arXiv:2603.08001v1 Announce Type: cross Abstract: Maximum inner product search (MIPS) is a crucial subroutine in machine learning, requiring the identification of key vectors that align best with a given query. We propose amortized MIPS: a learning-based approach that trains neural networks to directly predict MIPS solutions, amortizing the computational cost of matching queries (drawn from a fixed distribution) to a fixed set of keys. Our key insight is that the MIPS value function, the maximal inner product between […]

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arXiv:2601.02378v1 Announce Type: new Abstract: As the application of Embodied AI Agents in avatars, wearable devices, and robotic systems continues to deepen, their core research challenges have gradually shifted from physical environment interaction to the accurate understanding of social interactions. Traditional physical world models (PWM) focus on quantifiable physical attributes such as space and motion, failing to meet the needs of social intelligence modeling. In contrast, the Mental World Model (MWM), as a structured representation of humans’ internal […]

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arXiv:2603.06667v1 Announce Type: new Abstract: We design and implement on Radio Frequency System-on-Chip (RFSoC) software-defined radios (SDRs) a complete-graph network of four unmanned aerial vehicles and demonstrate real-time 4K video streaming over twelve always-on 2×2 multiple-input multiple-output (MIMO) links. The testbed operates at an aggregate network throughput of approximately 1.2 Gbps (i.e., 12 links of 99.84 Mbps) across a shared bandwidth of 200 MHz. To the best of our knowledge, this is the first demonstration of low-latency digitally […]

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I love Excalidraw for sketching system architectures. But sketches are static. When I want to show how a packet moves through a load balancer, or how a database shard splits, I have to wave my hands frantically or create 10 different slides. I wanted the ability to “Sketch Logic, Export Motion”. The Goal I didn’t want a timeline editor (like After Effects). That’s too much work for a simple diagram. n I wanted “Keyless Animation”: Draw Frame 1 (The start state). Clone […]

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RL involves exploration, search, planning, etc. Which of these steps could eventually be made much more performant with quantum computers, assuming the economics of said computers became realistic en masse? Off the cuff, maybe something like MCTS? submitted by /u/thecity2 [link] [comments]

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arXiv:2604.14237v1 Announce Type: new Abstract: Transistor topology optimization is a critical step in standard cell design, directly dictating diffusion sharing efficiency and downstream routability. However, identifying optimal topologies remains a persistent bottleneck, as conventional exhaustive search methods become computationally intractable with increasing circuit complexity in advanced nodes. This paper introduces TOPCELL, a novel and scalable framework that reformulates high-dimensional topology exploration as a generative task using Large Language Models (LLMs). We employ Group Relative Policy Optimization (GRPO) to […]

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Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA), optimizing low-rank matrices A and B. In distributed scenarios where privacy constraints necessitate Federated Learning (FL), however, the integration of LoRA is often unstable. Specifically, we identify that aggregating updates from multiple clients introduces statistical variance that scales with the client count, causing gradient collapse when using high-rank adapters. Existing scaling factor candidates, […]

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arXiv:2603.03305v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate executable outputs, JSON objects, and API calls, where a single syntax error can make the output unusable. Constrained decoding enforces validity token-by-token via masking and renormalization, but it can distort generation when the model assigns low probability mass to valid continuations, pushing decoding toward locally valid yet semantically incorrect trajectories. We propose emph{Draft-Conditioned Constrained Decoding (DCCD)}, a simple two-step, training-free inference procedure that decouples […]

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