Episodic memory allows LLM agents to accumulate and retrieve experience, but current methods treat each memory independently, i.e., evaluating retrieval quality in isolation without accounting for the dependency chains through which memories enable the creation of future memories. We introduce MemQ, which applies TD($\lambda$) eligibility traces to memory Q-values, propagating credit backward thro
Operational plan generation and verification are critical for modern complex and rapidly changing battlefield environments, yet traditional generation and verification methods still respectively face the challenges of generation infeasibility and verification insufficiency. To alleviate these limitations, we propose an Integrated Multi-Agent Framework for Generative Operational Planning and High-F
Maximizing performance on available GPU hardware is an ongoing challenge for modern AI inference systems. Traditional approaches include writing custom GPU kernels and using specialized model compilers to tune high-level code for specific GPU targets. Recent work shows that LLM-based multi-agent systems can effectively perform such tuning, often outperforming existing compilers and eliminating the
Chinese demonstrates high semantic compactness and rich metaphorical expressiveness, enabling limited text to convey dense meanings while increasing the difficulty of generation and verification, particularly in short-form creative natural language generation (CNLG). In the real world, users often require personalized, fine-grained creative constraints, making reliable verification critical to gui
Label-free single-cell imaging offers a scalable, non-invasive alternative to fluorescence-based cytometry, yet inferring molecular phenotypes directly from bright-field morphology remains challenging. We present a unified Deep Learning (DL) framework that jointly performs White Blood Cell (WBC) classification and continuous protein-expression regression from label-free Differential Phase Contrast
Capital is still concentrating in unusually large pools rather than a broad spread of venture rounds. The cycle points to money moving through institutional or balance-sheet channels more than startup formation.
Capital is still concentrating in unusually large pools rather than a broad spread of venture rounds. The cycle points to money moving through institutional or balance-sheet channels more than startup formation.
Capital is still concentrating in unusually large pools rather than a broad spread of venture rounds. The cycle points to money moving through institutional or balance-sheet channels more than startup formation.
Named movement is thin, but the desk still flags a leadership change tied to a major platform team. That kind of move matters when it shifts decision-making power inside an already central organization.
Named movement is thin, but the desk still flags a leadership change tied to a major platform team. That kind of move matters when it shifts decision-making power inside an already central organization.
Named movement is thin, but the desk still flags a leadership change tied to a major platform team. That kind of move matters when it shifts decision-making power inside an already central organization.
One system posted results across three evaluation tiers, making the benchmark desk the clearest signal of the cycle. The spread across levels matters more than any single score because it shows where capability is extending versus stalling.
One system posted results across three evaluation tiers, making the benchmark desk the clearest signal of the cycle. The spread across levels matters more than any single score because it shows where capability is extending versus stalling.
One system posted results across three evaluation tiers, making the benchmark desk the clearest signal of the cycle. The spread across levels matters more than any single score because it shows where capability is extending versus stalling.