{"total":12,"items":[{"citing_arxiv_id":"2607.02498","ref_index":137,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Alleviating prior dependencies for DESI DR1 clustering fits through reparameterization","primary_cat":"astro-ph.CO","submitted_at":"2026-07-02T17:57:15+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Jeffreys prior over EFTofLSS coefficients mitigates projection effects in DESI DR1 power spectrum multipole fits, recentering posteriors for late-time expansion parameters.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.20658","ref_index":26,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Expected Free Energy-based Planning as Variational Inference","primary_cat":"cs.AI","submitted_at":"2026-06-09T08:09:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EFE-based planning is formulated as variational free energy minimization with epistemic priors, decomposing into expected plan costs plus a complexity term.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.04935","ref_index":30,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"What Type of Inference is Active Inference?","primary_cat":"cs.AI","submitted_at":"2026-06-03T14:24:53+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"EFE-based active inference planning is characterized as VFE on an augmented model plus entropy and planning corrections, with a derived message-passing implementation and grid-world validation.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.02410","ref_index":180,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Optimal sequential two-stage Bayes Factor Design for two-arm clinical Phase II Trials with binary Endpoints","primary_cat":"stat.ME","submitted_at":"2026-06-01T15:53:21+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"Derives exact operating characteristic corrections and a numerical search over sample sizes to obtain optimal two-stage Bayes factor designs for two-arm binary-endpoint phase II trials that minimize expected sample size under the null.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.06249","ref_index":48,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Finite-size general security for relativistic phase shift keying via variable-length quantum key distribution","primary_cat":"quant-ph","submitted_at":"2026-05-07T13:27:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"The paper proves finite-size general security for relativistic phase shift keying (RPSK) achieving secret key rates beyond 12 dB with 10^5 signals via entropy accumulation, Rényi leftover hashing, and conic optimization.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"Hence, this consideration can only keep or decrease the total secret key rate (in particular, due the information about¯u) providing without loss of generality a lower bound for the protocol yield. Applying this consideration on the single-round map, we finally find the equivalence between the relativistic protocol and DPSK in terms of their final states N(ρ¯u ASR) =N([id A⊗VS(ρA ¯S)]⊗ρ˜u R) (48) →N(ωASR) (49) =M(ωASR).(50) In the first line, we used the decomposition of the state according to the first beam splitter, in the second line the substitution(47)and in the third one the equivalence between the maps(31)and (36)for the relativistic protocol and DPSK respectively according to the state from(47). Thanks to this reduction, we can proceed with the remaining elements of the finite-length analysis and"},{"citing_arxiv_id":"2605.02181","ref_index":63,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Beyond collective fluctuations: probing micro-image swarms in lensed quasars with intensity interferometry","primary_cat":"astro-ph.CO","submitted_at":"2026-05-04T03:25:33+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"Intensity interferometry offers a way to measure micro-image swarm sizes in lensed quasars, revealing stellar and compact dark matter mass functions beyond collective intensity fluctuations.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"extragalactic scale. ACKNOWLEDGMENTS The authors thank Luke Weisenbach and Liliya Williams for their useful comments. AKM acknowledges support from the Start-up Grant IE/CARE-25-0305 pro- vided by the IISc, Bengaluru, India. This research has made use of NASA's Astrophysics Data System Biblio- graphic Services. The work utilizes the following software packages: Julia[63],LensFactory.jl. DA T A A V AILABILITY The data supporting the findings of this article can be generated using the code available at:https://github. com/akmeena766/GL_II. 6 https://research.ast.cam.ac.uk/lensedquasars/indiv/ B1422+231.html 16 [1] P. Schneider, J. Ehlers, and E. E. Falco,Gravitational Lenses(1992). [2] P. Tisserand, L. Le Guillou, C."},{"citing_arxiv_id":"2604.03700","ref_index":2,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Robust self-testing with CHSH mod 3","primary_cat":"math.OC","submitted_at":"2026-04-04T12:09:24+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":8.0,"formal_verification":"none","one_line_summary":"CHSH mod 3 reaches its exact maximal quantum value only with maximally entangled qutrit pairs (unique up to symmetry) and any strategy within ε of the optimum is O(√ε)-close to a direct sum of those optimal strategies.