{"total":16,"items":[{"citing_arxiv_id":"2605.23286","ref_index":35,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Modeling the Quantum Photon Statistics in Hybrid Light-Matter Integrated Circuits","primary_cat":"quant-ph","submitted_at":"2026-05-22T06:58:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new modeling framework represents pulsed polariton waveguide dynamics as a dissipative bosonic quantum circuit to predict antibunching and sub-Poissonian statistics in single and multimode integrated circuit configurations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.10292","ref_index":119,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon 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Control","primary_cat":"eess.SY","submitted_at":"2025-11-07T23:02:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.18095","ref_index":48,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"MetaEmbed: Scaling Multimodal Retrieval at Test-Time with Flexible Late Interaction","primary_cat":"cs.IR","submitted_at":"2025-09-22T17:59:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"MetaEmbed trains fixed learnable Meta Tokens to produce granularity-organized multi-vector embeddings that support test-time scaling 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Manipulation","primary_cat":"cs.CV","submitted_at":"2025-07-17T03:48:57+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"AnyPos automates task-agnostic action collection and inverse-dynamics modeling with arm/end-effector decoupling plus a direction-aware decoder, delivering 51% higher test accuracy and 30-40% better success rates on bimanual tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2506.07969","ref_index":111,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"A Two-Phase Deep Learning Framework for Adaptive Time-Stepping in High-Speed Flow Modeling","primary_cat":"cs.LG","submitted_at":"2025-06-09T17:44:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ShockCast is a two-phase ML method that predicts adaptive timestep sizes to model high-speed flows with shocks more efficiently than fixed-step approaches.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2504.20472","ref_index":29,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Robustness via Referencing: Defending against Prompt Injection Attacks by Referencing the Executed Instruction","primary_cat":"cs.CR","submitted_at":"2025-04-29T07:13:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"The method prompts LLMs to output both answers and references to the executed instructions, then filters out any answers not linked to the original input instructions, reducing attack success rates to zero in tested scenarios while preserving utility.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.10471","ref_index":62,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Siamese Foundation Models for Crystal Structure Prediction","primary_cat":"cond-mat.mtrl-sci","submitted_at":"2025-03-13T15:44:16+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DAO pretrains Siamese diffusion-based models on stable/unstable crystal data to achieve 100% experimental match on Cr6Os2 and 2000x speedup over DFT on real superconductors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2502.04416","ref_index":28,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Analytical FFN-to-MoE Restructuring via Activation Pattern Analysis","primary_cat":"cs.LG","submitted_at":"2025-02-06T14:05:30+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"An analytical post-training method restructures FFNs into MoE by partitioning neurons based on activation patterns and building a router from statistics, achieving 1.17x speedup with minimal resources.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2402.17888","ref_index":55,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection","primary_cat":"cs.LG","submitted_at":"2024-02-27T21:02:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"ConjNorm reframes OOD detection score design as optimizing norm p in an exponential family density model via a Bregman divergence theorem, with a tractable Monte Carlo estimator, claiming SOTA gains on CIFAR-100 and ImageNet-1K.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2312.07104","ref_index":37,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"SGLang: Efficient Execution of Structured Language Model Programs","primary_cat":"cs.AI","submitted_at":"2023-12-12T09:34:27+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"SGLang is a new system that speeds up structured LLM programs by up to 6.4x using RadixAttention for KV cache reuse and compressed finite state machines for output decoding.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"In certain cases, with careful prompt engineering, the model can correctly match the template with high accuracy, saving us the latency and input costs of one API call. 6 Evaluation We evaluate the performance of SGLang across diverse LLM workloads. Subsequently, we conduct ablation studies and case studies to demonstrate the effectiveness of specific components. SGLang is implemented in PyTorch [37] with custom CUDA kernels from FlashInfer [59] and Triton [48]. 6.1 Setup Models. We test dense Llama-2 models [49], sparse mixture of experts Mixtral models [17], multi- modal LLaV A image [27] and video models [62], and API model OpenAI's GPT-3.5. For open-weight models, the number of parameters ranges from 7 billion to 70 billion, and we use float16 precision."},{"citing_arxiv_id":"2301.05217","ref_index":44,"ref_count":1,"confidence":0.55,"is_internal_anchor":false,"paper_title":"Progress measures for grokking via mechanistic interpretability","primary_cat":"cs.LG","submitted_at":"2023-01-12T18:56:49+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":8.0,"formal_verification":"none","one_line_summary":"Grokking arises from gradual amplification of a Fourier-based circuit in the weights followed by removal of memorizing components.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}