constraints

This paper introduces the Hybrid-to-NeSy (H2N) framework, which systematically translates hybrid mechanistic-data-driven models into neuro-symbolic AI designs, enabling the derivation of metrics for structural violation and belief dispersion as measures of epistemic uncertainty in the mechanistic part.

A developer built an architecture skill that explicitly handles assumptions, evidence, and constraints, and is now seeking agent-generated failure cases to test it.

A developer shares techniques for making LLM-generated podcasts sound natural, including using constraints to force disagreement and pre-editing content before generation.

CombEval is a dynamic benchmark for evaluating combinatorial counting in large language models, using typed specifications to generate problems with solver-verified answers. It tests 11 LLMs under direct and code-augmented settings and finds brittleness on ordered objects, indistinguishable elements, relative constraints, and nested dependencies.

This article discusses the concept of 'Verifier Tax' in AI agent benchmarks, distinguishing between safe success (completing tasks without violating constraints) and unsafe success (completing tasks but violating constraints), and questions how to properly measure agent performance considering safety tradeoffs.

Introduces a neurosymbolic framework that injects LTLf constraints into transformer-based reinforcement learning policies via differentiable automaton representations and a logic-based loss, improving constraint satisfaction while maintaining competitive returns.

AdaPlanBench is a dynamic benchmark for evaluating LLM agents' ability to adaptively plan under progressively revealed world and user constraints through multi-turn interactions, showing current models struggle especially with user constraints.

An informal research note describing a behavior in transformers where the model's inherent 'clarity-seeking' vectors can bypass constraints when discussing higher-order topics, potentially relevant to alignment and safety research.

The author argues against building fully autonomous email agents based on real-world failures, advocating for constrained 'propose-and-approve' workflows where AI prepares context and drafts but humans retain final approval on sends.

A blog post explaining how to use refinement mappings to preserve external properties during database schema changes, using examples of migrating a boolean column to a nullable timestamp and then to event sourcing.