AI News Today

arXiv:2604.01480v2 Announce Type: replace Abstract: Metasurface inverse design can realize complex optical functionality, but turning a target optical response into executable optimization code still requires substantial expertise in computational electromagnetics and solver-specific software engineering. We present a self-evolving agentic framework that lowers this barrier by coupling a coding agent, explicit human-readable skill files, and a deterministic physics-based evaluator. Rather than u

arXiv:2604.17267v2 Announce Type: replace Abstract: Large Language Models can generate synthetic survey responses at low cost, but their accuracy varies unpredictably across questions. We study the design problem of allocating a fixed budget of human respondents across estimation tasks when cheap LLM predictions are available for every task. Our framework combines three components. First, building on Prediction-Powered Inference, we characterize a question-specific rectification difficulty that

arXiv:2604.17502v4 Announce Type: replace Abstract: Misaligned artificial agents might resist shutdown. One proposed solution is to train agents to lack preferences between different-length trajectories. The Discounted Reward for Same-Length Trajectories (DReST) reward function does this by penalizing agents for repeatedly choosing same-length trajectories, and thus incentivizes agents to (1) choose stochastically between different trajectory-lengths (be NEUTRAL about trajectory-lengths), and (2

arXiv:2604.20140v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) is an effective framework for aligning large language models with human preferences, but it struggles with complex reasoning tasks. DPO optimizes for the likelihood of generating preferred over dispreferred responses in their entirety and lacks the granularity to provide feedback on subsections of many-step solutions typical of reasoning tasks. Existing methods excel at either stable preference learning (e.g

arXiv:2605.17393v2 Announce Type: replace Abstract: Coordination graphs are a central abstraction in cooperative multi-agent reinforcement learning (MARL), yet existing sparse-graph learners lack a theoretically grounded mechanism to decide which edges should exist and how much information each edge should carry. Current methods rely on heuristic criteria that offer no formal guarantee on the learned topology, and no principled way to allocate different communication capacities to structurally d

arXiv:2606.00726v2 Announce Type: replace Abstract: Strong reasoning depends not only on model knowledge but also on how effectively cognitive behaviors are deployed during generation. Existing methods often rely on explicit behavior-level control, making them insufficiently adaptive when failures and required corrections vary across reasoning states, tasks, and models. To this end, we propose Latent Reward Steering (LRS), an adaptive inference-time framework that promotes cognitive behaviors by

arXiv:2504.18902v3 Announce Type: replace-cross Abstract: In the forthcoming era of 6G networks, characterized by unprecedented data rates, ultra-low latency, and ubiquitous connectivity, effective management of Virtualized Network Functions (VNFs) is essential. VNFs are software-based counterparts of traditional hardware devices that facilitate flexible and scalable service provisioning. Service Function Chains (SFCs), structured as ordered sequences of VNFs, are pivotal in delivering complex n

arXiv:2506.10915v2 Announce Type: replace-cross Abstract: Text-to-video generation has significantly enriched content creation and holds the potential to evolve into powerful world simulators. However, modeling the vast spatiotemporal space remains computationally demanding, particularly when employing Transformers, which incur quadratic complexity in sequence processing and thus limit practical applications. Recent advancements in linear-time sequence modeling, particularly the Mamba architectu

arXiv:2506.22784v2 Announce Type: replace-cross Abstract: Point-pixel registration between LiDAR point clouds and camera images is a fundamental yet challenging task in autonomous driving and robotic perception. A key difficulty lies in the modality gap between unstructured point clouds and structured images, especially under sparse single-frame LiDAR settings. Existing methods typically extract features separately from point clouds and images, then rely on hand-crafted or learned matching strat

arXiv:2511.17113v3 Announce Type: replace-cross Abstract: Network Intrusion Detection Systems (NIDS) are essential tools for detecting network attacks and intrusions. While extensive research has explored the use of supervised Machine Learning for attack detection and characterisation, these methods require accurately labelled datasets, which are very costly to obtain. Moreover, existing public datasets have limited and/or outdated attacks, and many of them suffer from mislabelled data. To reduc

arXiv:2512.01317v3 Announce Type: replace-cross Abstract: Measurement-induced entanglement (MIE) captures how local measurements generate long-range quantum correlations and drive dynamical phase transitions in many-body systems. Yet estimating MIE experimentally remains challenging: direct evaluation requires extensive post-selection over measurement outcomes, raising the question of whether MIE is accessible with only polynomial resources. We address this challenge by reframing MIE detection a

