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The real AI race may no longer be at the frontier
Hugging Face CEO Clem Delangue argues that enterprise demand is shifting toward open-source models due to advantages in cost and data ownership.
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Unitree Robotics Partners With Hunan Steel: A Physical AI Infrastructure Company Emerges
Unitree Robotics has partnered with Hunan Steel and Looper Robotics to deploy legged robots for industrial inspection, signaling a move toward physical AI in heavy manufacturing.
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Chinese AI labs to challenge Thinking Machines Lab with new industry focus
Former Chinese AI lab leaders are launching a new venture focused on industry-specific AI solutions to compete with Thinking Machines Lab by prioritizing practical applications over general-purpose models.
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逐际动力再融2亿美元提速港股IPO，张巍：营收对赌不符合具身商业逻辑
LimX Dynamics has raised $200 million in new funding as it prepares for a potential IPO while focusing on the commercial viability of embodied AI robotics.
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Silent Alarm: A J-Space Protocol for Comparing Danger Recognition Across Models and Quantization Levels
A new protocol called JADR has been proposed to measure the underlying safety fragility of language models beyond standard jailbreak testing.
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Mistake gating leads to energy and memory efficient continual learning
A biologically inspired learning method called mistake-gated learning is introduced to reduce the energy and memory requirements of training neural networks.
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Attractor Geometry of Transformer Memory: From Conflict Arbitration to Confident Hallucination
Researchers identified a unified geometric mechanism in transformer models that explains how conflicting memory sources lead to confident hallucinations.
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DRIFTLENS: Measuring Memory-Induced Reasoning Drift in Personalized Language Models
The study introduces DRIFTLENS to measure how personalized memory injection in LLMs can alter the reasoning trajectories used to generate responses.
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dMX: Differentiable Mixed-Precision Assignment for Low-Precision Floating-Point Formats
The dMX framework introduces a differentiable approach to mixed-precision quantization, optimizing LLM performance and efficiency for low-precision hardware.
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Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Researchers proposed a compliance-aware federated learning framework that adjusts differential privacy noise to accommodate varying institutional data standards and resource levels.
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Tracking Drift: Variation-Aware Entropy Scheduling for Non-Stationary Reinforcement Learning
Researchers have developed a variation-aware entropy scheduling method to improve reinforcement learning performance in environments subject to drift.
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TADPO: Reinforcement Learning Goes Off-road
Researchers have introduced TADPO, a reinforcement learning approach designed to improve autonomous vehicle navigation in complex, unmapped off-road environments.
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Comparing Semantic Navigation in Humans and Large Language Models using Natural Language Processing
A comparative study using NLP metrics reveals similarities and differences in how humans and leading large language models navigate conceptual spaces during semantic memory retrieval.
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Are we Merging the Right Models? Impact of Expert Training Duration on Model Merging for LLMs
The authors investigate how the training duration of individual domain experts influences the performance of merged large language models.
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Plug Power sells Texas site to Stream Data Centers
Plug Power has sold a Texas industrial site to Stream Data Centers, which intends to repurpose the land for data center infrastructure.
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Visual Access Boundaries in Vision-Language Model Reasoning
Researchers have introduced Visual Access Sweep, a causal intervention method to study how Vision-Language Models utilize image tokens during long Chain-of-Thought reasoning.
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Cost-Governed RAG: Unified Per-Tenant Cost Attribution Across Retrieval and Generation in Multi-Tenant LLM Systems
Researchers introduced Cost-Governed RAG, an architecture designed to attribute both retrieval and generation costs to individual tenants in multi-user language model systems.
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PM-Bench: Evaluating Prospective Memory in LLM Agents
Researchers introduced PM-Bench, a text-based benchmark designed to evaluate the prospective memory capabilities of large language model agents.
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Evidence-Grounded AI for Musculoskeletal Care
This work discusses the application of evidence-grounded AI systems to support the longitudinal management and rehabilitation of musculoskeletal diseases.
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Win by Silence: Deletion Non-Monotonicity, Autonomous Exploitation, and Typed-State Gating in LLM Plan Evaluation
This paper investigates a vulnerability in large language model plan evaluators where strategic plans are rewarded for omitting explicit details.
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Audio-Native Speech Recognition with a Frozen Discrete-Diffusion Language Model
Researchers proposed a method for automatic speech recognition using a discrete diffusion language model to transcribe audio in parallel rather than through traditional autoregressive decoding.
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I'm Sorry, but I Can't Help with Braille: Revealing Accessibility Failures in State-of-the-Art LLMs
Research indicates that current state-of-the-art LLMs struggle significantly with bidirectional Korean-Braille translation, highlighting gaps in accessibility-focused capabilities.
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CARE-LoRA: Compressed Activation REconstruction for Memory-Efficient LoRA
The authors introduce a method called CARE-LoRA designed to reduce memory usage during the fine-tuning of large pre-trained models.
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Signal-Guided Optimization for Machine Unlearning
A new signal-guided optimization technique aims to improve machine unlearning by addressing the varying memorization strengths of individual training samples.