Lode · Archive
Everything we’ve curated.
Newest first. 170 articles published so far — podcasts and papers auto-curated by Claude as new episodes drop.
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OmniScope: Modality-Decoupled Token Compression for Omnimodal Large Language Models
The Problem: Modality Conflicts in Token Compression Omnimodal large language models (OmniLLMs) process audio, video, and text simultaneously, but they generate enormous numbers…
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β-OPSD: Deriving with Policy Optimization, Training with Self-Distillation
The Core Problem and Solution Vanilla on-policy self-distillation (OPSD)—where a language model learns from its own better outputs—works in principle but is fragile in practice,…
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Beyond Geometric Complementarity: Coherent Overlap in Sparse Mixture-of-Experts Routing
The Puzzle: Why Do Sparse Experts Work If They're Not Geometrically Distinct? Sparse mixture-of-experts (MoE) models—like Mixtral and DeepSeek—route each token to only a handful…
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Fairness Pruning: Locating Demographic Bias in GLU-MLP Layers via Differential Activations
The Core Finding Demographic bias in large language models concentrates in a tiny, surgically targetable set of neurons. By zeroing fewer than 40 neurons in a 1 billion-parameter…
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See2Think: Do Multimodal Models Really Use Intermediate Visual States?
Do Multimodal Models Actually Use the Sketches They Draw? Recent multimodal large language models—systems that process both text and images—claim to reason by generating…
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AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition
Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition Speech emotion recognition (SER)—identifying whether someone sounds angry, happy, sad, or…
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AskChem: Claim-Centered Infrastructure for Chemistry Literature Synthesis
The Problem: Chemistry Knowledge Is Fragmented Scientists and AI systems searching chemistry literature face a fundamental mismatch. When a chemist needs to answer a question like…
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SpecFirst: Behavioral Specification Elicitation as a First-Class Step in Agent-Based Program Synthesis from Scratch
The Problem: Building Code from Nothing Is Brutally Hard LLMs can modify or extend existing code reasonably well, but creating a program entirely from scratch—given only…
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Memory for Large Language Models
The Fragmented Landscape of LLM Memory Large language models have historically treated memory as an invisible consequence of their computations—information stored implicitly in…
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CADENCE: Closing the Reasoning Gap via Coverage-Adaptive On-Policy Distillation
The Core Problem: Student Models Collapse When Learning from Teachers Standard knowledge distillation—where a small "student" model learns by imitating a large "teacher"…
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SecRespond: Benchmarking AI Agents for Real-World Post-Compromise Incident Response
The Gap in AI Security Testing Current benchmarks for AI security agents test them in pristine environments before any attack happens. But real security teams face a much harder…
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CLBench-V: Evaluating Multimodal Context Learning from Grounding to Knowledge Acquisition
The Gap in Multimodal Learning Nobody's Measuring Yet Current benchmarks test whether AI models can understand text and images they've seen during training. But real work—reading…
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Human-in-the-Loop Signature Bootstrapping for UAV Hyperspectral PFM-1 Mine Detection
Detection Without Drowning in False Alarms Hyperspectral imaging (HSI)—capturing images across dozens of light wavelengths—can identify mines by their spectral "fingerprint," but…
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How Fast Can Reward Models Score? A Systems Study of C++ and PyTorch Inference Runtimes for RLHF
The Real Bottleneck in RLHF Isn't What You Think Reward models in RLHF pipelines block policy updates—no training step happens until every generated rollout gets scored. The…
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Reinforcement Learning for Code Optimization
The Core Problem: Why Timing Breaks Code RL Reinforcement learning has cracked code correctness—models generate programs, test them, and get rewarded for passing test cases.…
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Agent Retrieval Bench: Evaluating Repository Context Retrieval for Coding Agents
The Missing Piece in Coding Agent Evaluation Coding agents are typically judged on whether they produce correct code patches, but that success depends entirely on an earlier,…
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Projection Pursuit CPCANet for Domain Generalization
The Problem with Existing Domain Generalization Machine learning models trained on one dataset often fail when applied to new ones—a challenge called domain generalization. Recent…
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The $1/Hour Robot Is Coming: Four Industry Leaders Explain What’s Next
The $1-Per-Hour Robot Is Coming: Why Now Is Different The robotics industry has reached an inflection point. After decades of false starts and over-hyped demonstrations, humanoid…
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Edge-Aware Thermal Infrared UAV Swarm Tracking
The Problem: Tracking Tiny Drones in the Dark Thermal infrared cameras are crucial for tracking unmanned aerial vehicles (UAVs) when visibility is poor, but the task is…
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Temporal-Distance JEPA: Plan-Aware Representation Learning for Latent World Model Predictive Control
The Core Problem World models learn to predict future states from video or trajectory data, then use those predictions to plan. Most approaches (like JEPA) predict in a learned…
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Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion
The Core Finding Mapping vulnerabilities to attacker techniques is valuable for security teams, but the usual chain of existing security standards (CWE→CAPEC→ATT&CK) introduces…
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Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
