The Archive

OpenAI is overhauling ChatGPT's voice mode with a new model that it says is more like "talking to another person." The new GPT-Live-1 is designed to interrupt you less and will also wait for you to continue speaking if you pause mid-conversation. During a press briefing, OpenAI research lead Kundan Kumar called GPT-Live-1 the company's "smartest voice model" yet. It will automatically pass your queries to its best text models, like GPT-5.5, when it needs to reason or search the web, allowing it to more quickly transition from researching the topic you've asked about to talking about its findi...

OpenAI says its new voice mode can speak and listen at the same time, a key ability for live translation.

ALER-TI retrieval-augmented framework imputes time series using historical patterns to handle non-stationary dynamics.

Optimal control framework adapts neural network depth via posteriori error estimation for nonlinear problem approximation.

Hybrid quantum-classical architecture combines quantum neural networks with path signature kernels for time series classification.

Future Confidence Distillation measures pre- and post-solution confidence in LLM responses to improve reliability estimation for downstream tasks.

Android Bench is evolving, and developers can help guide that process.

Riemann normal coordinates improve Levenberg-Marquardt optimization for nonlinear least-squares via geodesic acceleration.

Systematic review of agentic AI governance identifies governance priorities and mechanisms for autonomous planning/execution systems.

Asymmetric focal loss improves graph neural network prediction of polypharmacy drug-drug interactions via difficult-example emphasis.

The round, led by Radical Ventures, values the two-year old startup at $1 billion.

CARLA-GS decouples representation, reasoning, and physics for photorealistic autonomous driving corner-case synthesis.

Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance... Presto is an open source, distributed SQL engine for running fast, interactive queries on very large datasets. On NVIDIA GPUs, Presto delivers peak performance for analytical query workloads and provides low latency for users and agents. GPU-accelerated Presto brings low latency to your analytical workloads, keeping you and your agents unblocked and iterating as fast as possible. Source

Multi-label BERT formulation outperforms multi-class for CVE-to-CWE vulnerability classification across taxonomy sizes.

FedKT-C combines one-shot federated learning with synthetic data generation to achieve privacy, low communication, and heterogeneity robustness simultaneously.

PALS adjusts per-layer sparsity in LLM pruning via activation percentiles, improving LLaMA-2-7B perplexity by 15% at 50% sparsity over uniform Wanda.

Empirical study on capacity allocation across hierarchical search agent roles (delegation, execution, generation) in multi-agent LLM systems.

There are a lot of fast-growing AI startups, but some are growing even faster, they say.

Bounds on failure region probability for Langevin dynamics training, proving exponential suppression of unsafe states with dimension.

Mahalanobis distance-based unified detection framework for LLM-generated text, hallucinations, watermarks, and adversarial examples.

Analysis of human-AI collaboration in structural design showing friction from constraints can drive creative exploration rather than final-answer generation.

GRiLS, a gradient-free Riemannian MCMC sampler for multimodal distributions, improves mode mixing without density gradient evaluations.

Theoretical proof that data augmentation induces graph-Laplacian regularization, achieving O(1/n_L) label-sample efficiency versus O(1/√n_L) supervised.

Analysis of permutation-equivariance and stability in continuous flow models parameterized by GNNs for graph signal generation.

Asynchronous single-rollout RL for agentic LLM post-training addresses training stability and efficiency limits of batch-synchronous GRPO.

HIVE benchmark systematically evaluates how hallucinated content in vision-language models influences downstream reasoning, moving beyond detection to understanding post-hallucination inference.

Empirical study compares LLM-generated versus expert-written skill modules for agentic data science tasks, finding trade-offs in automation cost versus performance across SQL, cleaning, and statistical testing.

Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are... Agentic systems often face a trade-off between accuracy and cost. The highest-performing proprietary frontier models and harnesses provide top accuracy but are expensive. Fine-tuning offers one way to address this problem. Smaller or more efficient open models starting with lower accuracy are taught to perform better with specific agents. However, fine-tuning requires expertise and hardware for… Source

TimEE is a 4.5M-parameter foundation model enabling end-to-end time series classification via in-context learning, eliminating per-dataset retraining.

RAID applies iterative reinforcement learning to automatically discover exploits in EA Sports NHL 26's goalie AI, reducing manual playtesting effort.