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Caltech Mathathon – first hackathon ever devoted to research level mathematics

Caltech will host the first research-level mathematics hackathon on October 30, giving 100 teams frontier AI models to attack open conjectures.

The Caltech Mathathon runs October 30 to November 1, assembling about 100 teams that will receive frontier models and over $2 million in AI credits to work on open mathematical problems. Teams will defend their results before leading mathematicians, with prize rounds before and after community verification of the results. The announcement cites recent AI-driven math results, including the disproof of Erdos's 80-year-old planar unit-distance conjecture, the first explicit non-sofic group, and a claimed complex structure on the six-sphere (unverified).

Hacker News · security·Sep 7, 09:26 UTC · 9d agoAI industryHN 264↑ · 92 comments1

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🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing

Caltech professor Anima Anandkumar discusses Neural Operators and FourCastNet for physics modeling, arguing inductive biases beat pure token scaling.

Anima Anandkumar, Bren Professor at Caltech and co-founder of Accelerated Understanding, describes Fourier Neural Operators that learn in frequency and spherical-harmonic domains to model weather, fusion, and fluid or heat flow. Her team built FourCastNet 3, a global weather model competitive with physics-based simulations that runs on consumer-grade GPUs. She also introduced TorchLean, a framework for writing PyTorch-style networks inside the Lean proof assistant for formal verification, and was appointed to the United Nations Scientific Advisory Board. She argues physical domains resist scaling due to tiny datasets and context lengths in the hundreds of billions, so progress comes from built-in structure and physical priors.

Latent Space·Aug 26, 15:15 UTC · 20d agoAI research1

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K-Bench: a clinically calibrated benchmark for evaluating large language models in high-risk mental health conversations

Clinician-calibrated K-Bench evaluates 125 LLM configurations on 200 high-risk mental health vignettes, exposing wide variation in suicide and violence risk handling.

K-Bench is a clinician-calibrated, protected benchmark evaluating 125 model configurations from 33 base models across 14 providers on 200 multi-turn vignettes covering suicide, self-harm, domestic violence, substance misuse and no-risk presentations. A frozen GPT-4o judge achieved 94.2% exact agreement with clinician consensus across 6,751 eligible comparisons from 151 clinician-rated transcripts. Leading models combined supportive conversation with combined-risk scores above 95, while risk exploration varied substantially among weaker configurations; therapeutic prompting helped weaker models and elevated reasoning produced no average improvement. A continuously updated public leaderboard is hosted at k-bench.ai with protected test materials.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:49 UTC · 1d agoAI safety & security

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Schools are catching on to Big Tech’s playbook

A new book warns AI firms are repeating Big Tech's education playbook, as New York City and Los Angeles restrict classroom AI use.

NYT education reporter Natasha Singer's book 'Coding Kids' documents how Apple, Microsoft and Google embedded proprietary curricula and Chromebooks in US schools over 15 years, building product loyalty and market position. Google's Chromebook and Classroom dominance positioned it to promote generative AI in classrooms. New York City banned AI in elementary and middle schools and Los Angeles imposed broader restrictions including high schoolers, as parents and teachers push back against screens and AI in classrooms.

The Verge · AI·Sep 10, 19:44 UTC · 5d agoAI industry

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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·Sep 14, 12:23 UTC · 1d agoAI researchHN 22↑ · 19 comments

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NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research1

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Education Under Attack: The Pattern Behind Recent University Breaches

Huntress finds four recent university breaches share one root cause, security misconfigurations, and outlines fixes for higher education.

Huntress analyzed four university breaches from 2026 and identified misconfiguration as the common root cause behind the incidents. The report describes the recurring attack pattern targeting higher education and offers remediation guidance to close the gap. Specific victim institutions, threat actor attribution, and breach volumes are not named in the announcement.

Huntress·Aug 13, 15:00 UTC · Aug 13, 2026Threat actor in the wild

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Unfold The World: Factorize 4D Properties in Reinforcing Spatial Reasoning

FactoSR factorizes 4D spatial reasoning into XY, Z, and T reinforcement-learning sub-objectives, boosting VLM performance on VSI-Bench by 5.9% and All-Angles-Bench by 4.5%.