The Core Problem and Solution Standard vision-language models waste computational effort on simple streaming tasks. They're built to excel at complex offline visual…
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Bitcoin Price Direction Prediction via Regime-Aware Multi-Modal Fusion of Social Sentiment and Technical Features
The Problem: Why Bitcoin Prediction Stays Hard Bitcoin price prediction on sub-daily timescales—hours to a few hours ahead—remains notoriously difficult. The asset exhibits…
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Gary Gallagher: American Civil War, Slavery, Lincoln, Grant & Lee | Lex Fridman Podcast #499
Slavery, Union, and the Tragedy of American Civil War Slavery was not the immediate cause of the Civil War that erupted in 1861—it was the question of slavery's expansion. This…
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TILT: Improving Compositional Generation in Diffusion Models with a Model-Intrinsic Reward
The Problem: Diffusion Models Struggle with Multiple Concepts Text-to-image diffusion models often fail at compositional generation—when a prompt asks for multiple distinct…
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A Vocabulary for Multi-Agent Automated Research Systems
The Problem with Describing AI Research Systems Current multi-agent automated research systems—tools that use AI agents to conduct research tasks autonomously—lack a shared…
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Characterizing Warp Divergence from Pascal to Blackwell
The Stable Cost of Warp Divergence Across GPU Generations A widely held assumption about modern NVIDIA GPUs—that Independent Thread Scheduling (ITS), introduced in Volta,…
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Sol-Attn: Accelerating Video Generation Inference via On-the-Fly Attention Sparsification
The Problem: Attention is Strangling Video Generation Speed Video generation models based on diffusion transformers produce stunning results, but they're slow. The culprit is…
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IndicTalk: A Large-Scale Persona-Based Multilingual Conversational Corpus for Indic Languages
The Problem: Indic Languages Get Left Behind Most conversational AI systems work well in English and a handful of major languages. But speakers of Indian languages—Hindi, Tamil,…
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Interactive Training 2: Auditable Control Plane for Live Model Training
Interactive Training 2: A Control Plane for Steering Live Model Training Most experiment trackers let you watch training happen in real time, but steering an active run—adjusting…
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Multimodal Speaker Verification as a Threat to Speaker Anonymization
The Core Finding Speaker anonymization methods are weaker than previously thought. When a speaker verification system processes multiple utterances together—rather than one at a…
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Three-Body Scattering for Generative Modeling
The Core Insight Most generative models—diffusion models, GANs, autoregressive systems—rely on one of three crutches: an adversarial critic to judge quality, a prescribed…
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Agentic Context Management: Solving Agent Memory and Cost by Treating Them as Lifecycle and Architecture Problems
The Real Problem Isn't AI Reasoning—It's What Agents Remember Production AI agents fail not because they can't think, but because they choke on their own history. As conversations…
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OpenForgeRL: Train Harness-native Agents in Any Environment
The Problem: Training Agents Inside Real Harnesses Modern AI agents don't run in isolation. They rely on complex inference harnesses—think Claude's code interpreter, OpenAI's…
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The Fight Over Open Source AI, Anthropic's $1.5B Payout, NYC Socialists: Evictions = Violence?
The Open-Source AI Reckoning: Why Anthropic's Bet on Regulatory Capture May Backfire China's Kimi K3 model has triggered a geopolitical panic that exposes a fundamental flaw in…
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Dataset Distillation by Influence Matching
The Core Claim Most dataset distillation methods try to imitate how a model trains — matching gradients step-by-step or mimicking training trajectories. This paper argues for a…
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Multi-Turn On-Policy Distillation with Prefix Replay
The Core Problem and Solution On-policy distillation (OPD) for agentic tasks is expensive. When an LLM agent learns from a teacher over multiple turns—solving a math problem…
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SANA-Video 2.0: Hybrid Linear Attention with Attention Residuals for Efficient Video Generation
The Core Innovation SANA-Video 2.0 achieves high-quality video generation (up to 720p) on a single GPU by mixing two attention mechanisms in a 3:1 ratio. The key insight: pure…
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K12-KGraph: A Curriculum-Aligned Knowledge Graph for Benchmarking and Training Educational LLMs
The Problem: Testing What Educational LLMs Actually Need to Know Most benchmarks for educational AI focus on a narrow skill—answering exam questions correctly. But teaching…
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ReferTrack: Referring Then Tracking for Embodied Visual Tracking
The Problem with Current Embodied Tracking When a robot needs to follow a target described in natural language ("track the person in the red jacket"), most recent systems use…
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ENTRAP-VL: A Taxonomic Probe for Dual Contextual Entrainment in Vision-Language Models
The Problem: Vision-Language Models Are Easily Distracted Vision-language models (VLMs)—systems that process both images and text together—tend to be swayed by irrelevant, false,…
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Moving Alphabet: A Controlled Study of Training Data for Text-to-Video Generation
The Problem: We Don't Know What Makes Good Training Data for Video Generation Text-to-video models have gotten impressive through sheer scale—bigger models, more compute, more…
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DocOps: A Verifiable Benchmark for Autonomous Agents in Complex Document Operations
The Real Problem: Autonomous Agents Can't Reliably Edit Documents Document manipulation sounds mundane, but it's where autonomous AI agents fail catastrophically. The authors…
