Core AI
Large Language Models
Deep dives into GPT, Claude, Gemini, Llama and the transformers powering modern AI
Skills in this topic
5 skills — Sign in to track your progress
LLM Foundations
beginner
Explain how transformers generate text
Prompt Craft
beginner
Write zero-shot and few-shot prompts
LLM Engineering
intermediate
Call LLM APIs with function/tool use
Fine-tuning LLMs
advanced
Prepare fine-tuning datasets
Multimodal LLMs
advanced
Use GPT-4V / Claude Vision for image understanding
Showing 5,061 reads from curated sources
ArXiv cs.AI
🧠 Large Language Models
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⚡ AI Lesson
2w ago
Justified or Just Convincing? Error Verifiability as a Dimension of LLM Quality
arXiv:2604.04418v1 Announce Type: cross Abstract: As LLMs are deployed in high-stakes settings, users must judge the correctness of individual responses, often
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Is Prompt Selection Necessary for Task-Free Online Continual Learning?
arXiv:2604.04420v1 Announce Type: cross Abstract: Task-free online continual learning has recently emerged as a realistic paradigm for addressing continual lear
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Training Transformers in Cosine Coefficient Space
arXiv:2604.04440v1 Announce Type: cross Abstract: We parameterize the weight matrices of a transformer in the two-dimensional discrete cosine transform (DCT) do
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Conversational Control with Ontologies for Large Language Models: A Lightweight Framework for Constrained Generation
arXiv:2604.04450v1 Announce Type: cross Abstract: Conversational agents based on Large Language Models (LLMs) have recently emerged as powerful tools for human-
ArXiv cs.AI
🧠 Large Language Models
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2w ago
DP-OPD: Differentially Private On-Policy Distillation for Language Models
arXiv:2604.04461v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly adapted to proprietary and domain-specific corpora that contain
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Discrete Prototypical Memories for Federated Time Series Foundation Models
arXiv:2604.04475v1 Announce Type: cross Abstract: Leveraging Large Language Models (LLMs) as federated learning (FL)-based time series foundation models offers
ArXiv cs.AI
🧠 Large Language Models
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2w ago
SLaB: Sparse-Lowrank-Binary Decomposition for Efficient Large Language Models
arXiv:2604.04493v1 Announce Type: cross Abstract: The rapid growth of large language models (LLMs) presents significant deployment challenges due to their massi
ArXiv cs.AI
🧠 Large Language Models
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2w ago
One Model for All: Multi-Objective Controllable Language Models
arXiv:2604.04497v1 Announce Type: cross Abstract: Aligning large language models (LLMs) with human preferences is critical for enhancing LLMs' safety, helpfulne
ArXiv cs.AI
🧠 Large Language Models
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2w ago
GAIN: Multiplicative Modulation for Domain Adaptation
arXiv:2604.04516v1 Announce Type: cross Abstract: Adapting LLMs to new domains causes forgetting because standard methods (full fine-tuning, LoRA) inject new di
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Multilingual Prompt Localization for Agent-as-a-Judge: Language and Backbone Sensitivity in Requirement-Level Evaluation
arXiv:2604.04532v1 Announce Type: cross Abstract: Evaluation language is typically treated as a fixed English default in agentic code benchmarks, yet we show th
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Mapping the Exploitation Surface: A 10,000-Trial Taxonomy of What Makes LLM Agents Exploit Vulnerabilities
arXiv:2604.04561v1 Announce Type: cross Abstract: LLM agents with tool access can discover and exploit security vulnerabilities. This is known. What is not know
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Paper Espresso: From Paper Overload to Research Insight
arXiv:2604.04562v1 Announce Type: cross Abstract: The accelerating pace of scientific publishing makes it increasingly difficult for researchers to stay current
ArXiv cs.AI
🧠 Large Language Models
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2w ago
PassiveQA: A Three-Action Framework for Epistemically Calibrated Question Answering via Supervised Finetuning
arXiv:2604.04565v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in question answering and retrieval-augmented ge
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Ruling Out to Rule In: Contrastive Hypothesis Retrieval for Medical Question Answering
arXiv:2604.04593v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) grounds large language models in external medical knowledge, yet standard
ArXiv cs.AI
🧠 Large Language Models
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2w ago
An AI Teaching Assistant for Motion Picture Engineering
arXiv:2604.04670v1 Announce Type: cross Abstract: The rapid rise of LLMs over the last few years has promoted growing experimentation with LLM-driven AI tutors.
