AI News Today

<p>Leave books alone!</p><ul><li><p><a href="https://www.theguardian.com/commentisfree/2014/jun/16/-sp-first-dog-on-the-moon-subscribe-by-email">Sign up here to get an email</a> whenever First Dog cartoons are published</p></li><li><p><a href="https://firstdogonthemoon.com.au/shop/">Get all your needs met at the First Dog shop</a> if what you need is First Dog merchandise and prints</p></li></ul> <a href="https://www.theguardian.com/commentisfree/picture/2026/jul/31/isnt-ai-terrible-enough-alrea

Build 100% ATS-compliant tech resumes in minutes. Import existing PDFs with Gemini AI, tailor keywords to job descriptions, and download high-quality PDFs with no paywalls or subscriptions. Comments URL: https://news.ycombinator.com/item?id=49119667 Points: 1 # Comments: 0

Article URL: https://multi-agent-systems-failure-taxonomy.github.io/AdaMAST/blogs/adamast_paper/ Comments URL: https://news.ycombinator.com/item?id=49119652 Points: 1 # Comments: 0

Article URL: https://pub.towardsai.net/saps-big-push-for-tabular-ai-what-it-means-for-enterprises-7dc3af2a1d8c?sk=73d9e33939f8f38b3d7ea49f87bb2e6a Comments URL: https://news.ycombinator.com/item?id=49119518 Points: 1 # Comments: 0

Article URL: https://pressgazette.co.uk/publishers/digital-journalism/first-tremors-of-ai-earthquake-showing-in-digital-revenue-hit/ Comments URL: https://news.ycombinator.com/item?id=49119344 Points: 5 # Comments: 1

Article URL: https://www.latimes.com/business/story/2026-07-29/nvidias-750-billion-ai-bet-deepens-fears-of-circular-tech-bubble Comments URL: https://news.ycombinator.com/item?id=49119284 Points: 4 # Comments: 1

Hi HN! We’re Akilan and Miguel, the creators of MarbleOS. The inspiration for Marble comes from the GUI work at Xerox PARC, the 1984 Macintosh, and later NeXTSTEP, which became the foundation for Mac OS X. Before GUIs, interacting with a computer was limited to strange terminal commands: C:\> DIR C:\> COPY FILE.TXT A: You had to remember the command, syntax, paths, and parameters. The GUI made those capabilities visible. Instead of remembering commands, you could point at files, drag them, click

<p> Any harness, any LLM — cloud-hosted agents on demand. </p> <p> <a href="https://www.producthunt.com/products/agentsky?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1211254?app_id=339">Link</a> </p>

Article URL: https://www.bbc.co.uk/news/articles/cz7dl7w8y7po Comments URL: https://news.ycombinator.com/item?id=49119165 Points: 13 # Comments: 4

Article URL: https://engineering.signaldrift.net Comments URL: https://news.ycombinator.com/item?id=49119156 Points: 2 # Comments: 0

Article URL: https://www.theguardian.com/technology/2026/jul/30/anthropic-ai-claude-hack Comments URL: https://news.ycombinator.com/item?id=49119138 Points: 7 # Comments: 1

Article URL: https://www.washingtonpost.com/technology/2026/07/30/anthropic-discloses-that-ai-models-testing-hacked-three-companies/ Comments URL: https://news.ycombinator.com/item?id=49119126 Points: 4 # Comments: 0

<p> AI Agents for Customer Support </p> <p> <a href="https://www.producthunt.com/products/ticketdesk-ai?utm_campaign=producthunt-atom-posts-feed&utm_medium=rss-feed&utm_source=producthunt-atom-posts-feed">Discussion</a> | <a href="https://www.producthunt.com/r/p/1211240?app_id=339">Link</a> </p>

Article URL: https://github.com/yashmahajan10/llm-differential-privacy-gateway Comments URL: https://news.ycombinator.com/item?id=49119049 Points: 2 # Comments: 0

Article URL: https://www.ft.com/content/dcf3873e-7b32-4a24-a90d-3bccf1d2c996 Comments URL: https://news.ycombinator.com/item?id=49118949 Points: 2 # Comments: 0

Article URL: https://greyswansignals.com/?theme=dark Comments URL: https://news.ycombinator.com/item?id=49118933 Points: 129 # Comments: 118

Article URL: https://www.prizmkey.com Comments URL: https://news.ycombinator.com/item?id=49118923 Points: 2 # Comments: 0

Article URL: https://www.ft.com/content/620bd668-d112-412d-aa52-22489b128d55 Comments URL: https://news.ycombinator.com/item?id=49118912 Points: 5 # Comments: 0

The company said it planned to release the model weights in the coming days, subject to applicable laws and regulations.