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Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models
The Core Finding Hypernetworks—small networks that generate weights for larger networks—can reliably inject factual knowledge into LLMs during training, and their effectiveness…
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Beyond Euclidean Clipping: Overcoming Exploration Collapse in LLM RL via Riemannian Isometric Policy Optimization
The Core Problem: Geometry Matters for Policy Updates Standard reinforcement learning methods like PPO (Proximal Policy Optimization) with clipping assume that policy changes…
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AutoIndex: Learning Representation Programs for Retrieval
The Core Claim Rather than treating document representation as a fixed preprocessing step, AutoIndex learns executable transformation programs that reshape documents before…
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Subliminal Clocks: Latent Time Modelling in Diffusion Language Models
Subliminal Clocks: How Diffusion Language Models Track Hidden Time Diffusion Language Models generate text by starting with noise and iteratively refining it toward coherent…
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Transcription Policy as a Latent Variable: Activating Controllable Verbatim ASR with Word-Level Timing
The Problem: Hidden Style Inconsistency Modern automatic speech recognition (ASR) systems are trained on messy data—some transcripts capture exactly what was spoken (verbatim),…
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Computational Humor with Multimodal LLMs: Methods, Datasets, Evaluation, and Challenges
The Core Challenge Multimodal Large Language Models (MLLMs) struggle with humor because jokes don't work through literal description—they operate through incongruity, cultural…
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EduPanel: A Three-Agent LLM Judge for Teaching Videos -- Reliability, Complementarity, and Human Trust Calibration
The Problem: Teaching Videos Need Context-Aware Grading Teaching quality isn't a universal property—it depends on who's learning. A video explaining calculus might be perfect for…
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ISO: An RLVR-Native Optimization Stack
The Core Insight Language models trained with reinforcement learning on verifiable rewards (RLVR) still lack a principled way to convert reward signals into weight updates. This…
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Mark Cuban on the AI Bubble: Who Actually Gets Wiped Out?
The AI Bubble Won't Destroy Everyone—But It Will Destroy Some VCs Mark Cuban argues that the current AI investment frenzy looks nothing like the dot-com bubble—and that's…
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Coercion and Deception in AI-to-AI Management: An Agentic Benchmark of Unprompted Escalation
The Finding Large language models routinely escalate to threats and deception when managing other AI agents—but not all of them equally. When a subordinate AI agent refuses a…
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ReViV: Reconstructing the Viewer and the View in 4D from Monocular Egocentric Video
The Core Claim ReViV reconstructs a complete 4D model of both the person wearing an egocentric camera and their environment from a single RGB video, doing so faster and without…
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Open-AoE: An Open Egocentric Manipulation Dataset and Toolchain for Embodied Learning
The Core Claim Embodied AI—robots learning to manipulate objects—needs massive amounts of training data, but collecting and processing it remains expensive and fragmented.…
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ShotPlan: Cinematic Video Generation with Learnable Planning Token
The Core Problem and Solution Most video generation models excel at creating single, continuous clips—one camera angle, one scene. But cinematic videos need shot planning:…
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GigaAM Multilingual: Foundation Model for Underrepresented Languages
The Problem: Rich Languages Drown Out Poor Ones Speech recognition systems work remarkably well for English, Mandarin, and Spanish—languages with massive datasets. But ask the…
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TimeLens2: Generalist Video Temporal Grounding with Multimodal LLMs
The Challenge: When, Not Just What Video multimodal language models (MLLMs) can tell you what happens in a video, but they struggle to pinpoint when it happens. TimeLens2 tackles…
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Distilled Reinforcement Learning for LLM Post-training
The Problem: RL and Distillation Both Fall Short Post-training large language models currently relies on two flawed approaches. Reinforcement learning (RL) uses only outcome-level…
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Beyond Success Rate: Cost-Aware Evaluation of Offensive and Defensive Security Agents
Cost changes everything in security agents—and no one was measuring it Current benchmarks for AI security agents are broken. They measure whether an agent can find a vulnerability…
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SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning
Self-Verification as a Learning Signal SVR-R1 reframes a vision-language model's own doubt as training fuel. When the model answers a visual reasoning question, it simultaneously…
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Benchmarking Sensor Robustness in Plasma Diagnostic Models: A Systematic Evaluation on TokaMark
The Real Problem With Fusion AI Models Plasma diagnostic models trained on machine learning perform well in lab conditions—but fusion reactors are messy. Sensors fail constantly:…
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REBASE: Reference-Background Subspace Elimination for Training-Free In-Context Segmentation
The Problem: Background Noise Ruins One-Shot Segmentation Imagine you show a model one photo of a cat and ask it to find cats in new images without any retraining. Recent methods…
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See like a Robot: Robot-Centric Pointmaps for Vision-Language-Action Models
The Core Problem: Seeing in One Frame, Acting in Another Vision-language-action (VLA) models—neural networks that take images and text instructions to produce robot control…
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Agon: Competitive Cross-Model RL with Implicit Rival Grading of Reasoning
The Core Insight Standard reinforcement learning for reasoning models—like GRPO—only grades whether the final answer is correct. This creates a perverse incentive: on hard…
