Spike sparsity is widely believed to enable efficient spiking neural network (SNN) inference on GPU hardware. We demonstrate this is an illusion: five distinct ...
Continuous prompt search offers a computationally efficient alternative to conventional parameter tuning in natural language processing tasks. Nevertheless, its...
In Jurassic World, there’s that iconic scene where Owen Grady Chris Pratt stands in a cage with three Velociraptors. He doesn’t run. He doesn’t try to punch th...
Why Most AI Agents Fail in Production And How to Fix It After running autonomous agents in production for months, I've noticed a pattern: agents fail in predic...
Article Abstract In the early days of building with AI, most teams focus on two things. 1. The model. 2. The application interface. Which model should we use?...
markdown !Manoj Mishrahttps://media2.dev.to/dynamic/image/width=50,height=50,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2F...
!Jon Groveshttps://media2.dev.to/dynamic/image/width=50,height=50,fit=cover,gravity=auto,format=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fu...
Just as useless of an idea as LLMs.txt was It’s all dumb abstractions that AI doesn’t need because AIs are as smart as humans so they can just use what was alre...
Introduction As a practical testing ground for verifying reasoning optimization and model handling, I first touched an OSS shogi software in January 2026. As a...
xAI's Rebuilding Effort “xAI was not built right the first time around, so it is being rebuilt from the foundations up,” Elon Musk said on Thursday on his soci...
One Good Use of AI: Helping People with Permanent Voice Loss Worldwide By Timothy Beck Werthhttps://mashable.com/author/timothy-beck-werth !Headshot of Timothy...
The Hidden Problem with Traditional RAG Most RAG pipelines follow a similar workflow: 1. Documents are split into chunks. 2. Each chunk is converted into embed...
The Problem Nobody Talks About When you go from 200 K to 1 M context, the natural instinct is to dump everything in: your entire codebase, all the docs, every...
Your move > When winning depends on intuiting a mathematical function, AIs come up short. Oddly, the training methods that work great for chess fail on far sim...
Introduction A recent guest lecturer in a Computer Science elective warned that AI is poised to take over the majority of jobs, even suggesting that a CS educa...
Spielberg's Comments on AI Legendary filmmaker Steven Spielberg spoke out against the use of AI technology in creative endeavors during an interview at the SXS...
In a spiking neural network, is it enough for each neuron to spike at most once? In recent work, approximation bounds for spiking neural networks have been deri...
Delay of Meta’s Next AI Model Meta has delayed the release of its next major AI modelhttps://www.nytimes.com/2026/03/12/technology/meta-avocado-ai-model-delaye...
Recent progress in text-conditioned human motion generation has been largely driven by diffusion models trained on large-scale human motion data. Building on th...
Machine learning approaches to spatiotemporal physical systems have primarily focused on next-frame prediction, with the goal of learning an accurate emulator f...
Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations wit...
Evolutions in the world, such as water pouring or ice melting, happen regardless of being observed. Video world models generate 'worlds' via 2D frame observatio...
Instruction Tuning (IT) has been proven to be an effective approach to unlock the powerful capabilities of large language models (LLMs). Recent studies indicate...
While large language models (LLMs) have transformed AI agents into proficient executors of computational materials science, performing a hundred simulations doe...
Large Language Models (LLMs) can generate persuasive influence strategies that shift cooperative behavior in multi-agent populations, but a critical question re...
Prior approaches for membership privacy preservation usually update or retrain all weights in neural networks, which is costly and can lead to unnecessary utili...
Spatio-temporal scene graphs provide a principled representation for modeling evolving object interactions, yet existing methods remain fundamentally frame-cent...
Brain tumor classification from magnetic resonance imaging, which is also known as MRI, plays a sensitive role in computer-assisted diagnosis systems. In recent...
Matrix multiplication performance has long been the major bottleneck to scaling deep learning workloads, which has stimulated the design of new accelerators tha...
The dynamics of Saturn's satellite system offer a rich framework for studying orbital stability and resonance interactions. Traditional methods for analysing su...
In modern human-robot collaboration (HRC) applications, multiple perception modules jointly extract visual, auditory, and contextual cues to achieve comprehensi...
Scaling Costs and Latency in RAG and AI Agents We’ve talked a lot about what an incredible tool RAG is for leveraging the power of AI on custom data. Whether w...
Large Language Models (LLMs) increasingly serve as autonomous reasoning agents in decision support, scientific problem-solving, and multi-agent coordination sys...
The ability to provide trustworthy maternal health information using phone-based chatbots can have a significant impact, particularly in low-resource settings w...
Concept Bottleneck Models (CBMs) are interpretable models that route predictions through a layer of human-interpretable concepts. While widely studied in vision...
Diffusion-based image compression has recently shown outstanding perceptual fidelity, yet its practicality is hindered by prohibitive sampling overhead and high...
As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in man...
Face de-identification (FDeID) aims to remove personally identifiable information from facial images while preserving task-relevant utility attributes such as a...
Deep learning models, despite their impressive achievements, suffer from high computational costs and memory requirements, limiting their usability in resource-...
Active learning (AL) aims to reduce annotation costs while maximizing model performance by iteratively selecting valuable instances. While foundation models hav...
Yield Multi-Corner Analysis validates circuits across 25+ Process-Voltage-Temperature corners, resulting in a combinatorial simulation cost of O(K times N) wher...
Understanding the theoretical foundations of attention mechanisms remains challenging due to their complex, non-linear dynamics. This work reveals a fundamental...
What is a diffusion model actually doing when it turns noise into a photograph? We show that the deterministic DDIM reverse chain operates as a Partitioned Iter...
Large Language Models (LLMs) have demonstrated remarkable capability in machine translation on high-resource language pairs, yet their performance on low-resour...
Supervised Semantic Differential (SSD) is a mixed quantitative-interpretive method that models how text meaning varies with continuous individual-difference var...
Federated learning on neuromorphic hardware remains unexplored because on-chip spike-timing-dependent plasticity (STDP) produces binary weight updates rather th...
Training capable software engineering (SWE) agents demands large-scale, executable, and verifiable environments that provide dynamic feedback loops for iterativ...
