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13 stories in the last 7d

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CategoryAllAI research 13AI industry 11AI safety & security 8Vulnerability 4Research 3Advisory 2Malware 2Phishing & fraud 2Policy & legal 2Tools 1Model release 1AI tools & infra 1Industry 1AI policy 1
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TART: A Modular Tool for Technique-Aware Audio-to-Tablature Guitar Transcription

TART pipeline transcribes guitar audio to tablature with fingering and technique labels, outperforming prior baselines by up to 8.5 Tab F1 points.

TART is a modular four-stage pipeline for audio-to-tablature guitar transcription, combining an audio-to-MIDI transcription model, an expressive technique classifier, an audio-conditioned T5 encoder-decoder for string-fret assignment, and an automated tablature generator. In zero-shot evaluation on GuitarSet, EGDB, and two noisy augmented benchmarks, it averages 81.35% audio-to-MIDI F50 (+6.67 over the best baseline), 71.8% string-fret Tab F1 (+8.5), and 54.08% end-to-end Tab F1. The authors state it is the first framework to generate tablature with both fingering and expressive technique annotations directly from guitar audio.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research2

Unsolved Problem by Fields Medalist Breached by Two High School Students

Two high school students used Claude Opus 5 and GPT-5.6 Sol to help solve an open Lorentzian polynomials problem, posting a 75-page arXiv proof.

Aayush Bathija and Prince Rohatgi of Oak Park High School, mentored by UCLA postdoc Daniel Soskin, published the 75-page paper 'Bounded Ratios for Lorentzian Polynomials' (arXiv 2609.05341), solving an open problem in Fields Medalist June Huh's Lorentzian polynomial theory. The main structural theorem extends bounded coefficient-ratio characterization from quadratic to arbitrary-degree polynomials via discrete convexity conditions. The students used Claude Opus 5 and GPT-5.6 Sol for exploration and proof ideas but independently verified all arguments; the result follows an open letter from 25 Fields Medalists voicing concerns about AI's impact on mathematical rigor.

Hacker News · security · 3d agoAI researchHN 22↑ · 19 comments

Prepared Or Unprepared? Evaluating Healthcare Workforce Readiness for Clinical Adoption of Artificial Intelligence in Nigeria

Survey of 761 Nigerian healthcare professionals finds high AI awareness (92.6%) but limited knowledge, preparedness, and major training and infrastructure barriers.

A cross-sectional study of 761 healthcare professionals across Nigeria, conducted from December 2025 to March 2026, found 92.6% awareness of AI in healthcare but 40.9% reporting low knowledge and only 63.0% feeling adequately prepared. Top barriers were lack of training (84.7%), poor infrastructure (71.1%), and high tool costs (61.0%). Willingness to adopt was strong, with 92.5% interested in training and 78.7% supporting AI in undergraduate curricula; preparedness differed significantly across geopolitical zones and professions.

arXiv cs.AI / cs.LG / cs.CL · 20h agoAI research

Recurrent GraphNeural NetworkswithSet-BasedAggregation

Paper proves two-directional equivalence between recurrent GNNs with set-based aggregation and Boolean closure of reachability/safety properties in modal mu-calculus, checkable from weights.

The authors study recurrent graph neural networks with set-based aggregation and identify sufficient conditions, checkable directly from network weights, for compiling networks into logical formulas and formulas into networks. They establish an effective two-directional equivalence with the Boolean closure of reachability and safety properties, the fragment BΣ°1 of the modal μ-calculus, shown to be the exact expressive level of stabilization over finite vocabulary. The correspondence needs no counting logic, external halting signal, or non-effective acceptance condition, yielding a verifiable path from weights to symbolic explanations.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

Bridging Control, Inference, Transport, and Thermodynamics: From Theory to Applications in Learning

Review connects control theory, optimal transport, probabilistic inference, thermodynamics, and machine learning via free-energy optimization under constraints.

The review unifies five fields: control theory, optimal transport, probabilistic inference, non-equilibrium thermodynamics, and machine learning. The common conceptual thread is optimization of free-energy-like functionals under dynamical or statistical constraints. Selected applications are presented in reinforcement learning, variational inference, and generative modeling. The tutorial-style text assumes no prior familiarity and begins from physics principles.

arXiv cs.AI / cs.LG / cs.CL · 2d agoAI research

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 · AI · 2d agoAI researchHN 41↑ · 23 comments

Claude Fable 5.1 Solves the Cyphral Distich, a 370-year-old cipher

Claude Fable 5.1 solved Sir Thomas Urquhart's 370-year-old Cyphral Distich cipher, recovering a hidden royalist prayer for Charles II.

