20 results for "ai errors"
Claude Status Update : Claude.ai unavailable and elevated errors on the API on 2026-04-28T18:33:55.000Z
This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Claude.ai unavailable and elevated errors on the API Check on progress and whether or not the…
Claude Status Update : Claude.ai unavailable and elevated errors on the API on 2026-04-28T17:51:36.000Z
This is an automatic post triggered within 2 minutes of an official Claude system status update. Incident: Claude.ai unavailable and elevated errors on the API Check on progress and whether or not the…
Pylon: Self-Host Your Own AI Agent Pipeline That Fixes Sentry Errors via
Pylon is a self-hosted daemon that triggers sandboxed Claude Code agents from webhooks (Sentry, cron, chat) and reports results with human approval —…
Claude Status Update : Elevated errors on Claude Haiku 4.5 on 2026-04-28T12:38:38.000Z
Claude.ai is unavailable
Claude's Status Page - Claude.ai unavailable and elevated errors on the API.…
CAP-CoT: Cycle Adversarial Prompt for Improving Chain of Thoughts in LLM Reasoning
Chain-of-Thought (CoT) prompting has emerged as a simple and effective way to elicit step-by-step solutions from large language models (LLMs). However, CoT reasoning can be unstable across runs on lon…
When Corrective Hints Hurt: Prompt Design in Reasoner-Guided Repair of LLM Overcaution on Entailed Negations under OWL~2~DL
We report a reproducible error pattern in GPT-5.4 on OWL~2~DL compliance queries: the model frequently answers ``unknown'' when the reasoner-entailed answer is ``no'' under \emph{FunctionalProperty} c…
FinGround: Detecting and Grounding Financial Hallucinations via Atomic Claim Verification
Financial AI systems must produce answers grounded in specific regulatory filings, yet current LLMs fabricate metrics, invent citations, and miscalculate derived quantities. These errors carry direct …
CT-FineBench: A Diagnostic Fidelity Benchmark for Fine-Grained Evaluation of CT Report Generation
The evaluation of generated reports remains a critical challenge in Computed Tomography (CT) report generation, due to the large volume of text, the diversity and complexity of findings, and the prese…
Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition
Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epistemic uncertainty (reducible model underspecifica…
Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus
Multiple myeloma is managed through sequential lines of therapy over years to decades, with each decision depending on cumulative disease history distributed across dozens to hundreds of heterogeneous…
OpenGame: Open Agentic Coding for Games
Game development sits at the intersection of creative design and intricate software engineering, demanding the joint orchestration of game engines, real-time loops, and tightly coupled state across ma…
RADIANT-LLM: an Agentic Retrieval Augmented Generation Framework for Reliable Decision Support in Safety-Critical Nuclear Engineering
Reliable decision support in nuclear engineering requires traceable, domain-grounded knowledge retrieval, yet safety and risk analysis workflows remain hampered by fragmented documentation and halluci…
When VLMs 'Fix' Students: Identifying and Penalizing Over-Correction in the Evaluation of Multi-line Handwritten Math OCR
Accurate transcription of handwritten mathematics is crucial for educational AI systems, yet current benchmarks fail to evaluate this capability properly. Most prior studies focus on single-line expre…
Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation
Parameter-Efficient Fine-Tuning (PEFT) has become the standard for adapting large language models (LLMs). In this work we challenge the wide-spread assumption that parameter efficiency equates memory …
DO-Bench: An Attributable Benchmark for Diagnosing Object Hallucination in Vision-Language Models
Object level hallucination remains a central reliability challenge for vision language models (VLMs), particularly in binary object existence verification. Existing benchmarks emphasize aggregate accu…
LLMs Corrupt Your Documents When You Delegate
Large Language Models (LLMs) are poised to disrupt knowledge work, with the emergence of delegated work as a new interaction paradigm (e.g., vibe coding). Delegation requires trust - the expectation t…
A Systematic Approach for Large Language Models Debugging
Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based reasoning. However, debugging these models remains…
Discovering Agentic Safety Specifications from 1-Bit Danger Signals
Can large language model agents discover hidden safety objectives through experience alone? We introduce EPO-Safe (Experiential Prompt Optimization for Safe Agents), a framework where an LLM iterative…
Causal Discovery as Dialectical Aggregation: A Quantitative Argumentation Framework
Constraint-based causal discovery is brittle in finite-sample regimes because erroneous conditional-independence (CI) decisions can cascade into substantial structural errors. We propose Quantitative …