{"total":49,"items":[{"citing_arxiv_id":"2606.12405","ref_index":89,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Bounding the Effect of HOD Assumptions on Small-Scale Clustering Constraints","primary_cat":"astro-ph.CO","submitted_at":"2026-06-10T17:59:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"The fraction of AbacusSummit cosmologies excluded at 3σ by small-scale clustering multipoles drops from 81% to 25% when moving from fixed HOD parameters to broad marginalization over the five-parameter HOD model.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.31494","ref_index":14,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Consolidating Rewarded Perturbations for LLM Post-Training","primary_cat":"cs.CL","submitted_at":"2026-05-29T16:16:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CoRP consolidates reward-weighted perturbations into a single model via low-rank structure, improving base LLMs by 8.1 points on average while using one-tenth the budget of prior ensembles and one forward pass.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.29234","ref_index":4,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Rethinking Literature Search Evaluation: Deep Research Helps, and Human Citation Lists Are Not a Ground Truth","primary_cat":"cs.AI","submitted_at":"2026-05-28T01:50:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Deep bibliography expansion in literature search achieves high recall while human citations are found to have only 51% moderate relevance compared to 86-88% for AI methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.28596","ref_index":44,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Dark Quest II: A Wide-Coverage Neural Network Emulator of the Nonlinear Matter Power Spectrum Across Extended Cosmologies","primary_cat":"astro-ph.CO","submitted_at":"2026-05-27T15:15:26+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A neural network emulator trained on multi-resolution N-body simulations reproduces the nonlinear matter power spectrum to subpercent accuracy up to the Nyquist scale across an extended nine-dimensional cosmological parameter space.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.21179","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"KSOS-BO: Improving Sampling in Bayesian Optimization via Kernel Sum of Squares","primary_cat":"cs.CE","submitted_at":"2026-05-20T13:46:50+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"KSOS-BO improves acquisition function optimization in Bayesian optimization by casting it as a kernel sum of squares semidefinite program, outperforming Sobol, DE, and CMA-ES baselines on 10/15 benchmarks with 81% average regret reduction.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.17137","ref_index":6,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Latent Heuristic Search: Continuous Optimization for Automated Algorithm Design","primary_cat":"cs.AI","submitted_at":"2026-05-16T20:03:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Latent Heuristic Search performs continuous optimization over learned embeddings of heuristics, using normalizing flows and LLM prompting to discover competitive solvers for TSP, CVRP, KSP, and OBP.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16995","ref_index":23,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Low Stage High Order Explicit Runge--Kutta Methods via Q- and D-Conditions: General Theory and Efficient Recursive Construction","primary_cat":"math.NA","submitted_at":"2026-05-16T13:43:57+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16844","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Artificial Adaptive Intelligence: The Missing Stage Between Narrow and General Intelligence","primary_cat":"cs.AI","submitted_at":"2026-05-16T07:04:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"Proposes Artificial Adaptive Intelligence as the regime between narrow and general AI, defined by elimination of human-specified hyperparameters, and introduces an adaptivity index plus parametric minimality principle grounded in minimum description length.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16520","ref_index":88,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Recasts sampling-based nonconvex optimization as smoothed gradient descent to obtain non-asymptotic convergence guarantees and introduces the DIDA annealed algorithm that converges to the global optimum.","context_count":1,"top_context_role":"background","top_context_polarity":"unclear","context_text":")d+6 4 κ −d 2 0 exp(−1 2C2 E β λD2 τ). By taking the maximum ofPout (which occurs atPout = 1/e), we obtain the sufficient condition 33τ2≤(1−Cα)(3κ2 0)−(d 2 +1) (4eC 1.1 d+ 6 )d+6 4 exp(−1 2C2 E β λD2 τ). In other words, for all sufficiently larged, the sufficient condition is satisfied if (3κ2 0)−(d 2) (8eD 2 τ 9d )d 4 exp ( −1 2C2 E β λD2 τ ) >33τ2 (88) Subcase 1.1.b:t> 4κ2 0.For t> 4κ2 0, we use another bound onPout[0|t]from Equation (80), which leads to the following condition: 48τ2 log 1 Pout Pout≤(1−Cα) (4κ2 0)2 4κ2 0 + 2κ2 0 . By taking the maximum ofPout log(1/Pout)(which is1/e), we obtain the sufficient condition 18τ2≤(1−Cα) (4κ2 0)2 4κ2 0 + 2κ2 0 (89) where the factor1/ecomes from the fact thatPout log(1/Pout)≤1/efor allPout∈(0,1)."