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Canes strike back in an instant classic Game 2: Gr...
The future is the past. In Game 2 of the Stanley Cup Final, the Carolina Hurricanes trailed by two goals, scored three straight to take a lead in the third, lost that lead and then won 4-3 in overtime against the Vegas Golden Knights. Both of the first two games really came alive once the clock was within the last five minutes.
GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks
arXiv:2506.16114v3 Announce Type: replace Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios. The majority of existing research efforts primarily concentrate on developing powerful item tokenizers or advancing LLM decoding strategies to attain superior performance. However, the critical fine-tuning step in GR frameworks, which is essential for...
Understanding Generative Recommendation with Semantic IDs from a Model-scaling View
arXiv:2509.25522v3 Announce Type: replace Abstract: Recent advancements in generative models have allowed the emergence of a promising paradigm for recommender systems (RS), known as Generative Recommendation (GR), which tries to unify rich item semantics and collaborative filtering signals. One popular modern approach is to use semantic IDs (SIDs), which are discrete codes quantized from the embeddings of modality encoders (e.g., large language or vision models), to represent items in an...
Gryphon: A Unified Architecture for Semantic-ID Generation and Item-Level Scoring in Industrial Recommendations
arXiv:2606.08604v1 Announce Type: new Abstract: Generative retrieval (GR) has become a scalable approach to candidate generation: each item is assigned a short hierarchical token sequence called a Semantic ID (SID), and the next item's SID is decoded autoregressively. A practical limitation is that the decoder's beam search optimizes the likelihood of token sequences, not the relevance of the underlying items. These objectives diverge when sequence likelihood is poorly calibrated due to beam...
AI Rater Discrimination Depends on Scoring Protocol in Complex Clinical Decision-Making
arXiv:2606.03198v1 Announce Type: new Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized. We address this gap through a factorial study of AI rater behavior in adult type 2 diabetes (T2D) pharmacotherapy at 12-month outpatient follow-up, a clinical task involving complex decision-making operationalized across seven evaluation...
Nonlocal Mean Field Schr\"{o}dinger Bridge with Learned Interactions
Announce Type: cross Abstract: The Schr\"odinger Bridge Problem constructs a stochastic process that connects an initial distribution to a terminal distribution with minimum energy. This work considers its mean-field extension, the Mean-Field Schr\"odinger Bridge, for interacting particle systems. With nonlocal interactions, evaluating the resulting particle-dependent distributional terms can scale quadratically with the population size, which makes large-scale problems intractable.
Lean 4 Machine-Verified Proof of P = NP via the Pedigree Polytope Membership Problem
arXiv:2606.03194v1 Announce Type: new Abstract: The Membership Problem for Pedigree Polytope (M3P) asks, given $X\in\mathbb{Q}^{\binom{n}{3}}$, whether $X\in\mathrm{conv}(P_n)$, where $P_n$ is the set of all pedigrees. A pedigree is a structured encoding of a Hamiltonian cycle construction in $K_n$. We establish that M3P is solvable in strongly polynomial time via a recursively constructed layered network $(N_k, R_k, \mu)$ and a multicommodity flow problem MCF$(k)$. The necessary and...
Investigating Energy Bounds of Analog Compute-in-Memory with Local Normalization
arXiv:2602.08081v2 Announce Type: replace Abstract: Modern edge AI workloads demand maximum energy efficiency, motivating the pursuit of analog Compute-in-Memory (CIM) architectures. Simultaneously, the popularity of Large-Language-Models (LLMs) drives the adoption of low-bit floating-point formats which prioritize dynamic range. However, the conventional direct-accumulation CIM accommodates floating-points by normalizing them to a shared widened fixed-point scale.
Peaks, lakes and coastal views: Here are the best trails in Europe to hike this summer
Lace up your hiking boots and swap crowded city breaks for volcanic landscapes, dramatic peaks and coastal trails across the continent. Europe has no shortage of awe-inspiring trails, whether you’re looking to tackle heart-pumping peaks or take a leisurely stroll through nature. If you’re struggling to pick where to go, AllTrails’ pick of the best trail destinations for 2026 is a good place to start.
Time-Aware Diffusion based on Preference Disentanglement for Generative Recommendation
arXiv:2606.01670v1 Announce Type: new Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indices (SIDs). Owing to the exceptional generative capabilities of diffusion models, a few pioneering works explore developing GRs with diffusion architectures as the backbone. However, a fatal limitation of existing diffusion-based GRs is that the diffusion process applies uniformly to all items...