Search: “usability”

13 stories in the last 3d

Survey of Tamarin and ProVerif users reveals usability barriers: debugging non-termination, model validation, and opaque proof failures hinder cryptographic protocol verification.

The paper presents an exploratory human-centered survey of researchers, graduate students, and practitioners with hands-on experience using Tamarin, ProVerif, and related cryptographic protocol verification tools. Findings reveal usability barriers across the verification workflow, including difficulties debugging non-termination and performance issues plus the lack of systematic methods for validating formal models against real protocols. When proofs fail without concrete attacks, users commonly simplify models, add helper lemmas, and revisit modeling abstractions. Participants called for actionable diagnostics, clearer explanations of results, visualization, and automation for recurring proof tasks.

arXiv cs.CRSep 14, 20:40 UTC · 2d agoResearch

Stanford Researchers Release Paper2Agent: Turning Research Papers Into AI Agents That Reproduce Results and Run on New Data

Stanford researchers released Paper2Agent, a Nature-published pipeline that turns research papers into MCP servers agents can execute.

A Stanford team led by Jiacheng Miao and James Zou published Paper2Agent in Nature on 16 September 2026. Built on Claude Code's agent SDK, it converts a paper and its codebase into a Model Context Protocol server with validated tools, resources, and prompts. In benchmarks, the AlphaGenome agent built 22 tools in about 45 minutes for US$14, scored 100% on 15 novel queries versus 78.7% for Claude Code with repository access, and cut median runtime 1.9x. In scale tests, 74 of 100 bioRxiv papers were converted and 593 of 599 proposed tools passed validation.

MarkTechPostSep 16, 21:59 UTC · 9h agoAI research1

After accusations of selling ‘perv glasses,’ Meta prepares to sell a pair without a camera

Meta is preparing camera-less Luna smart glasses with microphones and an AI button to access its chatbot and Muse agent, per reports.

The Information reports Meta is developing Luna, a camera-free smart glasses model with six built-in microphones and a side button that activates Meta's AI chatbot and its Muse consumer agent, possibly unveiled at next week's Meta Connect event. The move follows consumer backlash over camera-equipped glasses that critics called 'perv glasses.' Meta's Reality Labs continues to post large losses despite the smart glasses line's relative market success.

TechCrunch · AISep 16, 20:12 UTC · 11h agoAI industry

How Model Growth, Recursion, and Boundary Operators Influence Scaling Exponents

Researchers show model growth via looped transformers improves scaling exponents; a 7.4B architecture matches GPT-3 13B with roughly 20x less compute.

The paper shows that architectural interventions, contrary to conventional wisdom, can modify pre-training scaling exponents and yield exponential performance gains with compute. Looped transformers with increasing loop counts provide a model growth mechanism; a 7.4B model-growth architecture matches GPT-3 13B on CORE with roughly 20x less compute, with efficiency gains that increase with scale. A boundary operator that normalizes and injects an earlier block also improves compute efficiency, and in data-constrained multi-epoch settings increasing loops with scale is compute-optimal.

arXiv cs.AI / cs.LG / cs.CLSep 16, 17:36 UTC · 13h agoAI research

The true cost of a ransomware attack, with and without BCDR

Ransomware's true cost averages $5.08 million per incident versus a $139,875 median ransom, and mature BCDR strategies with immutable backups cut downtime and recovery expenses.

IBM's Cost of a Data Breach Report 2025 puts the average ransomware incident at $5.08 million, far above the $139,875 median ransom payment cited in Verizon's 2026 DBIR. Datto's State of BCDR Report 2025 found 60% of organizations believed they could recover within a day, but only 35% actually did. The Datto-sponsored piece argues mature BCDR with immutable WORM backups shortens downtime, citing a Techify case that restored 19 TB and returned a client to operations in under two hours without paying a ransom. It also flags compliance deadlines such as GDPR's 72-hour breach notification and the SEC's four-business-day disclosure rule.

BleepingComputerSep 16, 14:00 UTC · 17h agoIndustry

Apple Reference Image: A New Approach for Verified Photography

Apple introduces Reference Image, hardware-backed verifiable photography on iPhone 18 Pro using sensor signing and Private Cloud Compute to counter AI-generated fakes.

