TSMC will manufacture 3-nanometer semiconductors in Japan, targeting the surging demand for AI technologies and enhancing Japan's chipmaking capabilities.
Hard-braking events as indicators of road segment crash risk
Hard-braking events (HBEs) serve as a reliable leading indicator of crash risk, with a significant correlation established between HBE frequency and road segment crash rates, suggesting their potential for proactive safety assessments.
Eight more months of agents
Agents have evolved significantly, with the latest models like Opus now capable of writing 90% of code, a stark improvement from Claude Code's 25% last year, indicating a rapid advancement in coding capabilities.
[R] Really nice interactive explanation of Speculative Decoding
Speculative decoding enhances model performance by predicting future tokens, allowing for faster and more efficient text generation, as visualized in recent studies.
[D] Are autoregressive video world models actually the right foundation for robot control, or are we overcomplicating things?
LingBot-VA's autoregressive video world model claims to provide a unique foundation for robot learning, achieving 92.9% accuracy on RoboTwin 2.0 and outperforming π0.5 by over 20% on long horizon tasks with minimal adaptation.
[P] Built a real-time video translator that clones your voice while translating
Real-time video translation allows users to speak in one language while their voice is cloned and translated into another, achieving a latency of approximately 545ms, making it nearly imperceptible during calls.
InftyThink+: Effective and Efficient Infinite-Horizon Reasoning via Reinforcement Learning
InftyThink+ introduces a novel reinforcement learning framework that optimizes iterative reasoning by strategically managing summarization and continuation, addressing the limitations of traditional chain-of-thought models.
[D] Advice on journal for work between ML, data infrastructures, and robotics
The work presents dynamic and semantic "data-to-Knowledge pipelines" that enhance robot control by training a robust base model for inverse kinematics, demonstrating practical feasibility across multiple research institutes.
🤗Transformers.js v4 Preview: Now Available on NPM!
Transformers.js v4 is now available on NPM, featuring a new WebGPU Runtime that enhances performance across ~200 model architectures, enabling local execution in various JavaScript environments like Node and Deno.
[R] Identifying the "Complexity Kink": An Econometric Analysis of AI Marginal Productivity Collapse in Multi-Asset Tasks
The study reveals a significant Benchmark Curation Bias (p=0.03), indicating that current benchmarks favor low-coordination tasks, obscuring the true limits of AI productivity compared to human experts.
Endogenous Resistance to Activation Steering in Language Models
Endogenous Steering Resistance (ESR) allows large language models, particularly Llama-3.3-70B, to recover mid-generation from task-misaligned steering, enhancing response quality despite ongoing steering efforts.
Halluverse-M^3: A multitask multilingual benchmark for hallucination in LLMs
Halluverse-M^3 is a novel dataset that enables systematic analysis of hallucinations in large language models across four languages (English, Arabic, Hindi, Turkish) and two tasks (question answering and dialogue summarization), addressing a critical gap in multilingual model evaluation.
Rethinking Multi-Condition DiTs: Eliminating Redundant Attention via Position-Alignment and Keyword-Scoping
The proposed Position-aligned and Keyword-scoped Attention (PKA) framework significantly enhances multi-condition control in text-to-image models by eliminating redundant attention, achieving a 10.0× inference speedup and 5.1× VRAM saving.
POP: Online Structural Pruning Enables Efficient Inference of Large Foundation Models
POP (Partition-guided Online Pruning) introduces a novel framework for context-conditioned dynamic pruning, enhancing the efficiency of large foundation models (LFMs) during inference without the need for extensive preprocessing.
HyPER: Bridging Exploration and Exploitation for Scalable LLM Reasoning with Hypothesis Path Expansion and Reduction
HyPER introduces a dynamic expand-reduce control mechanism that optimizes the exploration-exploitation trade-off in multi-path reasoning, enhancing accuracy without the need for extensive retraining.