arXiv:2512.03238v2 Announce Type: replace-cross Abstract: High quality data is needed to unlock the full potential of AI for end users. However finding new sources of such data is getting harder: most publicly-available human generated data will soon have been used. Additionally, publicly available data often is not representative of users of a particular system -- for example, a research speech dataset of contractors interacting with an AI assistant will likely be more homogeneous, well articul

arXiv:2512.16861v2 Announce Type: replace-cross Abstract: Long-horizon manipulation has been a long-standing challenge in the robotics community. We propose ReinforceGen, a system that combines task decomposition, data generation, imitation learning, and motion planning to form an initial solution, and improves each component through reinforcement-learning-based fine-tuning. ReinforceGen first segments the task into multiple localized skills, which are connected through motion planning. The skil

arXiv:2601.09881v2 Announce Type: replace-cross Abstract: Large video diffusion and flow models have achieved remarkable success in high-quality video generation, but their use in real-time interactive applications remains limited due to their inefficient multi-step sampling process. In this work, we present Transition Matching Distillation (TMD), a novel framework for distilling video diffusion models into efficient few-step generators. The central idea of TMD is to match the multi-step denoisi

arXiv:2602.04078v2 Announce Type: replace-cross Abstract: Deep learning has achieved remarkable success across a wide range of domains, significantly expanding the frontiers of what is achievable in artificial intelligence. Yet, despite these advances, critical challenges remain -- most notably, ensuring robustness to small input perturbations and generalization to out-of-distribution data. These critical challenges underscore the need to understand the underlying fundamental principles that gov

arXiv:2602.07764v2 Announce Type: replace Abstract: Multi-objective reinforcement learning (MORL) seeks to train agents capable of balancing conflicting objectives. While single preference-conditioned policies offer a highly scalable solution, existing approaches remain brittle in practice, frequently failing to recover dense Pareto fronts. We demonstrate that this failure stems from two structural pathologies: destructive advantage cancellation caused by premature Early Scalarization (ES), and

arXiv:2606.03979v2 Announce Type: replace Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large Language Models (LLMs). Despite showing promising results in tasks that require instant prediction or in-context learning, existing models lack the ability to continually learn and effectively transfer their temporal in-context knowledge to their long-term parameters

arXiv:2606.31650v2 Announce Type: replace Abstract: Long-horizon language agents must repeatedly interact with tools, accumulate evidence, and make decisions under bounded context windows. Context-management methods make such rollouts feasible by simplifying past interactions through deletion, folding, or memory editing. However, when useful history is collapsed into compressed states, the reconstructed context may no longer reveal which earlier observations support a successful final answer. Th

arXiv:2605.05882v2 Announce Type: replace-cross Abstract: Artificial-intelligence systems are becoming ubiquitous in society, yet their predictions typically inherit biases with respect to protected attributes such as race, gender, or age. Classical fairness notions, most notably Statistical Parity (SP), demand that predictions be independent of the protected attributes, but are overly restrictive when these attributes influence mediating variables that are considered business necessities. Recen

I found an open source homebrew 3DS application that can connect to a remote host via SSH and run Claude Code on it. I tested it on my own 3DS,the experience was great—it even supports voice input. With the 3DS's dual screens, analog stick, multiple buttons (mapped to Ctrl, Alt, etc.), and voice input, using Claude Code is very smooth. I also ported the Tailscale library to the 3DS and used it in my local build, so I can use my 3DS to control my computer and run Claude Code/Codex anytime I'm in

Article URL: https://globalcareers-3qwcpfkw.manus.space/ Comments URL: https://news.ycombinator.com/item?id=48887718 Points: 1 # Comments: 0

<p>This is a submission for Weekend Challenge: Passion Edition<br> <em>(<a href="https://dev.to/challenges/weekend-2026-07-09">https://dev.to/challenges/weekend-2026-07-09</a>)</em></p> <h2> What I Built </h2> <p>Rivalry Radar — a live "Heat Index" for World Cup rivalries. Fans drop 280-character Terrace Takes on any matchup (Brazil vs Argentina, England vs France, whatever's got you shouting at the TV), rate how much the moment hurt or thrilled them from 1–10, and the app does the rest:</p> <p>