},{"citing_arxiv_id":"2605.15899","ref_index":145,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States","primary_cat":"cond-mat.dis-nn","submitted_at":"2026-05-15T12:30:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.18867","ref_index":22,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"EVA-0: Test-Time Model Evolution with Only Two Forward Passes per Sample","primary_cat":"cs.LG","submitted_at":"2026-05-15T09:26:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EVA-0 is a zeroth-order test-time adaptation method that uses scale-invariant loss, anchor-guided optimization, and symmetric two-sided perturbations to enable inference and adaptation in two forward passes, outperforming prior methods on ImageNet-C with ViT-Base.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12326","ref_index":5,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Black-Box Optimization of Mixed Binary-Continuous Variables: Challenges and Opportunities in Evolutionary Model Merging","primary_cat":"cs.NE","submitted_at":"2026-05-12T16:08:47+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Data flow space model merging is formalized as a mixed binary-continuous black-box optimization problem, where a structured approach respecting variable dependencies achieves 6.7% higher accuracy and 51.4% smaller search space than unstructured methods on real language models.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.09781","ref_index":29,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Parameter-Efficient Neuroevolution for Diverse LLM Generation: Quality-Diversity Optimization via Prompt Embedding Evolution","primary_cat":"cs.NE","submitted_at":"2026-05-10T22:00:15+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"fying base model weights, achieving competitive task performance with orders of magnitude fewer trainable parameters. P-Tuning v2 [41] demonstrated that deep prompt tuning across layers can match full fine-tuning performance. Parameter-efficient fine-tuning (PEFT) has emerged as a major paradigm for LLM adaptation [39]. LoRA [30] introduces low-rank weight updates, while adapter modules [29] insert small trainable layers. QLoRA [11] enables efficient fine-tuning of quantized 70B+ models on single GPUs. These methods demonstrate that effective LLM adaptation is possible with minimal parameter updates-our approach extends this insight to evolutionary optimization. Unlike discrete prompt optimization operating in token space with combinatorial complexity, soft prompts exist in continuous"},{"citing_arxiv_id":"2605.04531","ref_index":24,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Reward-Guided Semantic Evolution for Test-time Adaptive Object Detection","primary_cat":"cs.CV","submitted_at":"2026-05-06T06:17:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"RGSE adapts text embeddings at test time via evolutionary search, using cosine similarity rewards from high-confidence visual proposals to improve open-vocabulary object detection under distribution shifts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.27175","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Global Sampling-Based Trajectory Optimization for Contact-Rich Manipulation via KernelSOS","primary_cat":"cs.RO","submitted_at":"2026-04-29T20:24:41+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Global-MPPI integrates kernel SOS global search with MPPI local refinement and graduated non-convexity smoothing to achieve faster convergence and lower costs on high-dimensional contact-rich manipulation tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25458","ref_index":19,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Benchmarking Stopping Criteria for Evolutionary Multi-objective Optimization","primary_cat":"cs.NE","submitted_at":"2026-04-28T10:05:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Introduces a single-number performance measure, file-based benchmarking, and efficient text-file storage to evaluate and compare stopping criteria for EMO algorithms.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.25950","ref_index":22,"ref_count":4,"confidence":0.9,"is_internal_anchor":true,"paper_title":"A Complex-Valued Continuous-Variable Quantum Approximation Optimization Algorithm (CCV-QAOA)","primary_cat":"quant-ph","submitted_at":"2026-04-23T08:18:17+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CCV-QAOA is a new complex-valued continuous-variable variant of QAOA that solves real and complex multivariate optimization problems via a variational framework.","context_count":2,"top_context_role":"method","top_context_polarity":"use_method","context_text":"feasibility requirements, giving rise to complex chance-constrained programming (CCCP) [31]. Solving such problems typically relies on gradient-based schemes [5] or derivative-free methods. Among the latter, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) has emerged as particularly effective for highly nonconvex or black-box formulations, where