Search: “llm”

Liquid just dropped d1-omni-600M and it's a decision model! I ported it to runntime, the WebGPU inference library I'm currently working on. It's plain TypeScript on top of TypeGPU with no WASM and vir

arXiv:2610.07766v1 Announce Type: new Abstract: We present Open Telco (OTel), an open telecom AI resource that releases derived telecom datasets for retrieval, reranking, instruction tuning, and safet

arXiv:2507.02115v3 Announce Type: cross Abstract: Synthesizing second-language (L2) speech is potentially highly valued for L2 language learning experience and feedback. However, due to the lack of L2

arXiv:2602.02624v2 Announce Type: replace-cross Abstract: Several data protection laws restrict processing that reveals political opinions, irrespective of the controller's intent. Whether recommender

arXiv:2610.08621v1 Announce Type: new Abstract: Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experie

arXiv:2610.08561v1 Announce Type: new Abstract: Reinforcement learning (RL) has become a standard tool for post-training language models on reasoning tasks, where the policy is updated by reward feedb

arXiv:2610.08287v1 Announce Type: new Abstract: Understanding the structural relationships among attributes in tabular data is fundamental to machine learning and pattern recognition. While functional

arXiv:2610.07792v1 Announce Type: cross Abstract: Large language model agents are increasingly deployed to perform complex tasks in real-world environments. However, the knowledge required for correct

Hey everyone, I'm looking for a small uncensored model to run locally with Ollama. Nothing fancy. I just want something I can ask everyday questions without it refusing or lecturing me every other mes

arXiv:2610.07091v1 Announce Type: new Abstract: The evolution of machine learning has progressively changed where intelligence resides in an AI system. In conventional machine learning the task, data

arXiv:2610.06940v1 Announce Type: new Abstract: Continual adaptation of language models can change their output distribution on prompts learned earlier, while retaining every old prompt-answer pair ma

arXiv:2610.08161v1 Announce Type: cross Abstract: Ambient documentation systems are rapidly gaining adoption, yet their impact on clinical note quality remains poorly characterized. We introduce MedCo

arXiv:2610.08026v1 Announce Type: new Abstract: In recent years, several methods for detecting when large language models (LLMs) hallucinate have been developed. These methods are often benchmarked wi

arXiv:2610.07309v1 Announce Type: new Abstract: Long-term-memory agents can retrieve relevant information that is inadmissible for the current request because it belongs to another principal, violates

arXiv:2607.17524v2 Announce Type: replace Abstract: We propose Token-Level Off-Policy Labeling (TOPL), an off-policy training paradigm that reframes post-training as a token-level correctness predicti

arXiv:2610.07350v1 Announce Type: new Abstract: Long chain-of-thought reasoning increases sequential decoding cost while creating a growing history of potentially reusable continuations. We investigat

arXiv:2610.07261v1 Announce Type: new Abstract: A team of coding agents can look fine agent by agent yet fail as a team: each passes its own tests while the merged result is broken, and single-agent e

arXiv:2610.07405v1 Announce Type: new Abstract: pass@k, the fraction of problems a model solves within k sampled attempts, is the field's default protocol for deciding whether reinforcement-learning (

arXiv:2610.05281v2 Announce Type: replace Abstract: Modern agents increasingly ground their reasoning in observations returned by tools, such as file contents read from a workspace. However, the data

arXiv:2610.06992v1 Announce Type: new Abstract: Improved traffic forecasts do not necessarily yield better signal-control decisions. We investigate this gap through a layered diagnostic study using 29

arXiv:2610.07018v1 Announce Type: new Abstract: Vision-language models (VLMs) have achieved strong performance in multimodal reasoning, yet they remain prone to generating plausible but incorrect answ

arXiv:2610.07553v1 Announce Type: cross Abstract: LoRA fine-tuning adapts small language models (SLMs) to heterogeneous instruction data within a low-rank update subspace, making it vulnerable to thre

arXiv:2610.05334v1 Announce Type: cross Abstract: Frameworks that use large language models for scientific discovery typically rely on a fixed, human-designed algorithm that decides what the model see

