{"total":13,"items":[{"citing_arxiv_id":"2607.00341","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning","primary_cat":"cs.CL","submitted_at":"2026-07-01T02:32:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DiscoLoop adds a discrete embedding channel to looped transformers to fix representational misalignment in two-hop reasoning, yielding near-perfect accuracy on synthetic tasks and better pretraining loss on real data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.07157","ref_index":31,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Think Fast: Estimating No-CoT Task-Completion Time Horizons of Frontier AI Models","primary_cat":"cs.AI","submitted_at":"2026-06-05T11:17:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Frontier AI models' no-CoT 50% task-completion time horizons have doubled yearly over six years, reaching over 3 minutes for GPT-5.5 with projections to 25 minutes by 2030.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08221","ref_index":43,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"NoisyCoconut: Counterfactual Consensus via Latent Space Reasoning","primary_cat":"cs.LG","submitted_at":"2026-05-06T13:58:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Injecting noise into LLM latent trajectories creates diverse reasoning paths whose agreement acts as a confidence signal for selective abstention, cutting error rates from 40-70% to under 15% on math tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22951","ref_index":56,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"The Power of Power Law: Asymmetry Enables Compositional Reasoning","primary_cat":"cs.AI","submitted_at":"2026-04-24T18:49:08+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Power-law data sampling creates beneficial asymmetry in the loss landscape that lets models acquire high-frequency skill compositions first, enabling more efficient learning of rare long-tail skills than uniform distributions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.21027","ref_index":102,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering","primary_cat":"cs.AI","submitted_at":"2026-04-22T19:18:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"HypEHR is a hyperbolic embedding model for EHR data that uses Lorentzian geometry and hierarchy-aware pretraining to answer clinical questions nearly as well as large language models but with much smaller size.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17458","ref_index":172,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval","primary_cat":"cs.AI","submitted_at":"2026-04-19T14:18:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"EHRAG constructs structural hyperedges from sentence co-occurrence and semantic hyperedges from entity embedding clusters, then applies hybrid diffusion plus topic-aware PPR to retrieve top-k documents, outperforming baselines on four datasets with linear indexing cost and zero token overhead.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.15529","ref_index":46,"ref_count":3,"confidence":0.9,"is_internal_anchor":false,"paper_title":"LACE: Lattice Attention for Cross-thread Exploration","primary_cat":"cs.AI","submitted_at":"2026-04-16T21:19:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LACE enables concurrent reasoning paths in LLMs to interact via lattice attention and a synthetic training pipeline, raising accuracy more than 7 points over independent parallel search.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":", Zhang, R., Xu, R., Zhu, Q., Ma, S., Wang, P., Bi, X., et al. Deepseek-r1: In- centivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025. Hong, L. and Page, S. E. Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proceedings of the National Academy of Sciences, 101 (46):16385-16389, 2004. Hsu, C.-J., Buffelli, D., McGowan, J., Liao, F.-T., Chen, Y .-C., Vakili, S., and Shiu, D.-s. Group think: Multiple concurrent reasoning agents collaborating at token level granularity.arXiv preprint arXiv:2505.11107, 2025. Huang, J., Chen, X., Mishra, S., Zheng, H. S., Yu, A. W., Song, X., and Zhou, D. 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