Researchers present FactoSR, a factorized reinforcement learning framework that decomposes world-consistent reasoning into planar correspondence, depth consistency, and temporal reversibility sub-objectives. Optimizing these verifiable constraints turns the ill-posed projection recovery problem into tangible reasoning steps. Evaluations show gains of 5.9% on VSI-Bench and 4.5% on All-Angles-Bench for 3D and 4D reasoning, arguing VLMs' spatial bottleneck stems from training on 2D projections versus latent 3D geometry and temporal continuity.

Hugging Face daily papers·Sep 2, 20:00 UTC · 13d agoAI research

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How a researcher uses Codex and ChatGPT to search for new antimicrobial molecules

OpenAI profiles César de la Fuente's lab using ChatGPT and Codex alongside deep-learning models to accelerate antimicrobial molecule discovery.

OpenAI published a case study on bioengineer César de la Fuente's lab, which uses ChatGPT and Codex for hypothesis brainstorming, code writing, dataset processing, and bridging knowledge gaps across biology, chemistry, and computer science. The lab's deep-learning models scan genome and protein databases for antimicrobial peptide candidates, potentially cutting initial searches from years to hours. Bacterial antimicrobial resistance was associated with about five million deaths in 2021, a toll projected to roughly double by 2050.

OpenAI News·Sep 10, 16:00 UTC · 5d agoAI industry1

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Evaluating Time-Series Foundation Models and Multimodal Dietary Context for CGM Forecasting

Study finds zero-shot time-series foundation models underperform on CGM forecasting; fine-tuned Chronos-Bolt cuts RMSE up to 18.4% and dietary context adds signal.

The paper evaluates time-series foundation models for continuous glucose monitoring forecasting across eight public datasets covering Type 1 diabetes, Type 2 diabetes, and non-diabetes populations. Under a unified protocol, zero-shot foundation models did not consistently outperform baselines like Elastic Net and PatchTST, but lightweight fine-tuning did, with fine-tuned Chronos-Bolt reducing RMSE by 6.5%-18.4% in the T1D cohort and 8.6%-18.2% in the non-diabetes/T2D cohort. A residual-based fusion framework adding dietary context from CGMacros reduced overall RMSE by about 3% and postprandial RMSE by about 15% versus CGM-only baselines.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 5d agoAI research

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MacOS 27 - First Boot, (Tue, Sep 15th)

SANS ISC documents the expected network traffic macOS 27 'Golden Gate' generates on first boot to help defenders baseline their networks.

Johannes Ullrich of SANS Internet Storm Center captured roughly 300 packets from a macOS 27 'Golden Gate' system before user login, covering DHCP, IPv6 duplicate address discovery, DNS, and TCP behavior. macOS 27 resolves hostnames like albert.apple.com (device activation, certificate-pinned), push messaging hosts, and ipv4only.arpa for NAT64 networks. The OS still uses a TCP window scale of 6, ECN, and random timestamps, with only four TCP connections observed during boot. The analysis provides a reference baseline for security teams monitoring Apple endpoints.

SANS Internet Storm Center·Sep 15, 15:23 UTC · 19h agoResearch

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StudyBench: Can Self-Evolution Squeeze Textbooks for Olympiad Capability?

Researchers introduce StudyBench, a physics benchmark showing self-evolution gains on textbook problems rarely transfer to olympiad-level questions.

StudyBench is a controlled physics benchmark splitting test data into an Application Set of difficult textbook problems and a Transfer Set of olympiad-level problems. Across three base models, representative self-evolution methods improved on the Application Set but rarely transferred to the harder Transfer Set. A guidance ablation reveals a Guidance Gap, and every method hits a Compute Plateau, indicating the remaining limits are method problems rather than data or compute problems.

Hugging Face daily papers·Aug 31, 20:00 UTC · 15d agoAI research

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Bias-Induced Crossover in Absolute Capacity of Dense Associative Memory

Analysis shows biased patterns cut dense associative memory capacity from N^(n-1)/ln N to O(N^(n/2)), with a bias-induced crossover.

The paper analyzes dense associative memory capacity for biased centered binary patterns under the Krotov-Hopfield single-site criterion. Unbiased patterns (q=1/2) with order-n polynomial interactions yield capacity of order N^(n-1)/ln N, while fixed bias q<1/2 reduces capacity to O(N^(n/2)) for even n>=4 and O(N^((n+1)/2)) for odd n>=5. A bias-dependent crosstalk mean destabilizes sites carrying the frequent value, and an activity-dependent control potential restores the higher capacity within the conditioned-Gaussian approximation.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:16 UTC · 17h agoAI research

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Curriculum Learning as Transport: Understanding Curricula with Wasserstein Geodesics

Researchers model curriculum learning as Wasserstein transport over difficulty distributions, finding curriculum benefits are strongly task- and budget-dependent with no dominant strategy.