arXiv:2610.04801v1 Announce Type: cross Abstract: A useful model of AI inference infrastructure must specify the system state, the information available to an observer, and the decisions the model is

arXiv:2610.05367v1 Announce Type: cross Abstract: Proof auto-formalization translates natural-language (NL) theorems and proofs into a formal language (FL) such as Lean, enabling mechanical verificati

arXiv:2610.04927v1 Announce Type: cross Abstract: Agentic machine learning engineering (MLE) is an emerging AI4SE application for complex ML tasks and a step toward recursive self-improvement of AI sy

arXiv:2610.05745v1 Announce Type: new Abstract: Post-training quantization has been shown to preserve VLA performance under standard evaluation conditions, but whether it preserves the full-precision

arXiv:2610.05481v1 Announce Type: cross Abstract: Large language models for code generation often fail on execution, multilingual coverage, and contamination control, especially under frozen backbone

arXiv:2610.05936v1 Announce Type: cross Abstract: One of the major transformative factors in 6G will be the integration of Artificial Intelligence (AI) to become a native part of Radio Access Network

arXiv:2610.05615v1 Announce Type: cross Abstract: Multi-agent debate (MAD) has emerged as an effective paradigm to improve the reasoning capabilities of large language models (LLMs) and is increasingl

arXiv:2610.04087v1 Announce Type: new Abstract: Agentic workflows are increasingly used across domains such as technology, finance, and enterprise operations. As these agents become more widely deploy

arXiv:2610.04489v1 Announce Type: cross Abstract: Understanding parameter functions helps elucidate the internal mechanisms of large language models (LLMs). However, how to connect parameters from dif

arXiv:2610.04569v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed in safety-critical settings, yet their black-box nature makes it difficult to provide formal gu

arXiv:2610.05450v1 Announce Type: new Abstract: Preference-based alignment methods such as reinforcement learning from human feedback (RLHF) and Nash learning from human feedback (NLHF) aggregate pair

arXiv:2610.06437v1 Announce Type: new Abstract: We introduce Heuristic-Guided Fourier Fine-Tuning (HeuFouFT), a task-guided framework for selecting trainable frequency coordinates in Fourier fine-tuni

arXiv:2610.04344v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has been shown to improve the reasoning capability of large language models (LLMs) across diverse

My YouTube feed was mostly songs, pranks and celebrity clips, so I built a filter that judges each video title with a small decision model running inside Chrome. Nothing is sent anywhere: no server, n

Meet Bev. She is a decision model (the Jev / Nimble kind: you give her a situation and a question, she gives a probability for each answer), fine-tuned on Qwen3.5-9B to pick the worst answer on purpos

arXiv:2610.05428v1 Announce Type: cross Abstract: We study learning in continuous games when players receive only pairwise preference feedback, revealing which of two actions is preferred but neither

arXiv:2610.06844v1 Announce Type: new Abstract: Multimodal Diffusion Transformers (MM-DiTs) jointly process visual and textual representations throughout generation. These models repeatedly update the

arXiv:2610.04518v1 Announce Type: new Abstract: Length generalization is the ability of sequential models to perform well on context lengths unseen during training. In this work, we show that the chal

Last time when I posted on this subReddit to share my work, the response almost made me cry because a tiny demo got so many people talking about this In the past 2 months Ive been training a new model

arXiv:2610.03960v1 Announce Type: cross Abstract: Instruction fine-tuning adapts a pretrained language model to follow instructions by training it on instruction--response pairs from a collection of t

arXiv:2610.05501v1 Announce Type: new Abstract: In this study, we propose a novel rule-based interpretable choice model, Logic-Logit, designed to effectively learn and explain human choices. Choice mo

arXiv:2610.05051v1 Announce Type: new Abstract: Time-series data are often sampled irregularly at high frequencies and exhibit long-range dependencies, which makes long-horizon modelling difficult. Co

Mistral 4 is pretty good at lawyer bench and cyber security bench, but give it's European heritage, one can only assume it's also good at regulation bench Meet Mistral Large 4, aka Le Chonk. • 1T para