Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/

Alldetailsarecovered (c) Did you report error bars (e.g., with respect totherandom seed after running experiments multiple times)?YES.Mean andstandard deviation formetrics on4runsare reported.

Graph neural networks (GNNs) have emerged as a powerful tool for learning softwareengineering tasksincluding codecompletion, bugfinding,andprogram repair. The IPA-GNN can be seen either as a continuous relaxation of the RNN model or as a GNN variant more tailored to execution.

An interpreter broke down in tears at the European Parliament in Brussels while translating for an 11-year-old Ukrainian boy who was injured in a Russian missile strike on a hospital in central Ukraine in 2022. Roman Oleksiv's mother was killed in the attack and he has undergone multiple surgeries since. The aspiring ballroom dancer, who was also the subject of an award-winning film, has also received an award from the Ukraine's President Volodymyr Zelensky. A waterspout is a whirling column of air and mist that can form over oceans, seas or large lakes. 'I don't want to be part of this war machine': Young Germans protest against military service plans Germany is introducing voluntary military service to boost national defences after Russia's full-scale invasion of Ukraine.

This technical white paper introduces the Interactive Agents Call Tree (IACT), a computational model designed to address the limitations of static, hard-coded agent workflows. Unlike traditional systems that require pre-defined graphs or specialized programming, IACT operates as a general-purpose autonomous system driven purely by user dialogue. Given a high-level objective, the system autonomously grows a dynamic, recursive agent topology incrementally tailored to the problem's structure. This allows it to scale its organizational complexity to match open-ended tasks. To mitigate the error propagation inherent in unidirectional function calls, IACT introduces interactional redundancy by replacing rigid invocations with bidirectional, stateful dialogues. This mechanism enables runtime error correction and ambiguity resolution. We describe the architecture, design principles, and practical lessons behind the production deployment of this model in the kragent.ai system, presenting qualitative evidence from real-world workflows rather than exhaustive benchmark results.

Cognitive BASIC is a minimal, BASIC-style prompting language and in-model interpreter that structures large language model (LLM) reasoning into explicit, stepwise execution traces. Inspired by the simplicity of retro BASIC, we repurpose numbered lines and simple commands as an interpretable cognitive control layer. Modern LLMs can reliably simulate such short programs, enabling transparent multi-step reasoning inside the model. A natural-language interpreter file specifies command semantics, memory updates, and logging behavior. Our mental-model interpreter extracts declarative and procedural knowledge, detects contradictions, and produces resolutions when necessary. A comparison across three LLMs on a benchmark of knowledge extraction, conflict detection, and reasoning tasks shows that all models can execute Cognitive BASIC programs, with overall strong but not uniform performance.