arXiv:2607. 08774v1 Announce Type: new Abstract: Reliability in large language model (LLM) systems is typically framed as a function of model capability.
arXiv:2603.18614v2 Announce Type: replace Abstract: Tool-augmented large language models (LLMs) must tightly couple multi-step reasoning with external actions, yet existing benchmarks often confound...
CritICL is an inference-time framework that enhances reasoning in large language models by using failure patterns from weaker models as critique-based in-context examples. It offers two variants: CritICL-dynamic, which predicts input-specific failure modes, and CritICL-static, which applies a global failure mode profile. Experiments show that CritICL outperforms standard in-context learning and rivals test-time scaling methods while using fewer generations and lower token costs.
arXiv:2607. 18100v1 Announce Type: new Abstract: Extended reasoning has become standard for frontier Large Language Models (LLMs), yet the trajectories these models produce remain largely uncontrollable.
arXiv:2608.29988v1 Announce Type: new Abstract: Large language models (LLMs) achieve strong reasoning performance, which depends critically on inference-time decisions. Yet these decisions are common...
arXiv:2607. 11388v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) and vision-language models (VLMs) have enabled increasingly capable digital agents for computer use.
arXiv:2607. 23771v1 Announce Type: new Abstract: Large language model (LLM) performance increasingly depends not only on the base model, but also on the inference-time controller used to organize reasoning.
arXiv:2606. 03698v1 Announce Type: new Abstract: A central goal of large language model (LLM) research is to build agentic systems that can plan, act, and adapt through sustained interaction with dynamic environments.
arXiv:2510. 11713v4 Announce Type: replace-cross Abstract: Real-world applications of Large Reasoning Models (LRMs) often require reasoning about changing prompts or environments.
EasySteer is a unified framework for high‑performance, extensible large‑language‑model steering built on vLLM. It offers a modular architecture with pluggable interfaces for analysis‑based and learning‑based methods, fine‑grained parameter control, pre‑computed steering vectors for eight application domains, and an interactive demo system. Integrated with vLLM’s optimized inference engine, EasySteer delivers a 10.8–22.3× speedup over existing frameworks and demonstrates effectiveness in overthinking mitigation, hallucination reduction, and other key applications.
arXiv:2604.27251v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) acquire reasoning capabilities through shared inference patterns in pre-training data, which are further elicite...
PetriBench is a compact, fully self‑contained, and scalable benchmark that evaluates large language model (LLM) reasoning over dynamic state spaces using Petri nets. It organizes reasoning into four task families with Easy, Medium, and Hard levels, each generated by increasing structural complexity and evaluated against exact ground truth. Experiments across proprietary and open‑weight models show that accuracy consistently drops with difficulty, revealing distinct task‑specific capability profiles, while test‑time compute and procedural generation affect performance differently across tasks.
arXiv:2606. 14574v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments.