Search: “clay”

Smart search ranks by meaning as well as keywords (one row per story, last 45 days).

Clay Mathematics Institute says the Navier-Stokes Millennium Prize Problem has "apparently been settled"

Clay Mathematics Institute says the Navier-Stokes Millennium Problem appears settled amid accusations OpenAI misused a mathematician's leaked drafts.

The Clay Mathematics Institute stated the Navier-Stokes Millennium Prize Problem, one of seven problems worth $1 million each, has 'apparently been settled' and the solution is under review. A dispute has erupted around the work: mathematician Tristan Buckmaster accuses OpenAI of redirecting resources to the problem after rumors of his research leaked, using his drafts in training data, and excluding co-author Levent Alpöge, who works at Anthropic. CMI also noted new technologies' increasing ability to accelerate mathematical research.

The Decoder·Sep 14, 13:01 UTC · 2d agoAI industry1

15

Transfer Learning for Evolving Domains

TrED formalizes transfer learning for domains whose data availability evolves over time, arguing classical settings are regimes along one trajectory, and remains unsolved.

The paper introduces Transfer Learning for Evolving Domains (TrED), formalizing transfer learning as a trajectory problem where target data and labels are progressively collected. TrED is specified by a data availability process fixed by the environment, a freely chosen learning protocol, and an evaluation criterion scoring the whole trajectory of models. Classical settings like domain generalization, domain adaptation, and multi-domain learning are recovered as regimes within this framework. The authors survey the literature and find most methods are tailored to a single regime, leaving TrED a well-posed open problem.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:37 UTC · 5d agoAI research

17

How AI-native companies turn workflows into operating capability

OpenAI highlights Basis, Clay, and Exa Labs using AI agents for onboarding, account management, and developer integrations in enterprise workflows.

An OpenAI news post profiles AI-native companies Basis, Clay, and Exa Labs that use AI agents to improve onboarding, account management, and developer integrations. The piece positions these workflows as operating capability and draws lessons for enterprise leaders. It is promotional case-study content rather than product or research news.

OpenAI News·Sep 1, 17:00 UTC · 15d agoAI industry

20

Mines, Minds, and Machines: The Journey of AI

Recorded Future essay traces AI's supply chain from minerals and chips to embodied AI, mapping geopolitical rivalry and cyber operations at every stage.

Recorded Future published an analysis following the supply chain of the fourth industrial revolution: minerals become chips, chips become data centers, and data centers become models that are acquiring physical capabilities. It argues geopolitical rivalry and cyber operations now run along every link, from mine to embodied AI machines.

Recorded Future·Aug 11, 00:00 UTC · Aug 11, 2026AI industry

15

OpenAI just wants to win

OpenAI says roughly 10,000 agents and tens of millions in compute solved the Navier-Stokes Millennium Prize problem in 88 hours, amid controversy.

OpenAI claims an advanced unreleased model, run with about 10,000 agents and tens of millions of dollars of compute for 88 hours, produced a solution to the Navier-Stokes problem, one of seven $1 million Millennium Prize problems set by the Clay Mathematics Institute in 2000. NYU professor Tristan Buckmaster, who was pursuing the problem with Anthropic researcher Levent Alpöge, accused OpenAI of racing his team and of possibly benefiting from his Codex prompts; OpenAI categorically denied both. OpenAI researcher Sébastien Bubeck acknowledged offering Buckmaster compute and authorship arrangements, which Buckmaster characterized as a bribe, while many mathematicians expressed unease at OpenAI's competitive conduct.

The Verge · AI·Sep 12, 11:00 UTC · 4d agoAI industry1

78

Decomposition Buys Integrity, Not Yield

Study of 600 production deep-research traces finds agent-tree decomposition loses findings at rate N^(1-δ); flat architectures maximize yield.

