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Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing
This paper compares softmax attention with four recurrent linear-attention architectures to analyze their mechanisms and trade-offs for long-context processing.
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Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic
Omni-Sleep is a new foundation model designed to analyze sleep physiology by modeling the coordinated dynamics of the central and autonomic nervous systems.
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TAG: A Lightweight Framework for Test-Driven Agentic Artifact Generation
The paper introduces TAG, a lightweight framework designed to improve the reliability of LLM-generated structured artifacts through rigorous, test-driven validation.
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Behavior Foundations for Quadruped Robots: ABot-C0 Technical Report
The ABot-C0 technical report details a new framework for quadrupedal robot control designed to overcome the scarcity of animal motion-capture data.
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Echoes: A semantically-aligned music deepfake detection dataset
The Echoes dataset provides over 130 hours of audio across multiple genres to benchmark and train robust music deepfake detection systems.
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ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generation
Researchers have introduced ParamMute, a method that improves the faithfulness of retrieval-augmented generation by suppressing knowledge-critical feed-forward networks that cause models to ignore retrieved context.
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Persuasion Attacks Can Decrease Effectiveness of CoT Monitoring
Researchers have found that persuasion-based jailbreak attacks can undermine the effectiveness of chain-of-thought monitoring in detecting misaligned AI behavior.
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V-VLAPS: Value-Guided Planning for Vision-Language-Action Models
The V-VLAPS framework integrates value-guided planning with vision-language-action models to improve robotic manipulation performance under distribution shifts and long-horizon tasks.
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Retrieval-Augmented Generation Must Move Beyond Factual Grounding to Represent Diverse Opinions
This position paper argues that current retrieval-augmented generation systems are overly focused on factual grounding and fail to adequately represent diverse opinions.
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VocaDet: Sample-Driven Open-Vocabulary Object Detection and Segmentation via Visual Tokenization and Vector Database Retrieval
VocaDet is a new open-vocabulary object detection and segmentation framework that scales to large object categories using visual tokenization and vector database retrieval.
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TRACE: A Two-Channel Robust Attribution Watermark via Complementary Embeddings for LLM-Agent Trajectories
Researchers developed TRACE, a robust watermarking method designed to protect and verify the attribution of LLM agent trajectory logs against unauthorized rebranding or model substitution.
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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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苹果股票获得罕见的看跌评级，KeyBanc表示需求和估值令人担心
KeyBanc Capital Markets downgraded Apple's stock rating to underweight, citing concerns over demand and high valuation relative to historical averages.
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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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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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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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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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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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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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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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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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Predicting Scale-Up of Metal-Organic Framework Syntheses with Large Language Models
Researchers developed ScaleMOF, a dataset and fine-tuning strategy that enables large language models to predict and prioritize viable candidates for scaling up metal-organic framework syntheses.