·
DataBubble
  • Home
  • Models
  • News
  • Compare
  • Boards
  • Pricing
  • About
  • Newsletter
  • Methodology
  • Contact
Latest
The tech-broification of American science has officially begun40m◆OpenAI’s new voice mode makes it to the ChatGPT desktop app1h◆Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs11h◆DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions11h◆PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs11h◆Incomplete Prompt Jailbreaks in Large Language Models11h◆Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering11h◆Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants11h◆DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making11h◆Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversarial Mutation11h◆AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs11h◆MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation11h◆Telco-GAIA: Bilingual Benchmark for Agents in Telecom Domain11h◆SiGMA: Sign-Guided Merging and Adaptation for Multimodal Continual Instruction Tuning11h◆Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models11h◆StrideDiffusion: Accelerating Diffusion Models for Time-series Generation11h◆Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs11h◆Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks11h◆Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions11h◆Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority11h◆The tech-broification of American science has officially begun40m◆OpenAI’s new voice mode makes it to the ChatGPT desktop app1h◆Stochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs11h◆DecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions11h◆PlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs11h◆Incomplete Prompt Jailbreaks in Large Language Models11h◆Directional Hallucinations: Ideological Drift in News-Grounded LLM Question Answering11h◆Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants11h◆DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making11h◆Isolating LLM Alignment from Regex: Zero Coverage and Metric-Dependent Divergence Under Adversarial Mutation11h◆AISE-Bench: A Full-Cycle Curated Benchmark for Information Seeking on Academic Knowledge Graphs11h◆MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation11h◆Telco-GAIA: Bilingual Benchmark for Agents in Telecom Domain11h◆SiGMA: Sign-Guided Merging and Adaptation for Multimodal Continual Instruction Tuning11h◆Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models11h◆StrideDiffusion: Accelerating Diffusion Models for Time-series Generation11h◆Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs11h◆Efficient and Interpretable Body-Based Emotion Recognition with Lightweight Temporal Convolutional Networks11h◆Is Deep Research Reliable? Misleading Knowledge Induces False Conclusions11h◆Representing Entity Importance in AI Knowledge Systems: A Dual-Signal Framework of Audience Evaluation and Structural Authority11h◆
News/Case study: solving P-99 with LPTP and an LLM
arxiv
PublishedJuly 24, 2026 at 4:00 AM
—neutral

Case study: solving P-99 with LPTP and an LLM

Source
arxiv.orgfull article ↗
Read on arxiv→
Publisher summary· verbatim

arXiv:2607.21196v1 Announce Type: cross Abstract: Ninety-Nine Prolog Problems (P-99) is a famous set of Prolog exercises. We solved the first thirty three just by prompting an LLM (Large Language Model). We used Claude from Anthropic. By solved we mean: generate the Prolog code and a test file, run

Stay posted· Newsletter

A 5-min weekly brief — top movers, price watch, story of the week.

// no spam · unsubscribe one-click · free forever

Discussion
Source
↗
arxiv
Read original ↗All from arxiv →

No replies yet. Be first.

Source
↗
arxiv
Read original ↗All from arxiv →

Related coverage

More from ARXIV
arxivStochastic Sampling is Epistemically Shallow: The Dimensionality Gap Between Temperature Variation and Model Diversity in LLMs11harxivDecodeShare: Tracing the Shared Subspace of LLM Decode-Time Decisions11harxivPlanE: Meta Planning of Data, Tuning, and Inference for Extractive-based LLMs11harxivIncomplete Prompt Jailbreaks in Large Language Models11h
The Bubble Brief
WEEKLY

Read AI insights every Tuesday — top movers, new releases, story of the week.

// no spam · unsubscribe one-click · free forever

Originally published on arxiv ↗
HomeModelsNews