates superior performance ($\geq$ 0.93) compared to state-of-the-art baseline architectures. A comprehensive deployment analysis utilizing TensorFlow Lite (TFLite), Open Neural Network Exchange (ONNX), and TensorRT achieves sub-millisecond inference latency, making it suitable for real-time operation on resource-constrained edge platforms. Hence, validating AIMformer is viable for both in-vehicle and roadside deployment.

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18.
arXiv (CS.AI) 2026-06-12

Democracy in the Era of Artificial Intelligence

Authors:
Evangelos Pournaras↗Srijoni Majumdar↗Carina Hausladen↗Dirk Helbing↗

arXiv:2606.13026v1 Announce Type: cross Abstract: Interfacing Artificial Intelligence (AI) with democracy is one of the most profound challenges of our times. On the one hand, AI comes with opportunities to overcome long-standing challenges in democracy, such as low participation in deliberative and voting processes with poor representation of people. On the other hand, new risks arise from AI algorithms that are privacy-intrusive, biased, manipulative, spread misinformation and influence election results. Moving beyond the over-simplistic question of whether AI is good or bad for democracy, the Handbook on Democracy in the Era of Artificial Intelligence asks instead: how to upgrade democracies and the principles they are built on, using AI? How to engage with AI and on what terms? Which new values and design principles are required to build democratic resilience? In 34 chapters by 59 authors across the world from different disciplines, we explore how AI can empower collective intelligence for democracy (Part 1) and what is the future of deliberative democracy using large language models and social media (Part 2). We also illustrate the role of AI for building resilient self-governance systems (Part 3) and the challenges of transforming democracy in the age of AI (Part 4). We conclude with broader perspectives (Part 5) that re-imagine the interplay of democracy and AI.

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19.
arXiv (CS.AI) 2026-06-24

When Language Overwrites Vision: Over-Alignment and Geometric Debiasing in Vision-Language Models

Authors:
Harshvardhan Saini↗Samyak Jha↗Yiming Tang↗Dianbo Liu↗

arXiv:2605.08245v4 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) increasingly power high-stakes applications, from medical imaging to autonomous systems, yet they routinely hallucinate, confidently describing content not present in the input. We investigate the root causes of these failure modes with a mechanistic analysis focusing on the decoder-based VLMs. We trace these failure modes to a geometric over-alignment: to bridge the modality gap required by attention mechanisms, decoder-based VLMs over-align visual embeddings with the text manifold, injecting a statistical linguistic bias that systematically overshadows fine-grained visual evidence. While prior work either aggressively closes this gap or suppresses hallucinations through expensive black-box decoding strategies, none addresses the underlying geometric cause. We provide the first quantitative characterization of this over-alignment, demonstrating that linguistic bias concentrates in the top principal components of a universal, dataset-agnostic text subspace. Building on this insight, we propose two complementary remedies: a training-free inference strategy and a bias-aware fine-tuning paradigm, both of which explicitly project out this subspace from visual representations. Our methods significantly reduce hallucinations across POPE, CHAIR, and AMBER benchmarks, and improve CLAIR scores on long-form captioning tasks, with the training-free variant adding no computational overhead over the base model.

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20.
arXiv (CS.AI) 2026-06-15

An Analysis of the Coordination Gap between Joint and Modular Learning for Job Shop Scheduling with Transportation Resources

Authors:
Moritz Link↗Jonathan Hoss↗Noah Klarmann↗

arXiv:2604.24117v2 Announce Type: replace Abstract: Efficient job-shop scheduling with transportation resources is critical for high-performance manufacturing. With the rise of "decentralized factories", multi-agent reinforcement learning has emerged as a promising approach for the combined scheduling of production and transportation tasks. Prior work has largely focused on developing novel cooperative architectures while overlooking the question of when joint training is necessary. Joint training denotes the simultaneous training of job and automatic guided vehicle scheduling agents, whereas modular training involves independently training each agent followed by post-hoc integration. In this study, we systematically investigate the conditions under which joint training is essential for optimal performance in the job-shop scheduling problem with transportation resources. Through a rigorous sensitivity analysis of resource scarcity and temporal dominance, we quantify the coordination gap – the performance difference between these two training modalities. In our evaluation, joint training outperforms the majority of dispatching rule combinations and modular training approaches. However, the coordination gap advantage diminishes in bottleneck environments, particularly under severe transport and processing constraints. These findings indicate that modular training represents a viable alternative in environments where a single scheduling task dominates. Overall, our work provides practical guidance for selecting between training modalities based on environmental conditions, enabling decision-makers to optimize reinforcement learning-based scheduling performance.

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21.
arXiv (CS.LG) 2026-06-24

Machine Learning Classification and Portfolio Construction: Does the Loss Function Matter?

Authors:
Yang Bai↗Kuntara Pukthuanthong↗

arXiv:2108.02283v3 Announce Type: replace-cross Abstract: Classification outperforms regression across matched machine learning models in portfolio construction. A stacking ensemble of gradient boosted tree, random forest, and neural network yields a value-weighted annualized Sharpe ratio of 1.83 for classification and 1.11 for regression. This outperformance persists in multiclass settings, across subsamples, and after transaction costs. Spanning tests show that classification retains economically large alphas after we control for regression, whereas regression alphas shrink substantially once we control for classification. These results indicate that classification extracts more return information than matched regression. Our diagnostics trace classification's advantage to sharper and more precise separation of return deciles.

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22.
arXiv (CS.CV) 2026-06-19

ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?