The paper models multi-agent decomposition as a tree where an agent holding b items retains each with probability r(b); with r(b)=1/b every tree delivers exactly one finding regardless of shape. Analysis of 600 production deep-research traces estimates delta=0.34 retention decay, and 1,012 annotated traces show one brief in sixteen goes off-target per tier, giving an alignment penalty of 0.536. Depth still cuts root context exposure from N to N^(1/k) and is cheaper at scale, with a hazard model over 743,819 production tool calls showing delegation is an opening move rather than a response to filling context.

arXiv cs.AI / cs.LG / cs.CL·Sep 15, 17:04 UTC · 1d agoAI research

30

OpenAI fought dirty on career-making math problem, says NYU mathematician

NYU mathematician Tristan Buckmaster alleges OpenAI learned of his team's Navier-Stokes approach and raced ahead using massive compute to claim a full proof first.

NYU mathematics professor Tristan Buckmaster and Anthropic mathematician Levent Alpöge announced preliminary proofs toward the Navier-Stokes existence and smoothness problem, a $1 million Clay Millennium Prize problem, developed using OpenAI's Codex and Claude. They allege OpenAI learned of their progress and that an OpenAI team then used an 'insane amount of compute' to announce a full proof first. OpenAI research lead Sebastian Bubeck denies the claims as 'false and inflammatory'. Buckmaster also raised concerns that OpenAI could have learned from his Codex interactions, which the company may use for model training.

TechCrunch · AI·Sep 8, 17:32 UTC · 8d agoAI industry

38

Molecular Déjà Vu: Digit-Level Retrieval of Published Values in Frontier Language Models

Audit of 22 frontier models finds widespread verbatim retrieval of published molecular property values, with higher reasoning increasing recall of memorized numbers.

An arXiv audit tests 22 frontier LLMs across 12 molecular regression benchmarks for verbatim retrieval of published values. More than 50% of the LLMs show verbatim retrieval on five datasets, and identical experiments are flagged 89% more often at a high reasoning level than at the lowest one. Suppressing retrieval moves model prediction errors closer together in relative terms, suggesting predictive capability is not determined solely by memorized values.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:32 UTC · 12d agoAI research1

42

AIR raises $50M to help companies vet the skills and add-ons AI agents use

AI security startup AIR exits stealth with $50M across two seed rounds to continuously vet skills, plugins, and MCP servers used by enterprise AI agents.

AIR, founded by Unit 8200 veterans Yair Saban and Niv Hoffman, raised $10 million led by Sequoia and $40 million led by Greenoaks to secure the emerging AI agent software supply chain. Its platform discovers agents in enterprise environments, intercepts their actions, and continuously vets skills, add-ons, and MCP servers against a maintained whitelist, filtering out about 27% of found add-ons. The company claims over 20 customers, roughly a quarter large enterprises, with strong demand in financial services and pharma, and competes with Noma Security, Zenity, Astrix Security, and Operant AI.

TechCrunch · Security·Sep 1, 15:45 UTC · 15d agoAI industry

46

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 · 5d agoAI research1

28

OpenAI's millennium proof dispute raises the question of whether researchers can trust AI labs

Mathematician Tristan Buckmaster accused OpenAI of pressuring him and possibly training on his drafts amid OpenAI's race to claim a Navier-Stokes millennium proof.

OpenAI published a blog post and Sam Altman defended the team behind its AI-generated proof of the Navier-Stokes Millennium Problem after mathematician Tristan Buckmaster accused the company of academic misconduct. Buckmaster and co-author Levent Alpöge, who works at Anthropic, allege OpenAI pressured Buckmaster, sidelined Alpöge, and may have trained on drafts they entered into OpenAI's systems. OpenAI acknowledges it cannot rule out that de-identified data from their product usage helped improve its models. Mathematician Terence Tao warned the episode could discourage researchers from sharing work, reversing centuries of open science.

The Decoder·Sep 9, 10:27 UTC · 7d agoAI industry1

48

Climate-ModernBERT: Revisiting Corpus Composition for Domain-Adaptive Continued Pretraining

Climate-ModernBERT domain-adapted encoders reach 76.3 average F1 across nine climate benchmarks, 2.8 points above vanilla ModernBERT-Base.

