arXiv:2601.11044v4 Announce Type: replace
Abstract: Large Language Models (LLMs) based autonomous agents demonstrate multifaceted capabilities to contribute substantially to economic production. However, existing benchmarks remain focused on single ag…
arXiv:2604.08016v2 Announce Type: replace
Abstract: Regardless of its foundational role in human discovery and sense-making, abductive reasoning–the inference of the most plausible explanation for an observation–has been relatively underexplored in …
arXiv:2604.12867v4 Announce Type: replace
Abstract: As agentic foundation models continue to evolve, how to further improve their performance in vertical domains has become an important challenge. To this end, building upon Tongyi DeepResearch, a powe…
arXiv:2604.20300v2 Announce Type: replace
Abstract: For LLM agents, memory management critically impacts efficiency, quality, and security. While much research focuses on retention, selective forgetting–inspired by human cognitive processes (hippocam…
arXiv:2507.01829v2 Announce Type: replace-cross
Abstract: Multi-timescale sequence modeling relies on capturing both local fast dynamics and global slow context; yet, maintaining these capabilities under the strict memory constraints common to edge de…
arXiv:2604.21508v1 Announce Type: new
Abstract: Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated bioactivity extraction …
arXiv:2505.11336v4 Announce Type: replace
Abstract: Despite the growing adoption of large language models (LLMs) in academic workflows, their capabilities remain limited in supporting high-quality scientific writing. Most existing systems are designed…
arXiv:2511.11439v2 Announce Type: replace-cross
Abstract: Binary security has increasingly relied on deep learning to reason about malware behavior and program semantics. However, the performance often degrades as threat landscapes evolve and code rep…
arXiv:2604.21584v1 Announce Type: new
Abstract: Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a target outcome while avoiding leakage, proxies, and po…
arXiv:2604.21251v1 Announce Type: cross
Abstract: Large language models (LLMs) trained on unfiltered corpora inherently risk retaining sensitive information, necessitating selective knowledge unlearning for regulatory compliance and ethical safety. Ho…