The framework represents curricula as trajectories of training distributions over discrete difficulty levels, decoupling ordering, matched exposure, endpoint smoothness, and pacing. Across a calibrated suite of 12 tasks and 33 difficulty axes under fixed training budgets, no single strategy dominates, though easy-to-hard ordering improves hard-level performance relative to exposure-matched static sampling. Endpoint smoothness and pacing substantially affect where along the difficulty spectrum a curriculum is effective, and the transport view supports extensions to learned pacing and structured difficulty spaces.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:39 UTC · 7d agoAI research1

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Rewiring Democracy Series on The Renovator

Schneier and Sanders link a four-part Rewiring Democracy essay series covering civic AI in Japan, Switzerland, Brazil and Scotland.

Bruce Schneier and Nathan E. Sanders announced links to their Rewiring Democracy essay series on The Renovator. The four parts cover Japan's digital democracy party Team Mirai, Switzerland's public AI model Apertus, Brazil's Open Knowledge civic technologists, and civic AI in Scotland. The post is a brief index without technical details.

Schneier on Security·Sep 1, 09:59 UTC · 15d agoAI policy

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UniH^3: Unifying Hierarchical Homogeneity and Heterogeneity for All-in-One Medical Image Restoration

UniH3 unifies hierarchical homogeneity and heterogeneity modeling for all-in-one medical image restoration across modalities and degradation types.

UniH3 introduces a Hierarchical Homogeneity Memory module that distills shared anatomical priors from high-quality images, injected via a Homogeneity-Guided Attention mechanism. A Hierarchical Heterogeneity Balancer mitigates inter- and intra-task conflicts during multi-task optimization. It achieves state-of-the-art on MedIR-2D-500K and MedIR-3D-3D benchmarks for both all-in-one and single-task restoration, with code released on GitHub.

Hugging Face daily papers·Sep 9, 20:00 UTC · 6d agoAI research

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Who Should Grade My Work? Student Perspectives on Transparent AI-Assisted Writing Assessment in Higher Education

A Saudi university study finds students value ChatGPT writing feedback but treat human instructors as the final grading authority.

Thirteen male undergraduate computing students at a Saudi public university completed handwritten writing tasks that were scored by ChatGPT using a rubric-based prompt, then reflected after being told the score and feedback were AI-generated. Inductive thematic analysis identified four themes: perceived feedback usefulness, awareness of AI's contextual and pedagogical limitations, conditional trust, and reflection on the instructor's institutional role. Participants accepted GenAI feedback for surface-level revision but consistently positioned human instructors as the authority over grading decisions, distinguishing feedback utility from evaluative authority.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:49 UTC · 11d agoAI research

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AuK Technical Report: An Open-Source Foundational Model for Speech Generation and Editing

Open-source speech foundation model AuK unifies generation and editing, trained on 1.95 million hours, with distilled AuK-Flash achieving 4.5x speedup.

AuK is an open-source foundational model that unifies speech generation and editing through natural-language instructions and audio context, trained on approximately 3.03 billion instruction-audio instances and 1.95 million hours of supervision across five task families including generation, content editing, and acoustic editing. It combines a multimodal LLM for semantic conditioning, a VAE jointly trained on speech, general audio, and music, and a hybrid rectified-flow Transformer using dual-stream MMDiT blocks followed by unified single-stream DiT blocks. Post-training applies human-feedback preference optimization for editing and reward-based reinforcement learning for generation, and the distilled AuK-Flash performs 4-step inference without classifier-free guidance at a 4.5x wall-clock speedup. Source code and model weights are released.

Hugging Face daily papers·Sep 7, 20:00 UTC · 8d agoModel release1

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Quoting Laurie Voss

Laurie Voss argues AI collapses code-writing and review costs, leaving product discovery and precise definition as the core of software engineering.

Simon Willison quotes Laurie Voss's essay "We are all Product Engineers now," which argues that AI is collapsing the cost of writing code and will likewise collapse the cost of reviewing, fixing, and operating it. Voss contends the remaining work is finding out what people want, defining it precisely, and making software pleasant to use. He expects the amount of software to grow without limit because demand has no ceiling, making product-definition skills the whole job. No specific models, tools, or incidents are named; this is career and industry commentary.

