The Archive

Modern deep neural networks rely on Euclidean scalar activations (e.g., ReLU) and global normalization techniques (e.g., LayerNorm) to prevent gradient instability in deep architectures. However, these mechanisms inherently cause dead neurons, discard critical directional information, and destroy the orthogonality of feature representations. Inspired by the frequency-modulation transmission of biological axons, we propose the Z-Plane Neural Network, which maps hidden states into 2D phasor bundles on a hypersphere. We introduce a novel geometric activation function, Radial Bounding($\mathbf{x}...

This paper studies the information gap between mixture detection and label recovery in binomial logistic mixtures. Standard likelihood-based criteria such as the Bayesian information criterion (BIC) can detect the presence of two components, but this does not guarantee that the corresponding labels are recoverable. We show that this gap is intrinsic to binomial logistic mixtures with a fixed number of trials: observed-data evidence for mixture structure and per-observation information for label recovery have different local orders in the component separation, and only the former accumulates w...

Real-world robot task planning must operate under both stochastic action execution and partial observability, yet constructing Partially Observable Markov Decision Process (POMDP) models for real robotics domains remains difficult and labor-intensive. We introduce PO-PDDL, a symbolic formulation of POMDPs that preserves the relational structure and LLM-friendly syntax of the Planning Domain Definition Language (PDDL), while explicitly modeling partial observability, stochasticity, and beliefs. Building on this formulation, we propose a demonstration-driven pipeline for learning PO-PDDL models...

4-bit quantization significantly reduces the memory footprint and accelerates the inference of large language models (LLMs). However, its limited bit-width representation struggles to faithfully capture both dense common values (\emph{inliers}) and rare large-magnitude values (\emph{outliers}), causing substantial accuracy degradation. Existing mixed-precision methods mitigate this by retaining outliers in high precision, but at the cost of breaking the uniformity of low-bit execution, introducing precision conversion and extra data movement that undermine practical speedup. We propose \textb...

Multilingual benchmarks are central to evaluating large language models (LLMs) across languages, but they suffer from three issues: exhaustive evaluation scales linearly with the number of languages, automatic translation introduces errors that are easily missed at scale, and some items conflate general and culture-specific knowledge. We address all three with a unified statistical framework, Multilingual-IRT, which extends Item Response Theory with per-language difficulty deviations, split discriminability separating content from language effects, and per-language ability residuals. Fitting ...

Accurate traffic flow prediction remains challenging in cross-city, data-scarce scenarios where limited historical data hinders model generalisation. The chaotic nature of traffic dynamics, complex spatio-temporal dependencies, and heterogeneous urban networks complicate few-shot learning across cities. Existing deep learning approaches either treat traffic as purely deterministic or lack mechanisms to model wave-like interference patterns essential for cross-regime traffic dynamics. To address these limitations, this paper proposes CIWI-CKT, a novel Chaos-Informed Wave Interference Feature F...

In-context learning (ICL) is the standard method for low-resource classification, yet its efficacy in specialized domains remains largely unexplored. We address the challenge of classifying semantically complex, multi-party B2B conversations, where traditional ICL encounters significant limitations, especially as context length increases due to the concatenation of multiple few-shot examples. We introduce the \texttt{Call Playbook} dataset, featuring five classification tasks derived from real-world B2B conversations targeting core sales concepts. To bridge the gap between performance and pra...

Audit risk assessment increasingly benefits from combining heterogeneous evidence sources, yet existing approaches typically produce point predictions without quantifying how well different evidence streams agree. We propose UMAR (Uncertainty-Aware Multi-Agent Risk Assessment), a framework that employs three specialized agents: an MD&A Text Agent, a Financial Ratio Agent, and a CAM Agent, each producing independent risk scores with calibrated uncertainty estimates. An Uncertainty Aggregator based on Dempster-Shafer evidence theory fuses these scores while explicitly measuring inter-agent conf...

A digital twin (DT) of a patient-specific heart offers significant potential in personalized medicine. However, its rapid and dynamic adaptation to an individual's live data and its predictive capability after adaptation remains central challenges. We examine this challenge from its two building blocks: DT formulation where mechanistic and data-driven models show competing merits and limitations, and DT optimization strategies that are largely driven by a reconstruction objective leading to un-identifiable models. We address both bottlenecks via HAPI -- an AI framework for building hybrid, ad...

Large Language Models (LLMs) have shown promise for reasoning over Text-Attributed Graphs (TAGs). However, applying LLMs to graphs requires linearizing their structure into sequences, introducing distortion rooted in the graph bandwidth problem. While this distortion has been shown to degrade performance, it is often attributed to prompt design or model scale, leaving the underlying mechanism unclear. In this work, we show \textit{how} rotary positional embeddings turn graph linearization into bandwidth-dependent attention decay, suppressing attention between graph-adjacent nodes that are for...

Extending a vision-language-action (VLA) policy to a new task typically requires task-specific teleoperated demonstrations and per-task fine-tuning, making adaptation costly in both data collection and compute. In this paper, we show that this target-side per-task adaptation cost can be replaced by retrieval. Our retrieval-augmented policy is trained once on paired demonstrations from the target embodiment (query) and a cheaper embodiment (pool, e.g., human-hand video), then frozen. New tasks are added at deployment by appending pool-side demonstrations to a retrieval pool. The frozen policy ...

