arXiv:2509.19736v1 Announce Type: new Abstract: Reinforcement learning (RL) has shown promise in training agentic models that move beyond static benchmarks to engage in dynamic, multi-turn interactions. Yet, the ultimate value of such agents lies in their ability to assist users,…
arXiv:2506.03135v2 Announce Type: replace-cross Abstract: Spatial reasoning is a key aspect of cognitive psychology and remains a bottleneck for current vision-language models (VLMs). While extensive research has aimed to evaluate or improve VLMs’ understanding of basic spatial relations, such as…
arXiv:2509.19489v1 Announce Type: new Abstract: Systems often repeat the same prompt to large language models (LLMs) and aggregate responses to improve reliability. This short note analyzes an estimator of the self-consistency of LLMs and the tradeoffs it induces under a…
arXiv:2509.19517v1 Announce Type: new Abstract: The scaling of Large Language Models (LLMs) has exposed a critical gap between their performance on static benchmarks and their fragility in dynamic, information-rich environments. While models excel at isolated tasks, the computational limits that…
arXiv:2509.19524v1 Announce Type: new Abstract: Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoal-level reporting should become routine: for each trajectory,…
arXiv:2509.19590v1 Announce Type: new Abstract: Evaluations of generative models on benchmark data are now ubiquitous, and their outcomes critically shape public and scientific expectations of AI’s capabilities. Yet growing skepticism surrounds their reliability. How can we know that a reported…
arXiv:2509.19566v1 Announce Type: new Abstract: We investigate the application of Small Language Models (<10 billion parameters) for genomics question answering via agentic framework to address hallucination issues and computational cost challenges. The Nano Bio-Agent (NBA) framework we implemented incorporates task…
arXiv:2509.19681v1 Announce Type: new Abstract: Advanced test-time computing strategies are essential for scaling reasoning models, but their effectiveness is capped by the models’ poor self-evaluation. We propose a pairwise Explanatory Verifier, trained via reinforcement learning (GRPO), that produces calibrated confidence…
arXiv:2509.19623v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle with complex Text-to-SQL queries that demand both sophisticated mathematical reasoning and intricate schema navigation. Existing methods often tackle these challenges in isolation, creating a fractured reasoning process that compromises logical…
arXiv:2509.20146v1 Announce Type: cross Abstract: Recent benchmarks for medical Large Vision-Language Models (LVLMs) emphasize leaderboard accuracy, overlooking reliability and safety. We study sycophancy — models’ tendency to uncritically echo user-provided information — in high-stakes clinical settings. We introduce EchoBench, a…