The authors continue pretraining ModernBERT-Base on three climate corpora - academic text, climate-filtered web data, and synthetic documents - and compare joint mixtures against parameter-space merging of specialized checkpoints. The best model achieves 76.3 average F1 across nine climate NLP benchmarks, a 2.8-point improvement over the vanilla baseline. Academic climate corpora provide the strongest adaptation signal, and parameter-space merging outperforms joint multi-source training while preserving complementary corpus information; all variants are released.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:41 UTC · 9d agoAI research

22

What OpenAI’s latest controversy tells us about the future of math

OpenAI says its agents solved the Navier–Stokes Millennium Problem using an internal model, amid uncredited-work accusations from mathematicians Buckmaster and Alpöge.

OpenAI announced that its AI agents produced a proof that the full Navier–Stokes existence and smoothness problem can break down, using an internal model that outperforms the recently released Astra. NYU's Tristan Buckmaster and Anthropic's Levent Alpöge had posted a proof for a simplified version the previous day after nearly a year of work with public OpenAI and Anthropic models. OpenAI denies using their transcripts or training on them; chief research officer Mark Chen reiterated the denial, and the company says it will not claim the $1 million Clay Mathematics Institute prize. The episode fuels debate over attribution norms as frontier labs concentrate mathematical breakthroughs.

MIT Technology Review · AI·Sep 9, 03:10 UTC · 7d agoAI industry

78

LLM Agents as Computational Typologists

AUTOTYPOLOGIST is an LLM agent that performs evidence-grounded linguistic typology analysis over 25 open-source reference grammars.

The agent retrieves relevant grammar sections, analyzes interlinear glossed text (IGT), and iteratively reasons over typological hypotheses in a ReAct-style workflow. It was evaluated on typological feature coding against expert annotations and hypothesis testing against universals using 25 open-source reference grammars. Results suggest LLM agents can support scalable, inspectable crosslinguistic analysis but still require expert validation.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:35 UTC · 9d agoAI research1

16

OpenAI researcher allegedly pressured mathematician to drop Anthropic co-author from math breakthrough paper

OpenAI researcher Sébastien Bubeck allegedly pressured mathematician Tristan Buckmaster to drop his Anthropic co-author from an AI-assisted Navier-Stokes breakthrough paper.

Mathematician Tristan Buckmaster says OpenAI, after learning of his and Levent Alpöge's AI-assisted progress on the Navier-Stokes equations, pressed him to drop his Anthropic-employed co-author and dictated how any results would be announced. He says Sébastien Bubeck claimed an internal OpenAI model had produced a roughly 100-page proof for Navier-Stokes with forcing and allegedly told him 'Why would you ruin your career?' when he threatened to go public. Buckmaster published a public statement detailing the exchanges; OpenAI has not yet responded. The pair had worked with models including Claude and OpenAI Codex running GPT-5.6 Sol on the Clay Millennium Problem, which carries a $1 million prize.

The Decoder·Sep 8, 17:23 UTC · 8d agoAI industry1

45

Show HN: Panel – A research workspace where the agent can build its own panes

Panel is an open-source research workspace where AI agents dynamically build their own panes, posted on Hacker News.

Panel, shared as a Show HN project on GitHub, is a research workspace in which the agent can construct its own panes rather than using a fixed interface. The post received 41 points and 8 comments on Hacker News. It targets agentic research workflows with a dynamically generated UI.

Hacker News · AI·Sep 15, 13:58 UTC · 1d agoAI tools & infraHN 41↑ · 8 comments

15

Viggle/Viggle-Animate — new model trending #28 on Hugging Face

Viggle released Viggle-Animate, a 33.1B MiniMax-H3 finetune replacing video characters from one repainted frame, rendering 124 frames in 26 seconds on one GPU.

Viggle-Animate replaces the character in a video using only a driving video and one of its own repainted frames, with no pose estimator, segmentation mask, face tracker, or text encoder. It is a 33.1B full finetune of MiniMax-H3's ref2va transformer, jointly distilled with DMD across two teachers split by noise level, so rendering takes three forward passes per clip. On a B200 GPU it renders 124 frames in 26 seconds, 6.1x faster per clip than Wan2.2-Animate-14B in matched comparisons. The method assumes no person-specific representation, so it generalizes beyond humans; a demo, research write-up, and ComfyUI nodes are available.

