The latest AI news, in focus.

AtomicFaaS: Offloading Side-Effect Compensation to the Serverless Platform

arXiv:2611.06601v1 Announce Type: new Abstract: Function-as-a-Service (FaaS) simplifies distributed application development by offloading operational concerns to the platform, but developers remain responsible for recovering from failures. Retries require idempotent functions, which means that developers have to write complex, error-prone code that undoes or skips the already completed operations of a failed function. To address this, we propose AtomicFaaS, a platform extension that provides semantic atomicity for the external side effects of a function invocation, whether the function fails or its instance crashes, without developer-written compensation code. AtomicFaaS intercepts outbound calls and records their effects in a fault-tolerant log, from which the platform compensates leaked effects outside the failed instance. Compared with a manually implemented saga, AtomicFaaS eliminates compensation code and leaves no compensatable effects after any tested failure, while adding ~1 ms per intercepted call. Under load, it lowers the sustainable throughput of CPU-bound functions, while its latency overhead becomes negligible once calls to external services dominate the invocation time.

Discarded or Unreachable? Diagnosing and Repairing Spatial Relation Failure in Vision--Language Similarity

arXiv:2611.05565v1 Announce Type: new Abstract: Contrastive vision-language models score left/right relations near chance, but the symptom has two causes that call for opposite repairs: the relation may survive in the pooled embedding yet be unreachable by native image-text similarity, or pooling may discard it while the dense features retain it. On clean two object photographs, across ten encoders, the lateral polarity is linearly decodable from the flip odd component of the pooled vector at 0.77-0.99, yet a search over a million captions leaves nine of the ten at chance under native scoring: an access failure. On controlled C4 orbits, where the relation is the only thing that varies, pooling keeps the arrangement axis at 0.998-1.000 and loses its polarity, which the patch grid retains: a pooling failure. Both sit downstream of the dense features, so one readout covers both. Our zero parameter, query conditioned spatial moment recomputes the geometry from the patch grid and stores it as a vector beside the native embedding. On a 118,287 image index it raises left/right pairwise accuracy against reversed geometry negatives from 0.51-0.53 to 0.81-0.96 and conditional R@1 among 21 category matched candidates from 0.05 to 0.37-0.75, at under a millisecond per batched query, while reproducing the native ranking exactly for queries that name no relation.

Physics-Informed Identification of Tensegrity Prestress across Taut-Slack Transitions

arXiv:2611.04007v1 Announce Type: new Abstract: Prestress governs the stability, stiffness, and dynamic behavior of tensegrity structures, but it is not directly observable from sparse structural measurements, and it may decrease during service life. Under strong excitation, the cables may slacken and the dynamics become nonsmooth. Existing identification methods are limited to the taut regime, and physics-informed networks for tensegrities solve only the forward problem of predicting the response from known member parameters. This study formulates the inverse problem. Neural networks are trained on the equation-of-motion residual in constrained Cartesian coordinates, where the mass matrix is constant and the force densities enter linearly. The force densities, damping, and axial stiffness are trainable parameters, and the tension-only cable law is kept. A Fisher-information analysis shows, before inversion, which member parameters a record can determine. The method is tested on simulated planar tensegrity frames with up to five modules and a three-dimensional prism, and on measured records of a biomimetic leg and a full-scale tensegrity simplex. On the one-module frame, the identifiable force densities are recovered within 1.5% from noisy accelerations at half the free coordinates, on records with and without taut-slack transitions. Records with transitions determine members that taut records leave undetermined. On the five-module frame, a 16-cable two-span bridge, the median force-density error rises moderately, to 4.2%. On measured records, an identified spring stiffness is within 0.1% of its independently measured value on average, and the identified simplex prestress at 13 levels agrees with its cable force transducers to 2.9% root-mean-square.

