Knowledge catalogue
arXiv:2607.21437v1 Announce Type: new Abstract: Deep learning models can effectively use Rapid Evaporative Ionization Mass Spectrometry (REIMS) data for surgical margin assessment. However, their clin
arXiv:2607.21482v1 Announce Type: new Abstract: Large language models (LLMs) and agents are now widely used tools in code development, with data typically sent to third-party cloud-based models. Their
arXiv:2607.21503v1 Announce Type: new Abstract: Production AI agents' failures are less often due to an inability to reason well and more often because they cannot manage what is in their reasoning co
arXiv:2607.20866v1 Announce Type: new Abstract: Generating realistic interior furniture layouts that strictly adhere to architectural constraints (e.g., walls, doors, and windows) remains a fundamenta
arXiv:2607.21162v1 Announce Type: new Abstract: Outsourced Transformer inference exposes clients to model substitution and incomplete execution, while direct replay removes the computational benefit o
arXiv:2607.21306v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used as tutors and thought partners, helping users reason through problems. While guidance from AI assis
arXiv:2607.20554v1 Announce Type: new Abstract: Reliable and low-latency NR-V2X communications are essential for smart mobility in dense urban environments. However, limited Road-Side Unit (RSU) densi
arXiv:2607.20577v1 Announce Type: new Abstract: Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibi
arXiv:2607.20452v1 Announce Type: new Abstract: Modern software quality assurance demands intelligent, autonomous systems capable of adaptive decision-making across distributed cloud environments. Thi
arXiv:2607.20498v1 Announce Type: new Abstract: Large language models (LLMs) augmented with tools are emerging as autonomous agents capable of using Web engine, APIs, and code to solve complex, long-h
arXiv:2607.20636v1 Announce Type: cross Abstract: As humans, we face many decisions that require us to choose between sticking to something and giving up. This thesis uses algorithmic tools to derive
arXiv:2607.20943v1 Announce Type: cross Abstract: Quantum Phase Estimation (QPE) is a foundational algorithm for molecular ground-state energy estimation, but its deep circuit requirements make direct
arXiv:2607.21424v1 Announce Type: new Abstract: Recent advancements in automated audio captioning (AAC) have shifted from monolithic sentence generation toward structured formats that explicitly disen
arXiv:2607.21292v1 Announce Type: new Abstract: We present a structured large-language-model-driven workflow for automated multi-variable control design from dynamic process models. The workflow decom
arXiv:2607.21192v1 Announce Type: cross Abstract: ProB is a Prolog-based model checker, animator and constraint solver for high-level formal specifications. One can also use ProB to animate transition
arXiv:2607.20440v1 Announce Type: cross Abstract: Proprietary large language models (LLMs) entail substantial intellectual and financial investment, making them valuable intellectual property (IP). Ho
arXiv:2607.20902v1 Announce Type: cross Abstract: Goal reasoning in Non-Axiomatic Logic (NAL) explains how an adaptive system derives means for realizing desired events under insufficient knowledge an
arXiv:2607.21405v1 Announce Type: new Abstract: Mobius RoPE is a rotary positional encoding built on the anti-periodic frequency ladder heta_i=pi(2i+1)/N: every rotation plane advances by an odd multi
arXiv:2607.18042v2 Announce Type: replace-cross Abstract: End-to-end vision-language navigation (VLN) with causal vision-language models maps instructions and egocentric observations directly to actio
arXiv:2607.21121v1 Announce Type: cross Abstract: In this work, a quantum architecture search framework for approximate quantum state preparation (QSP) is proposed. QSP is a challenging task, since th
arXiv:2607.20536v1 Announce Type: new Abstract: Tool-use agents that address day-to-day digital tasks such as ordering groceries must not only operate applications, but also interact with the user, e.