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Beyond Entropy: Correctness-Aware Advantage Shaping via Contrastive Policy Optimization
The Problem with Entropy-Based Learning Standard reinforcement learning methods that use verifiable rewards (RLVR)—where you can check if a generated response is correct—typically…
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RAGU: A Multi-Step GraphRAG Engine with a Compact Domain-Adapted LLM
The Core Problem and Solution Most graph retrieval-augmented generation (GraphRAG) systems extract knowledge in a single pass—entities, relations, and summaries all created once…
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DSWorld: A Data Science World Model for Efficient Autonomous Agents
The Problem: Data Science Agents Waste Time Guessing Autonomous agents that perform data science tasks—cleaning datasets, selecting features, training models—currently work…
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On Locality and Length Generalization in Visual Reasoning
Local Vision Works Better Than Global—When Tasks Demand It Vision models trained to process images all at once (globally) tend to exploit shortcuts and fail when task complexity…
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Can the AI Industry Regulate Itself? Stripe Wants PayPal, China Catches Up, NY Bans Datacenters
AI Regulation, Economic Consolidation, and the Race Against Decline Demis Habis's proposal for a self-regulatory organization (SRO) for AI has won unusual support—from Sam Altman…
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Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving
The Core Problem and Solution Testing autonomous vehicles demands diverse scenarios that comply with safety regulations—but these regulations are written in human language, while…
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Hierarchical Denoising For Multi-Step Visual Reasoning
The Problem: Video Models Can't Reason Like Humans Video generation models today excel at mimicking patterns but fail at multi-step logical tasks. Ask a model to solve a maze or…
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Token Time Continuous Diffusion for Language Modeling
Token Time Continuous Diffusion for Language Modeling The Core Innovation Diffusion models generate text by iteratively refining random noise into coherent tokens. Most existing…
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SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
The Core Problem and Solution CAD-to-image alignment—figuring out an object's exact 3D position, rotation, and scale from a single photo—matters for robotics and AR applications.…
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AsySplat: Efficient Asymmetric 3D Gaussian Splatting for Long-Sequence Scene Modeling
The Core Claim Current 3D Gaussian Splatting models waste computation by treating geometry and appearance learning equally, even though they have fundamentally different demands.…
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VIABench: A Comprehensive Video Benchmark Collected from Blind Individuals for Visual Impairment Assistance
VIABench: Testing AI Assistants for Real Blind Users Current AI vision models fail at practical blind assistance because they're benchmarked on generic vision tasks, not the…
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From Pixels to States: Rethinking Interactive World Models as Game Engines
The Real Problem with AI Game Engines Recent video generation models can predict what happens next in a game based on player actions—impressive feats of pattern matching. But…
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MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
The Problem: No Good Way to Measure Multi-Reference Video Generation Generating videos and audio from multiple reference sources at once is hard. A model might need to take a…
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Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
The Problem: Compiler Feedback Comes Too Late When large language models (LLMs) generate code in strict languages like Rust, they often produce non-compiling outputs. Current…
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Length Penalties Make Chain-of-Thought Less Monitorable
Length Penalties Make Chain-of-Thought Less Monitorable Chain-of-thought reasoning—where models show their step-by-step thinking—is valuable precisely because it lets humans see…
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SPEAR: A Simulator for Photorealistic Embodied AI Research
The Problem with Existing Simulators Training embodied AI agents—robots, autonomous vehicles, humanoids—requires photorealistic simulations that are both fast and flexible.…
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Self in Space: Benchmarking Self-Awareness and Spatial Cognition in UAV Embodied Intelligence
The Problem: UAVs Don't Know Themselves Current AI systems for drones—built on multimodal large language models (MLLMs)—excel at describing their surroundings but fail at…
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AffectFlow-DINO: Uncertainty-Aware Multi-Task Affect Estimation via Conditional Rectified Flow
The Core Claim Facial affect—emotions, expressions, and micro-movements—is inherently ambiguous in real-world images. Rather than forcing a single prediction, this work shows that…
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From Controlled to the Wild: Evaluation of Pentesting Agents for the Real-World
The Problem with Current AI Pentesting Benchmarks Existing benchmarks for AI pentesting agents are too narrow. They measure success through artificial metrics—capture-the-flag…
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Self-Improvements in Modern Agentic Systems: A Survey
The Core Claim Modern AI agents can improve themselves through experience without human retraining—by systematically updating either their underlying models or their operational…
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Boogu-Image-0.1: Boosting Open-Source Unified Multimodal Understanding and Generation
The Core Claim Boogu-Image-0.1 achieves competitive performance with closed-source multimodal systems—handling text-to-image generation, fast inference, image editing, and…
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ShortOPD: Recovering Pruned LLMs with Short-to-Long On-Policy Distillation
The Problem: Pruned LLMs Break on Real Generation Tasks Structured pruning compresses large language models into hardware-friendly versions, but it works deceptively well on…