The continuous scaling of large language models (LLMs) incurs prohibitive computational costs, making Mixture-of-Experts (MoE) a scalable alternative for efficient fine-tuning via sparse activation. While federated learning (FL) emerges as the paradigm for privacy-preserving collaborative optimization, integrating MoE into FL under data heterogeneity may trigger conflicting expert optimizations. Client-specific data distributions force same-indexed experts to optimize under inconsistent or even conflicting feature-label correlations. This mismatch induces destructive interference during aggre...

Tech leaders debate whether the Anthropic episode is a wake-up call for India’s AI ambitions.

Meta starts dismantling its $2 billion Manus acquisition after Beijing ordered the deal reversed.

The Pyodide 314.0 release announcement (via Hacker News ) includes news I've been looking forward to for a long time: You can now publish Python packages built for Pyodide (or any Python runtime compatible with the PyEmscripten platform defined in PEP 783 ) directly to PyPI and install them at runtime. Previously, the Pyodide maintainers had to maintain, build, and host over 300 packages ourselves. This created a significant burden on our maintainers and became a major bottleneck for the community, as every new package required manual review. Moving forward, package maintainers can simply bui...

Release: luau-wasm 0.1a0 See Publishing WASM wheels to PyPI for use with Pyodide for details. Tags: lua , webassembly , pyodide

Research: Mapping SQLite result columns back to their source `table.column` It would be neat if arbitrary SQL queries in Datasette could be rendered with additional information based on which columns from which tables were included in the results. To build that, we would need to be able to look at a SQL query like select users.name, orders.total from users join orders on orders.user_id = users.id and programmatically identify the table.column for each result - navigating not just joins but also more complex syntax like CTEs. I decided to set Claude Code (Opus 4.8, since Fable is currently ban...

According to the Wall Street Journal, the export control directive that led to Anthropic cutting off access to Fable 5 and Mythos 5 was triggered in part by cybersecurity research from Amazon and conversations between CEO Andy Jassy and the White House. According to the report, the paper from Amazon claims that, through a series of prompts, it was able to get Fable 5 to serve up information that could be used in cyberattacks. Amazon has yet to respond to a request for comment. Shortly after Jassy shared the company's findings with the government, it made the call to block its use by foreign n...

Once again, AI proves to be an unreliable source of information about AI.

Amazon CEO Andy Jassy may have been the source of security concerns that led Anthropic to cut off worldwide access to two models on Friday.

It's not clear which states are involved, but they're asking about everything from OpenAI's ad policies to its handling of health data.

When I returned to my computer five minutes after giving Gemini a lengthy prompt, I had two things: a functional app in a preview window, and a message about a bug. "~ Channel is unrecoverably broken and will be disposed!" Sounded bad! But right below it was a button to fix the bug. Pretty weird that I just instructed a computer to build a whole app for me with a single prompt, but it needed me to click a button to fix a bug. I did anyway, and in 233 seconds Gemini reported back that it had succeeded, using words like "blockages" and "race conditions." I didn't understand a bit of it. It was ...

iPhone owners are getting real, native AI photo editing for the first time. The most popular camera in the world just got its first set of serious AI photo editing features, and I don't think any of us are ready. As far as AI photo editing goes, the new features in iOS 27 are pretty tame compared to what you can do on, say, Google's Pixel phones. But for the iPhone, they represent a tipping point in what the native photos app allows you to do to your photos. I mean memories. I mean, I don't know anymore. These new features are part of the iOS 27 developer beta right now, so bear in mind that ...

Concept art from Dear Upstairs Neighbors that used to train custom builds of Google’s Veo and Imagen models. | Image: Google DeepMind For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of visually inconsistent footage. And some of Hollywood's biggest AI partnerships have suddenly evaporated in ways that make it seem like stu...

We are in the strangest timeline.

Andrew Yang made a list of everything Americans overpay for — housing, food, wireless — and thinks the next startup gold rush is giving that money back.

Commerce dept. worries that a Fable 5 "jailbreak" could be a national security threat.

Anthropic isn't hiding its frustration. "We disagree that the finding of a narrow potential jailbreak should be cause for recalling a commercial model deployed to hundreds of millions of people," the company wrote in a blog post.

Statement on the US government directive to suspend access to Fable 5 and Mythos 5 Well this is nuts : The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Anthropic models will not be affected. We received the directive from the government toda...

OpenAI WebRTC Audio Session, now with document context I built the first version of this tool in December 2024 to try out the then-new OpenAI WebRTC API for interacting with their realtime audio models. Last month OpenAI introduced a brand new model to that API called GPT‑Realtime‑2 , which they promoted as "our first voice model with GPT‑5‑class reasoning" - with a Sep 30, 2024 knowledge cut-off. I've been waiting for that model to show up in the ChatGPT iPhone app but it still hasn't, so I revisited my old playground. You can now pick the better model, and you can also paste in a big chunk ...