Vals AI reports that Claude Fable 5.1 solved the Cyphral Distich, a 64-number cryptogram from Sir Thomas Urquhart's Logopandecteision unsolved since 1653, in 44 minutes using 176k tokens with no human hints. The key insight was that the cipher's key was the book itself: each number indexes a word in the corresponding Proquiritation, taking the first letter, yielding 'O GOD UPHOLD KING CHARLS THE SECOND AND MAKE HIM THE SUPREME RULER OF THIS LAND'. The model also deciphered the larger Cyphral Octastich (285 numbers) from The Jewel (1652) using page-based word indexing, recovering all but nine letters of a royalist prayer. The puzzle had been listed among Klaus Schmeh's Top 50 unsolved encrypted messages.

Hacker News · AI · 3d agoAI researchHN 63↑ · 6 comments1· 1 read

CMA-OT: Hierarchical Expert Supervision for Dance-to-Music Generation

CMA-OT aligns a music generator's latent features with hierarchical expert representations via curriculum learning and scale-aware optimal transport, improving dance-to-music quality.

CMA-OT introduces curriculum-guided multi-scale representation alignment with scale-aware optimal transport for dance-to-music generation. An external music expert provides hierarchical supervision over the generator's latent features, progressively transferring musical knowledge for stable representation learning. The optimal transport mechanism handles temporal mismatch and semantic variation across expert scales. Experiments on two datasets show state-of-the-art rhythmic synchronization, perceptual quality, and overall music generation.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

Continue, Adapt, or Yield: In-Turn Adaptation to Overlapping Speech in Full-Duplex Agents

Duplex Cue evaluation shows PersonaPlex full-duplex agents adapt in-turn to listener contributions in only 34.8% of collaborative cases versus 68.2% for humans.

The paper introduces Duplex Cue, an evaluation of in-turn adaptation in full-duplex voice agents that separates listener intent (backchannel, collaboration, interruption) from speaker behavior (continue, adapt, yield). Using 208 scorable pairs from 300 human-confirmed cues in unscripted English conversations, it compares recorded human responses with PersonaPlex continuations generated while replaying listener audio. Humans adapt within the turn in 68.2% of collaborative pairs versus 34.8% for PersonaPlex, which otherwise continues unchanged (42.4%) or yields (22.7%).

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

Benign Loss Landscapes Can Coexist with Worst-Case Hardness

Theory paper shows tree tensor networks contain worst-case hard targets yet benign loss landscapes, with difficulty arising from degenerate saddles.

The paper studies tree tensor networks (TTNs), which generalize deep linear networks and Tucker decompositions and embed arbitrary read-once Boolean formulas. It proves that every local minimum that is minimum-norm is global for every realizable target, so bad local minima do not distinguish typical from worst-case problems. Instead, learning difficulty arises from high-order degenerate saddle points caused by rank-deficiency, illustrated via a parity function case study, linking landscape geometry to computational hardness.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

Paper unifies regularization-based robust RL methods via new performance-gap upper bounds and jointly learned Lagrange multipliers.

The authors derive new upper bounds on the gap between nominal and worst-case deep RL policies, each expressible as an existing regularization objective plus a KL-divergence penalty. Robust training is reformulated as constrained optimization, where prior methods correspond to a fixed Lagrange multiplier. The multiplier is instead updated jointly with the policy, auto-tuning the regularization weight. Adversarial evaluations across several continuous control tasks validate the theory.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

MCRL2: Multi-resource Cross-attention-based Representation Learning-augmented Reinforcement Learning for Cloud Microservice Scheduling

MCRL2 augments reinforcement learning with multi-resource cross-attention representations to improve cloud microservice scheduling and load balancing.

MCRL2 combines a multi-resource cross-attention representation learning module (MCRL) with an actor-critic architecture and maximum entropy objective for microservice scheduling. The approach captures interdependencies among nodes, resources, and microservices in data centers. Experiments on real production cluster traces show improvements in load balancing, scheduling success rate, and average completion time versus baselines.

arXiv cs.AI / cs.LG / cs.CL · 5d agoAI research1

On the Regularization Landscape for the Linear Recommendation Models

Study shows leading linear recommendation models reduce to nuclear-norm or Frobenius-norm regularization, with two new closed-form low-rank solutions proposed.

The paper unifies top-performing linear recommendation algorithms under a single regularization framework, showing they effectively apply either nuclear-norm or Frobenius-norm regularizers. Nuclear-norm solutions have a rigid structure, are low-rank, and have closed form, while Frobenius-norm solutions are more expressive but full-rank or require hard-to-tune procedures such as ADMM. The authors derive two new low-rank, closed-form solutions that combine the advantages of both regularization families.

arXiv cs.AI / cs.LG / cs.CL · 6d agoAI research