Stacking-Based Weighting for Large Language Bayes in M-Open Settings

arXiv:2611.03966v1 Announce Type: new Abstract: Large Language Bayes (LLB) transforms a natural language problem description into a collection of candidate probabilistic programs and performs inference within each model, combining their predictions using Bayesian model averaging. While this provides a flexible framework for probabilistic inference without manual model specification, the resulting candidate model set is often misspecified in practice, and likelihood-based aggregation can concentrate on a single model, under-representing uncertainty across the sampled models. We propose a stacking-based aggregation method for LLB that selects model weights by optimizing predictive performance rather than marginal likelihood, while preserving the original language-model-driven model generation and within-model inference pipeline. Because leave-one-out predictive quantities are intractable in latent-variable models, we derive a variational lower bound that yields a tractable ELBO-based stacking objective. Across real-world probabilistic inference tasks, our method achieves comparable predictive performance to Bayesian model averaging while yielding more dispersed model weights, reducing concentration on a single candidate model under misspecification.

Code with CodeQwen1.5

GITHUB HUGGING FACE MODELSCOPE DEMO DISCORD Introduction The advent of advanced programming tools, which harnesses the power of large language models (LLMs), has significantly enhanced programmer productivity and accuracy. Notwithstanding these advancements, dominant coding assistants like Github Copilot, built upon proprietary LLMs, pose notable challenges in terms of cost, privacy, security, and potential copyright infringement. Recognizing the imperative for a more transparent and accessible alternative, the open-source community has embarked on a concerted endeavor to develop open codeLLMs.

EvoCache: Accelerating Browser Large Language Model Delivery with Public Cache Anchors

arXiv:2611.04638v1 Announce Type: new Abstract: Large Language Models (LLMs) can run directly in a browser, but first use typically requires downloading model files that are hundreds of MiB or even several GiB in size. The same LLM is often repackaged by different publishers; these files share substantial content, yet byte differences prevent whole-file caches from reusing them. We propose EvoCache, a browser-managed LLM delivery system. EvoCache uses public cache anchors that are stored in advance by the browser and independent of browsing history to reconstruct losslessly the publisher version requested by a site. A site specifies only the target, while the browser selects the anchor and performs reconstruction, keeping both hidden from the site. An audit of 1,000 public repositories finds that 61 of the 100 most popular LLMs are offered by multiple publishers and that 98.1% of overlapping versions cannot be reused by whole-file caching. Across 23 cross-publisher model-package pairs, EvoCache achieves a median traffic reduction of 77.3% and outperforms every comparator. Across 240 real end-to-end browser experiments, EvoCache reduces network traffic by 78.9% and page-ready time by 64.2% relative to ordinary full downloads. For cached data left by other sites, EvoCache also protects users' cross-site cache privacy by using the same return time for cache hits and misses.

Exclusive: Aina’s Smart Ring Wants to Be a Remote Control for Your AI Agent

The startup Aina bets you won’t need to rely on your phone to control virtual assistants. It’s making a smart ring that lets you control the actions of your autonomous agents with one tap.

Google Is Forcing AI Overviews in Classrooms. Schools Are Searching for a Way Out

Google is tying up AI Overviews with traditional search, angering parents and educators who worry that easy, machine-generated answers are leaving kids less capable.

Minimizing Bottleneck Overload Risk in Online Edge Task Placement under Workload Uncertainty