<h2> The problem </h2> <p>Websites have SSL certificates. Browsers verify them. Users trust them. It's the foundation of the web.</p> <p>AI agents have <strong>nothing</strong>.</p> <p>When Agent A connects to Agent B:</p> <ul> <li>❌ No way to verify B's identity (anyone can impersonate)</li> <li>❌ No way to check B's trustworthiness (no audit, no reputation)</li> <li>❌ No encryption (messages are plaintext)</li> <li>❌ No standard payment method</li> <li>❌ No way to translate between frameworks

<pre style='white-space:pre-wrap;width:81ex'>test: standardize HTTP mocks on MockAgent (#105925)</pre>

<p>Anyone who has used AI tools for a while has probably run into this annoyance. You ask it to write a weekly report in the morning and it doesn't know your KPI framework was overhauled last week. You ask for a technical proposal in the afternoon and it has no idea you spent three months locking down your tech stack. Every new conversation means re-explaining the project background, which decisions were made and why.</p> <p>In multi-person collaboration the problem scales up fast. Five people e

Pulls AI-powered image tweaker after allowing free-for-all

<!-- SC_OFF --><div class="md"><p>Hi folks,</p> <p>I am looking for research group or individuals that are interested in joining this competition. </p> <p>We can form a team.</p> <p><a href="https://aiboost-project.eu/ai-challenge-competition/">https://aiboost-project.eu/ai-challenge-competition/</a></p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/Tony-Me1998"> /u/Tony-Me1998 </a> <br/> <span><a href="https://www.reddit.com/r/MachineLearning/comments/1uv0crm/look_for_a

<p>I spent a chunk of last year around legacy modernization work — the kind of project where a bank or an insurer is taking twenty years of accumulated code and rebuilding it as modern services, one system at a time. Every one of those systems starts the same way: a PRD or a requirements document says what the business needs, that gets translated into a spec precise enough for an AI to implement, and eventually someone tests what came out.</p> <p>What struck me, watching this happen at scale, wa

<h2> Resources vs Tools </h2> <p>The split:<br> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>Tools → actions the LLM executes (verbs) LLM decides when to call; calls may have side effects Examples: create_issue, update_status Resources → data the LLM reads (nouns) Host decides when to inject; read-only, no side effects Examples: current Sprint status, project statistics </code></pre> </div> <p>The rule: "reading a state" → Resource. "Executing an operatio

Article URL: https://github.com/hkjarral/AVA-AI-Voice-Agent-for-Asterisk Comments URL: https://news.ycombinator.com/item?id=48887276 Points: 2 # Comments: 0

Article URL: https://www.sixthtone.com/news/1018753/TheChineseVoiceActorForcedtoProveHe’sHuman Comments URL: https://news.ycombinator.com/item?id=48887264 Points: 1 # Comments: 1

Article URL: https://jackpeplinski.bearblog.dev/thoughts-on-ai/ Comments URL: https://news.ycombinator.com/item?id=48887218 Points: 1 # Comments: 0

Article URL: https://github.com/neilkpatel/baton Comments URL: https://news.ycombinator.com/item?id=48887186 Points: 1 # Comments: 0

<p>Senior China correspondent <strong>Amy Hawkins </strong>on China’s embrace of AI, from medical avatars to food delivery drones and state surveillance</p><p>While the spread of AI has been met perhaps with a lot of scepticism in the west, China has fully embraced the technology, explains <strong>Amy Hawkins</strong>, from millions of users talking to AI doctors, to the use of intelligent robots in factories, and drones delivering food on the Great Wall of China.</p><p>AI has also been eagerly

"This discovery of light-induced quantum friction fundamentally changes our understanding... " ScienceAlert stories are written, fact-checked, and edited by humans, never generated by AI. Don't miss a story, subscribe here.

Article URL: https://academic.oup.com/dsh/advance-article/doi/10.1093/llc/fqag067/8694305?login=false Comments URL: https://news.ycombinator.com/item?id=48886873 Points: 1 # Comments: 2

Article URL: https://github.com/Arman-Luthra/aftr Comments URL: https://news.ycombinator.com/item?id=48886809 Points: 6 # Comments: 2

Article URL: https://tinier.app/ Comments URL: https://news.ycombinator.com/item?id=48886792 Points: 1 # Comments: 0

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