the objective function is not analytically available [22, 23]. Quantum computing leverages superposition, interference, and entanglement to offer potential advantages in optimization, motivating variational algorithms such as the Variational Quantum Eigensolvers (VQE) [50]. However, most existing methods target qubit architectures, where out- puts are fundamentally discrete. As a result, continuous problems are typically reformulated as"},{"citing_arxiv_id":"2604.20639","ref_index":49,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Distributed Quantum-Enhanced Optimization: A Topographical Preconditioning Approach for High-Dimensional Search","primary_cat":"quant-ph","submitted_at":"2026-04-22T14:50:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"D-QEO framework uses quantum topographical preconditioning on separable functions via small parallel subcircuits to generate seeds that accelerate classical global optimization and avoid exponential failure rates.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.20336","ref_index":7,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Stability-Driven Motion Generation for Object-Guided Human-Human Co-Manipulation","primary_cat":"cs.CV","submitted_at":"2026-04-22T08:31:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A flow-matching model derives manipulation strategies from object affordance, adds an adversarial interaction prior, and uses stability simulation to generate natural, effective human-human co-manipulation motions.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.18196","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Similarity-based Portfolio Construction for Black-box Optimization","primary_cat":"cs.NE","submitted_at":"2026-04-20T12:48:28+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A k-nearest-neighbor approach constructs problem-specific algorithm portfolios that outperform both single solvers and the virtual best solver in fixed-budget black-box optimization.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17753","ref_index":13,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Evolutionary Negative Module Pruning for Better LoRA Merging","primary_cat":"cs.AI","submitted_at":"2026-04-20T03:13:18+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"ENMP prunes negative LoRA modules via evolutionary search to boost merging performance to new state-of-the-art levels across language and vision tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.17402","ref_index":10,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"On the Generalization Bounds of Symbolic Regression with Genetic Programming","primary_cat":"cs.LG","submitted_at":"2026-04-19T12:12:11+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Derives a generalization bound for GP-based symbolic regression that decomposes the gap into structure-selection complexity and constant-fitting complexity under tree constraints.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.13945","ref_index":63,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Optimal Majoranas in Mesoscopic Kitaev Chains","primary_cat":"cond-mat.mes-hall","submitted_at":"2026-04-15T14:57:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Microscopic treatment of the hybrid segment in mesoscopic Kitaev chains shows that Andreev bound state parity crossings define optimal sweet spots for localized Majoranas with large gaps.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.12834","ref_index":20,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Rapid LoRA Aggregation for Wireless Channel Adaptation in Open-Set Radio Frequency Fingerprinting","primary_cat":"eess.SP","submitted_at":"2026-04-14T14:53:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"LoRA pretraining per environment plus weighted aggregation at inference cuts EER by 15% and training time by 83% for open-set RFF authentication under varying channels.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.11090","ref_index":34,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Simulator Adaptation for Sim-to-Real Learning of Legged Locomotion via Proprioceptive Distribution Matching","primary_cat":"cs.RO","submitted_at":"2026-04-13T07:10:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Proprioceptive distribution matching adapts simulators for legged robot policies by comparing observation and action distributions, reducing sim-to-real gaps with minimal real data and no external sensing.