AI News Today

arXiv:2607.16877v1 Announce Type: cross Abstract: The increasing complexity of next-generation wireless networks has driven the integration of artificial intelligence (AI) into wireless communications. However, most existing studies focus on developing task-specific deep learning techniques for single scenarios, which limits their ability to generalize across diverse tasks, channel conditions, and system configurations. To address this generalization bottleneck, we propose a hierarchical wireles

arXiv:2607.16888v1 Announce Type: cross Abstract: Deploying a medical imaging model that must later accommodate a modality it has never seen is a recurring practical problem: retraining the shared representation is expensive and destroys performance on the modalities already in service. We study this \emph{onboarding} problem under a strict leave-one-domain-out protocol, in which a convolutional backbone is pre-trained on source modalities (Kidney CT and Brain MRI), frozen permanently, and then

arXiv:2607.16987v1 Announce Type: cross Abstract: Over the past few years, diffusion-based Schr\"odinger bridge models have been proposed to approximate optimal transport dynamics between two prescribed boundary distributions, with successful applications to generative modeling. More precisely, these methods aim to estimate a path measure whose initial and terminal marginals match the two boundary distributions, while minimizing the Kullback-Leibler divergence with respect to a reference Markov

arXiv:2607.17008v1 Announce Type: cross Abstract: In many cases, the outage of one transmission line in a system can be localized by monitoring the power flow of another line, and machine learning methods can be used to distinguish the cases under uncertainty. In this study, we examine the improvements in line outage localization performance achieved by various ensemble classifiers compared to single-model methods. In the case studies, we compared the classification results with measurement data

arXiv:2607.17091v1 Announce Type: cross Abstract: Processing-In-Memory (PIM) has emerged as a promising technology for accelerating machine learning (ML) workloads. Specifically, non-volatile memory-based PIM architectures have enabled effective ML acceleration due to their ability to perform energy-efficient matrix-vector multiplication operations. However, these devices suffer from non-idealities such as thermal noise. This noise alters the stored values in the memory cells which correspond to

arXiv:2607.17154v1 Announce Type: cross Abstract: Although mixture-of-experts, MoE, models have been increasingly adopted to scale large language models with moderate computation cost, it remains challenging to deploy MoE inference over resource-constrained and bandwidth-limited edge infrastructures. Existing distributed MoE serving methods mainly rely on exact expert placement, caching, replication, or communication scheduling, while overlooking the functional similarity among experts, which pr

arXiv:2607.17201v1 Announce Type: cross Abstract: In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning. This is an active sequential hypothesis testing problem in which the learner's objective is to identify an optimal policy in a Markov Decision Process (MDP) with high confidence, while minimizing the expected sample complexity to do so. We consider an online setting with deterministic rewards, where the agent must strategically navigate t

arXiv:2607.17327v1 Announce Type: cross Abstract: Quantum machine learning models define probabilistic input--output maps through coherent quantum evolution and measurement. While such models can exhibit computational advantages, their internal functioning and decision making generally resists interpretation in terms of stochastic trajectories through intermediate configurations. In contrast to classical (Markovian) stochastic processes, quantum dynamics generically violates the Chapman--Kolmogo

arXiv:2505.17964v2 Announce Type: replace Abstract: Cycle count statistics are fundamental tools in statistics and engineering, with applications in motif counting, channel coding, and statistical inference of network and matrix data. However, how to compute high-order cycle count statistics efficiently is still an open problem. In this paper, we aim to derive Computationally Efficient Equivalent Forms (CEEF) for cycle count statistics of any given order, where we express each cycle count statis

arXiv:2509.02522v3 Announce Type: replace Abstract: Recent advances in Reinforcement Learning with Verifiable Rewards (RLVR) have empowered large language models (LLMs) to tackle challenging reasoning tasks such as mathematics and programming, however existing RLVR methods often suffer from sparse reward signals and unstable policy gradient updates inherent to RL-based approaches. To address the challenges, we propose PACS, a novel RLVR framework that achieves imPlicit Actor Critic coupling via

arXiv:2509.23071v2 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories. Existing datasets provide questions, answers, and evidence, but lack fine-grained supervision for retriever invocation, dynamic planning, and stepwise decision-making. Reinforcement learning offers a potential solution, but often suffers from sparse rewards and cold-start failures when base large

arXiv:2510.05969v3 Announce Type: replace Abstract: Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an essential capability for adaptive reasoning and efficient resource allocation. In this work, we investigate whether LLMs implicitly encode problem difficulty in their internal representations. Using a linear probe on the final-token representations of LLMs, we demonstra

arXiv:2511.07457v2 Announce Type: replace Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks. However, effectively adapting LLMs to structural data, such as knowledge graphs or web graphs, remains a fundamental challenge. Some approaches adopt complex strategies to convert graphs into text sequences, resulting in significant token overhead and rendering them impractical for large-scale graphs.

