5
On-Device Deep Research at 4B: Exposure Bounds Faithfulness, Retrieval Bounds Coverage
Researchers analyzed the citation faithfulness and coverage of a four-billion parameter research agent running locally on a consumer laptop.
5
The Model Knows Your Project, Not You: Measuring Recognition in LLMs with NameRank
The paper introduces NameRank, a metric designed to measure how well large language models recognize specific researchers and tools within their parametric memory.
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独家｜把芯片设计交给AI，上海AI Lab李林阳创业获数千万元首轮融资
Chinese startup Novasilicon has secured multi-million yuan in seed funding to develop AI-driven chip design solutions.
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Firefox in WebAssembly
Developers have successfully compiled the Firefox browser into WebAssembly, allowing it to run entirely within another web browser.
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Alphabet spin-out Verrus plans three-building data center campus outside Portland, Oregon
Verrus, an Alphabet spin-out, is planning the construction of a new grid-reactive data center campus in Oregon to support infrastructure needs.
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德明利：预计上半年净利润为57亿元–65亿元
Chinese storage company Demingli projects a first-half net profit of up to 6.5 billion yuan, reversing a previous loss due to surging AI-driven storage demand.
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In China’s electronics hub, a memory chip crisis is hitting consumers hard
The global artificial intelligence boom has driven up memory and solid-state drive prices in Shenzhen's Huaqiangbei electronics market, significantly increasing costs for PC builders.
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DIVE: Embedding Compression via Self-Limiting Gradient Updates
DIVE is a new dimensionality reduction technique for language model embeddings that uses self-limiting gradient updates to improve compression efficiency.
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Federated Explainable Artificial Intelligence: Roles, Architectures, Evaluation, and Open Challenges
This review examines the integration of explainable AI techniques within federated learning architectures to improve transparency in privacy-preserving distributed model training.
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Interaction Protocol Shapes Moral Judgment in Multi-Agent Debate
A study demonstrates that the interaction protocols used in multi-agent debates significantly influence the moral reasoning and judgment outcomes of large language models.
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STOCKTAKE: Measuring the Gap Between Perception and Action in LLM Agents with a Fair Oracle
A new evaluation framework called STOCKTAKE aims to distinguish between perception errors and execution failures in LLM agents during long-term decision-making tasks.
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Experience Memory Graph: One-Shot Error Correction for Agents
A new error-correction method called Experience Memory Graph aims to help LLM agents recover from failures in long-horizon tasks more efficiently than traditional reflection techniques.
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Interventional Grounding Audits: Black-Box Premise-Dependency Tests for LLM Chain-of-Thought via Predicate Substitution
A new auditing method uses predicate substitution to test whether large language models genuinely rely on stated premises during chain-of-thought reasoning.
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Generative Compilation: On-the-Fly Compiler Feedback as AI Generates Code
The authors present a method for integrating compiler feedback directly into the autoregressive decoding process to improve the quality of AI-generated code.
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Unleashing Multimodal Large Language Models for Training-free HOI Detection in the Wild
Researchers have proposed a training-free approach for detecting human-object interactions in the wild by leveraging the capabilities of multimodal large language models.
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MedDiffuseMix: Preserving Diagnostic Evidence with Saliency-Aware Diffusion Medical Image Data Augmentation
MedDiffuseMix provides a saliency-guided diffusion framework to augment medical imaging data while preserving critical diagnostic features.
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Networked Intelligence: Active Shared Context Graphs for Human-AI Team Science
This paper proposes a framework for networked intelligence that uses shared context graphs to facilitate collaboration among multiple AI agents in scientific research.
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Learning to Learn-at-Test-Time: Language Agents with Learnable Adaptation Policies
A new method for test-time learning introduces learnable adaptation policies to help language agents improve their performance through iterative interaction.
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Set-shifting Behavioral Test for Harnessed Agents
A new benchmark based on cognitive psychology tests has been developed to evaluate how AI agents adapt when the reliability of their tools changes during operation.
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How Far Can Root Cause Analysis Go on Real-World Telemetry Data?
A study evaluating root cause analysis in microservice failures reveals that current AI and classical methods struggle to effectively process large-scale, multimodal telemetry data.
5
When Bots Join the Team: Bot Adoption and the Institutional Fabric of Open-Source Software Projects
Researchers analyzed nearly 3,000 GitHub projects to understand how the integration of automated bots as active participants influences the organizational structure of open-source software teams.
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A Self-Evolving Agent for Longitudinal Personal Health Management
HealthClaw is a proposed self-evolving agent architecture designed to provide longitudinal health management by maintaining private memory of user routines and medical history.
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When Audio Separation Hurts Zero-Shot ASR: Evaluating SAM-Audio with Whisper on Bengali and English Speech
The study evaluates the impact of audio separation preprocessing on zero-shot automatic speech recognition performance, finding that cleaner audio does not always improve transcription accuracy.
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FixItFlow: Automated Troubleshooting Guide Generation from Cloud Incidents
FixItFlow is an automated system that leverages large language models to generate technical troubleshooting guides from historical cloud incident data.