ArXiv cs.AI
🧠 Large Language Models
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2w ago
MUXQ: Mixed-to-Uniform Precision MatriX Quantization via Low-Rank Outlier Decomposition
arXiv:2604.04701v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved outstanding performance across a wide range of natural language pro
ArXiv cs.AI
🧠 Large Language Models
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2w ago
BiST: A Gold Standard Bangla-English Bilingual Corpus for Sentence Structure and Tense Classification with Inter-Annotator Agreement
arXiv:2604.04708v1 Announce Type: cross Abstract: High-quality bilingual resources remain a critical bottleneck for advancing multilingual NLP in low-resource s
ArXiv cs.AI
🧠 Large Language Models
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2w ago
What Makes Good Multilingual Reasoning? Disentangling Reasoning Traces with Measurable Features
arXiv:2604.04720v1 Announce Type: cross Abstract: Large Reasoning Models (LRMs) still exhibit large performance gaps between English and other languages, yet mu
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Individual and Combined Effects of English as a Second Language and Typos on LLM Performance
arXiv:2604.04723v1 Announce Type: cross Abstract: Large language models (LLMs) are used globally, and because much of their training data is in English, they ty
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Metaphors We Compute By: A Computational Audit of Cultural Translation vs. Thinking in LLMs
arXiv:2604.04732v1 Announce Type: cross Abstract: Large language models (LLMs) are often described as multilingual because they can understand and respond in ma
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Discovering Failure Modes in Vision-Language Models using RL
arXiv:2604.04733v1 Announce Type: cross Abstract: Vision-language Models (VLMs), despite achieving strong performance on multimodal benchmarks, often misinterpr
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Hallucination Basins: A Dynamic Framework for Understanding and Controlling LLM Hallucinations
arXiv:2604.04743v1 Announce Type: cross Abstract: Large language models (LLMs) hallucinate: they produce fluent outputs that are factually incorrect. We present
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Cog-DRIFT: Exploration on Adaptively Reformulated Instances Enables Learning from Hard Reasoning Problems
arXiv:2604.04767v1 Announce Type: cross Abstract: Reinforcement learning from verifiable rewards (RLVR) has improved the reasoning abilities of LLMs, yet a fund
ArXiv cs.AI
🧠 Large Language Models
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2w ago
SkillX: Automatically Constructing Skill Knowledge Bases for Agents
arXiv:2604.04804v1 Announce Type: cross Abstract: Learning from experience is critical for building capable large language model (LLM) agents, yet prevailing se
ArXiv cs.AI
🧠 Large Language Models
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2w ago
LiveFact: A Dynamic, Time-Aware Benchmark for LLM-Driven Fake News Detection
arXiv:2604.04815v1 Announce Type: cross Abstract: The rapid development of Large Language Models (LLMs) has transformed fake news detection and fact-checking ta
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Plausibility as Commonsense Reasoning: Humans Succeed, Large Language Models Do not
arXiv:2604.04825v1 Announce Type: cross Abstract: Large language models achieve strong performance on many language tasks, yet it remains unclear whether they i
ArXiv cs.AI
🧠 Large Language Models
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2w ago
InfBaGel: Human-Object-Scene Interaction Generation with Dynamic Perception and Iterative Refinement
arXiv:2604.04843v1 Announce Type: cross Abstract: Human-object-scene interactions (HOSI) generation has broad applications in embodied AI, simulation, and anima
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Strengthening Human-Centric Chain-of-Thought Reasoning Integrity in LLMs via a Structured Prompt Framework
arXiv:2604.04852v1 Announce Type: cross Abstract: Chain-of-Thought (CoT) prompting has been used to enhance the reasoning capability of LLMs. However, its relia
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
arXiv:2604.04868v1 Announce Type: cross Abstract: Tabular foundation models (TFMs) such as TabPFN (Tabular Prior-Data Fitted Network) are designed to generalize
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Rethinking Exploration in RLVR: From Entropy Regularization to Refinement via Bidirectional Entropy Modulation
arXiv:2604.04894v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has significantly advanced the reasoning capabilities of
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Agentic Federated Learning: The Future of Distributed Training Orchestration
arXiv:2604.04895v1 Announce Type: cross Abstract: Although Federated Learning (FL) promises privacy and distributed collaboration, its effectiveness in real-wor
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Vero: An Open RL Recipe for General Visual Reasoning
arXiv:2604.04917v1 Announce Type: cross Abstract: What does it take to build a visual reasoner that works across charts, science, spatial understanding, and ope
