Search: “models”

Post-Training Quantization of Autoregressive Weather Models

arXiv:2610.02511v1 Announce Type: new Abstract: Advancements in high-resolution numerical weather prediction (NWP) and data assimilation (DA) have shaped the developments in deep learning (DL) archite

5 Oct 2026

Human

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

SpectralCache: Accelerating Diffusion-Based World Models via Spectral Feature Caching

DGX agent

arXiv:2610.02660v1 Announce Type: new Abstract: Diffusion-based world models enable high-quality interactive environment generation but suffer from substantial inference overhead due to repeated Trans

researcharxiv-cs-cv

5 Oct 2026

To Jev or Not? Evaluating the Accuracy and Efficiency of Structured Decision Models for Hate-Speech Moderation

arXiv:2610.03324v1 Announce Type: new Abstract: The scale of online content makes hate-speech moderation challenging, while Large Language Models (LLMs) enable harmful material to be produced and adap

researcharxiv-cs-cl

5 Oct 2026

VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models

arXiv:2311.18837v2 Announce Type: replace-cross Abstract: Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, wh

5 Oct 2026

Local Ai

Two ~300B MoE models, each on ONE 128 GB mini PC (AMD Strix Halo): GLM-5.3-Flash at ~580 tok/s prefill, MiMo-V2.6-Flash up to 44 tok/s decode. EXL3 weights + open ROCm engine

We built an engine, Kyojin, on top of ExLlamaV3 for Strix Halo (gfx1151, ROCm), and packed two 300B-class MoE models so each fits one 128 GB machine. First release, all measured on Ryzen AI Max+ 395.

local-air-localllama

3 Oct 2026

Local Ai

Adaptive Conformal Prediction for Image Regression Models with Application to an Inertial Confinement Fusion Emulator

arXiv:2610.00535v1 Announce Type: cross Abstract: Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes se

local-aiarxiv-cs-lg

AURAL: Adaptive Latent Reasoning with Joint Chunk for Speech Language Models

arXiv:2610.01560v1 Announce Type: cross Abstract: Model intelligence and fast response jointly shape the quality of interaction with speech language models, yet remain difficult to achieve together. E

CortexBridge: Cortical Alignment of EEG Montages for Foundation Models

arXiv:2610.01124v1 Announce Type: new Abstract: Electroencephalography (EEG) foundation models are often pretrained with a fixed channel vocabulary or a limited set of montages, making transfer diffic

DeepJEPA: Scaling World Models from Within

arXiv:2610.00368v1 Announce Type: cross Abstract: World-model planners typically scale outward by rolling farther, sampling more trajectories, or optimizing longer, while assigning the same computatio

Evaluating Physical Consistency and Plausibility in Generative Scenario Models for Autonomous Driving

arXiv:2610.01581v1 Announce Type: new Abstract: Generative AI models are increasingly used for scenario generation in autonomous driving. While they can generate realistic-looking scenarios, they ofte

Explainability of Complex AI Models with Correlation Impact Ratio

arXiv:2601.06701v2 Announce Type: replace-cross Abstract: Complex AI systems make better predictions but often lack transparency, limiting trustworthiness, interpretability, and safe deployment. Commo

Grounding Large Language Models in DSGE Simulators for Policy Generation and Forecasting

arXiv:2610.01128v1 Announce Type: new Abstract: Large language models can produce economic policy responses that sound reasonable, but this does not show that their actions are consistent with economi

JEPA-Bisim: Learning Robust Visual Representations for Planning with Joint-Embedding Predictive World Models

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

The Benchmarking Epistemology: Validity Theory for Evaluating Machine Learning Models

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

VETO: Video Efficient Token Optimization for Vision Language Models

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

Video Generation Models: A Survey of Post-Training and Alignment

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

Applications

White Men Without Degrees Receive the Lowest Ratings from Large Language Models

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

applicationsarxiv-cs-ai

Anthropic urges Australia to consider 'conditional approval' for Big Tech to train models on copyrighted works, giving copyright holders a choice to opt out (Josh Butler/The Guardian)

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

model-releasestechmeme

Applications

Anthropomorphism in the age of Large Language Models: An overview of potential risks and mitigations

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

applicationsarxiv-cs-cl

Cognitive Enhancement: Rethinking the Necessity of Role-Playing for Large Language Models

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

safetyarxiv-cs-cl

CollageAttack: Exploiting Cross-Modal Alignment Flaws in T2I Models through Spatial Text Composition

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

Differentiable Structure Learning for Cyclic Linear Gaussian Models with Latent Confounders

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

Fenchel Tilting: Weighted Correction for Efficient Finetuning of Generative Models

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

Fork-dLLM: Avoiding the Flexibility Trap in Diffusion Language Models

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

safetyarxiv-cs-lg

MatGPTQ: Efficient and Accurate Inference over Nested Quantized Models

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

MCD: Causal Distillation of Multimodal In-Context Learning in Large Vision-Language Models

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

safetyarxiv-cs-cl

RATIO: Reasoning Analysis and Token-level Inference Optimization for Quantized Reasoning Models

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

SEAR: Spoofing Evidence-Grounded Audio Reasoning Benchmark for Audio Language Models

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

Security-Enhanced Seed-Based Weight Quantization for Large Language Models

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

SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations

model-releasesarxiv-cs-ro

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

SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models

model-releasesarxiv-cs-ro

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

T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models

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

Applications

UBTree: Parallel Tree Drafting via Unigram and Bigram Models for Speculative Decoding

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

applicationsarxiv-cs-cl

WinoTS: Wavelet-based Self-Distillation for Time Series Models

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

Agents

A thing to know about the AI business is labs that have frontier models can release half-built products and they work surprisingly well beca…

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

agentsethan-mollick--x

Behavioral Capacity Certificates for Quantized Language Models

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,

Beyond Semantics: How Temporal Biases Shape Retrieval in Transformer and State-Space Models

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

Cross-attention encoding models reveal dynamic spatiotemporal routing across human higher visual cortex

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

DatalogBench: Evaluating Large Language Models on Text-to-Datalog Synthesis

model-releasesarxiv-cs-ai

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

Distilling What Matters: Confidence-Aware Selective Distillation for Large Language Models

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

safetyarxiv-cs-cl

Dyad: Extending Large Language Models with Native Typed Decision-Making

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

safetyarxiv-cs-lg

Five local models, 6.8 GB of weights: my open-source Mac meeting notetaker

model-releasesr-localllama

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

HEAR: Real Voices, Real Bias: A Large-Scale Human-Recorded, Demographically Diverse Benchmark for Audio Language Models

model-releasesarxiv-cs-ai

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

i would like to learn deeply about fine-tuning local models before burning money

model-releasesr-localllama

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

MetaCtrl: Your Large Language Models Can Reason Better and More Concisely with a Metacognitive Controller

model-releasesarxiv-cs-ai

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

OmniRoute: Mapping Temporal Semantic Evidence to Audio-Visual Token Budgets for Efficient Omnimodal Large Language Models

model-releasesarxiv-cs-cv

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

Rho: A Foundation for Efficiently Adaptable VLA Models

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

safetyarxiv-cs-ro

S^3: Spectral Null-Space Swap Makes Reasoning Models Efficient

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