Knowledge catalogue
arXiv:2608.24568v1 Announce Type: cross Abstract: Deep neural networks generalize well despite their highly nonconvex, overparameterized loss landscapes, a phenomenon often associated with the geometr
arXiv:2601.19435v2 Announce Type: replace-cross Abstract: Sustainable monetization of large language models (LLMs) remains a critical open challenge. Traditional search advertising, which relies on st
arXiv:2402.11215v4 Announce Type: replace Abstract: The choice of batch size in minibatch stochastic gradient optimization is critical for both optimization and generalization performance in large-sca
arXiv:2502.21099v3 Announce Type: replace-cross Abstract: This paper proposes {sf AEPG-SPIDER}, an Adaptive Extrapolated Proximal Gradient (AEPG) method with variance reduction for minimizing composit
arXiv:2511.02605v3 Announce Type: replace Abstract: Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specificati
arXiv:2608.24361v1 Announce Type: new Abstract: Multi-agent LLM systems are increasingly deployed in real-world applications, where failures can be costly and difficult to localize. Despite growing ef
arXiv:2509.13577v3 Announce Type: replace Abstract: Trustworthy trajectory prediction grounds autonomous vehicle (AV) safety, yet deployed models inevitably face out-of-distribution (OOD) scenes. Prio
arXiv:2512.00342v2 Announce Type: replace Abstract: Real-world intelligence systems usually operate by combining offline learning and online adaptation with highly correlated and non-stationary system
arXiv:2603.10068v2 Announce Type: replace-cross Abstract: Most adversarial evaluations of large language model (LLM) safety assess single prompts and report binary pass/fail outcomes, which fails to c
arXiv:2608.23864v1 Announce Type: new Abstract: Visual tokenizers increasingly inject semantic supervision into latent spaces to make downstream diffusion easier. Yet how these semantics should be org
arXiv:2608.23740v1 Announce Type: new Abstract: Concurrent multi-agent coding promises division of labor across modules, robustness through redundancy, and parallel exploration at the natural granular
arXiv:2608.24076v1 Announce Type: new Abstract: Evaluation of agentic information retrieval remains limited to scripted interactions with uniform users, missing both natural personality diversity and
arXiv:2608.24114v1 Announce Type: new Abstract: Training multi-turn LLM agents with reinforcement learning typically relies on trajectory-level rewards, which assign a uniform advantage to every step
arXiv:2608.23642v1 Announce Type: new Abstract: AI agents pose significant risks as they are granted increasing autonomy. A commonly proposed solution is human oversight and keeping a ''human in the l
arXiv:2608.23875v1 Announce Type: new Abstract: Artificial Intelligence (AI) algorithms frequently learn creative and unexpected solutions, surprising even expert researchers who develop and study the
arXiv:2608.24046v1 Announce Type: new Abstract: When an AI algorithm makes decisions that affect more than one person, aligning it becomes a problem of social choice: how should people's divergent pre
arXiv:2608.24051v1 Announce Type: new Abstract: Can a sequence model remain competitive with only a few thousand parameters and an explicitly auditable prediction interface? We introduce ALPHABET, a c
arXiv:2608.24175v1 Announce Type: new Abstract: Iterated Function Systems (IFS) generate self-similar fractals from a few contractive affine maps. The forward map from parameters to images is computat
arXiv:2601.19509v2 Announce Type: replace Abstract: In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into t
arXiv:2608.24127v1 Announce Type: cross Abstract: Telephone fraud is pervasive and costly, but its inner workings are rarely observed at scale. We analyze a complete corpus of 10,211 inbound scam and
arXiv:2602.03702v2 Announce Type: replace-cross Abstract: Large language models are increasingly trained in continual or open-ended settings, where the total training horizon is not known in advance.
arXiv:2608.23816v1 Announce Type: new Abstract: Quantized fine-tuning (QLoRA) saves memory but not time. It dequantizes every 4-bit weight on the fly, so it trains more slowly than fp16 LoRA. We prese
arXiv:2608.24099v1 Announce Type: new Abstract: GUI agents often encounter dynamic anomalies when deployed on Android devices, from unexpected pop-ups to action misuse, yet existing benchmarks lack sy
arXiv:2512.23245v3 Announce Type: replace Abstract: Recent text-to-image diffusion models have significantly improved visual quality and text alignment. However, generating a sequence of images while
arXiv:2506.02825v3 Announce Type: replace-cross Abstract: We present the OmniMatch algorithm for seeded multiple graph matching. In the setting of d-dimensional Random Dot Product Graphs (RDPG), we pr
arXiv:2608.23640v1 Announce Type: new Abstract: When a large language model (LLM) is asked to write a person's life, how much of what it writes actually happened? We present a scene-level case-study a
arXiv:2510.21536v5 Announce Type: replace-cross Abstract: Free-space segmentation is essential for autonomous robots to identify drivable regions and navigate safely across indoor, outdoor, and road-s
arXiv:2608.23670v1 Announce Type: new Abstract: LLM-based agents execute multi-step tasks, but their behavioral structure remains opaque: long unstructured traces resist the safety auditing and runtim
arXiv:2608.23842v1 Announce Type: cross Abstract: DevOps programming (e.g., using CLI/API scripts or IaC frameworks) is key to cloud infrastructure management. Unlike traditional programming tasks, De
arXiv:2608.24807v1 Announce Type: cross Abstract: Model cards are structured documents that summarize key information about machine learning models to improve transparency, usability, and accountabili