Amazon raised its full-year capital expenditure forecast to US$220 billion from US$200 billion and said most of the spending will go to AI.

arXiv:2607.26159v1 Announce Type: new Abstract: An AI benchmark result rarely reaches a consequential claim in one step. Evaluators generalize it to further cases, interpret it as evidence of capability, extrapolate it to new tasks, transport it to another system or site, and combine it with assumptions about human review and downstream consequences. Validity-centred approaches require evidence for each claim. This paper identifies a further epistemic problem: warranted links don't automatically

arXiv:2607.26393v1 Announce Type: new Abstract: Social deduction games (SDGs) such as Werewolf have become challenging testbeds for AI agents. These games require complex social skills such as reasoning, deception, and collaboration. While recent advances in large language models (LLMs) have driven significant progress in SDG agents, current approaches are predominantly text-based, overlooking the multimodal nature that is fundamental to human social interaction. To bridge this gap, we introduce

arXiv:2607.26119v1 Announce Type: new Abstract: Large reasoning models trained via reinforcement learning (RL) have been increasingly shown to outperform their supervised fine-tuned (SFT) counterparts on mathematical reasoning tasks; Yet the mechanistic basis for this advantage remains unclear. We therefore ask, what internal representational differences enable RL models' superior performance? Our work presents two converging lines of evidence: First, linear probes trained on layer-wise hidden s

arXiv:2607.26120v1 Announce Type: new Abstract: Large Language Models (LLMs)-powered multi-agent systems are increasingly deployed in mixed-motive environments, where agents operate under asymmetric information and strategic deception due to conflicting or hidden objectives. In these settings, misalignment with collective goals becomes a central concern. We propose a novel framework for evaluating objective misalignment using the social deduction game Werewolf, modifying the objective of a singl

arXiv:2607.26155v1 Announce Type: new Abstract: Clinical data-science agents must transform heterogeneous longitudinal records into auditable analyses, yet existing benchmarks largely isolate medical question answering, structured-table reasoning, or generic scientific repositories. We introduce CLINLENS, a benchmark of 200 executable tasks over five linked MIMIC resources spanning structured electronic health records, notes, electrocardiograms, chest radiographs, and echocardiograms. A 4 x 5 ta

arXiv:2607.26160v1 Announce Type: new Abstract: Clinical practice guidelines (CPGs) encode diagnostic criteria, but LLM systems typically retrieve guideline text or absorb it through training rather than execute its rules. We introduce GuideSkill, an external reasoning layer that compiles disease-specific criteria into executable functions returning ordinal diagnostic-support scores. GuideSkill-Zero is initialized from guidelines, while GuideSkill-Evo uses case--diagnosis pairs to refine covered

arXiv:2607.26181v1 Announce Type: new Abstract: Functional verification dominates integrated circuit (IC) front-end engineering effort, and a single missed bug that escapes to silicon can trigger a costly respin. Recent large language models (LLMs) offer new opportunities to automate this process, yet existing LLM-based approaches generate each component through independent single-turn calls with no shared context, leaving interface mismatches undetected and reported coverage disconnected from s

arXiv:2607.26191v1 Announce Type: new Abstract: Evaluation methodologies for language models increasingly combine multiple signals, from automated metrics and LLM-as-judge ratings to human assessments and benchmark suite results. When these signals are aggregated via averaging, evaluation confidence can then substantially exceed the reliability of the weakest signal: a phenomenon we call trust inflation in evaluation. We argue that evaluation scores should be treated as epistemic claims with thr

arXiv:2607.26307v1 Announce Type: new Abstract: Contemporary LLM-based coding agents produce code as black-box outputs: the rationale behind each line is hidden, the evolution of the code through benchmark-driven repair is ephemeral, and post-hoc auditing is impossible. We present a code generation concept that addresses these shortcomings through three complementary mechanisms: (i) a relational snippet-history schema that records, per repair event, the benchmark reference, round number, failure