Apple announced Reference Image, an opt-in camera mode debuting on the main sensor of iPhone 18 Pro and iPhone 18 Pro Max that produces securely timestamped, verifiable photographs. The design splits into two phases: a secure digital negative created by cryptographically signing pixel data at the sensor immediately after capture (preventing injection or tampering), then developing that negative into a reference image. Private Cloud Compute handles processing without exposing image contents to anyone, including Apple, and fraudulent reference images can be revoked without revealing the photographer's identity. Apple positions the system as stronger than C2PA-based approaches, which sign metadata after capture, are vulnerable to editing-chain compromise, and can tie images to a device or individual.

Hacker News · securitySep 16, 02:07 UTC · 1d agoResearch HN 25↑ · 18 comments

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPostSep 15, 21:37 UTC · 1d agoAI tools & infra2

Most Firms Unable to Recover Quickly from Ransomware

Fenix24's first State of Recoverability report finds only 0.5% of 800+ ransomware clients neared 24-48 hour recovery targets, with identity failures central.

Drawing on 500+ ransomware recoveries, Fenix24 found only four of 800+ clients (0.5%) came close to their own 24-48 hour recovery targets, and none reached full operations for weeks. 99.2% lacked a documented identity recovery plan, Active Directory typically fell first, and 94% tied backup systems to the compromised directory. In 38% of engagements backups survived but could not carry recovery; storage ran short in 82% of cases and 95% lacked meaningful MFA on critical infrastructure consoles.

Infosecurity MagazineSep 15, 14:30 UTC · 1d agoResearch

ENISA: Frontier AI Is Changing the Speed of Cyberattacks. Europe Needs to Catch Up

ENISA warns frontier AI compresses attack lifecycles to minutes, with exploits possible within 15 minutes of disclosure and median 72-minute breach-to-exfiltration times.

ENISA's July 2026 paper 'ENISA's view on Cybersecurity in the Frontier AI Era' argues AI-assisted attackers may weaponize vulnerabilities within 15 minutes of disclosure and achieve initial-access-to-data-exfiltration in a median 72 minutes, creating a 'negative time-to-exploit' problem. The report cites one organisation whose CVE volume rose from roughly 80 in Q1 2025 to almost 500 in Q1 2026, then about 500 reports per day when frontier-AI tools were used. ENISA recommends machine-speed defence under 'Cybersecurity as Code', EPSS and VEX-based prioritisation, AI-assisted incident response with human oversight, and an assume-breached architecture.

Security AffairsSep 14, 20:35 UTC · 2d agoAdvisory

When LLM judges agree, should we believe them?

Amazon ICML paper uses Ising models to correct correlated LLM-judge votes, beating accuracy-weighted panels by 9-14%.

Amazon Science describes an ICML paper, "Dependence-aware label aggregation for LLM-as-a-judge via Ising models," addressing how correlated judge outputs inflate majority-vote confidence. The unsupervised method models pairwise dependence between judges, learning both reliability and similarity without human reference labels. Tested on relevance, toxicity, and summarization tasks with 10 judge models at temperature zero, it outperformed accuracy-weighted voting by 9% to 14%.

Hacker News · AISep 14, 16:29 UTC · 2d agoAI research HN 41↑ · 23 comments

RAPID: A Real-Time Defense Against Unauthorized Model Distillation for Text-to-Image Services

RAPID embeds defensive perturbations in a T2I model's shared VAE decoder to block unauthorized black-box distillation in real time.

The paper defends text-to-image services against model theft via black-box output-based distillation, where adversaries collect prompt-image pairs to train substitute models. RAPID integrates defensive perturbations into the shared VAE decoder using self-referenced latent maximization plus reconstruction-guided color regularization, avoiding costly sample-wise online optimization. Across four T2I models and four datasets versus five baselines, it consistently degrades substitute-model generation quality while preserving visual fidelity.

arXiv cs.CRSep 14, 16:11 UTC · 2d agoAI safety & security

Event-Native Symbolic-Temporal Spike Encoding Framework for Heterogeneous Cyber Streams

An event-native spike encoding framework lets recurrent spiking neural networks run intrusion detection directly on heterogeneous packet and CAN bus streams.

The paper maps heterogeneous cyber events—categorical identifiers, local frequency context, and inter-event timing—directly into sparse spike-compatible inputs for spiking neural networks. This avoids flow aggregation and fixed windows that add buffering latency and obscure temporal structure in traditional IDS pipelines. Compact recurrent SNNs under edge-oriented neuromorphic hardware constraints achieve a hybrid anomaly metric of 0.987 on packet-level Network IDS and 0.980 on message-level CAN IDS.

arXiv cs.CRSep 14, 15:55 UTC · 2d agoResearch1