Knowledge catalogue
arXiv:2602.18639v2 Announce Type: replace Abstract: World models learned from high-dimensional visual observations allow agents to make decisions and plan directly in latent space, avoiding pixel-leve
arXiv:2510.23191v2 Announce Type: replace Abstract: Predictive benchmarking, evaluating machine learning models based on predictive performance and competitive ranking, is central to machine learning
arXiv:2610.01785v1 Announce Type: cross Abstract: Processing long videos with Vision-Language Models (VLMs) is bottlenecked by the quadratic cost of visual tokens, making long-form inference prohibiti
arXiv:2610.00812v1 Announce Type: cross Abstract: Video generation has rapidly progressed from short, low-quality clips to high-resolution, long-duration sequences with complex spatiotemporal dynamics
arXiv:2610.00185v1 Announce Type: cross Abstract: White men without an undergraduate degree receive the lowest average ratings among eight gender-race-education groups in controlled large-language-mod
Josh Butler / The Guardian: Anthropic urges Australia to consider “conditional approval” for Big Tech to train models on copyrighted works, giving copyright holders a choice to opt out — Maker of Clau
arXiv:2609.38486v1 Announce Type: cross Abstract: Large Language Models (LLMs) and more broadly Artificial Intelligence (AI) systems are often described and understood in human-like terms, a phenomeno
arXiv:2609.39853v1 Announce Type: new Abstract: Role-playing prompting has become a popular yet simple technique for improving LLM reasoning and output quality. However, whether it consistently boosts
arXiv:2609.38253v1 Announce Type: cross Abstract: Text-to-image (T2I) models have substantially improved in language understanding, in-image text rendering, and visual composition, while their safety
arXiv:2609.38618v1 Announce Type: cross Abstract: We study causal structure learning from observational data in linear Gaussian structural causal models in the presence of directed cycles and an unkno
arXiv:2609.40030v1 Announce Type: cross Abstract: Adapting a pretrained generative model to an arbitrary preference expressed as a utility function underlies reward alignment, guided design, and const
arXiv:2609.39859v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have shown strong potential for faster inference through parallel token generation when combined with confidenc
arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize
arXiv:2609.39920v1 Announce Type: cross Abstract: Large vision-language models (LVLMs) exhibit strong multimodal in-context learning (ICL) capabilities, yet this ability degrades substantially as mode
arXiv:2609.39801v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a widely adopted technique for reducing the memory footprint and inference cost of large language models (LL
arXiv:2609.39847v1 Announce Type: cross Abstract: Audio language models (ALMs) are increasingly used for audio deepfake detection (ADD), yet existing benchmarks assess their verdicts or rationale plau
arXiv:2609.38477v1 Announce Type: cross Abstract: Large language models (LLMs) incur substantial storage, memory-bandwidth and energy costs, motivating compact weight representations. Existing seed-ba
arXiv:2609.39873v1 Announce Type: new Abstract: World action models (WAMs) are large embodied policies that jointly predict future video and the actions to execute, emitting a fixed-length action chun
arXiv:2609.39056v1 Announce Type: new Abstract: World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous s
arXiv:2609.39466v1 Announce Type: new Abstract: In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by
arXiv:2609.39972v1 Announce Type: new Abstract: Speculative decoding accelerates language model inference by verifying multiple draft tokens in a single target-model pass. Recent parallel drafters hav
arXiv:2609.39337v1 Announce Type: cross Abstract: Self-supervised pre-training of time series models is currently dominated by next-token prediction and reconstruction objectives. In continuous-valued
A thing to know about the AI business is labs that have frontier models can release half-built products and they work surprisingly well because the AI can just figure stuff out and improvise. Its like
arXiv:2609.37887v1 Announce Type: new Abstract: Activation and key-value cache precision change what a quantized language model computes without altering its stored weights. Direct weight-code bounds,
arXiv:2510.22752v2 Announce Type: replace-cross Abstract: In-context learning is governed by both temporal and semantic relationships, shaping how Large Language Models (LLMs) retrieve contextual info
arXiv:2609.36366v1 Announce Type: cross Abstract: Understanding how the brain parses actions and events from time-varying natural inputs is a central challenge in neuroscience. Recent work has used de
arXiv:2609.37233v1 Announce Type: cross Abstract: Datalog underpins reasoning tasks such as program analysis, but its programs are hard to write. Existing synthesizers automate this task but require u
arXiv:2609.36734v1 Announce Type: new Abstract: Knowledge Distillation (KD) trains a smaller-capacity student model to imitate a larger-capacity teacher model by matching output distributions, implici
arXiv:2609.36116v1 Announce Type: new Abstract: We study how to build more capable general-purpose agents by extending large language models (LLMs) with native typed decision-making. We introduce Dyad
https://preview.redd.it/xs01ds930nsh1.png?width=4800&format=png&auto=webp&s=939762bcf3fbd93e137d4f97e927dda38aaf30fd Back in July I shared LokalBot here. It's a free, open-source Mac app that records