Hugging Face trending models·Aug 31, 20:14 UTC · 16d agoModel release

40

Why AI food looks like that

Experts explain why AI-generated food images look unappetizing, citing diffusion model limitations, weak structural reasoning, and stylized training data.

The Verge examines why AI-generated food imagery from restaurants and brands often appears grotesque, citing researchers from Oxford, Naples, Zurich, and London. Diffusion models recover coarse structure before fine texture, so structural errors like extra fingers or donut shrimp get baked in early. Researchers note the models are weak at thin, continuous, terminating structures such as noodles, and reproduce the glossy conventions of professional food photography without understanding the objects. Odd internet imagery and memes in training data further skew outputs toward strange textures and clustered holes.

The Verge · AI·Sep 4, 11:00 UTC · 12d agoAI research

25

OpenAI’s sly mathematical breakthrough sends a chill through academia

OpenAI claims an unreleased model solved the Navier-Stokes Millennium Prize problem in 88 hours using ~10,000 agents, sparking academic scooping controversy.

OpenAI announced that one of its unreleased internal models took 88 hours, running a swarm of roughly 10,000 AI agents, to produce a solution to the Navier-Stokes Millennium Prize problem, a $1 million Clay Mathematics Institute challenge unsolved by humans for nearly 90 years. The announcement triggered allegations from NYU professor Tristan Buckmaster that OpenAI scooped his joint work with Anthropic researcher Levent Alpoge and questioned whether OpenAI accessed his Codex sessions; OpenAI denies using specific user data but concedes de-identified data influence cannot be ruled out. Critics say the rushed, reportedly million-dollar effort violates academic norms around trust and openness, potentially chilling collaboration in mathematics.

The Verge · AI·Sep 9, 21:16 UTC · 6d agoAI industry2

72

GPT-6 built this earth exploration site in 5 prompts

Hacker News post showcases an interactive earth exploration website that GPT-6 generated from only five prompts.

The item is a Hacker News submission (23 points, 32 comments) linking to earth.ethanplus.ai, an earth exploration website the author says was built by GPT-6 in five prompts. It functions as a community demo of the model's web development and generative capabilities rather than a research or security disclosure.

Hacker News · security·Sep 11, 21:39 UTC · 4d agoAI industryHN 23↑ · 32 comments

15

CausalArena: Benchmarking Causal Discovery in the Foundation Model Era

Researchers introduce CausalArena, a unified benchmark revealing that causal discovery rankings shift substantially across structural causal model families and protocols.

The paper presents CausalArena, a unified and evolvable benchmark for causal discovery combining synthetic structural causal models, semantically grounded operational SCMs, formula-grounded scientific SCMs, and public real-world datasets. Experiments across classical, neural, and pretrained causal discovery foundation models show large ranking shifts between benchmark regimes. The authors identify pretraining-evaluation overlap and benchmark diversity as central evaluation challenges.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:53 UTC · 6d agoAI research

30

Technical Manual for a Toolkit for Measuring Contextual Individuation in Transformer Language Models

An open methodology toolkit measures whether transformer language models contextualize fixed word forms across domains using bridge forms and layer-wise silhouette analysis.

The manual documents an open toolkit built around 'bridge forms' - identical written words recurring across two or more subject domains with a different sense in each - to test whether transformer language models individuate word occurrences by context beyond the embedding layer. It covers declarative specification of bridge forms, Wikipedia corpus acquisition, occurrence localization, layer-wise representation extraction, domain-pairwise silhouette measurement, and visualization, justifying each choice against failure modes such as sense contamination and subword-tokenization misalignment. It is a methodological and implementation reference and reports no empirical results.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:38 UTC · 12d agoAI research

14

Opaque recurrence, and other AI terms that you should probably know

TechCrunch updates its plain-English glossary defining common AI terms from AGI and agents to chain-of-thought reasoning.

TechCrunch maintains a regularly updated glossary of AI terminology, defining terms such as AGI, AI agents, API endpoints, chain of thought, coding agents, compute, deep learning, and diffusion. It highlights 'opaque recurrence', the reasoning technique in OpenAI's new Astra model that has drawn attention from AI safety researchers. The piece is an educational living document rather than new research or a product announcement.

