All Updates
Page 771 of 1666
May 1, 2026
API Probes Estimate Secret LLM Params
Researchers propose IKP framework to estimate black-box LLM parameter counts via API calls probing rare factual knowledge. Estimates GPT-5.5 at ~9T params, Claude Opus 4.7 at ~4T, sparking heated community debate on accuracy.
UK Job Seekers Hate AI Interviews
Greenhouse survey shows 47% of UK job seekers have faced AI interviews, often described as awkward and unnatural. 30% abandoned hiring processes due to AI involvement. The study polled 2,950 active seekers, including 1,132 from the UK.
Universities Launch AI Majors Long-term
Chinese universities adjusted majors, revoking 12.2K vs adding 10.2K in '14-5th', with low employment rates driving cuts in e-commerce, public management. New 2026 directory adds embodied intelligence, brain-computer tech amid AI job disruptions. Warns against short-term hot-chasing without faculty support.
Fujitsu Ends Mainframes in 2035 for Quantum AI
Fujitsu has confirmed it will shut down its mainframe business by 2035, coinciding with the anticipated rise of quantum AI supercomputers. The company is also engaging in talks with Japan, the UK, and Australia on defense technologies to promote global stability.
Google Cloud AI Surge Crushes Nvidia, Goldman Pivot
Alphabet shares jumped 10%โlargest ever single-day gainโfueled by Google Cloud's 63% revenue growth and AI-driven profit margin expansion to 32.9%. Nvidia dropped 4%; Goldman Sachs recommends overweighting cloud hyperscalers like Alphabet while underweighting overvalued semiconductors.
PhDs vs Practice: DeepSeek Success Story
Wang Shuguo questions if PhD paths would yield DeepSeek, Unitree, DJI successes. DeepSeek V4 amplifies China's cost-performance edge over top global models. Argues tech practice historically drives scientific theory via tacit knowledge.
AI PhD Blues Music Fest Goes Viral
A researcher used AI to create 42 songs about PhD struggles like rejected papers and burnout, launching 'Don't Pursue PhD Music Fest' that amassed 50M Bilibili views. Drawing from personal injury hiatus, he refined generations via trial-and-error. The project healed him and resonated with thousands via fan letters.
Intel EMIB Yield Hits 90% for AI Chips
Analyst Jeff Pu reports Intel's EMIB packaging tech achieved 90% yield, matching FCBGA while enabling higher interconnect density for AI data center chips. EMIB-T variant supports large-scale HBM integration. Intel plans EMIB-T expansion to over 12x reticle by 2028.
ICML 2026 Position Track Decision Thread
A dedicated Reddit thread for discussing ICML 2026 Position Track decisions. The niche track risks being overshadowed by main track discussions. Posted by /u/Striking-Warning9533 to centralize conversations.
Claude Agents Nail Office Deal Negotiations
Anthropic's Project Deal had 69 Claude agents negotiate flea market deals with $100 budgets, achieving 186 trades worth $4K+. Opus 4.5 outperformed Haiku 4.5 by ~$2-3 per deal. One agent bought 19 ping pong balls as 'gift' for itself.
Vibe Coding: Top Students Inquiry, Low Delegate to AI
Generative AI enables vibe coding, where students use natural language for programming help. Analysis of 19,418 interactions shows top performers use instrumental help-seeking for inquiry, while low performers delegate tasks. AI must evolve to detect unproductive patterns and promote learning.
Unsupervised ML Classifies Keta Basin Electrofacies
This arXiv paper introduces an unsupervised ML workflow using K-means clustering on wireline logs from Well C in Ghana's offshore Keta Basin. It identifies four electrofacies with a silhouette score of 0.50, linking them to clay content, porosity, and rock properties. The approach enables robust subsurface characterization without core data.
TRUST: Decentralized AI Auditing Framework
TRUST is a new decentralized framework addressing limitations in verifying Large Reasoning Models and Multi-Agent Systems, including robustness, scalability, opacity, and privacy issues in centralized systems. It features HDAGs for parallel reasoning auditing, DAAN protocol for multi-agent root-cause attribution, and stake-weighted multi-tier consensus guaranteeing correctness under 30% adversaries. Benchmarks show 72.4% accuracy (4-18% above baselines) and resilience to 20% corruption.
TabPFN Excels in Low-Data Alzheimer's Prediction
Researchers evaluated TabPFN against traditional ML models like XGBoost and LightGBM for predicting 3-year MCI-to-AD conversion on the TADPOLE dataset from ADNI. TabPFN achieved top AUC of 0.892, outperforming LightGBM's 0.860, with strong results even at N=50 training samples. Foundation models prove promising for data-limited disease prediction.
Step-Level Optimization for Efficient GUI Agents
Proposes an event-driven step-level cascade for computer-use agents, defaulting to small policies and escalating to large models only on detected risks. Features Stuck Monitor for progress stalls and Milestone Monitor for semantic drift in long-horizon GUI tasks. Modular framework layers onto existing agents without retraining.
Self-Healing Agents Automate ML Pipelines
A five-agent AI system automates end-to-end ML pipelines from datasets and natural language goals. It integrates code-grounded RAG, explainable recommenders, and LLM-based self-healing for robustness. Achieves 84.7% success on 150 diverse tasks, outperforming baselines.
Qiushi Engine Masters Autonomous Optical Discovery
Qiushi Discovery Engine is an LLM-based agentic system that achieves end-to-end autonomous scientific discovery on a real optical platform. It reproduces a published transmission-matrix experiment, observes coherence-order structures, and discovers a novel optical bilinear interaction mechanism analogous to Transformer attention. This is the first AI agent to experimentally validate a previously unreported physical mechanism.
Optimized Exits Boost AI Trading Swarm
This arXiv paper tests stop-loss and take-profit settings on 900+ historical crypto trades for autonomous trading agent swarms. Stronger exit configs improve risk-adjusted performance via tighter loss limits and earlier profit capture. Randomized data splits address market distortions from war periods.
LAM-PINN Boosts PINNs Against Task Heterogeneity
LAM-PINN introduces compositional meta-learning to address task heterogeneity in physics-informed neural networks (PINNs) for parameterized PDEs. It clusters tasks using PDE parameters and learning-affinity metrics from brief transfers, decomposing the model into specialized subnetworks with learned routing. Achieves 19.7-fold MSE reduction on unseen tasks using only 10% of conventional PINN training iterations.
Interval Orders and Biorders for Belief Revision
This paper explores interval orders and biorders as generalizations of total preorders for rational belief revision. It provides axiomatic characterizations of corresponding revision operators and introduces non-prioritised revisions that ensure consistency by deeming inconsistent inputs incredible. These approaches link to credibility-limited revision and suit scenarios where agents reject new info initially.