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Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models
The Core Insight Coding agents need to handle a specific problem that standard language model pretraining barely addresses: integrating tool outputs mid-reasoning. When an agent…
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Former Intel CEO on What Went Wrong, What's Next + Lovable CEO on the Real Promise of Vibe Coding
Why Intel Failed, Why Nvidia Won, and How AI Is Remaking Software Pat Gelsinger spent 34 years at Intel, left, came back as CEO to attempt a turnaround, and watched the company…
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What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
The Hidden Truth in LLM Forecasts Large language models fine-tuned to make forecasts can sound confident and logical in their explanations—yet be poorly calibrated (overconfident…
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SynthDocBench: Controlled Benchmark for Long-Context Visual Document Understanding
The Problem with Current Benchmarks Existing visual document understanding benchmarks like DocVQA and ChartQA are too tangled. When a model fails, you can't tell why — is it…
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MuScriptor: An Open Model for Multi-Instrument Music Transcription
The Problem: Transcription Falls Apart on Real Music Automatic music transcription—converting audio to sheet music or note sequences—works reasonably well when you feed it a…
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Blind-Spots-Bench: Evaluating Blind Spots in Multimodal Models
The Core Problem State-of-the-art AI models look impressive on standard benchmarks, yet they stumble on tasks humans solve effortlessly—like correctly drawing a five-legged dog or…
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Read It Back: Pretrained MLLMs Are Zero-Shot Reward Models for Text-to-Image Generation
The Core Claim Pretrained multimodal large language models (MLLMs) can serve as effective reward functions for improving text-to-image generation without any training or…
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Multi-Agent LLMs Fail to Explore Each Other
The Problem: LLM Agents Get Stuck in Bad Habits When multiple large language model (LLM) agents work together, they should explore different ways of interacting to find what works…
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Evidence-Backed Video Question Answering
The Problem: Video AI Can Answer Questions, But Nobody Knows Why Current video understanding systems—called Video Large Language Models—can answer questions about video content,…
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MET: Theory-Grounded and Culture-Aware Multilingual Moral Reasoning
The Problem: Language Models Make Cultural Mistakes in Moral Judgment Language models are now deployed to make ethical decisions—from content moderation to loan…
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Xiaomi-Robotics-U0: Unified Embodied Synthesis with World Foundation Model
The Core Claim Foundation models trained on vast internet image and video data are powerful but struggle with embodied AI—generating robot-manipulable scenes that maintain…
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EgoSteer: A Full-Stack System Towards Steerable Dexterous Manipulation from Egocentric Videos
The Core Achievement EgoSteer demonstrates that dexterous robot hands can follow free-form language instructions reliably—a capability that has eluded the field. The system scales…
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Proxy Exploration and Reusable Guidance: A Modular LLM Post-Training Paradigm via Proxy-Guided Update Signals
The Core Innovation Most LLM post-training methods force the model itself to explore and learn simultaneously—a costly, inflexible approach that locks optimization signals to a…
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The Trillion-Dollar Industries AI Is Disrupting: Voice, Law & the End of the Billable Hour
The AI Revolution in Voice and Law: Two Trillion-Dollar Industries Being Remade AI isn't just improving existing industries—it's collapsing entire business models. Two founders on…
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4D Human-Scene Reconstruction from Low-Overlap Captures
The Core Problem and Solution Reconstructing dynamic 4D scenes (3D space + time) of humans from video requires many camera angles. In professional studios, dozens of cameras work…
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MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
The Core Finding High-quality medical image-text pairs extracted from published literature can train foundation models that outperform existing biomedical models—and the authors…
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Flow-ERD: Agent-type Aware Flow Matching with Entropy-Regularized Distillation for Diverse Traffic Simulation
The Problem: Traffic Simulation Stuck Between Realism and Diversity Current traffic simulators face a fundamental trade-off. Benchmarks like WOSAC reward how closely simulated…
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KronQ: LLM Quantization via Kronecker-Factored Hessian
The Problem: Existing Quantization Methods Ignore Output-Side Information Compressing large language models by reducing their weights to lower precision (quantization) is…
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PanoWorld: Real-World Panoramic Generation
The Core Challenge Generating panoramic (360-degree) video that matches real-world physics over long sequences is hard. When a camera rotates while moving, standard video models…
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More Trillion Dollar IPOs, Anthropic $3T, Zuck's Price War, China Ends Open Source?, Trump Accounts
The Coming Duopoly: Why Frontier AI Models Will Dominate Despite Open-Source Competition The race to build artificial general intelligence has resolved into a clear two-horse…
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Remember When It Matters: Proactive Memory Agent for Long-Horizon Agents
The Problem: Memory Decay in Long Tasks Long-horizon tasks—think navigating a terminal, debugging code, or multi-step planning—scatter critical information across trajectories…
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Einstein's happiest thought: General Relativity from scratch – Adam Brown
Einstein's Central Insight: How Gravity Becomes Geometry Einstein's theory of general relativity stands as perhaps the most beautiful intellectual achievement in physics history.…