TechCrunch · AI·Sep 7, 19:24 UTC · 9d agoAI industry1

18

ReCite: Agentic Reasoning for Faithful Citation

ReCite is an agentic citation framework using claim-level reasoning and verification, outperforming large generative models in strict citation accuracy.

ReCite is a decoupled agentic framework that orchestrates location perception, intent-aware query planning, and reflective verification for citation recommendation. Trained on synthesized reasoning trajectories, the agent verifies claim-evidence consistency and triggers self-correction loops when retrieved candidates lack logical support. Experiments show the lightweight framework outperforms state-of-the-art massive generative models in strict citation accuracy, addressing misattribution where cited papers are real but logically unsupportive.

arXiv cs.AI / cs.LG / cs.CL·Sep 8, 17:59 UTC · 8d agoAI research

25

DianShi-RxnDB: A Large-Scale, Fine-Grained Organic Reaction Data Platform Built via a Fully Automated Pipeline for Researchers and AI Agents

Researchers release DianShi-RxnDB, a database of roughly 24 million organic reaction instances extracted automatically from USPTO and EPO patents since 1976.

DianShi-RxnDB is built by a fully automated pipeline integrating patent text, images, and reaction schemes, yielding about 24 million reaction instances, of which 14.8 million (61.7%) pass automated qualification checks. Manual evaluation of 1,300 sampled instances showed 92.95% field-level accuracy, and comparisons with Pistachio found advantages in deduplicated record counts and granularity. The platform offers a web research workbench and a Model Context Protocol (MCP) service enabling AI agents to perform composable structured retrieval.

Hugging Face daily papers·Sep 5, 20:00 UTC · 11d agoAI research

28

CoRA-NAS: Coarse Ranking and Anchor-Residual Refinement for Neural Architecture Search

CoRA-NAS combines zero-cost proxy ranking with low-cost learning-curve refinement, achieving the best worst-space Spearman correlation across NAS benchmarks.

The paper proposes CoRA-NAS, a two-stage neural architecture search framework pairing a static ranking prior (CoRA-Rank) with learning-curve refinement (CoRA-Refine) that extrapolates early validation curves for sampled anchors and propagates residual corrections with an ExtraTrees model at about 1% of full training cost. Across NAS-Bench-201, NAS-Bench-101, TransNAS-Bench-101, and NATS-SSS it achieves mean Spearman correlations of 0.946, 0.715, 0.786, and 0.894 respectively, with the best worst-space correlation of 0.715 among compared methods. On NAS-Bench-201/CIFAR-100 its selected architecture reaches 73.32% accuracy versus a 73.37% ground-truth best.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:49 UTC · 6d agoAI research

28

Augustinian BabyLM: What Ostensive Definition Can and Cannot Teach a Small Language Model

Study shows visually grounded token embeddings in a small masked LM persist through training and improve object-property knowledge, but escape standard BabyLM benchmarks.

The paper implements ostensive definition for a small DeBERTa masked language model trained on 10M words, seeding visually grounded tokens with embeddings derived from labeled image regions before training. Visual initialization leaves a persistent, seed-replicated advantage on object-property knowledge (COMPS) and a corpus-tailored Visual-Property Swap benchmark covering color, material, size, and shape, but has no effect on most BabyLM grammar benchmarks. Synthetic grounding of previously unseeded words causally transfers the advantage to exactly those words.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:43 UTC · 6d agoAI research

20

Does Your Agent's Memory Survive a Model Upgrade? A Controlled Study of Memory Portability

A controlled study finds agent memory portability varies sharply: fixed-schema knowledge graphs survive model swaps while compressed notes degrade.

