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
Late-stage money dominates the cycle, with one AI growth round far larger than the rest of the slate. The spread also shows how capital can be diluted by unrelated large allocations in the same feed.
Late-stage money dominates the cycle, with one AI growth round far larger than the rest of the slate. The spread also shows how capital can be diluted by unrelated large allocations in the same feed.
Late-stage money dominates the cycle, with one AI growth round far larger than the rest of the slate. The spread also shows how capital can be diluted by unrelated large allocations in the same feed.
Named moves are sparse, but one legal-side hire and one long-tenured OpenAI-linked transition show how even small personnel shifts can matter when they touch high-stakes teams. The rest of the desk is too thin to suggest broader hiring momentum.
Named moves are sparse, but one legal-side hire and one long-tenured OpenAI-linked transition show how even small personnel shifts can matter when they touch high-stakes teams. The rest of the desk is too thin to suggest broader hiring momentum.
Named moves are sparse, but one legal-side hire and one long-tenured OpenAI-linked transition show how even small personnel shifts can matter when they touch high-stakes teams. The rest of the desk is too thin to suggest broader hiring momentum.
A single model family posts three fresh scores on one benchmark suite, making this a surface-level update rather than a leaderboard shakeup. The signal is in coverage breadth across difficulty tiers, not in a crowded field of new top entries.
A single model family posts three fresh scores on one benchmark suite, making this a surface-level update rather than a leaderboard shakeup. The signal is in coverage breadth across difficulty tiers, not in a crowded field of new top entries.
A single model family posts three fresh scores on one benchmark suite, making this a surface-level update rather than a leaderboard shakeup. The signal is in coverage breadth across difficulty tiers, not in a crowded field of new top entries.