Abstract

Ensuring the freshness of drinking water at the point of use remains a challenge in building-scale storage systems, where intermittent demand can lead to prolonged storage. This paper presents a data-driven framework for estimating and managing water age using IoT-enabled flow monitoring, eliminating the need for tracer studies or detailed hydraulic models. An eight-month dataset collected from a cumulative flow meter installed on a system of three 80 L drinking water coolers is used to reconstruct continuous consumption behavior from discrete refilling events through interpolation and event aggregation. The reconstructed demand profile enables continuous estimation of water age, revealing persistent staleness under baseline operation, with water age exceeding 14 days for a significant portion of the monitoring period and reaching peaks of approximately 30 days. To address this, two operational strategies are investigated: demand-aware refilling based on observed hourly consumption patterns, and reduction of storage capacity to promote faster turnover. Both approaches demonstrate substantial reductions in water age; specifically, demand-aware refilling reduces the mean water age from 13.6 days to 5.3 days and the peak from approximately 30 days to 17 days, while capacity reduction to 120 L reduces the mean to 6.9 days. The results highlight the potential of IoT-based monitoring combined with simple control strategies to enhance water freshness in decentralized storage systems, offering a scalable and low-cost solution for smart water management.

Abstract

We present CadLLM, a training-free method
to accelerate the inference throughput of
diffusion-based LLMs (dLLMs). We first in
vestigate on the dynamic nature of the token
unmasking confidence across blocks and steps.
Based on this observation, we then present a
lightweight adaptive approach that can control
the generation block size, step size, and thresh
old based on the average confidence score of
the unmasked tokens. We further reduce the
softmaxing overhead of token probability gen
eration by dynamically leveraging a subset of
vocabulary size to regulate sampling breadth.
CadLLM is a plug-and-play model-agnostic
with KV caching based dLLMs. Extensive ex
periments on four popular tasks demonstrate
the efficacy of CadLLM to yield throughput
improvements ranging from 1.1-2.28× over
the state-of-the-art baseline with competitive
accuracy.

Abstract

The growing scale, complexity, interconnectivity, and autonomy of
modern software ecosystems introduce unprecedented levels of un
certainty, challenging the foundations of traditional self-adaptation.
Existing self-adaptive techniques, often based on rule-driven con
trollers or isolated learning components, struggle to generalize
across novel contexts and coordinate responses across distributed
subsystems, leaving them ill-equipped to handle emergent "un
known unknowns" in complex, dynamic environments. Recent dis
cussions on Self Adaptation 2.0 established an equal partnership
between AI and adaptive systems, merging learning-driven intelli
gence with adaptive control to enable predictive, data-driven, and
proactive adaptation. Building on this foundation, we introduce a
new wave of self-adaptation through POLARIS a three-layer multi
agentic self-adaptation framework that moves beyond reactiveadap
tation by combining (1) a low-latency Adapter layer for monitoring
and safe execution, (2) a transparent Reasoning layer that gener
ates and verifies plans using tool-aware, explainable agents, and (3)
a Meta layer that records experiences and meta-learns improved
adaptation policies over time. By using shared knowledge and pre
dictive models, POLARIS can handle uncertainty, learn from its
past actions, and evolve its strategies, enabling the engineering
of autonomous systems that can anticipate change to ensure re
silient and goal-directed behavior under uncertainty. Preliminary
evaluation across two distinct self-adaptive exemplars, SWIM and
SWITCH, demonstrates that POLARIS consistently outperforms
existing state-of-the-art baselines. With this, we motivate a shift
towards Self-Adaptation 3.0 akin to Software 3.0, a new paradigm
where systems move beyond merely learning from their environ
ment to reasoning about and evolving their own adaptation. In this
vision, adaptation contributes to a self-learning process that enables
systems to continuously improve and respond to novel challenges.

Abstract

A fundamental challenge in reasoning is navigating hypothetical, counterfactual worlds where logic may conflict with ingrained knowledge. We investigate this frontier for Large Language Models (LLMs) by asking: Can LLMs reason logically when the context contradicts their parametric knowledge? To facilitate a systematic analysis, we first introduce CounterLogic, a benchmark specifically designed to disentangle logical validity from knowledge alignment. Evaluation of 11 LLMs across six diverse reasoning datasets reveals a consistent failure: model accuracy plummets by an average of 14% in counterfactual scenarios compared to knowledge-aligned ones. We hypothesize that this gap stems not from a flaw in logical processing, but from an inability to manage the cognitive conflict between context and knowledge. Inspired by human metacognition, we propose a simple yet powerful intervention: Flag & Reason (FaR), where models are first prompted to flag potential knowledge conflicts before they reason. This metacognitive step is highly effective, narrowing the performance gap to just 7% and increasing overall accuracy by 4%. Our findings diagnose and study a critical limitation in modern LLMs' reasoning and demonstrate how metacognitive awareness can make them more robust and reliable thinkers.