The study compares preserving an agent's history as raw long context, RAG chunks, compressed natural-language notes, or fixed-schema knowledge graphs across model upgrades, using 48 synthetic histories and two open-weight sub-10B-parameter models. Fixed-schema KG accuracy changed by only +0.0004 ± 0.0020 after a writer swap, while compressed NOTES shifted asymmetrically by +9.91 or -13.28 percentage points depending on migration direction. Mixed 50/50 embedding migrations captured only 4.96 of an 11.90-point RAG re-embedding gain; 80% of the NOTES deficit came from information lost at construction, and 81% of the RAG deficit from retrieval failures. Store-only repair of NOTES failed to reach 90% recovery in all 48 cases, while retaining raw histories enabled recovery in 34 of 48 for one direction.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:44 UTC · 12d agoAI research1

32

Real-World Knowledge-Guided Change Data Synthesis for Remote Sensing

Researchers introduce KnowChange, a framework that uses pretrained vision-language models to synthesize realistic change-detection training data for remote sensing.

KnowChange is a knowledge-guided change data synthesis framework that leverages pretrained vision-language models to reason about plausible change locations and class transitions from pre-change scenes and desired change types. It addresses the limited class-transition coverage and inflexibility of handcrafted rule-based synthesis methods, enabling diverse change types in a unified pipeline. Experiments show KnowChange-generated data outperforms existing synthetic datasets in both synthetic-to-real transfer and synthetic data augmentation, despite compact generation scale.

Hugging Face daily papers·Aug 24, 20:00 UTC · 23d agoAI research

25

From Parameters to Answers: How LLMs Retrieve and Use Their Internal Knowledge

Interpretability study traces how Qwen, Llama, and Gemma route query information and internal knowledge across layers when answering questions.

Researchers used layerwise interventions on hidden states to separate query-routing signals from target knowledge in language models, testing Qwen, Llama, and Gemma on country-continent questions with varied answer types. A pair-conditioned request direction strengthens before interventions alter downstream knowledge, opening a causal window while answer-supporting content is still forming. Trajectories differ by model: Gemma shows a partially overlapping mid-layer routing profile, while Llama has no sustained routing-effect window under the same gates.

arXiv cs.AI / cs.LG / cs.CL·Sep 10, 17:39 UTC · 6d agoAI research1

25

RelateAnything: Real-Time Open-Vocabulary Relation Prediction From Any Inputs

RelateAnything is a 53M-parameter open-vocabulary relation prediction model running at 20 ms/frame, with 2.3-3.5x higher mean recall than comparable open-vocabulary methods.

RelateAnything predicts scored relations between image regions using any predicate vocabulary supplied at inference as text embeddings, with object labels never required as input, so region sources can change without retraining. Training covers 19,103 predicates using positive-unlabeled supervision; the authors release RA-4M (474k images, 4.3M geometrically verified relations over 10,102 free-text predicates) and the OV-SGG-Bench evaluation suite. The 53M-parameter model runs at 20 ms/frame and achieves 2.3-3.5x the mean recall of the strongest comparable open-vocabulary method across cross-dataset and zero-shot benchmarks. Model, corpus, and benchmark are public.

Hugging Face daily papers·Sep 10, 20:00 UTC · 6d agoAI research

40

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 · 8d agoAI research1

52

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 · 9d agoAI research1

21

VeriScene: Reconstructing Crime Scenes from Legal Evidence via World-Model Agent

Researchers present VeriScene, a world-model agent that reconstructs crime scenes from forensic photos and witness statements with traceable, physically plausible output.

VeriScene orchestrates a world model to fuse forensic photographs and witness statements of varying reliability into cited narratives and physically plausible re-enactment videos. On a 25-scenario benchmark with planted unreliable testimony, it reaches 0.9014 evidence coverage and 0.7217 factual consistency on 20 test scenes. It outperforms an end-to-end multimodal-LLM baseline by 20.35% in factual consistency and 34.88% in temporal coherence at USD 1.82 per scene.

arXiv cs.CR·Sep 8, 07:14 UTC · 8d agoAI research

26

Diffusion TV: Experiencing Diffusion Models through Tangible, Embodied Interaction

Researchers present Diffusion TV, a CRT-based installation where antenna manipulation lets audiences physically experience diffusion model denoising.

