All Articles
16580 articles total
From Dependency to Compositionality: A Neurosymbolic Lifting of LLM Outputs via Combinatory Categorial Grammar
A neurosymbolic framework that lifts LLM outputs into typed compositional derivations using Combinatory Categorial Grammar for better auditability.
Measuring Reward-Seeking via Contrastive Belief Updates
Researchers developed a method to measure reward-seeking in RL models, finding that models often prioritize grader preferences over intended objectives.
When Does Machine Learning Beat Value Sorting? A Three-Dataset Diagnostic of Exposure-Weighted Shipment Prioritization
A study evaluating whether machine learning models for shipment prioritization outperform simple value-based sorting in supply chain contexts.
SciHazard: A Benchmark for Measuring Scientific Safety Risks with Decomposed Harm Scoring
Introduction of SciHazard, a benchmark and evaluation framework designed to measure scientific safety risks and misuse guidance in LLMs.
Semantic Primes as Explanans for Emotion in Large Language Models
Research exploring the use of Natural Semantic Metalanguage (NSM) semantic primes as more effective internal explanations for emotion in LLMs.
Do AI-Native Biotechs Need Departments? Benchmarking Company World Models for AI-Driven Drug Development
A proposal for 'Company World Models' in AI-native biotechs, suggesting asset-centric architectures over traditional human-mimicking organizational charts.
DWM: Separating World Effects from Actions in Latent World Models
Introduction of DWM (Decomposed World Model), a framework to separate action-driven transitions from action-invariant world effects in latent world models.
One Rewrite to Fix Them All? Type-Aware Repair Allocation for Text-to-Image Prompt Optimization
Presentation of TARA, a training-free framework for text-to-image prompt optimization that uses type-aware repair allocation to fix semantic failures.
AgentDebugX: An Open-Source Toolkit for Failure Observability, Attribution, and Recovery in LLM Agents
AgentDebugX, an open-source toolkit for failure observability, attribution, and recovery in LLM agents using a closed-loop debugging workflow.
SkillSight: Seeing Through Shared Descriptions for Accurate Skill Retrieval
SkillSight, a training-free retrieval framework that improves skill selection for LLM agents by calibrating shared descriptive background bias.
AI Tour Meeting: Group Travel Planning by LLM Agents
AI Tour Meeting, a group travel planning framework utilizing multiple LLM agents with distinct personas to collaboratively create itineraries.
Evaluating medical AI under missing information: same-provider judges and human raters change apparent safety
An evaluation of medical AI safety under missing information, revealing that LLM judges are more permissive than human clinicians.
Intel Starts Shipping High-NA EUV Silicon
Intel has begun shipping silicon manufactured using High-NA EUV lithography, marking a significant step in semiconductor manufacturing precision.
MAGE: Human-Like Macro Placement via Agentic Multimodal Reasoning
MAGE is a multimodal multi-agent framework that improves macro placement in physical chip design using natural-language directives and iterative refinement.
Engineering Trustworthy Agentic AI for Critical Systems
A survey on engineering trustworthy agentic AI for critical systems, proposing a cross-domain assurance framework for safety, reliability, and auditability.
Attacking Graph Foundation Models Through Their Shared Representation
Research demonstrates that graph foundation models have a specific attack surface in their alignment layer, making them susceptible to representation-space perturbations.
Show HN: ReadKinetic – a free, local-first speed reader for your own books
ReadKinetic is a free, local-first speed reading application designed for users to read their own books locally.
Show HN: A new kind of FPS aim trainer
A new type of FPS aim trainer is introduced to help players improve their accuracy in first-person shooter games.
Fence: Specialized SLM Guardrails for LLM Applications
The authors propose 'Fence', which uses Small Language Models (SLMs) trained on synthetic data as specialized guardrails to enhance safety and reduce hallucinations in LLM applications.
Wisdom of LLM Crowds: Aggregation and Contamination in Language Model Ensembles
Research on LLM ensembles suggests that learned aggregation methods outperform individual models, though training data contamination remains a significant issue for evaluation.