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.10351","ref_index":40,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Trajectory-based actuator identification via differentiable simulation","primary_cat":"cs.RO","submitted_at":"2026-04-11T21:36:34+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Differentiable simulation enables torque-sensor-free actuator model identification from trajectory data, achieving 1.88x better position tracking than a stand-trained baseline and 46% longer travel in downstream locomotion policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.09095","ref_index":14,"ref_count":2,"confidence":0.98,"is_internal_anchor":true,"paper_title":"GeoPAS: Geometric Probing for Algorithm Selection in Continuous Black-Box Optimization","primary_cat":"cs.LG","submitted_at":"2026-04-10T08:24:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"GeoPAS uses multi-scale 2D geometric slices of optimization landscapes with validity-mask pooling and a learned-plus-prior composite score to select from 12 solvers, cutting mean relative expected running time from 30.37 to around 3.1-3.6 on within-suite benchmarks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.08508","ref_index":14,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"Sumo: Dynamic and Generalizable Whole-Body Loco-Manipulation","primary_cat":"cs.RO","submitted_at":"2026-04-09T17:49:40+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Test-time steering of pre-trained whole-body policies via sample-based planning lets legged robots generalize dynamic loco-manipulation to varied heavy objects and tasks without additional training or tuning.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"creative ways: Their ability to gracefully make and break contact with their environment enables them to traverse treach- erous terrain and move objects several times larger than themselves. Achieving similar levels of athletic intelligence for robotic systems has been a long-standing challenge. Over the past decade, developments in numerical optimization [9, 11, 4] and machine learning [14, 7] have made significant progress towards building robots with agility and dexterity [38, 33]. The next research frontier lies in enabling robots to manipulate objects during locomotion, or so-called loco-manipulation. So far, research on loco-manipulation has largely focused on learning from human demonstrations through teleoperation or video imitation, which are usually limited to quasi static table-"},{"citing_arxiv_id":"2604.02730","ref_index":12,"ref_count":1,"confidence":0.9,"is_internal_anchor":true,"paper_title":"PhDLspec: physical-prior embedded deep learning method for spectroscopic determination of stellar labels in high-dimensional parameter space","primary_cat":"astro-ph.GA","submitted_at":"2026-04-03T04:44:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"PhDLspec combines differential spectra from physical stellar models with a transformer to derive approximately 30 stellar parameters from low-resolution spectra hundreds of times faster than traditional calculations.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"timal individuals of theg-th generation to update the algorithm's strategy parameters, including the step sizeσ, mean valuem, and covariance matrix C. Then, generate individuals in the next gener- ation distribution using mutation operations based on these parameters. The updating expression ofσ, m, andCis as follows: σ(g+1) =σ (g) exp( cσ dσ ( ∥p(g+1) σ ∥ E∥N(0, I)∥ −1)) (12) m(g+1) = µX i=1 wix(g+1) i:λ (13) C(g+1) = (1−c µ X wi)C(g) +c µ λX i=1 wiy(g+1) i:λ y(g+1)T i:λ (14) The fitting procedure is formulated as the minimiza- tion of a reducedχ 2 objective function that quantifies the difference between the observed and model spectra: X 2(θ) = 1 n nX i=1 \u0012 fobs,i(λ)−f i(λ;θ) σi(λ) \u00132 , (15) wheref obs(λ) is the observed spectrum,f(λ;θ) is the"},{"citing_arxiv_id":"2602.17517","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Depth Augmented and FE Free 3D/2D Liver Registration for Laparoscopic Liver AR","primary_cat":"cs.CV","submitted_at":"2026-02-19T16:31:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A depth-augmented rigid pose refinement combined with a patient-specific statistical deformation model from NICP correspondences achieves 14.73 mm mean TRE for 3D-2D liver registration in controlled laparoscopic settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.11398","ref_index":13,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Evolution With Purpose: Hierarchy-Informed Optimization of Whole-Brain Models","primary_cat":"cs.NE","submitted_at":"2026-02-11T22:03:37+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Hierarchy-informed curricular optimization of heterogeneous whole-brain models enables generalization to new subjects and prediction of behavioral abilities from parameters.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.09368","ref_index":16,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Certified Gradient-Based Contact-Rich Manipulation via Smoothing-Error Reachable Tubes","primary_cat":"cs.RO","submitted_at":"2026-02-10T03:19:42+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"A certified gradient-based method for contact-rich manipulation that quantifies smoothing-induced errors via set-valued discrepancies and incorporates them into analytical reachable sets for robust affine feedback policies.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2512.24497","ref_index":35,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?","primary_cat":"cs.AI","submitted_at":"2025-12-30T22:50:03+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An empirical study of JEPA world models 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