Simon Willison·Sep 14, 14:34 UTC · 1d agoAI industry

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Reference-Based Bias Detection in LLMs via Relative Representations of Hidden States

Researchers propose auditing LLM bias via relative hidden-state representations, detecting bias increases with 3-50x less compute than output-level benchmarks.

The paper introduces a reference-based bias auditing method that compares hidden-state representations across model variants, such as before and after fine-tuning, by encoding sentences relative to a fixed anchor set. The resulting Representational Bias Shift (Delta-B) correlates with output-level bias change in 15 of 18 tested settings, reaching |r| = 0.84 under full fine-tuning across WildGuardMix, DecodingTrust, and ToxiGen benchmarks. Thresholding Delta-B detects checkpoints whose bias increased with ROC AUC between 0.65 and 0.99 and beats a SEAT-based baseline, while auditing a model in about three minutes with 3-50x less compute.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

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CanvasAnneal: Curriculum Reinforcement Learning for Diffusion Language Models

CanvasAnneal injects teacher reasoning traces into diffusion canvases during curriculum RL, improving diffusion LLMs on MATH500, Countdown, and Tau2.

CanvasAnneal is a curriculum-guided reinforcement learning framework for diffusion language models that addresses exploration bottlenecks in standard RL. It warm-starts exploration by injecting teacher-generated reasoning traces into the initial diffusion canvas, then gradually removes this guidance so the model generates reasoning trajectories independently. Across mathematical reasoning and tool-use benchmarks, it improves over standard diffu-GRPO on MATH500, Countdown, and Tau2 and accelerates reward improvement, though gains are task-dependent.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:59 UTC · 4d agoAI research1

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Can Foundation Models Moderate Online Content? Evaluating Instruction- vs. Example-Driven Policy Operationalization

ModerationBench shows foundation models can nearly triple Bluesky's moderation F1 (0.60 vs 0.22), with instruction- and example-driven guidance performing comparably.

Researchers built ModerationBench, a new benchmark of 4,000 manually annotated in-the-wild posts from Bluesky, to test whether foundation models can reliably operationalize content moderation policies. They systematically compare instruction-driven guidance (reasoning from policy precepts) with example-driven guidance (generalizing from precedents) for Vision-Language Models. Both paradigms achieve comparable peak effectiveness, and foundation models nearly triple the F1 of Bluesky's deployed moderation system on Random Posts (0.60 vs 0.22).

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 16:30 UTC · 6d agoAI research1

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Right Tool, Right Job: Native-Language Evaluation, Tokenizer Sensitivity, and Methodological Findings from a French-Only BabyLM

French BabyLM entry METRON-FR (125M GPT-2, 92.47M words) shows tokenizer artifacts dominate child-scale zero-shot evaluation; proposes standard diagnostics.

METRON-FR is a 125M-parameter GPT-2 pretrained on 92.47M French words, submitted to the BabyLM 2026 Strict track, scoring 85.97% on the native Quebec-French QFrBLiMP benchmark and 62.80% on the BabyLM-weighted leaderboard. A cross-lingual GLUE protocol combining French task-data translation with rank-16 LoRA shows relational tasks gain while world-knowledge tasks regress. Bilingual Lexicon Induction reaches p@1 of 68.84%, 18x above chance, and ablations show single-token zero-shot scoring is dominated by tokenizer and template artifacts at child scale.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 16:43 UTC · 18h agoAI research

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Does Syntax Matter? A Graph-Augmented Variational Topic Model for Computational Social Sciences

SCPTM graph-augmented variational topic model shows syntax aids topic diversity and descriptor quality but gains stem mainly from the variational encoder.

The Structural Contextual Probabilistic Topic Model represents corpora as heterogeneous document-word graphs with lexical and syntactic edges processed by a Graph Attention Network inside a VAE for mixed-membership topic distributions. Across four corpora, neural gains in document-topic alignment are attributable to the variational encoder rather than syntax, while graph-augmented variants improve topic diversity everywhere. Dependency paths add value on argumentative deliberative texts but are redundant in technical and institutional registers.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:39 UTC · 8d agoAI research

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OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

OracleZoom enables recursive extreme-scale image super-resolution via reference-constrained on-policy distillation, reducing hallucinations at deep zoom scales.