arXiv:2607.20764v1 Announce Type: new Abstract: We introduce ARBIGRAPH, a benchmark generator for evaluating whether tool-assisted language agents can retain, update, compose, and discard task-relevan
arXiv:2606.21262v2 Announce Type: replace Abstract: Reinforcement learning for multi-step LLM agents often relies on scalar rewards that indicate success but cannot explain why a trajectory is good or
arXiv:2607.20768v1 Announce Type: cross Abstract: Majority voting over LLMs is widely assumed to benefit from diversity, and diversity measures are used to choose which models to combine. We ask wheth
arXiv:2607.20596v1 Announce Type: cross Abstract: Sparse autoencoder (SAE) features are used to interpret and steer large language models, yet whether a feature's causal role is stable across SAE fami
arXiv:2607.21461v1 Announce Type: new Abstract: Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candida
arXiv:2607.21498v1 Announce Type: cross Abstract: A rhetorical figure that Cicero and Quintilian catalogued two thousand years ago reappears, systematically, in the text of large language models: epan
arXiv:2607.21370v1 Announce Type: new Abstract: Quantitative drug-induced sleep endoscopy (DISE) requires reliable airway boundaries at specific anatomical levels. Pixel-level DISE annotations are sca
arXiv:2607.20439v1 Announce Type: new Abstract: Political evasion is difficult to detect because evasive answers often appear cooperative while avoiding concrete commitment. We present AsymVerify, a c
arXiv:2607.20493v1 Announce Type: new Abstract: Deep learning has led to remarkable progress in artificial intelligence, particularly in robotics, imaging and sound processing. However, a major limita
arXiv:2607.20524v1 Announce Type: new Abstract: Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual retrieval rema
arXiv:2607.20694v1 Announce Type: new Abstract: Personal and organizational planning systems maintain two records that drift apart: what was planned (a task's effort budget) and what was done (a logge
arXiv:2607.21106v1 Announce Type: new Abstract: Effective memory is crucial for LLM agents, yet constructing it effectively remains challenging. A memory-construction policy decides what information t
arXiv:2607.21004v1 Announce Type: new Abstract: Recently, cross-domain few-shot facial expression recognition (CF-FER) has received considerable attention. However, the performance of existing CF-FER
arXiv:2607.20848v1 Announce Type: new Abstract: Medical LLMs are often evaluated by whether they select the correct diagnosis, but diagnostic accuracy alone does not show whether the model used the ca
arXiv:2607.20827v1 Announce Type: new Abstract: LLM agents choose tools and arguments from context that mixes user requests, tool outputs, retrieved records, memory, and untrusted text. Evidence can b
arXiv:2607.21173v1 Announce Type: new Abstract: While automated research systems promise to accelerate empirical analysis, they are prone to silent failures: instances in which analysis code executes
arXiv:2607.21083v1 Announce Type: cross Abstract: B-spline regression constitutes a widely used framework for nonparametric modeling. The performance of this methodology depends on specifying the numb
arXiv:2607.20525v1 Announce Type: new Abstract: OpenAI's recent disproof of the Erdos unit distance conjecture marked a milestone for AI in mathematics. It also inspired another breakthrough: a human
arXiv:2607.20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time. When an individual agent becomes overloaded at runtime, for example by mixing
arXiv:2607.21588v1 Announce Type: new Abstract: Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale b
arXiv:2607.20660v1 Announce Type: new Abstract: Recent 3D generative models produce high-quality geometry from a single image using large-scale priors and diffusion architectures. However, they assume
arXiv:2601.07556v2 Announce Type: replace-cross Abstract: Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) face significant deployment challenges due to inter-subject variability, sig
arXiv:2607.21579v1 Announce Type: cross Abstract: Barzilai--Borwein (BB) method has shown strong practical performance in continuous optimization, yet its convergence dynamics remains poorly understoo
arXiv:2607.21267v1 Announce Type: new Abstract: Comprehensive basketball video understanding requires resolving not only what event occurs, but also who is responsible and when the key evidence appear
arXiv:2607.20582v1 Announce Type: cross Abstract: Machine learning models for medical image analysis typically lack a reliable measure of confidence, limiting their use in ambiguous or atypical cases.
arXiv:2607.20441v1 Announce Type: new Abstract: Every information ecosystem produces beliefs that shape strategic decisions. Both human analysts and AI systems inherit the blind spots of their informa
arXiv:2607.20476v1 Announce Type: new Abstract: We present an empirical benchmark evaluating how five large language models assess multisensor physical hazard data. Testing 60 scenarios across three c
arXiv:2607.20471v1 Announce Type: new Abstract: Personalization, the act of varying a message to induce action from a specific receiver while keeping sender, channel, and time fixed, has a long tradit
arXiv:2602.20114v2 Announce Type: replace-cross Abstract: Machine unlearning (MU) refers to the post-training capability to remove (the influence of) training examples that are incorrect, biased, or l
arXiv:2607.20950v1 Announce Type: new Abstract: BoN improves model outputs by sampling several candidates and selecting one with a proxy score, but it assumes that complete candidates can be evaluated
arXiv:2607.21571v1 Announce Type: new Abstract: Embodied question answering (EQA) is traditionally evaluated under an episodic formulation, where agents solve each task independently and reset interna
arXiv:2607.20832v1 Announce Type: cross Abstract: Advanced Persistent Threats (APTs) remain difficult to detect because only a small fraction of events in large-scale logs are attack-related, and inve
arXiv:2607.20981v1 Announce Type: new Abstract: Efficient multimodal inference is increasingly constrained not only by model quality or FLOP count, but also by the cost of preserving, moving, routing,
arXiv:2607.20479v1 Announce Type: new Abstract: Training probes to detect deceptive outputs from large language models is still an open problem. Recent work has demonstrated that detection probes fail
arXiv:2607.20550v1 Announce Type: cross Abstract: The traditional 'one drug, one target' paradigm of structure-based drug design (SBDD) frequently proves inadequate for treating multifactorial disease
arXiv:2607.21573v1 Announce Type: cross Abstract: Faithful explanations of time-series classifiers should identify subsequences that are not only sufficient to preserve a black-box model's prediction,
arXiv:2607.21558v1 Announce Type: new Abstract: Building socially calibrated large language models, which can learn from others without simply yielding to them, requires more than reducing sycophancy
arXiv:2512.04223v2 Announce Type: replace Abstract: Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging. We demonstrate ActVAE, a deep conditional-g
arXiv:2512.16607v2 Announce Type: replace-cross Abstract: Sampling configurations in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators address this pr