Knowledge catalogue
arXiv:2610.17836v1 Announce Type: cross Abstract: Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form
arXiv:2610.14252v1 Announce Type: new Abstract: Looped transformers reuse the same layer multiple times, allowing inference compute to be adjusted without changing the model. Recent adaptive-depth met
arXiv:2610.15308v1 Announce Type: new Abstract: Combining aerial robots with soft continuum manipulators promises safe, compliant interaction with the environment, but the coupled dynamics of a free-f
arXiv:2503.11147v2 Announce Type: replace Abstract: Sharpness-Aware Minimization (SAM) is an optimization method that improves the generalization performance of machine learning models. Despite its ef
arXiv:2610.14589v1 Announce Type: new Abstract: Walking sequences reveal human-interpretable attributes related to body structure, posture, and motion. Gait attribute recognition provides a concrete r
models: drop the t_embd expand of the embedding graphs (#30461) These graphs only produce embeddings, and build_pooling() already expands its output for every pooling type, so the expand at the end of
arXiv:1808.02932v5 Announce Type: replace-cross Abstract: In stochastic multi-armed bandits, rewards are generated by an unknown distribution; the world's reward uncertainty induces the exploration-ex
arXiv:2506.00482v2 Announce Type: replace Abstract: LLM benchmarks are often treated as coherent measurement units, yet they are heterogeneous collections of instances spanning diverse domains, skills
arXiv:2607.01416v2 Announce Type: replace Abstract: Concept Bottleneck Models (CBMs) provide an intrinsically interpretable alternative to post-hoc explanations. However, existing CBMs often rely on p
arXiv:2610.18167v1 Announce Type: new Abstract: End-to-end autonomous driving has achieved remarkable progress by planning trajectories directly from raw sensor data. Yet the step from planning to con
arXiv:2610.14862v1 Announce Type: new Abstract: Machine learning models for protein-ligand interactions commonly rely on atomic coordinates, which are constructed by fitting molecular models to experi
arXiv:2610.17983v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models, such as pi_{0.5}, have demonstrated impressive generalization capabilities by scaling behavior cloning acros
arXiv:2409.17397v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are increasingly being integrated into various medical fields, including mental health support systems. However,
arXiv:2610.14413v1 Announce Type: new Abstract: Computational coarse-graining aims to replace a sequence of updates with a cheaper module while preserving task performance. We present a continuation-r
arXiv:2610.17651v1 Announce Type: cross Abstract: Recent coding large language models (LLMs) can synthesize dynamic 4D worlds from natural language, but the resulting content remains a visual artifact
arXiv:2606.15320v2 Announce Type: replace Abstract: Multimodal large language models have made rapid progress in video temporal grounding, yet real-world applications routinely require localizing ever
arXiv:2610.17713v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has been widely used, yet the adapted model suffers catastrophic forgetting of pre-trained knowledge. While most
Connect your ChatGPT and Claude subscriptions to Grok @bot with the prompt below (@elonmusk approved): Connect my Claude and ChatGPT subscriptions so all of my bots can use them. 1. On your computer,
arXiv:2510.06640v3 Announce Type: replace-cross Abstract: State Space Models (SSMs) have emerged as efficient, linear-time alternatives to Transformer-Based Models (TBMs) for long-context processing.
arXiv:2610.15250v1 Announce Type: new Abstract: Reconstruction-based detectors for multivariate time series anomaly detection typically learn a single entangled representation of normal behavior and f
arXiv:2610.16546v1 Announce Type: new Abstract: Basic probabilistic models form the basis of modern deep learning largely by defining the final activation layer of a network and allowing the networks
arXiv:2610.14710v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for text-attributed graph learning by leveraging rich pre-trained knowledge and language unders
arXiv:2610.18609v1 Announce Type: cross Abstract: Direct Preference Optimization (DPO) has become a widely adopted approach for aligning vision-language models (VLMs), yet the security of multimodal p
arXiv:2610.18819v1 Announce Type: new Abstract: Complex image editing requires faithful task interpretation and accurate execution, but a failed edit does not reveal which responsibility failed. We pr
arXiv:2610.18018v1 Announce Type: new Abstract: Low-light image enhancement (LLIE) is an important image/video processing problem for reliable visual systems under poor illumination, yet it remains ch
arXiv:2601.06157v2 Announce Type: replace Abstract: Alignment in large language models (LLMs) is still largely static: after training, the policy is frozen. DPO, GRPO methods typically imprint one beh
arXiv:2610.16176v1 Announce Type: new Abstract: Situated 3D Question Answering requires models to interpret a 3D scene relative to an agent's pose. Existing methods commonly convey that pose through a
arXiv:2610.16172v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) achieve strong results on video question answering, yet often fail to recover the temporal structure tha
arXiv:2610.14948v1 Announce Type: new Abstract: Temporal point processes are used both to measure market mechanisms, for example how fast prices absorb news and how much of the reaction is the market
arXiv:2610.16161v1 Announce Type: new Abstract: A fruitful approach towards understanding generalization in machine learning involves characterizing how models separate generalizable structure from id
arXiv:2610.17552v1 Announce Type: new Abstract: Recent video super-resolution (VSR) models achieve efficient one-step generation through diffusion distillation. However, high-resolution inference rema
arXiv:2610.14445v1 Announce Type: new Abstract: In film production, chroma keying composites an approved foreground performance into a new environment, but conventional workflows require coordinated b
arXiv:2610.15390v1 Announce Type: cross Abstract: Audio Large Language Models (Audio LLMs) are embedded in agents for smart homes, vehicles, wearables, and embodied systems, where acoustic inputs can
arXiv:2610.16984v1 Announce Type: new Abstract: Offline reinforcement learning aims to learn effective policies from fixed datasets without environment interaction. Although generative models such as
arXiv:2610.18634v1 Announce Type: new Abstract: Recent studies investigate the weight geometry through its spectral structure, which characterizes how the weights transform different input directions.