OracleZoom tackles recursive super-resolution, where repeated feeding of predictions back into the same model leaves deeper-scale outputs unsupervised as required source resolution grows geometrically. The framework trains on its own trajectory while carrying the last ground-truth evidence beyond the supervision boundary, combining direct and cross-scale supervision, a no-reference quality objective, a KL-constrained pretrained latent prior, and EMA consistency. Across seven datasets it achieves state-of-the-art SR quality across zoom scales, averaging 0.713 CLIPIQA with larger gains at deeper scales and significantly reduced hallucinations. Code, data, and models are publicly released.

Hugging Face daily papers·Sep 5, 20:00 UTC · 10d agoAI research

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Cross-Model Agreement as a Deployment-Time Reliability Signal for Automatic Polyp Segmentation

Referee-Based Quality Estimation flags unreliable polyp segmentations at inference without ground truth, reaching ROC-AUC 0.960 with SegFormer-B0 referees.

RBQE measures agreement between a primary segmentation model and an independently trained referee on a 1,223-image external benchmark drawn from four public datasets. A cross-architecture SegFormer-B0 referee achieves the strongest signal (ROC-AUC 0.960), beating a Test-Time Augmentation baseline by 0.055 ROC-AUC under an identical protocol. Excluding trivially separable empty-mask cases, ROC-AUC falls to 0.876 (SegFormer-B0) and 0.783 (same-architecture control), but RBQE's margin over baselines widens. Progressive rejection of low-agreement predictions increases mean Dice of retained outputs, supporting selective prediction at the cost of one extra forward pass.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:32 UTC · 6d agoAI research

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One resignation turned the embers of AI fear into a wildfire

Interconnects argues a frontier-lab researcher's safety resignation went viral via media coordination, reigniting AI existential-risk discourse.

The essay analyzes why researcher Jacob Coxon's resignation over AI safety risks went viral, aided by a Wall Street Journal exclusive, advocacy-group amplification, and Daniel Kokotajlo's same-day Joe Rogan appearance. It notes Evan Hubinger's >10% extinction-risk figure and criticizes existential-risk discourse for conflating very different meanings of the term. The author assigns near-zero probability to complete extinction but argues concrete risks such as cyberattacks on critical infrastructure and bio-risks merit debate. It also rejects recursive self-improvement forecasts, proposing 'lossy self-improvement' where models excel at math and code but remain limited elsewhere.

Interconnects·Sep 10, 15:28 UTC · 5d agoAI safety & security

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One Symptom, Three Levers: A Critical Review of On-Policy Self-Distillation

A review paper frames on-policy self-distillation collapse as governed by three levers: token weighting, privileged information, and guidance decay.

The paper critically reviews On-Policy Self-Distillation (OPSD), where a language model trains on its own generations scored token-by-token by a teacher conditioned on privileged information such as reference solutions or environment feedback. It identifies collapse, the progressive narrowing of producible reasoning paths, as the dominant failure mode and analyzes it through three levers: signal weighting, the nature of privileged information, and teacher dynamics. The review is restricted to mathematical reasoning, reports no new experiments, and offers a shared vocabulary separating settled findings from disputed ones.

Hugging Face daily papers·Aug 25, 20:00 UTC · 21d agoAI research

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MetroLLM-Bench: Evaluating Language Models as Transit Kiosk Runtimes

MetroLLM-Bench is a 955-case benchmark testing language models as transit kiosk tool-calling runtimes across six real metro systems.

The benchmark covers 37-414-station metro systems and eleven task categories including routing, fare calculation, disruptions, accessibility, and adversarial input, with 14 deterministic and 8 semantic scoring components. Of 26 models from six vendors, a PEFT-tuned 4B Qwen 3.5 student scored 91.3 on Tier 1, exceeding GPT-5.6 (90.6/90.0), while Muse Glimmer 30B led the composite ranking. A deterministic rule-based baseline reached 84.6, and PEFT gains over base models shrank from +7.03 points at 2B to -0.91 at 27B.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research

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Better answers, broader thinking: What students gain from ChatGPT and critical-thinking training

OpenAI published a randomized study of over 1,000 students examining ChatGPT's effects on critical thinking and performance.

OpenAI released results from a randomized study of more than 1,000 students using ChatGPT on a real-world university assignment. The research examined effects on critical thinking, originality, and student performance. Findings inform ongoing debates about AI's role in education and learning outcomes.

OpenAI News·Aug 27, 09:00 UTC · 20d agoAI research

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