arXiv:2609.35952v1 Announce Type: cross Abstract: We introduce HEAR (Human-recorded Evaluation of Audio-LLM bias by Real speakers), a large-scale, ecologically valid benchmark comprising 87k real huma
There's so many new techniques like RL, RL LoRA, QLoRA, CPT LoRA. I believe i would have a usecase for them, but i don't know where to learn, youtube is filled with bad quality tutorials if i just sea
arXiv:2609.37304v1 Announce Type: new Abstract: Large reasoning models improve performance on challenging problems by allocating additional computation before answering, but longer reasoning does not
arXiv:2609.37052v1 Announce Type: new Abstract: Omnimodal large language models (Omni-LLMs) encode audio and visual streams into temporally interleaved token sequences for multimodal reasoning. Howeve
arXiv:2609.38164v1 Announce Type: new Abstract: General-purpose physical AI models must combine broad visual and linguistic capabilities with precise control across robot embodiments and efficient ada
arXiv:2609.37976v1 Announce Type: cross Abstract: LLMs trained with Chain-of-thought excel in reasoning capability, but often come with excessive token cost. We find that the core of reasoning capacit
arXiv:2609.37759v1 Announce Type: cross Abstract: Vision-language models (VLMs) exhibit strong multimodal capabilities but remain vulnerable to backdoors implanted through poisoned fine-tuning data. E
arXiv:2609.36562v1 Announce Type: cross Abstract: While Multimodal Large Language Models (MLLMs) are increasingly deployed in safety-critical domains, their reliability is threatened by multimodal imp
arXiv:2609.36636v1 Announce Type: cross Abstract: Looped language models (LoopLMs) increase computational depth through parameter sharing, offering a path to scale inference computation without adding
arXiv:2609.32341v1 Announce Type: new Abstract: Transformers and state-space models (SSMs) are two prominent sequential learning architectures, yet their comparison remains largely empirical and exist
arXiv:2609.34982v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models have shown great promise for robotic manipulation by mapping multi-modal semantics to physical actions. However, thi
arXiv:2609.32462v1 Announce Type: new Abstract: Motion-language models are typically scored by motion-language evaluators, but how well these evaluators ground language structure remains unclear. Here
arXiv:2609.32328v1 Announce Type: new Abstract: Data unlearning in diffusion models aims to remove the influence of specific training examples without suppressing the broader concepts they represent.
arXiv:2609.31893v1 Announce Type: new Abstract: Deep reinforcement learning has become a prominent approach to autonomous cyber defense. Existing methods are predominantly model-free and consequently
arXiv:2609.34933v1 Announce Type: new Abstract: Memorization has been proposed as a mechanism to explain how language models fit the tail of their training distributions, but its training dynamics are
arXiv:2609.33936v1 Announce Type: new Abstract: Recent research argues that language models exhibit pronounced homogeneity in open-ended generation, framing such behavior as an Artificial Hivemind tha
arXiv:2603.11950v2 Announce Type: replace-cross Abstract: Multimodal language models have demonstrated strong semantic understanding and reasoning over physiological and behavioral signals captured fr
arXiv:2512.06983v2 Announce Type: replace-cross Abstract: World models enable agents to plan within imagined environments by predicting future states conditioned on past observations and actions. Howe
arXiv:2609.32298v1 Announce Type: new Abstract: Drifting Models train a one-step generator by recomputing a finite-sample drift field at every iteration and taking an optimizer step toward the drifted
arXiv:2609.33510v1 Announce Type: new Abstract: Deep generative models are used to solve partial differential equations and model distributions of physical system states, but ensuring that the generat
arXiv:2306.00206v3 Announce Type: replace Abstract: Embedding models, often obtained via self-supervised learning, extract general-purpose representations from data. Quantifying the reliability of the
arXiv:2609.34826v1 Announce Type: new Abstract: Humans often solve spatial problems by mentally simulating visual transformations. In contrast, conventional vision-language models (VLMs) reason primar
arXiv:2609.34206v1 Announce Type: new Abstract: Latent world models offer a promising way to improve Vision-Language-Action policies by capturing the consequences of actions. However, models trained p
arXiv:2602.02043v2 Announce Type: replace-cross Abstract: Modern vision-language models struggle with basic compositional reasoning, failing to bind attributes to objects or relations to their referen
arXiv:2609.30540v1 Announce Type: cross Abstract: Text-to-music systems are assessed on audio quality and on how faithfully the music follows its prompt, and the CLAP score, the cosine similarity betw
arXiv:2609.30634v1 Announce Type: new Abstract: How many assignments can a language model recall before it loses track of which value belongs to which entity? We measure this limit using continuous re
arXiv:2609.30773v1 Announce Type: new Abstract: Tandem speech-to-speech architectures couple a responsive speech frontend with an asynchronous text backend. In KAME, a large language model (LLM) serve
arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the
arXiv:2609.30578v1 Announce Type: new Abstract: Language models gain accuracy from more compute at test time, but majority voting over independent samples saturates: as samples grow, the vote converge
arXiv:2609.31245v1 Announce Type: new Abstract: Individuals turn to large language models (LLMs) for guidance across a wide range of economic tasks, from comparing loan options and planning savings to