arXiv:2512.02924v3 Announce Type: replace Abstract: While Neural Processing Units (NPUs) offer high theoretical efficiency for edge AI, state-of-the-art Vision--Language Models (VLMs) tailored for GPUs often falter on these substrates. We attribute this hardware-model mismatch to two primary factors: the quantization brittleness of Vision Transformers (ViTs) and the I/O-bound nature of autoregressive attention mechanisms, which fail to utilize the high arithmetic throughput of NPUs. To bridge th

arXiv:2601.03043v4 Announce Type: replace Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency. Prior work typically decomposes inference into prefill and decode stages, with the decode stage dominating total latency. To reduce time and memory complexity in the decode stage, a line of work introduces sparse-attention algorithms. In this paper, we show

arXiv:2601.07121v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used not only for problem solving but also for creative ideation; however, generating ideas that are both novel and coherent remains challenging. While high-temperature sampling can promote originality, it often compromises consistency and usefulness. Here, we propose ReMIND, a four-stage framework comprising wake, which establishes a stable semantic baseline through low-temperature generation; drea

arXiv:2601.13359v3 Announce Type: replace Abstract: Prefill attacks are an effective and low-cost jailbreaking method, as they directly insert an acceptance sequence (e.g., "Sure, here is...") at the start of an LLM's output and lead the model to continue the response. We make two contributions to this prior work. First, we show that an unsophisticated adversary can improve the well-known prefill attacks by ensembling a small number of prefill variants. Running three easy-to-generate prefills yi

arXiv:2602.11961v3 Announce Type: replace Abstract: Open large language models (LLMs) have demonstrated improving multilingual capabilities in recent years. In this paper, we present a study of open LLMs for multilingual machine translation (MT) across a range of languages, and investigate the effects of model scaling and data scaling when adapting open LLMs to multilingual MT through continual pretraining and instruction finetuning. Based on the Gemma3 model family, we develop MiLMMT-46, which

arXiv:2603.14355v2 Announce Type: replace Abstract: Safety tuning through supervised fine-tuning and reinforcement learning from human feedback has substantially improved the robustness of large language models. However, it typically suppresses rather eliminates unsafe behaviors, leaving rare but critical failures hidden in the long tail of the output distribution. While most red-teaming work emphasizes adversarial prompt search, we show that these hidden risks can be systematically exposed thro

arXiv:2603.27752v2 Announce Type: replace Abstract: Large language models can still hallucinate in retrieval-augmented generation (RAG), producing claims that are unsupported by or conflict with the retrieved context. Detecting such errors remains challenging when faithfulness is judged solely against the retrieved context: many existing detectors return holistic answer-level scores, while others target open-domain factuality or fail to provide evidence-grounded diagnostics. We present RT4CHART,

arXiv:2604.01457v2 Announce Type: replace Abstract: Large language models are often not just wrong, but \emph{confidently wrong}: when they produce factually incorrect answers, they tend to verbalize overly high confidence rather than signal uncertainty. Such verbalized overconfidence can mislead users and weaken confidence scores as a reliable uncertainty signal, yet its internal mechanisms remain poorly understood. We present a circuit-level mechanistic analysis of this inflated verbalized con

arXiv:2604.08523v2 Announce Type: replace Abstract: AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites? Everyday online tasks offer a realistic yet unsolved testbed for evaluating the next generation of AI agents. To this end, we introduce ClawBench, an evaluation framework comprising 153 everyday online tasks that people need to accomplish regularly in their lives and work, spanning 144 platforms across 15 categor

arXiv:2604.09497v2 Announce Type: replace Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases. In practice, however, evaluating generative outputs typically relies on rigid lexical methods to extract and assess answers, which can conflate a model's true problem-solving ability with its compliance with predefined formatting guidelines. While recent LLM-as-a-Judge approaches mitigate this iss

arXiv:2605.01224v2 Announce Type: replace Abstract: This paper argues that contemporary multilingual NLP has converged on a fragile and misleading paradigm of incidental multilingualism. Today's LLMs appear multilingual largely because they are trained on massive, uneven web corpora, not because multilingual or multicultural competence has been treated as a core design objective. We contend that this paradigm systematically produces unequal, brittle, and opaque behavior across languages, with se

arXiv:2605.12225v2 Announce Type: replace Abstract: While deep transformer-based models have advanced rapidly, their internal mechanisms remain largely a mystery. Recent work has prioritized understanding text-based transformer models, leaving ASR systems largely unexplored. In order to address this gap, we examine the internal representations of Whisper's encoder using a sparse autoencoder. We find diverse monosemantic features across linguistic and non-linguistic boundaries, spanning a hierarc