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Your Pre-trained Diffusion Model Secretly Knows Restoration
arXiv:2604.04924v1 Announce Type: cross Abstract: Pre-trained diffusion models have enabled significant advancements in All-in-One Restoration (AiOR), offering
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Early Stopping for Large Reasoning Models via Confidence Dynamics
arXiv:2604.04930v1 Announce Type: cross Abstract: Large reasoning models rely on long chain-of-thought generation to solve complex problems, but extended reason
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Combining Tree-Search, Generative Models, and Nash Bargaining Concepts in Game-Theoretic Reinforcement Learning
arXiv:2302.00797v4 Announce Type: replace Abstract: Opponent modeling methods typically involve two crucial steps: building a belief distribution over opponents
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Barriers to Complexity-Theoretic Proofs that "AGI" Using Machine Learning is Impossible
arXiv:2411.06498v2 Announce Type: replace Abstract: A recent paper (van Rooij et al. 2024) claims to have proved that achieving human-like intelligence using le
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Reflection of Episodes: Learning to Play Game from Expert and Self Experiences
arXiv:2502.13388v3 Announce Type: replace Abstract: StarCraft II is a complex and dynamic real-time strategy (RTS) game environment, which is very suitable for
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Cite Pretrain: Retrieval-Free Knowledge Attribution for Large Language Models
arXiv:2506.17585v3 Announce Type: replace Abstract: Trustworthy language models should provide both correct and verifiable answers. However, citations generated
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Similarity Field Theory: A Mathematical Framework for Intelligence
arXiv:2509.18218v5 Announce Type: replace Abstract: We posit that transforming similarity relations form the structural basis of comprehensible dynamic systems.
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Autonomous Agents for Scientific Discovery: Orchestrating Scientists, Language, Code, and Physics
arXiv:2510.09901v2 Announce Type: replace Abstract: Computing has long served as a cornerstone of scientific discovery. Recently, a paradigm shift has emerged w
ArXiv cs.AI
🧠 Large Language Models
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2w ago
PRISM: Prompt-Refined In-Context System Modelling for Financial Retrieval
arXiv:2511.14130v2 Announce Type: replace Abstract: With the rapid progress of large language models (LLMs), financial information retrieval has become a critic
ArXiv cs.AI
🧠 Large Language Models
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2w ago
The Drill-Down and Fabricate Test (DDFT): A Protocol for Measuring Epistemic Robustness in Language Models
arXiv:2512.23850v2 Announce Type: replace Abstract: Current language model evaluations measure what models know under ideal conditions but not how robustly they
ArXiv cs.AI
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2w ago
Circuit Mechanisms for Spatial Relation Generation in Diffusion Transformers
arXiv:2601.06338v2 Announce Type: replace Abstract: Diffusion Transformers (DiTs) have greatly advanced text-to-image generation, but models still struggle to g
ArXiv cs.AI
🧠 Large Language Models
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2w ago
ConvoLearn: A Dataset for Fine-Tuning Dialogic AI Tutors
arXiv:2601.08950v2 Announce Type: replace Abstract: Despite their growing adoption in education, LLMs remain misaligned with the core principle of effective tut
ArXiv cs.AI
🧠 Large Language Models
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2w ago
The Paradox of Robustness: Decoupling Rule-Based Logic from Affective Noise in High-Stakes Decision-Making
arXiv:2601.21439v2 Announce Type: replace Abstract: While Large Language Models (LLMs) are widely documented to be sensitive to minor prompt perturbations and p
ArXiv cs.AI
🧠 Large Language Models
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2w ago
TSPO: Breaking the Double Homogenization Dilemma in Multi-turn Search Policy Optimization
arXiv:2601.22776v2 Announce Type: replace Abstract: Multi-turn tool-integrated reasoning enables Large Language Models (LLMs) to solve complex tasks through ite
ArXiv cs.AI
🧠 Large Language Models
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2w ago
Enhancing Foundation VLM Robustness to Missing Modality: Scalable Diffusion for Bi-directional Feature Restoration
arXiv:2602.03151v2 Announce Type: replace Abstract: Vision Language Model (VLM) typically assume complete modality input during inference. However, their effect
ArXiv cs.AI
🧠 Large Language Models
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2w ago
IV Co-Scientist: Multi-Agent LLM Framework for Causal Instrumental Variable Discovery
arXiv:2602.07943v2 Announce Type: replace Abstract: In the presence of confounding between an endogenous variable and the outcome, instrumental variables (IVs)
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