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"In principle, we can take all optimal statesψ i to be equal, which gives a state of the formϕ⊗ψopt withψ opt maximally entangled. However, because there are different optimal irreducible representations, this will not simplify equations (14) and (15). We provide the proof of the theorem in Appendix D, and give here a sketch of the proof. The proof follows the idea of the proof of [CMMN20, Lemma 2.4], compared to which the main differences are that we require robustness instead of exact equalities, and that there are multiple optimal irreducible representations. Sketch of the proof.ByLemma7,f A andf B are(ε, ψ)-representationsofG, sobyTheorem6, there is a local isometryU=U A ⊗U B such that ψ∗(fA(x)⊗f B(y)−U ∗ AτA(x)UA ⊗U ∗ BτB(y)UB)ψ≤ε Thenf A(x)⊗f B(y)ψ≈τ A ⊗τ BU ψ."},{"citing_arxiv_id":"2512.05210","ref_index":180,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"A Framework for Quantum Simulations of Energy-Loss and Hadronization in Non-Abelian Gauge Theories: SU(2) Lattice Gauge Theory in 1+1D","primary_cat":"quant-ph","submitted_at":"2025-12-04T19:31:09+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A quantum simulation framework is developed and demonstrated for energy loss and hadronization of a heavy quark in 1+1D SU(2) lattice gauge theory on 18 qubits of IBM hardware, with results matching classical simulations.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team. This work was enabled, in part, by the use of advanced computational, storage and networking infrastructure provided by the Hyak supercomputer system at the University of Washington.22 We have made extensive use of Wol- fram Mathematica [177], python [178, 179], julia [180], jupyter notebooks [181] in the Conda environment [182], and IBM's quantum programming environmentqiskit [154]. This research used resources of the National Energy Research Scientific Computing Center (NERSC), a Department of Energy Office of Science User Facility using NERSC award NP-ERCAP0032083. [1] K. Adcoxet al.(PHENIX), Formation of dense partonic matter in relativistic nucleus-nucleus collisions at RHIC: Exper-"},{"citing_arxiv_id":"2511.10584","ref_index":27,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Finite-size quantum key distribution rates from R\\'enyi entropies using conic optimization","primary_cat":"quant-ph","submitted_at":"2025-11-13T18:25:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"A general conic optimization solver computes finite-size QKD rates from Rényi entropies more reliably than prior Frank-Wolfe methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2509.13528","ref_index":42,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Evaluating the Limits of QAOA Parameter Transfer at High-Rounds on Sparse Ising Models With Geometrically Local Cubic Terms","primary_cat":"quant-ph","submitted_at":"2025-09-16T20:48:53+00:00","verdict":"CONDITIONAL","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Systematic numerical study of QAOA parameter transfer on heavy-hex Ising models with local cubic terms shows transferred angles from small instances yield improving expectation values up to 49 layers on instances up to 156 qubits, with hardware runs confirming gains up to p=10.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2305.01582","ref_index":98,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","primary_cat":"astro-ph.IM","submitted_at":"2023-05-02T16:31:35+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":5.0,"formal_verification":"none","one_line_summary":"PySR delivers a distributed evolutionary symbolic regression tool with a new EmpiricalBench for recovering historical scientific equations from data.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"discovery in a variety of fields. It is our hope that PySR will continue to grow as a community tool, and provide value to researchers, helping discover in- terpretable symbolic relationships in data and ulti- matelyleadingtonewinsights, theories, andadvance- ments in their respective fields. Acknowledgements This software was built in the Python [97] and Julia [98] programming languages. Direct dependencies of PySR include nUmPy [68], SymPy [67], SkLeaRn [99], and PanDaS [100], with export functionality provided by JaX [70] and Py- tORCh [69]. Key dependencies of SymbolicRe- gression.jl include /Ptim.JL [64], ,OOPVeCtORiZa- tiOn.JL [101], :ygOte.JL [102], and 3ymbOLiC5- tiLS.JL [103]. The packagesmatPLOtLib [104] and"},{"citing_arxiv_id":"1906.11199","ref_index":7,"ref_count":1,"confidence":0.98,"is_internal_anchor":true,"paper_title":"Deployable probabilistic programming","primary_cat":"cs.PL","submitted_at":"2019-06-20T15:17:02+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Design guidelines and a Go library (Infergo) for deploying probabilistic programming in production systems, with benchmark comparisons.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}