Diffusion TV is an interactive installation built around a modified CRT television where turning the antenna controls the clarity of AI-generated images and sounds, mirroring the denoising process of diffusion models. Three channels present AI-generated animals from the past, present, and future within a temporal and ecological narrative. The authors frame the work as an embodied, non-verbal alternative to explainable AI that highlights intermediate generative states rather than final outputs.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 17:51 UTC · 12d agoAI research

12

LLMs and Contextual Integrity

Bruce Schneier highlights two papers: the CIMemories benchmark shows frontier LLMs leak memory attributes up to 69%, and an RL method reduces inappropriate disclosures.

Bruce Schneier discusses contextual integrity in LLMs, referencing the CIMemories benchmark, which uses synthetic profiles with 100+ attributes per user to test whether models with persistent memory disclose sensitive information appropriately. Evaluation showed frontier models exhibit up to 69% attribute-level violations, with GPT-5's violation rate rising from 0.1% to 9.6% across 40 tasks and reaching 25.1% with repeated prompting, showing unstable leakage behavior. A second paper introduces a reinforcement learning framework trained on a synthetic 700-example dataset that substantially reduces inappropriate disclosure while maintaining task performance, with improvements transferring to the human-annotated PrivacyLens benchmark.

Schneier on Security·Aug 18, 10:40 UTC · 29d agoAI safety & security

48

Embodied-BenchForge: A Closed-Loop Agentic Workflow for Embodied Benchmark Construction

Embodied-BenchForge automates embodied benchmark construction via closed-loop synthesis with verification and repair, yielding seven benchmarks for MLLM evaluation.

Embodied-BenchForge is an agentic framework that transforms user-specified evaluation intents into complete embodied benchmark artifacts via Closed-Loop Benchmark Synthesis. Skill-Orchestrated Artifact Synthesis composes typed reusable skills while an artifact dependency graph records intermediate outputs; Requirement-Guided Verification and Repair triggers local re-execution or upstream rollback on failures. It constructs six Offline EQA benchmarks plus one interactive benchmark with 220 executable tasks, distinguishing MLLM and embodied agent capabilities in observation-based understanding and closed-loop execution.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 17:16 UTC · 5d agoAI research

18

LexFlip: A Dissociation Diagnostic for Legal Meaning Preservation Metrics

LexFlip releases 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving tokens, exposing weaknesses in embedding-based meaning preservation metrics.

LexFlip provides 373 minimal perturbations of Quebec statutory French that reverse legal force while preserving 0.93 of tokens, creating dissociation items that break monotone token-overlap metric validation. The seven embedding and BERTScore metrics tested register only 0.022-0.039 of their identical-to-unrelated range on these edits, versus 0.670 for bidirectional NLI. Against FrJudge, with a measured human ceiling of r=0.597, a bare length feature outscores every semantic metric tested.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 15:49 UTC · 12d agoAI research

22

Diffusion Models and Concept Formation

Paper argues diffusion models implicitly form Cobweb-like concept hierarchies, with a basic level emerging at intermediate noise levels.

The authors draw a formal correspondence between diffusion models and Cobweb, a classic incremental concept-hierarchy learner, noting both are hierarchical Bayesian density models with Gaussian prototypes. Modes of the diffusion model's noisy marginals form a hierarchy whose basic level sits at intermediate noise, where class identity commits. The correspondence is tested on MNIST and Fashion-MNIST via mode-finding. Diffusion is reframed as a cognitive model of concept formation.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:43 UTC · 5d agoAI research

15

LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction

LongAgent autonomously searches variable sets and temporal windows to predict longitudinal medical outcomes, beating the strongest non-agent baseline on synthetic data.

The paper proposes LongAgent, an agent-based method that searches over combinations of variable sets, temporal windows and aggregation functions for outcome prediction on heterogeneous medical longitudinal data. It uses a history memory of previous searches and numerical evidence to guide exploration. On synthetic data it achieves mean RMSE 1.7376, improving over the best non-agent baseline by 0.0151 (95% CI [0.0045, 0.0260]; p=0.0273), and performs comparably to the best baseline on a real clinical dataset.

arXiv cs.AI / cs.LG / cs.CL·Sep 14, 16:51 UTC · 2d agoAI research

16