Authors:
Yuyang Zhang↗Wenyao Zhang↗Zekun Qi↗He Zhang↗Haitao Lin↗Jingbo Zhang↗Yao Mu↗Xiaokang Yang↗Wenjun Zeng↗Xin Jin↗

World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense multi-frame future tokens make inference costly, full video prediction spends capacity on action-irrelevant temporal and appearance details, and long-horizon future imagination may introduce errors that mislead action prediction. These issues raise a simple question: Does world action model really need video generation? We propose ImageWAM, a simple WAM framework that repurposes pretrained image editing models for robot action prediction. In contrast to video generation, image editing provides a better-matched prior: it only needs to model a target-frame transformation, focuses on action-relevant current-to-target visual differences, and grounds task instructions to localized visual changes through edit pretraining. In practice, ImageWAM does not decode the target frame at inference time; instead, it conditions a flow-matching action expert on the KV caches produced by image-editing denoising, using them as a compact world-action context. ImageWAM outperforms standard VLA baselines and matching competitive WAMs without additional policy pretraining across different simulator and real-world experiments. It also reduces FLOPs to 1/6 and latency to 1/4 of video-based WAMs. Attention analysis further shows that editing caches focus on task-relevant change regions, supporting image editing as an effective alternative to video-based world-action modeling.

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23.
arXiv (CS.AI) 2026-06-17

Agentic AI-based Framework for Mitigating Premature Diagnostic Handoff and Silent Hallucination in Healthcare Applications

Authors:
Divyansh Srivastava↗Shreya Ghosh↗Anshul Verma↗Rajkumar Buyya↗

arXiv:2606.18068v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) and multi-agent systems have driven the rise of Agentic AI, showing promise for medical reasoning. However, open-ended conversational agents remain prone to two critical failure modes: premature diagnostic handoff and silent clinical hallucinations that may go undetected before reaching the patient. In this work, we propose a multi-agent framework that addresses both issues by replacing ``LLM-as-a-judge'' routing with deterministic orchestration constraints. The framework incorporates two safety mechanisms. First, a neuro-symbolic state-tracking gate enforces completeness of the OLDCARTS clinical protocol (Onset, Location, Duration, Character, Aggravating/Alleviating factors, Radiation, Timing, and Severity) by blocking diagnostic transitions until all required dimensions are collected. Second, an epistemic uncertainty quantification (UQ) gate computes semantic entropy (H) across K=5 independent diagnostic samples to identify and intercept divergent outputs before delivery. We evaluate the system using simulated patient agents powered by the llama-3.1-70b-instruct model on 150 test cases. The full architecture achieves 49.3% diagnostic precision, representing an absolute improvement of 11.3 percentage points over an unconstrained baseline. Additionally, we observe a statistically significant negative correlation (r = -0.181, p < 0.05) between OLDCARTS completeness (\sigma) and semantic entropy (H), suggesting that structured information gathering is associated with reduced diagnostic uncertainty.

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24.
arXiv (CS.AI) 2026-06-18

AI Sovereignty as National Learning Capacity: A Human-Centered Learning Mechanics Viewpoint on France, the United States, and China

Authors:
Kim Phuc Tran↗

arXiv:2606.00729v2 Announce Type: replace Abstract: Artificial intelligence in France is often discussed through separate dimensions such as investment, compute, regulation, employment, sovereignty, and education. This viewpoint paper proposes a unified interpretation: France can be analyzed as a national AI learning system. Building on Human-Centered Learning Mechanics (HCLM), we use HCLM not as a validated econometric model, but as a conceptual and diagnostic lens for interpreting national AI development as a balance between information injection, absorptive capacity, and institutional dissipation. Information injection includes compute, data, talent, research, capital, industrial deployment, and policy experimentation. Institutional dissipation refers to avoidable frictions such as administrative overload, coordination failures, energy constraints, regulatory uncertainty, talent mobility pressures, and weak industrial absorption. Regulation is not treated as mere friction: adaptive governance, trusted data spaces, and safety-oriented standards may increase long-term learning capacity by improving legitimacy, interoperability, and social trust. The central claim is not that a country follows neural-network equations, but that AI sovereignty depends on how effectively it converts distributed information into absorbed, coordinated, and socially legitimate capability. The paper connects HCLM with neural scaling laws, endogenous growth theory, creative destruction, absorptive capacity, and coordination mechanisms. It offers a formal heuristic, policy indicators, illustrative scenarios, and implications for France. The numerical results are diagnostic scenarios, not econometric estimates or official rankings. The proposed viewpoint reframes AI policy as the governance of an open, strategic, non-equilibrium learning system that should be tested with historical and cross-country data.

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25.
arXiv (quant-ph) 2026-06-12

Theoretical Study for Generating Optical GKP State via a Single-Photon-Added Squeezed Vacuum

Authors:
Deriyan Senjaya↗

arXiv:2606.12467v1 Announce Type: new Abstract: A theoretical framework is developed to analyze the generation of the optical GKP state using a single-photon-added squeezed vacuum. This state, defined by the squeezing parameter $r$, is injected into a 50:50 beam splitter, and the optical GKP state is obtained through conditional measurement at one output port. The single-photon-added squeezed vacuum is especially prominent in this context because it provides a simpler and more experimentally accessible ingredient than Schrodinger cat states, while conditional measurement ensures projection onto a state that closely approximates the finite-energy GKP form. Fidelity is employed to quantify this closeness, and the analysis demonstrates that the scheme achieves a maximum fidelity of 85% at a squeezing level of $3.76 \ dB$. This performance surpasses approaches based on squeezed optical odd Schrodinger cat states, underscoring the single-photon-added squeezed vacuum as a practical and effective pathway toward fault-tolerant photonic quantum computing.

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