arXiv:2611.04425v1 Announce Type: new Abstract: In edge computing, tasks must be placed on servers of finite capacity before workloads are realized, often with only workload means and variances known. An assignment balanced on expected load can still leave a server with more fluctuations than its residual capacity can absorb. Per-server risk constraints can enforce a prescribed overload tolerance but do not distinguish the assignments that meet it by how close the riskiest server comes to the limit. We show that, for independent tasks, placement preserves a period's total variance and total residual capacity, and decides only how fluctuation, measured by standard deviation, is paired with residual capacity across servers. The risk ratio, the ratio of fluctuation to residual capacity, parameterizes a server's worst-case overload probability over all aggregate workload distributions with given mean and variance. We minimize the largest risk ratio, attained at the risk bottleneck. Risk-Bottleneck Placement (RBP) places tasks in descending order of standard deviation, each on the server with the smallest post-assignment risk ratio, and refines the assignment with relocations and swaps within a finite per-period online budget. On small instances solved exactly by enumerating every assignment, minimizing the maximum expected utilization leaves the largest risk ratio 25.66-29.19% above its exact minimum on average, even with risk-minimizing tie-breaking, and the corresponding gap for RBP is 1.77-2.64%. In trace-derived multi-period simulations at two workload pressures, RBP reduces the worst-server overload probability by 26.0% and 18.0% relative to the best baseline and remains the lowest at every tested pressure and scale.

Probabilistic Steering of Generative Priors for Lab-in-the-Loop Antibody Optimization

arXiv:2611.03844v1 Announce Type: new Abstract: Therapeutic antibody development is often framed as comprising two discrete stages: initial discovery of novel binders followed by iterative optimization to refine these hits into therapeutic-grade molecules. Models and methods often reflect these stages, with generative structure models applied to discovery and probabilistic black-box optimization methods applied to refinement. Here we propose to unify these stages by smoothly transitioning a generative discovery model into iterative optimization, allowing the handoff between the structure-based prior and supervision from wet lab data to be guided by the posterior uncertainty of a probabilistic surrogate. We show that this yields improvements in optimization efficiency in the low-N regime, as well as improved diversity amongst the optimized molecules.

Reasoning is topologically simple

arXiv:2611.03739v1 Announce Type: new Abstract: We formulate and investigate the hypothesis that reasoning text has simpler co-occurrence topology than non-reasoning text, and that higher-quality reasoning has simpler co-occurrence topology than lower-quality reasoning. We discuss and test applications to AI safety and AI evaluation.

Self-stabilising approximate clock synchronisation requires linear time

arXiv:2611.04725v1 Announce Type: new Abstract: Synchronising the clocks of processes is a fundamental problem in distributed computing. We consider message-passing systems in which processes start in an arbitrary initial state (self-stabilisation) and up to $f$ processes may be non-transiently faulty. It is well known that synchronising clocks \emph{exactly} is at least as hard as consensus and requires $\Theta(f)$ time. In contrast, without self-stabilisation, clocks can be synchronised \emph{approximately} much faster, in time independent of the number of non-transient faults [Srikanth and Toueg, JACM 1987; Welch and Lynch, Inf. Comput. 1988]. In this work, we show that deterministic self-stabilising approximate clock synchronisation tasks require $\Omega(f)$ time to stabilise. This gives the first non-trivial time lower bound for these tasks, whose time complexity has remained open for over two decades [Dolev and Welch, JACM 2004; Dolev, F\"ugger, Schmid and Lenzen, JACM 2014]. In particular, this answers the open question whether self-stabilising approximate clock synchronisation tasks are as hard as consensus or as easy as approximate agreement [Lenzen and Rybicki, JACM 2019]. The results show that guaranteeing \emph{both} transient and non-transient fault-tolerance simultaneously is expensive even for approximate synchronisation tasks. The lower bounds hold in a very strong model: the faulty processes can only crash, the local clocks of processes have no clock drift, and the message delay is bounded and known to all processes. The proof is based on a combinatorial topological argument showing that no fast finite-state protocol can ensure that the clocks behave consistently across all executions. To our knowledge, this is the first use of topological arguments to prove time lower bounds in the self-stabilising setting.

Setting a new standard for responsible AI infrastructure

OpenAI introduces its National Community Compact and Compute Campus Policy Blueprint to protect local resources, create jobs, and guide responsible AI infrastructure development.

