Search: “models”

arXiv:2605.31108v1 Announce Type: new Abstract: In this work we introduce a novel approach to domain incremental learning, adapting models over time to evolving, non-stationary data. In contrast to ot

arXiv:2510.02060v2 Announce Type: replace Abstract: In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specifi

arXiv:2605.31284v1 Announce Type: cross Abstract: The morphological analysis of mitochondria in fluorescence microscopy (FM) is crucial for understanding cellular health, energy production, and metabo

arXiv:2601.05134v2 Announce Type: replace Abstract: Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches propos

arXiv:2605.31210v1 Announce Type: cross Abstract: Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory predic

arXiv:2511.11440v3 Announce Type: replace-cross Abstract: Performance gains of Vision Language Models (VLMs) obtained by fine-tuning are generally based on ad hoc data collection and annotation of rea

arXiv:2503.21168v3 Announce Type: replace Abstract: Robots navigating human-populated environments must avoid collisions while respecting the social structure of crowds, particularly the implicit boun

arXiv:2605.30537v1 Announce Type: new Abstract: Data selection is increasingly used to reduce the cost of large language model (LLM) fine-tuning, with recent methods prioritizing samples by current ut

arXiv:2512.05038v2 Announce Type: replace Abstract: Concept vectors aim to enhance model interpretability by linking internal representations with human-understandable semantics, but their practical u

arXiv:2605.31294v1 Announce Type: new Abstract: Recent advances in Audio-LLMs like GPT-4o have ushered in an era of conversational interaction with language models. Conversational avatars however, sti

arXiv:2411.19463v3 Announce Type: replace-cross Abstract: Retrieval-Augmented Generation (RAG) has emerged as a critical technique for enhancing large language model (LLM) capabilities. However, pract

arXiv:2605.31378v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) still struggle with fine-grained translation quality estimation (QE), even with long reasoning chains. We argue that LRMs

arXiv:2605.30916v1 Announce Type: new Abstract: AI benchmarks have well-documented limitations, with prior work examining contamination, saturation, and construct underspecification. Aggregation has r

Z-Image is a fast, open-source image model from Alibaba Tongyi Lab that has gained popularity for its speed and image quality. Users on r/StableDiffusion likely discuss techniques for forcing the mode

// The Efficiency Frontier // Cool paper on context management. As agents reuse the same documents and histories across many turns, the cheapest context strategy is not fixed. This work describes a pr

Gary Marcus argues that while Anthropic has better prospects for long-term profitability compared to OpenAI, the overall probability of either company achieving sustained profitability remains modest.

How we contain Claude across products A complaint I often have about sandboxing products is that they are rarely thoroughly documented, and in the absence of detailed documentation it's hard to know h

The efficiency frontier! Where do you think GPT-5.6 will land? Claude Opus 4.8 has landed on DeepSWE Bench, posting a 58% Pass@1 and taking #2 overall behind GPT-5.5. It continues a broader trend: sli

arXiv:2605.29688v1 Announce Type: new Abstract: This paper presents the Tensor Product Network (TPNet), a novel neural architecture for efficient and accurate function approximation and PDE solving. T

arXiv:2605.28975v1 Announce Type: new Abstract: We study the log-alignment ratio (LAR), a measure of parameter-activation alignment, introduced in parameterization theory. We reformulate it as the ove

arXiv:2605.29926v1 Announce Type: new Abstract: Accurate prediction of drug-target interactions (DTI) is critical for drug discovery. Existing methods often rely on single-modal representations (e.g.,

