arXiv:2605.00742v1 Announce Type: new
Abstract: LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to consult, or how many reso…
arXiv:2605.00347v1 Announce Type: cross
Abstract: Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has emerged as a promising frontier. However, existing a…
An AI agent that revealed sensitive data without being asked. An agent that overruled its own guardrails. Another that sent credentials to an attacker via Telegram, because it forgot it wasn’t supposed to do so after a reset.
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arXiv:2604.27780v1 Announce Type: cross
Abstract: Large Language Models (LLMs) have rapidly improved in performance across code-related tasks, making their integration into Register Transfer Level (RTL) development increasingly attractive. Mimicking t…
arXiv:2604.27852v1 Announce Type: cross
Abstract: Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retrieval performance do not consistently translate to c…
arXiv:2604.28123v1 Announce Type: cross
Abstract: The standard post-training recipe for large multimodal models (LMMs) applies supervised fine-tuning (SFT) on curated demonstrations followed by reinforcement learning with verifiable rewards (RLVR). Ho…
arXiv:2604.28139v1 Announce Type: cross
Abstract: LLM agents are expected to complete end-to-end units of work across software tools, business services, and local workspaces. Yet many agent benchmarks freeze a curated task set at release time and grad…
arXiv:2604.26969v1 Announce Type: cross
Abstract: Modern large-scale recommendation systems are typically constructed as multi-stage pipelines, encompassing pre-ranking, ranking, and re-ranking phases. While traditional recommendation research typical…
arXiv:2412.16720v2 Announce Type: replace
Abstract: The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought. These advanced reasoning capabilities provide new avenues for improving the safety and robustn…
arXiv:2602.13559v2 Announce Type: replace
Abstract: To fulfill user instructions, autonomous web agents must contend with the inherent complexity and volatile nature of real-world websites. Conventional paradigms predominantly rely on Supervised Fine-…