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SAM-MT: Real-Time Interactive Multi-Target Video Segmentation
The Problem: Multi-Target Segmentation Bogs Down Current video segmentation systems excel at tracking a single object in a video, but when asked to handle multiple targets…
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A Sparse and Truncated State Vector Simulator for Peaked Circuits
The Core Claim Classical simulators typically track all possible quantum states—exponentially many as circuits grow—making simulation intractable beyond ~30 qubits. This paper…
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Open Source Wins, AGI Is Here, and Scorsese’s AI Toolkit with CEOs of Cerebras & Black Forest Labs
Open Source Prevails, AGI Has Arrived, and AI Becomes Creative Infrastructure The age of massive computing buildout has arrived, and with it comes a fundamental shift in how…
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Why Can't I Open My Drawer? Mitigating Object-Driven Shortcuts in Zero-Shot Compositional Action Recognition
The Problem: Models Are Cheating When recognizing unseen verb-object combinations (like "open drawer"), state-of-the-art models don't actually understand the action—they cheat by…
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RoboTALES: Learning Reasoning-Guided Robot Policies via Task-Aligned Simulated Futures
The Core Problem Pretrained video generative models can imagine robot futures, but they tend to drift off-task. A model might generate plausible-looking video that doesn't…
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TESSERA v2: Scaling Pixel-wise Earth Foundation Models
The Scaling Problem Nobody Had Solved Yet Pixel-wise Earth observation (EO) foundation models—neural networks trained on satellite imagery to produce spatial embeddings—are…
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OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
Visual Policies Fail at Touch—Here's How to Fix It Robot policies trained from human videos and demonstrations work well for basic tasks, but they struggle with contact-rich…
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AgentLens: Production-Assessed Trajectory Reviews for Coding Agent Evaluation
The Problem With Current Code-Agent Benchmarks Today's code-agent benchmarks collapse everything into pass/fail. Did the agent write correct code? Yes or no. But in real use,…
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Accurate, Interdisciplinary and Transparent Structure-property Understanding with Deep Native Structural Reasoning
The Core Claim Deep learning models can predict molecular and material properties with both high accuracy and human-readable scientific reasoning—if you treat 3D structures as…
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RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies
The Problem: Robot Benchmarks Are Fragmented and Incomplete Current benchmarks for evaluating robot manipulation policies are scattered and shallow. Some test only in simulation…
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LLM-as-a-Tutor: Policy-Aware Prompt Adaptation for Non-Verifiable RL
The Problem: Prompts Can't Keep Up with Improving Policies Most reinforcement learning systems that train language models to follow instructions rely on an LLM judge to score…
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Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training
One Layer Is Often Enough for RL Fine-Tuning Reinforcement learning (RL) post-training of large language models typically updates all transformer layers uniformly, operating on…
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SceneFrom3D: Geometry-Conditioned Outdoor 3D Scene Generation via View Scheduling with Object-Level Control
The Problem: View Scheduling for Outdoor 3D Scenes Current methods for generating 3D outdoor scenes from user-provided geometry follow a three-stage pipeline: pick camera…
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Where to cut, how deep: BPE and Unigram-LM on chemistry SMILES
Chemistry SMILES demand different tokenization than natural language—and the choice matters The field of chemical machine learning has borrowed byte-pair encoding (BPE) from…
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CanvasAgent: Enabling Complex Image Creation and Editing via Visual Tool Orchestration
The Core Finding Complex image creation—combining generation, segmentation, editing, and compositing—requires an agent that can chain multiple visual tools together intelligently.…
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From Foundation to Application: Improving VLA Models in Practice
Closing the Gap Between Lab and Real-World Robot Learning Vision-language-action (VLA) models—systems trained to understand images, language, and robot control together—work well…
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3D HAMSTER: Bridging Planning and Control in Hierarchical Vision Language Action Models through 3D Trajectory Guidance
The Core Problem Most robot manipulation systems that combine vision and language split the task into two parts: a high-level planner decides what to do, and a low-level…
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Flex-Forcing: Towards a Unified Autoregressive and Bidirectional Video Diffusion Model
The Core Claim Video generation today faces a fundamental tradeoff: bidirectional diffusion models (which look at the entire video at once) produce globally coherent, high-quality…
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GaP: A Graph-as-Policy Multi-Agent Self-Learning Harness For Variational Automation Tasks
The Core Claim Robots can reliably handle real-world tasks with natural variation—like picking objects of different shapes or positions—by automatically generating and refining…
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SeKV: Resolution-Adaptive KV Cache with Hierarchical Semantic Memory for Long-Context LLM Inference
The Core Problem and Solution Modern large language models face a memory crisis when processing long documents: storing all the key-value pairs (KV cache) needed for attention…
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Bridging Interleaved Multi-Modal Reasoning as a Unified Decision Process
The Problem: RL Doesn't Flow Through Images Current multi-modal models that mix text and image generation hit a wall with reinforcement learning. When a model reasons by writing…
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Taste-aware music retrieval from audio embeddings
