CodeMender helps patch critical software vulnerabilities, and rewrites and secures existing code. CodeMender helps patch critical software vulnerabilities, and rewrites and secures existing code. Read More
[[{“value”:” Just a few years ago, AI coding assistants were little more than autocomplete curiosities—tools that could finish your variable names or suggest a line of boilerplate. Today, they’ve become an everyday part of millions of developers’ workflows, with entire products and startups built around… Read More »Mapping the Design Space of AI Coding Assistants Sam Lau and Philip Guo AI & ML – Radar
Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity. Time series data have the added complexity of temporal dependencies, seasonality, and possible non-stationarity. Read More
[[{“value”:” Why treat LLM inference as batched kernels to DRAM when a dataflow compiler can pipe tiles through on-chip FIFOs and stream converters?StreamTensor is a compiler that lowers PyTorch LLM graphs (GPT-2, Llama, Qwen, Gemma) into stream-scheduled dataflow accelerators on AMD’s Alveo U55C FPGA. The… Read More »StreamTensor: A PyTorch-to-Accelerator Compiler that Streams LLM Intermediates Across FPGA Dataflows Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” Salesforce AI Research released CoDA-1.7B, a diffusion-based language model for code that generates by denoising whole sequences with bidirectional context, updating multiple tokens in parallel rather than left-to-right next-token prediction. The research team published both Base and Instruct checkpoints and an end-to-end training/evaluation/serving stack.… Read More »Salesforce AI Research Releases CoDA-1.7B: a Discrete-Diffusion Code Model with Bidirectional, Parallel Token Generation Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Table of contents Why WER Isn’t Enough ? What to Measure (and How) ? Benchmark Landscape: What Each Covers Filling the Gaps: What You Still Need to Add A Concrete, Reproducible Evaluation Plan References Optimizing only for Automatic Speech Recognition (ASR) and Word Error… Read More »How to Evaluate Voice Agents in 2025: Beyond Automatic Speech Recognition (ASR) and Word Error Rate (WER) to Task Success, Barge-In, and Hallucination-Under-Noise Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” Can a speech enhancer trained only on real noisy recordings cleanly separate speech and noise—without ever seeing paired data? A team of researchers from Brno University of Technology and Johns Hopkins University proposes Unsupervised Speech Enhancement using Data-defined Priors (USE-DDP), a dual-stream encoder–decoder that… Read More »This AI Paper Proposes a Novel Dual-Branch Encoder-Decoder Architecture for Unsupervised Speech Enhancement (SE) Michal Sutter Artificial Intelligence Category – MarkTechPost
[[{“value”:” We will build a Regression Language Model (RLM), a model that predicts continuous numerical values directly from text sequences in this coding implementation. Instead of classifying or generating text, we focus on training a transformer-based architecture that learns quantitative relationships hidden within natural language… Read More »A Coding Implementation to Build a Transformer-Based Regression Language Model to Predict Continuous Values from Text Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” What if, instead of re-sampling one agent, you could push Gemini-2.5 Pro to 34.1% on HLE by mixing 12–15 tool-using agents that share notes and stop early? Google Cloud AI Research, with collaborators from MIT, Harvard, and Google DeepMind, introduced TUMIX (Tool-Use Mixture)—a test-time… Read More »Google Proposes TUMIX: Multi-Agent Test-Time Scaling With Tool-Use Mixture Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Researchers from Cornell and Google introduce a unified Regression Language Model (RLM) that predicts numeric outcomes directly from code strings—covering GPU kernel latency, program memory usage, and even neural network accuracy and latency—without hand-engineered features. A 300M-parameter encoder–decoder initialized from T5-Gemma achieves strong rank… Read More »Can a Small Language Model Predict Kernel Latency, Memory, and Model Accuracy from Code? A New Regression Language Model (RLM) Says Yes Asif Razzaq Artificial Intelligence Category – MarkTechPost