arXiv:2602.03945v2 Announce Type: replace Abstract: Simple row-wise predictors that score each row independently remain competitive with far more expressive architectures on tabular benchmarks, which
arXiv:2610.14224v1 Announce Type: cross Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a p
arXiv:2610.17684v1 Announce Type: new Abstract: High-fidelity surgical video generation is increasingly important for surgical education, simulation, and downstream AI training, as conventional simula
arXiv:2603.21862v2 Announce Type: replace Abstract: Scaling laws govern macroscopic resource allocation for LLMs, yet precise architectural configurations for Mixture-of-Experts (MoE) models remain gu
arXiv:2610.16589v1 Announce Type: new Abstract: Electroencephalography (EEG) representation learning is challenged by noisy signals, inter-subject variability, and heterogeneous electrode montages in
arXiv:2603.15228v3 Announce Type: replace Abstract: Unified Multimodal Models struggle to bridge the fundamental gap between the abstract representations needed for visual understanding and the detail
arXiv:2610.17061v1 Announce Type: cross Abstract: We study in-context binary classification in a simple attention model trained to minimise a logistic loss. Each prompt has a latent logistic teacher,
arXiv:2610.16224v1 Announce Type: new Abstract: Text-to-image models have made remarkable progress in visual fidelity and instruction following, yet complex compositional generation remains challengin
arXiv:2610.17637v1 Announce Type: new Abstract: Since scaling laws were introduced for modern LLMs (Kaplan et al., 2020), several works have extended them to account for quantization, tracking the eme
arXiv:2610.18642v1 Announce Type: new Abstract: The Key-Value (KV) cache constitutes a dominant source of memory consumption during large language model (LLM) inference, particularly in long-context a
arXiv:2610.17347v1 Announce Type: new Abstract: We revisit the statistical mechanics of Transformer attention via modern Hopfield models. We present a free-energy formalism to characterize parameter r
arXiv:2610.17203v1 Announce Type: new Abstract: Large language models can answer the same question in different styles, but users' preferred styles are hidden and vary across users and questions. We s
arXiv:2610.18670v1 Announce Type: cross Abstract: Reliable robot autonomy requires reasoning about the world from partial and noisy observations. However, many perception systems model only visible sc
arXiv:2511.18825v3 Announce Type: replace Abstract: Evaluating AI-generated video (AIGV) quality hinges on three crucial dimensions: visual quality, dynamic quality, and text-video alignment. While nu
MacStudio 128GB M1 ultra I'm curious how people who can run both models are deciding which one to work with. DeepSeekv4 benchmarks right below GPT 5.5 high. Flash Next benchmarks right above 5.4/5.5 x
arXiv:2610.16427v1 Announce Type: new Abstract: Recursive reasoning models such as the Tiny Recursive Model (TRM) iteratively refine two latent states: an answer state y, decoded into the output, and
arXiv:2606.15734v2 Announce Type: replace-cross Abstract: Continual post-training enables models to absorb emerging knowledge after deployment, but repeatedly updating shared parameters can accumulate
arXiv:2610.16396v1 Announce Type: new Abstract: Guided sampling with sparse observations and PDE residuals has recently been combined with neural operator priors to solve partial differential equation
arXiv:2610.15163v1 Announce Type: cross Abstract: A language model's ability to solve a task individually does not ensure that a team can solve it under unequal access to information and communication
arXiv:2610.14439v1 Announce Type: new Abstract: Large multimodal models (LMMs) need to understand how entities move and interact as a scene develops. This requires following the same entities over tim
arXiv:2610.15117v1 Announce Type: new Abstract: To evaluate whether a compact bidirectional state-space autoencoder improves joint classification of species, cell type, and disease state from single-n
arXiv:2610.15617v1 Announce Type: cross Abstract: Recursive synthetic-data training can degrade a fitted model, but its effects on the variable-screening stage that precedes model fitting are less und
arXiv:2610.14644v1 Announce Type: cross Abstract: Fine-grained mixture-of-experts (MoE) models commonly increase expert count by narrowing each expert, treating expert shape as a by-product of routing
arXiv:2606.16953v2 Announce Type: replace Abstract: Urban sidewalk navigation presents significant challenges due to complex structural layouts, dynamic pedestrian behaviors, and long distances. While
arXiv:2610.14512v1 Announce Type: new Abstract: Objective. To evaluate whether spatial context and temporal modeling improve tissue recognition during optical trocar entry, where multiple abdominal-wa