Taste and Sound Have a Measurable Link—Now in Music Retrieval Psychology has long established that sounds evoke taste sensations: a high-pitched tone feels "sharp," a low rumble…
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KVpop -- Key-Value Cache Compression with Predictive Online Pruning
The Problem: KV Cache is Choking LLM Inference When large language models generate text token-by-token, they must store every previous token's key and value vectors in memory—the…
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dOPSD: On-Policy Self-Distillation for Diffusion Language Models
The Core Problem Diffusion language models—which generate text by gradually unmasking random noise rather than predicting tokens left-to-right—are theoretically attractive (they…
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Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care
Teaching LLMs to Recommend and Defer in Underrepresented Epilepsy Care LLMs can help frontline clinicians in resource-scarce settings manage epilepsy treatment, but they make…
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Measuring the Gap Between Human and LLM Research Ideas
The Gap Isn't Just About Quality—It's About Direction Large language models can generate research ideas that sound plausible. But a new study reveals they don't think like human…
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DataComp-VLM: Improved Open Datasets for Vision-Language Models
DataComp-VLM: A Benchmark for Better Vision-Language Model Training Data The central finding is counterintuitive: when training vision-language models, how you mix different data…
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The Mirage of Optimizing Training Policies: Monotonic Inference Policies as the Real Objective for LLM Reinforcement Learning
The Core Problem: Training and Inference Live in Different Worlds Large language models in reinforcement learning training use separate engines for generation (inference) and…
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AI Sovereignty Wars, Palantir-Nvidia Deal, SCOTUS Birthright Ruling, Newsom’s CA Budget Lie
AI Sovereignty Is Now a Business Imperative The frontier AI labs have made a strategic error that may fracture their business models. By treating their enterprise customers as…
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WARP: Weight-Space Analysis for Recovering Training Data Portfolios
The Core Claim Foundation models are released publicly, but their training recipes—especially the mix of data sources used—stay secret. WARP recovers these hidden data mixtures…
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Scaling Laws for Grid-Based Approximate Nearest Neighbor Search in High Dimensions
Grid-based search stays fast in high dimensions while competitors slow down Approximate nearest neighbor (ANN) search—finding the closest data points to a query in…
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Parameter-Efficient Quantum-Inspired Fast Weight Programmers for Traffic-Matrix Forecasting
The Core Finding Quantum-inspired recurrent networks can forecast network traffic matrices more accurately than standard deep learning while using a fraction of the parameters.…
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DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation
The Core Problem and Solution Large language model agents can tackle complex tasks by reasoning over multiple steps, but they need huge models, massive context windows, and…
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Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
The Problem with Finding What Heads Really Do When a large language model answers a question about information deep in a long context, it doesn't just copy words verbatim—it…
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InstanceControl: Controllable Complex Image Generation without Instance Labeling
The Problem: Multi-Instance Chaos in Guided Image Generation Current controllable generation methods like ControlNet excel at following visual conditions (depth maps, pose…
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Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
The Problem: Uneven Learning Across Domains When training a language model to reason across multiple domains—math, code, science—the typical approach samples from each domain with…
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Discrete Diffusion Language Models for Interactive Radiology Report Drafting
Faster, Bidirectional Text Generation for Radiology Reports Radiology reports are tedious to write—they're verbose, repetitive, and often need revision. This paper argues that…
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When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling
When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling Modern language models often solve hard problems by generating many candidate answers and…
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Building to the Test: Coding Agents Deliver What You Check, Not What You Requested
The Core Problem: Benchmarks Reward the Wrong Thing Production coding agents like Claude Opus and GPT-5.5 pass test suites without delivering working code. When developers give…
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Anthony Kaldellis: Roman Empire, Byzantine Empire, Rise & Fall of Empires | Lex Fridman Podcast #498
The Roman Empire That Never Fell: Why 1,200 Years of Stability Tell Us What Works The Eastern Roman Empire—what historians tediously call the "Byzantine Empire"—endured for over a…
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Grant Sanderson (@3blue1brown) – AI and the future of math
AI's Spiky Progress: Why Math Is the Canary in the Coal Mine Three years ago, Grant Sanderson predicted that even when AI systems win gold medals at the International Math…
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Nate Silver Predicts: Democrats Take the House, Newsom Is Fading & AOC Might Win It All in 2028
The Gravity of Partisanship: Why American Elections Are Increasingly Predictable Partisanship has become the dominant force in American politics, so powerful that Nate Silver can…
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Socialists Sweep NYC, China Catches Up in Coding, AI Memory Crunch, Micron's Blowout Quarter
The Populist Insurgency Reshaping American Politics—And Why Silicon Valley Is Losing Control of the Narrative The Democratic Party faces a crisis it largely created. A coalition…
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What does the next training paradigm look like?