arXiv:2607.26367v1 Announce Type: new Abstract: An important skill in theoretical physics is to recognize when a new problem can be transformed into a known model. We study this skill as an AI-agent task: can LLM-based agents discover statistical mechanical mappings from a raw partition function to a tractable representation? To probe this question, we introduce StatMechBench-v0, a benchmark of six Ising-type problems covering transfer-matrix methods, gauge-removable disorder, and planar/Pfaffia

arXiv:2607.26452v1 Announce Type: new Abstract: World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually capture only part of this structure. We introduce CG-World, a large-scale world-state dataset and protocol derived from industrial computer graphics production pipelines. CG-World explicitly records intermediate states, including multimodal semantics, spatial structure, skeletal and controller states

arXiv:2607.26465v1 Announce Type: new Abstract: Multimodal Large Language Models have sparked significant interest due to their potential for social intelligence; however, their ability to perform sequential motivation reasoning remains insufficiently studied. Existing evaluations predominantly examine static text or isolated visual snapshots, which do not reflect the cumulative nature of real-world behavioral drivers. To address this gap, we introduce MultivationBench, a benchmark designed to r

arXiv:2607.26490v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have emerged as a powerful paradigm for solving partial differential equations (PDEs), yet their performance heavily relies on the manual, trial-and-error engineering of neural representations, loss formulations, and optimization dynamics. While Large Language Models (LLMs) offer a promising avenue for automated design, unconstrained code generation often yields mathematically invalid or numerically unstable

arXiv:2607.26512v1 Announce Type: new Abstract: AI agents can draft claims faster than authors can check whether the cited or retrieved evidence supports them. We study evidence-ledger adjudication: a claim-evidence traceability workflow that pairs each claim with an evidence packet, assigns a support relation, and routes unsupported, contradicted, or mixed-evidence claims back to the author. The empirical core is a 2,335-row blind benchmark built from independent external labels in AVeriTeC, CL

arXiv:2607.26588v1 Announce Type: new Abstract: The rapid development of large language models (LLMs) has renewed interest in agent-based modeling (ABM). However, current LLM-based ABM research faces several key challenges: modeling evolving agent-environment interactions, enabling flexible counterfactual reasoning, and automating simulation workflows for scientific research. In this paper, we propose Eco3S, a socio-economic system simulation framework for economic research and policy analysis t

arXiv:2607.26611v1 Announce Type: new Abstract: AI-assisted coding increasingly translates informal user intent into executable software, yet coding requests often contain ambiguities that recur in user-specific ways across tasks and sessions. Existing disambiguation methods typically address each ambiguous request in isolation within the current coding session, often through eliciting additional clarification. However, whether resolved session history from the same user can serve as memory for

arXiv:2607.26642v1 Announce Type: new Abstract: Automated alpha mining has increasingly adopted large language model (LLM) agents for factor generation and iterative discovery. However, existing LLM-based systems often delegate both factor construction and search decisions to the agent itself, without an explicit exploration space or a principled mechanism for navigating that space. As a result, exploration remains largely implicit and difficult to control or optimize systematically. We introduc

arXiv:2607.26643v1 Announce Type: new Abstract: Enabling large language model (LLM) agents to accumulate and reuse experience from past interactions remains a central challenge in real-world applications. A promising solution is to treat skills as trainable states and optimize them in the same way as model parameters in neural network training. However, data-driven skill optimization is prone to overfitting to the limited trajectories collected from real environments. Overexploiting these trajec

arXiv:2607.26661v1 Announce Type: new Abstract: Ascend C operator optimization is critical for NPU (Neural Processing Unit) inference performance but requires deep hardware expertise.While large language models (LLMs) have shown promise in automated CUDA kernel generation, the fundamentally different programming model of Ascend C introduces unique challenges that remain unexplored. In this paper, we propose AgenticCANN, a knowledge-augmented agentic evolution framework specifically tailored for

arXiv:2607.26724v1 Announce Type: new Abstract: Large language model (LLM) agents have been widely applied in automating data science tasks. However, existing methods typically rely on a limited set of provided datasets, and they face challenges in data-intensive scenarios that require discovering and leveraging relevant information from large-scale and heterogeneous data repositories. Urban tasks are representative examples of such scenarios, as urban data are not only large-scale and multi-sou