arXiv:2608.23691v1 Announce Type: new Abstract: We study autonomous mathematical discovery in the Station, an open-world multi-agent environment in which AI agents from different model families pursue
arXiv:2608.24364v1 Announce Type: new Abstract: Self-supervised pretraining enables transferable representations for medical imaging, yet most CT encoders remain biased toward coarse semantic understa
arXiv:2608.24627v1 Announce Type: new Abstract: We study adversarial bandit maximization of monotone submodular functions under a matroid constraint. For a rank-k matroid on n elements, we give a rand
arXiv:2603.05335v3 Announce Type: replace-cross Abstract: Modern predictive systems combine predictors, sequential monitors, prediction sets, and online strategies, each with a different certificate o
arXiv:2608.24219v1 Announce Type: new Abstract: Facial attractiveness has been linked to statistical regularities such as symmetry and averageness, suggesting that beauty may depend on the ease with w
arXiv:2608.24858v1 Announce Type: new Abstract: Marginalized importance weighting evaluates a target policy by reweighting offline state-action samples with its discounted occupancy ratio, characteriz
arXiv:2608.23898v1 Announce Type: new Abstract: We introduce BenchBench-Protocol, a benchmark for large language models of 149 protocol-modification tasks recovered from modifications that scientists
arXiv:2608.24314v1 Announce Type: new Abstract: Evaluating conversational voice agents at scale re- quires reliable assessment methods that capture both observ- able interaction quality and the contex
arXiv:2608.24258v1 Announce Type: new Abstract: AI systems are increasingly evaluated for legally accountable settings, where correct outputs must also be justifiable against an applicable legal stand
arXiv:2608.24024v1 Announce Type: new Abstract: Confidence-based voting aggregates parallel LLM rollouts by weighting each with internal signals such as token log probabilities, and has been actively
arXiv:2608.23653v1 Announce Type: cross Abstract: AI agents are increasingly used for simulation-driven engineering. Physical system modeling presents different requirements from general-purpose code
arXiv:2603.17170v2 Announce Type: replace-cross Abstract: AI agents increasingly execute users' natural-language (NL) tasks by calling Web services, yet today's Web authorizes these calls through OAut
arXiv:2608.24482v1 Announce Type: cross Abstract: Mechanistic Localization bridges mechanistic interpretability and post-training optimization by isolating critical parameters via interpretative appro
arXiv:2608.23858v1 Announce Type: cross Abstract: The Agent Payments Protocol (AP2), introduced by Google, enables large language model (LLM)-driven shopping agents to authorize and execute payments o
arXiv:2608.24762v1 Announce Type: cross Abstract: Many disentanglement methods represent generative factors using Euclidean product coordinates, although the underlying factor spaces may wrap, collaps
arXiv:2608.24823v1 Announce Type: new Abstract: Spatially resolved biology requires representations that preserve biological neighborhood structure rather than only exact cross-modal correspondences.
arXiv:2608.23974v1 Announce Type: new Abstract: Breast ultrasound (BUS) is widely used for breast cancer diagnosis yet remains operator-dependent. While deep learning shows promise, ensuring diagnosti
arXiv:2608.24372v1 Announce Type: new Abstract: AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that ar
arXiv:2608.23994v1 Announce Type: new Abstract: Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in mac
arXiv:2511.00260v2 Announce Type: replace Abstract: Anatomy-aware colonoscopic navigation requires localizing partial endoscopic observations on a stable 3D reference to support coverage assessment, r
arXiv:2608.24794v1 Announce Type: new Abstract: Outcome-supervised search agents learn when and how to retrieve evidence, but terminal rewards neither localize intermediate errors nor redirect an ongo
arXiv:2608.23744v1 Announce Type: new Abstract: Split conformal prediction, not the pruning rule, supplies finite-sample marginal coverage once a pruned model is fixed independently of the conformal c
arXiv:2608.24319v1 Announce Type: new Abstract: An intelligent system does not merely reason: it governs its own reasoning - how much to compute, when to stop, which module to activate. Can that role
arXiv:2507.21790v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are becoming widely used to support various workflows across different disciplines, yet their potential in discre
arXiv:2601.00282v2 Announce Type: replace-cross Abstract: Quantization is widely used to accelerate inference and streamline the deployment of large language models (LLMs), yet its effects on self-exp
arXiv:2608.24282v1 Announce Type: new Abstract: Adaptive LiDAR scanning concentrates a limited sensing budget on regions of interest predicted from past object tracks, lowering data volume in autonomo
arXiv:2608.24217v1 Announce Type: new Abstract: We propose CARO, a contact-agnostic residual observation framework for policy adaptation. CARO embeds a fixed-base Euler--Lagrange model into the reinfo
arXiv:2608.24303v1 Announce Type: new Abstract: Transitioning from bespoke time series models towards time series foundation models changes the relationship of model and application from one-to-one to
arXiv:2608.24632v1 Announce Type: new Abstract: Accurate assessment of student competencies is essential for enabling educators to identify individual needs, design targeted interventions, and evaluat
arXiv:2608.24033v1 Announce Type: cross Abstract: Time series classification underpins applications in healthcare, sensing, and industrial monitoring. Although time series foundation models support fo