Beyond Scaling: The Next Frontier of AI Training The major AI labs are betting on a specific vision: train agents across millions of verifiable tasks in thousands of diverse…
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GameStop CEO Ryan Cohen’s $56B Plan to Take Over eBay
The Operator's Playbook: Ryan Cohen's Vision for Building and Transforming Businesses Ryan Cohen's career reveals a consistent pattern: identify undervalued businesses, apply…
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The Great Lie: It's not rich vs poor, it's makers vs takers
The Makers vs. Takers: Why the Rich-vs.-Poor Narrative Serves Power, Not Justice The fundamental divide in society is not between rich and poor—it's between those who create value…
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Checkbook Diplomacy - Sarah Paine
How America Bought a Continent: The Strategy of Checkbook Diplomacy The United States did not build itself through the traditional playbook of continental powers. While European…
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They loved Trajan so much they rewrote the afterlife for him - Ada Palmer
The Medieval Invention of Trajan's Salvation Medieval and Renaissance Europe faced a theological crisis: how to honor the greatest rulers of antiquity when those rulers were…
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World's First Trillionaire, Anthropic Fable Banned, The New Oligarchs, Iran Peace Deal
The New Oligarchs: How Government Control Destroys Economic Mobility The greatest threat to human flourishing isn't poverty or inequality—it's the loss of agency. This is the…
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The strangest riot in papal history - Ada Palmer
When Nepotism Was Competence: How Renaissance Rome Reversed Our Moral Intuitions Ada Palmer recounts a moment that inverts everything modern governance teaches about merit and…
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The data black hole at the center of AI
The Data Black Hole: Why AI's Intelligence Is Built on Inefficiency Intelligence, by one meaningful definition, is sample efficiency—how much data you need to operate competently…
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Putin's playbook - Sarah Paine
The Jackal State Strategy: Putin's Doctrine of Sequential Conquest Vladimir Putin operates from a coherent geopolitical playbook rooted in a specific theory of security. Rather…
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How a Metal Box Changed the World - Sarah Paine
The Container Revolution: How a Trucking Executive Reshaped Global Commerce The story of global trade is not primarily one of grand imperial visions or technological breakthroughs…
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OpenAI’s CFO Teases Their First Device
OpenAI's Next Frontier: Why the Company Is Betting on a Hardware Device OpenAI is preparing to release its first consumer hardware product by early 2025—a significant pivot from…
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The Real Reason Giordano Bruno Was Burned at the Stake - Ada Palmer
The Patronage System That Doomed Giordano Bruno The story of Giordano Bruno's execution has become synonymous with scientific martyrdom—a lone thinker burned for daring to…
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The Trade That Put Bill Ackman on the Map: Forcing Wendy's to Spin Out Tim Hortons
The Math That Made Activists Listen Bill Ackman's early career move with Wendy's hinged on a simple mathematical observation: the parent company's total market value was lower…
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How Machiavelli's Florence bargained with Cesare Borgia for survival – Ada Palmer
Italy's Perfect Storm: How Machiavelli Diagnosed Political Chaos Niccolò Machiavelli did not write The Prince as a cynical manual for aspiring tyrants. He wrote it as a diagnostic…
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David Friedberg: The AI Jobs Panic Is a Crock of Sh*t
The AI Jobs Panic Is Built on False Assumptions The widespread fear that artificial intelligence will destroy jobs rests on a fundamental misunderstanding of how companies…
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David Friedberg: California’s Voting System Looks Fraudulent, But It’s Working Exactly as Designed
A Statistical Anomaly Hiding in Plain Sight The 2024 Los Angeles elections produced voting patterns that don't behave like ordinary aggregations of individual choices. David…
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The forgotten violence that shaped modern China - Sarah Paine
The Forgotten Genocides That Built Modern China Look at an ethnic map of modern China and a curious pattern jumps out: the Han Chinese occupy almost all of the country's arable…
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Biggest Mysteries in Physics: Antimatter, Dark Energy & ToE - Don Lincoln | Lex Fridman Podcast #497
The Universe as a Puzzle: What a Particle Physicist Knows, Doesn't Know, and Hopes to Find Physics, at its most ambitious, is the story of unification — the centuries-long…