AlloyDB for PostgreSQL is a fully managed, PostgreSQL-compatible database service designed for the most demanding enterprise workloads. It combines the best of PostgreSQL with the power of Google, del

arXiv:2605.30229v1 Announce Type: new Abstract: We use a mean-field-based transformer model to theoretically investigate how auxiliary variables, such as positional encoding, prevent mode collapse of

arXiv:2605.28869v1 Announce Type: cross Abstract: Multimodal learning often suffers from modality imbalance, where modalities that converge faster dominate optimization while others remain undertraine

arXiv:2601.12699v2 Announce Type: replace Abstract: Deep Brain Stimulation (DBS) is an effective treatment for Parkinson's disease, but conventional fixed-parameter stimulation can reduce battery life

arXiv:2605.29560v1 Announce Type: new Abstract: Parameterizing high-fidelity 'digital twins' of batteries is a critical yet challenging inverse problem that hinders the pace of battery innovation. Pre

arXiv:2602.04729v2 Announce Type: replace Abstract: We present a large-scale human evaluation benchmark for assessing cultural localisation in machine translation produced by state-of-the-art multilin

arXiv:2605.29856v1 Announce Type: new Abstract: As a widespread form of informal settlements, urban villages present significant challenges for sustainable urban development and governance. Precise ma

arXiv:2605.30235v1 Announce Type: new Abstract: We present BullingerDB, a large-scale benchmark dataset for historical document analysis based on the correspondence of Heinrich Bullinger (1504-1575).

arXiv:2504.06022v3 Announce Type: replace Abstract: Recently, image-to-video (I2V) diffusion models have demonstrated impressive scene understanding and generative quality, incorporating image conditi

arXiv:2605.29008v1 Announce Type: new Abstract: Driving a system from one state to another through targeted interventions is a fundamental challenge in science, yet most predictive models offer limite

arXiv:2605.29467v1 Announce Type: cross Abstract: Stacking probabilistic building blocks into deeper architectures typically breaks closed-form inference. We show that closed-form inference can be pre

arXiv:2605.30103v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as generators in iterative neural architecture search (NAS), yet no formal convergence theory exists

arXiv:2605.29939v1 Announce Type: cross Abstract: Integrated sensing, communication, and computation (ISCC) provides a promising framework for indoor human-centric applications. In these applications,

arXiv:2605.30215v1 Announce Type: new Abstract: Recent feed-forward 3D reconstruction transformers have scaled to over a billion parameters, following the broader trend of increasing model capacity in

arXiv:2511.05924v3 Announce Type: replace Abstract: Estimating probability density and its score from samples remains a core problem in generative modeling, Bayesian inference, and kinetic theory. Exi

arXiv:2506.05985v3 Announce Type: replace Abstract: A generalist agent must continuously learn and adapt throughout its lifetime, achieving efficient forward transfer while minimizing catastrophic for

arXiv:2605.28890v1 Announce Type: cross Abstract: Large Language Models with Chain-of-Thought reasoning capabilities represent valuable intellectual property, yet existing black-box watermarking metho

arXiv:2605.29669v1 Announce Type: cross Abstract: Recent work in random matrix theory (RMT) has developed the notion of deterministic equivalents: typically linear surrogate models that approximate th

ElevenLabs: ElevenLabs launches Dubbing v2, which it says preserves the original speaker's emotion, tone, and pacing across 90+ languages while staying synced to content — Today we're launching Dubbin

arXiv:2605.29072v1 Announce Type: new Abstract: Accurate estimation and forecasting of energy consumption are important for power-system operation, planning, and demand-side management. In practice, h

arXiv:2511.08949v2 Announce Type: replace Abstract: High-quality datasets are critical for training and evaluating reliable NLP models. In tasks like natural language inference (NLI), human label vari

arXiv:2605.25975v2 Announce Type: replace-cross Abstract: Capturing relightable 3D assets from real-world objects is a widely researched problem. Several per-scene optimization-based methods, based on

arXiv:2605.30062v1 Announce Type: new Abstract: The development of generative artificial intelligence technologies has propelled the visual realism of synthetic images to an unprecedented level. Altho

arXiv:2605.29587v1 Announce Type: cross Abstract: In transcriptomics, gene-set-aware factorization methods such as the Pathway Level Information Extractor (PLIER) are most effective when trained on la

arXiv:2605.30045v1 Announce Type: new Abstract: Video object removal frequently struggles to simultaneously eliminate target objects and their associated physical effects (e.g., smoke, reflections, li