{"total":16,"items":[{"citing_arxiv_id":"2606.29727","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DeepTrans Studio: Turning Expert Interventions into Shared Team Knowledge in Agentic Translation Workflows","primary_cat":"cs.AI","submitted_at":"2026-06-29T03:08:43+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"DeepTrans Studio is a demo system that intercepts agentic translation workflows to let experts review, revise, and store decisions in shared team memory for propagation across segments and members.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.27274","ref_index":268,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"BetXplain: An Explanation-Annotated Dataset for Detecting Manipulative Betting Advertisements on Social Media","primary_cat":"cs.LG","submitted_at":"2026-06-25T16:51:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Introduces BetXplain, an explanation-annotated dataset of social media betting ads collected from Instagram and Reddit for detecting manipulative and deceptive advertising.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.21894","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Skills for the future software profession: beyond agentic AI!","primary_cat":"cs.SE","submitted_at":"2026-06-20T06:09:05+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":2.0,"formal_verification":"none","one_line_summary":"Round-table discussions with researchers and practitioners indicate verification and validation skills will become central for software engineers in the agentic AI era.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.18832","ref_index":118,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Target-confidence Recourse Using tSeTlin machines: TRUST","primary_cat":"cs.LG","submitted_at":"2026-06-17T09:07:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"TRUST searches for minimal input changes that achieve a user-defined confidence target in PTM models, claiming perfect robustness and low cost on benchmarks versus standard boundary-crossing methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06924","ref_index":114,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"From Sampled Outcomes to Capability Distributions: Rethinking Supervision for LLM Routing","primary_cat":"cs.LG","submitted_at":"2026-06-05T05:42:00+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"DARS replaces single-shot response labels with distribution-aware supervision derived from input and output uncertainty to produce more reliable LLM routing policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.06811","ref_index":106,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Dependencies and Dataflow in Seed-Filter-Extend Pipelines","primary_cat":"cs.PF","submitted_at":"2026-06-05T01:24:49+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"The paper analyzes dependencies in genome alignment pipelines and implements synthesized optimizations from four prior tools into LASTZ to reduce serial bottlenecks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23192","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Occlusion-Aware Physics-Semantic Keyframe Selection for Robust Video Editing","primary_cat":"cs.CV","submitted_at":"2026-05-22T03:19:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A new keyframe selection framework combines structural, tracking, and semantic criteria to select reliable anchor frames for diffusion-based video editing under occlusion.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21479","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"WikiVQABench: A Knowledge-Grounded Visual Question Answering Benchmark from Wikipedia and Wikidata","primary_cat":"cs.CV","submitted_at":"2026-05-20T17:58:24+00:00","verdict":"CONDITIONAL","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"WikiVQABench is a human-curated collection of Wikipedia-based VQA items that require both visual evidence and external knowledge from Wikidata to answer correctly.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20646","ref_index":93,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"DisImpact: Quantifying the Physi-Social Impact of Natural Disasters Through Social Media","primary_cat":"cs.SI","submitted_at":"2026-05-20T03:09:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DisImpact introduces a two-stage MLLM framework to classify disaster-related social media posts into ten impact categories and compute a unified physi-social impact index validated against FEMA and NASA ground-truth data.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16813","ref_index":97,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"QuadLink: Autoregressive Quad-Dominant Mesh Generation via Point-Relation Learning","primary_cat":"cs.GR","submitted_at":"2026-05-16T05:04:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"QuadLink generates anisotropic quad-dominant meshes from point clouds via autoregressive anchor prediction and centroid-conditioned linking, with a Tri-to-Quad data converter and quad-first assembly.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08583","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection","primary_cat":"cs.CL","submitted_at":"2026-05-09T00:53:24+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":7.0,"formal_verification":"none","one_line_summary":"CiteTracer detects citation hallucinations at 97.1% accuracy on synthetic and real-world benchmarks by combining structured extraction, multi-source retrieval, deterministic matching, and class-specialist agents.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"Moreover, bibliography styles vary widely across papers, and even references within the same paper may exhibit different surface formats. As a result, purely rule-based extraction is often brittle and difficult to scale across bibliography styles, and learning-based approaches such as soft-constrained citation field extractors trained on the UMass Citations corpus [4, 3] still leave residual character-level errors that propagate into downstream verification. To address these issues, we use the OCR model as a high-recall citation-block proposer rather than as the final parser. Let Mocr denote the OCR model. Given the bibliography region Pbib of an input paperP, the OCR model returns citation blocks together with their initial transcriptions:"},{"citing_arxiv_id":"2605.04524","ref_index":91,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"High-Fidelity Single-Image Head Modeling with Industry-Grade Topology","primary_cat":"cs.CV","submitted_at":"2026-05-06T06:07:35+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A single-image head reconstruction method uses coarse-to-fine optimization with normal consistency, landmarks, and geometry-aware constraints on curvature and conformality to produce meshes with industry-grade topology and preserved facial identity.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.00658","ref_index":243,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"UniVidX: A Unified Multimodal Framework for Versatile Video Generation via Diffusion Priors","primary_cat":"cs.CV","submitted_at":"2026-05-01T13:40:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"UniVidX unifies diverse video generation tasks into one conditional diffusion model using stochastic condition masking, decoupled gated LoRAs, and cross-modal self-attention.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.19351","ref_index":91,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Detecting Hallucinations for Large Language Model-based Knowledge Graph Reasoning","primary_cat":"cs.CL","submitted_at":"2026-04-27T12:20:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"LUCID detects hallucinations in LLM-KG reasoning by extracting node/edge features from attention and semantics then integrating them with KG structure in a GNN, achieving SOTA on nine new benchmark datasets versus 15 baselines.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17325","ref_index":91,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Align Documents to Questions: Question-Oriented Document Rewriting for Retrieval-Augmented Generation","primary_cat":"cs.CL","submitted_at":"2026-04-19T08:39:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"QREAM rewrites documents to question-focused style using iterative ICL and distilled FT models, boosting RAG performance by up to 8% relative improvement.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16294","ref_index":99,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Are Researchers Being Replaced by Artificial Intelligence?","primary_cat":"cs.CY","submitted_at":"2026-04-14T19:07:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":3.0,"formal_verification":"none","one_line_summary":"AI is shifting researchers from creators to curators of generated content, risking loss of intellectual ownership and genuine understanding of science.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}