arXiv:2606.02780v3 Announce Type: replace Abstract: The success of the transformer architecture as the backbone of modern LLMs is in large part due to its use of attention layers. An attention layer follows the standard neural network paradigm: it takes the residual stream as input and thereby produces context-dependent query, key, and value vectors. However, we find that model performance meaningfully improves when deeper layers learn only a context-free value vector to preserve the original to

arXiv:2606.06197v3 Announce Type: replace Abstract: Question answering (QA) systems have achieved notable progress with the advent of large language models (LLMs). However, they still face challenges in accurately extracting and generating precise answers from given contexts, particularly when dealing with complex or ambiguous queries. Existing approaches often struggle with contextual understanding, answer consistency, and generalization across diverse domains. In this work, we propose a questi

arXiv:2606.07559v2 Announce Type: replace Abstract: Fine-tuning a language model often fails silently when its correct completion must outrank a near-synonym competitor. Cross-entropy loss falls monotonically while the correct token never overtakes the competitor. We study five transformer architectures of two families over a fivefold parameter range, on ten contexts where the correct completion and nearest competitor overlap substantially. We construct an order parameter combining the predicted

Article URL: https://asia.nikkei.com/business/technology/five-us-tech-giants-hidden-debts-soar-to-1.65tn-on-opaque-ai-funding Comments URL: https://news.ycombinator.com/item?id=48987863 Points: 43 # Comments: 1

Article URL: https://www.thedial.world/articles/news/copyright-law-ai-intellectual-property Comments URL: https://news.ycombinator.com/item?id=48987723 Points: 6 # Comments: 0

Article URL: https://garymarcus.substack.com/p/china-has-all-but-caught-up-the-us Comments URL: https://news.ycombinator.com/item?id=48987713 Points: 4 # Comments: 0

<pre style='white-space:pre-wrap;width:81ex'>fix(plugin-sdk): bound Anthropic Vertex ADC credential read (#111120) * fix(plugin-sdk): bound Anthropic Vertex ADC credential read canReadAnthropicVertexAdc read the entire GOOGLE_APPLICATION_CREDENTIALS file via unbounded fs.readFileSync solely to check readability (the contents are discarded). The provider-local copy of this logic in extensions/anthropic-vertex/region.ts was already bounded with tryReadSecretFileSync + a 1 MiB limit in #109260 ("re

Article URL: https://medium.com/@robsandhu/ai-is-changing-the-economics-of-native-apps-e81c7f325948 Comments URL: https://news.ycombinator.com/item?id=48987649 Points: 4 # Comments: 1

A while back I needed an ID photo for something urgent. The only way to get one was to find a photo studio, book a time, and wait around for it to be printed, and just one set cost about $13 (converted from my local currency). With how far AI has come, I felt like this should be easier and cheaper by now. So, I built Portraify. It turns 1–3 casual photos into professional headshots, and also does US passport photos and 1-inch / 2-inch ID photos. The thing I cared most about was likeness. A heads

<pre style='white-space:pre-wrap;width:81ex'>feat(chat): collapse long user prompts (#111866) * feat(chat): collapse long user prompts * fix(chat): sync disclosure strings * fix(android): reuse disclosure labels * chore(i18n): refresh native inventory * perf(ui): trim prompt disclosure chunk</pre>

Article URL: https://www.bleepingcomputer.com/news/security/hugging-face-breach-autonomous-ai-agent-system-internal-datasets-credentials/ Comments URL: https://news.ycombinator.com/item?id=48987577 Points: 4 # Comments: 0

Article URL: https://www.wsj.com/tech/ai/top-american-ai-execs-sound-alarm-on-chinese-models-3c74f8c1 Comments URL: https://news.ycombinator.com/item?id=48987556 Points: 2 # Comments: 1

Comments URL: https://news.ycombinator.com/item?id=48987547 Points: 2 # Comments: 4

Article URL: https://www.i-programmer.info/professional-programmer/103-i-programmer/18759-why-software-engineering-will-never-die-revisited-in-the-age-of-spec-driven-development.html Comments URL: https://news.ycombinator.com/item?id=48987524 Points: 2 # Comments: 0

Hey all. I made this because, where I work, we aren't allowed to use any app that allows cloud-sync or AI within the application. I started with the things I needed (to-do lists, due dates, and interlinking) and I kept building up from there. I am hoping others in the community find it as useful as I do! Feedback is always appreciated. cheers. Comments URL: https://news.ycombinator.com/item?id=48987509 Points: 4 # Comments: 0