Abstract

This study presents efforts focused on extracting and structuring doctor notes, specifically Magnetic Resonance Imaging (MRI) reports, into a standardized format using large language models (LLMs). We introduce a novel benchmark dataset comprising of 55 clinically relevant variables given by doctors, making it the first of its kind in the automated processing of unstructured medical texts. The annotations to the dataset were generated using a systematic prompt-tuning approach that was manually validated. It was then evaluated across three experimental stages: baseline, intermediate, and fine-tuned. Each stage assessed the impact of different prompt strategies on the performance of various LLMs (LLaMA, Qwen, and DeepSeek). Among the models tested, LLaMA 3.1 8B Instruct consistently achieved the highest composite score in both the intermediate and final phases, resulting in an 18.42% improvement in performance.

Abstract

In this paper we present our solution to MEDIQA-SYNUR 2026 shared task organized at LREC-ClinicalNLP workshop. The goal of the task is to populate Electronic Health Record (EHR) flowsheets using the transcriptions of nurse dictations, to alleviate the extensive manual labor associated with sifting through large flowsheets of clinical concepts. We propose a modular architecture combining heuristic-driven Retrieval-Augmented Generation (RAG) with grammar-constrained decoding on an open-weight, quantized, 8B-parameter model (Llama 3.1 Instruct). Our system achieves an F1 score of 0.57, significantly trailing the initial zero-shot experiments with GPT-4o (F1 ≈ 0.88) and placing it towards the lower end of the current leaderboard. We conduct a failure analysis of this approach while establishing a baseline for privacy-preserving, zero-shot documentation assistants.

Abstract

Extracting structured clinical data from unstructured patient transcripts is challenging due to large target
schemas and inherent linguistic ambiguity. We address the extraction of 193 heterogeneous clinical
attributes from nursing notes and clinician-patient dialogues, and demonstrate that zero-shot large
language models (LLMs) are ineffective in this setting, achieving an F1 score below 0.15 due to context
window saturation and hallucination. We propose a four-stage framework that combines semantic
schema clustering, role-based chain-of-thought prompting, supervised fine-tuning of Llama-3.1-8B, and
transcript-verified post-processing. Our approach achieves an F1 score of 0.66, representing a 4.4x
improvement over the baseline, by balancing high recall from generative models with high precision
from verification. These results highlight the effectiveness of hybrid pipelines for high-stakes clinical
information extraction.

Abstract

Visual navigation ability is strongly tied to its underlying representation of the world. Unlike classical 3D maps that require globally-consistent geometry, image- or object-relative topological graphs almost entirely do away with geometric understanding. But, this comes at the cost of navigation capability, often limiting it to merely teach-and-repeat. In this work, we propose a novel map representation in the form of pixel-relative connectivity, which is geometrically accurate but does not require global geometric consistency. Inspired by recent progress in 3D grounded image matching, we construct a map from an image sequence through inter-image connectivity based on pixel correspondences in the relative 3D coordinate systems of individual image pairs. We then use this pixel-level graph to perform global path planning by approximating and sparsifying intra-image pixel connectivity. Through this, we derive a ''WayPixel Costmap'' representation and train a controller conditioned on it to predict a trajectory rollout. We show that this dense pixel-level costmap based on relative geometry is a more accurate conditioning variable for control prediction than its image- and object-level counterparts. This enables a highly capable navigation system, as validated on four types of navigation tasks in the simulator and through real world demonstrations.

Abstract

Recent advances in LLM agentic systems have improved the
automation of offensive security tasks, particularly for Capture
the Flag (CTF) challenges. We systematically investigate the
key factors that drive agent success and provide a detailed
recipe for building effective LLM-based offensive security
agents. First, we present CTFJudge, a framework leverag-
ing LLM as a judge to analyze agent trajectories and provide
granular evaluation across CTF solving steps. Second, we pro-
pose a novel metric, CTF Competency Index (CCI) for partial
correctness, revealing how closely agent solutions align with
human-crafted gold standards. Third, we examine how LLM
hyperparameters, namely temperature, top-p, and maximum
token length, influence agent performance and automated cy-
bersecurity task planning. For rapid evaluation, we present
CTFTiny, a curated benchmark of 50 representative CTF
challenges across binary exploitation, web, reverse engineer-
ing, forensics, and cryptography. Our findings identify optimal
multi-agent coordination settings and lay the groundwork for
future LLM agent research in cybersecurity.

Abstract

This study examines key factors associated with digital payment systems use behaviour in India, focusing on effort expectancy, grievance redressal, trust, performance expectancy, perceived cybersecurity risks, and social influence. Additionally, it explores the moderating effects of cybercrime experience, education level, and age on these relationships. Survey data were collected from urban regions in North India, and structural equation modelling was employed to analyse the determinants of use behaviour among active users. The analysis reveals that effort expectancy, grievance redressal, and performance expectancy are positively associated with use behaviour. Trust did not exhibit a significant direct association with use behaviour; however, it is positively associated with performance expectancy and is associated with social influence and perceived cybersecurity risks. Cybercrime experience moderates the association between perceived cybersecurity risks and trust, highlighting heterogeneity in trust-formation pathways across user subgroups. This research offers new perspectives on the relationships among trust, cybersecurity concerns, and digital payment use behaviour. It is underpinned by a unique dataset collected from diverse urban regions in North India, offering a novel perspective on user behaviour and use determinants. The findings underscore the importance of mitigating cybersecurity risks and strengthening trust-related mechanisms to support sustained use, offering practical recommendations for policymakers and digital payment providers.