Uncertainty-Aware Resource Orchestration for Agentic AI Systems

arXiv:2611.05392v1 Announce Type: new Abstract: Agentic artificial intelligence (AI) systems powered by large language models (LLMs) execute as autonomous multi-step pipelines that dynamically invoke tools, integrate observations into memory, and iterate until a task is resolved or a resource budget is exhausted. Unlike classical workflows with fixed graphs, the aggregate compute, bandwidth, and end-to-end latency demands of such pipelines are unknown at dispatch time: they emerge at runtime from intermediate observations and stochastic token sampling, yielding heavy-tailed, multi-modal random variables. This unpredictability forces infrastructure orchestrators into a dilemma: under-provisioning risks mid-execution throttling and deadline violations, while over-provisioning wastes scarce edge or cloud capacity. Existing schedulers require a fully known pipeline graph, while edge resource-management methods rely on parametric or stationary demand models, and neither fits the unknown, non-stationary profiles of agentic pipelines. We formalize this setting as the uncertainty-aware agentic orchestration problem, where the goal is to reserve, at dispatch time, compute and bandwidth allocations that cover actual pipeline demands, and to verify that the pipeline can meet its deadline, each with a prescribed probability. We then propose Conformal Reservation for Orchestration (CRO), an online algorithm that translates a sliding window of past execution traces into dispatch-time resource allocations with finite-sample, distribution-free coverage guarantees via conformal prediction. Simulations over heterogeneous task mixes with multi-modal and heavy-tailed profiles show that CRO is the only evaluated method that simultaneously satisfies target probabilistic coverage across all risk levels while, among coverage-feasible methods, attaining the lowest over-reservation, roughly halving the resource waste of the Chebyshev-bound baseline.

More details on Fable 5’s cyber safeguards and our jailbreak framework

More details on Fable 5’s cyber safeguards and our jailbreak framework

Securing the agentic enterprise with data lineage

A sales representative asks an AI agent for customer pricing, and the agent pulls data from multiple sources: restricted pricing systems, internal communications, and archived quotes. The output looks accurate to the agent, so a second agent uses the data to draft the proposal for the sales rep. No human checks either handoff. When it’s…

I discovered a sound that leaks your exact location (and it's everywhere) - YouTube

I discovered a sound that leaks your exact location (and it's everywhere) - YouTube Genius piece of OSINT by Colsto, who demonstrates how you can take almost imperceptible background noise of planes from a video soundtrack, figure out their Doppler curves, then cross reference them against ADS-B data to find the exact location of the video based on their speed, distance, and gaps between different flights. Via Lobste.rs Tags: privacy, youtube

The race to deploy AI agents finds a familiar hurdle in data access

Enterprises racing to put AI agents into production are discovering that role-based access control, not model choice, decides whether those agents can be trusted. Rising inference costs, privacy worries and sovereignty demands are also pulling more AI workloads back inside corporate data centers. That shift favors companies that have spent years governing sensitive data at […] The post The race to deploy AI agents finds a familiar hurdle in data access appeared first on SiliconANGLE.

ChatGPT credit officially doubled, but free resets may soon be a thing of the past

After being criticized by users for a month, OpenAI has finally decided to stop tinkering with everyone’s usage limits. Just now, OpenAI’s head of product, Tibo, suddenly announced that he would significantly increase the usage limits for the ChatGPT Pro series and Business....

Meta May Once Again Delay Implementation of Controversial New Commercial Terms

Meta stated that, due to strong opposition from publishers regarding AI training terms, it may further postpone the implementation of its new commercial terms—originally scheduled for October 30, 2026, and later extended to November 20. The company emphasized that it will only proceed with policy adjustments after completing comprehensive consultations with industry organizations and leaders.

Why it mattersThis news originates from an authoritative tech report published by The Verge on October 28, 2026, which includes Meta’s official statement (Andy Stone’s announcement on X), clear timelines (initially October 30, now tentatively November 20), specific points of contention (authorization for AI model training), and confirmed policy revisions (removal of “training”-related language). The information chain is complete and timely.