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GitLake: Git-for-data for the agentic lakehouse
GitLake introduces a version-control system for data lakehouses that allows autonomous AI agents to work on isolated branches before merging changes.
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Chasing the 2030 Power Generation Target: China's Artificial Sun Race Heats Up
Driven by the massive energy demands of artificial intelligence, China's private nuclear fusion sector is accelerating development toward a 2030 power generation goal.
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Nvidia is a victim of the compute marketplace it created
An analysis suggests Nvidia faces intense competition and market pressures as a direct result of popularizing the high-value AI compute market.
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What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness
Probing the internal activations of forecasting language models reveals that their hidden representations contain more accurate and better-calibrated probability estimates than their generated text.
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Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
This paper provides a comprehensive survey of methods, datasets, and benchmarks for multimodal machine unlearning across various data modalities.
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DreamCharacter-1: From 3D Generative Foundation Models to Product-Ready Character Generation
DreamCharacter-1 is a post-adaptation framework designed to refine pretrained 3D foundation models for high-fidelity, production-ready character generation.
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Reaction-network reasoning with frontier models for experimentally confirmed catalyst-selectivity hypotheses
The study demonstrates how frontier AI models can be used to reason through reaction networks and generate experimentally validated hypotheses for catalyst selectivity.
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A Stochastic--Geometric Theory of Scaling Laws in Grokking
This study provides a theoretical characterization of the mechanisms behind delayed generalization, or grokking, in neural networks using a stochastic-geometric framework.
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The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs
A new study proposes a decision-level metric called correctness agreement to better capture the behavioral changes in large language models caused by post-training quantization.
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Different Teachers, Different Capabilities: Sub-1B On-Device Distillation for Structured Text Enrichment
Researchers evaluated the effectiveness of distilling structured text extraction capabilities from an 8-billion parameter reasoning model into a sub-1-billion parameter on-device model.
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DrugGen 2: A disease-aware language model for enhancing drug discovery
Researchers have introduced DrugGen-2, a generative language model designed to design small molecules based on both target protein sequences and disease ontology.
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A First-Principles Theory of Slow Thinking and Active Perception
This paper provides a mathematical formulation of slow thinking and active perception to guide the design and training of reasoning-focused language models.
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Bridging Modal Isolation in Interleaved Thinking: Supervising Modality Transitions via Stepwise Reinforcement
This paper proposes a stepwise reinforcement learning approach to prevent text and image modalities from diverging during complex, interleaved multimodal reasoning tasks.
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LoKA: Low-precision Kernel Applications for Recommendation Models At Scale
The LoKA framework introduces low-precision FP8 kernels tailored to the numerical sensitivities and communication demands of large-scale recommendation models.
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Agentic Neural Architecture Search
Researchers propose a framework that combines large language model generation with neural architecture search to automate the design of neural networks in open-ended spaces.
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Infinity-Parser2 Technical Report
Researchers have introduced Infinity-Parser2, a multimodal document parsing model trained using a controllable data-synthesis pipeline and reinforcement learning.
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Adaptive Generation of Bias-Eliciting Questions for LLMs
Researchers developed a method to adaptively generate bias-eliciting questions to better identify and evaluate inherent biases in large language models.
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Theoria: Rewrite-Acceptability Verification over Informal Reasoning States
Researchers have developed Theoria, a verification architecture that audits large language model outputs by rewriting solutions into typed state transitions to ensure correctness.
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Deployment-Time Memorization in Foundation-Model Agents
This research analyzes how deployment-time memory configurations in foundation-model agents affect personalization utility, data extraction risks, and deletion fidelity.
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Dual-Difficulty Curriculum Learning for Direct Preference Optimization
Researchers introduced a two-dimensional curriculum learning framework that categorizes alignment difficulty by prompt complexity and pairwise distinguishability to optimize direct preference optimization.
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DocMaster: A Hierarchical Structure-Aware System for Document Analysis
DocMaster is a document analysis system that preserves the hierarchical structure of complex files to improve retrieval and question-answering performance in large language models.
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KVpop -- Key-Value Cache Compression with Predictive Online Pruning
Researchers introduced KVpop, a method that compresses key-value caches in autoregressive decoding by learning a fixed-budget eviction policy through direct supervision.
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WCog-VLA: A Dual-Level World-Cognitive Vision-Language-Action Model for End-to-End Autonomous Driving
The WCog-VLA framework integrates semantic world forecasting and generative world evolution to enable proactive decision-making in end-to-end autonomous driving.
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PhasorFlow: A Python Library for Unit Circle Based Computing
PhasorFlow is a new open-source Python library designed to facilitate complex-valued computations on the unit circle manifold.