pith. sign in

super hub Mixed citations

librosa/librosa: 0.6.3

Mixed citation behavior. Most common role is background (55%).

192 Pith papers citing it
Background 55% of classified citations

hub tools

citation-role summary

background 15 method 9 other 3 baseline 1 extension 1

citation-polarity summary

co-cited works

clear filters

representative citing papers

Kinetic energy from the cubic sum rule of the dynamic structure factor

physics.plasm-ph · 2026-06-29 · unverdicted · novelty 7.0

The cubic sum rule of S(q,ω) is tested as a kinetic energy estimator using PIMC data and dielectric models for the uniform electron gas, confirming consistency with thermodynamics but exposing flaws in semi-classical approximations.

The $(1 + 1)$-EA in Dynamic Environments

cs.NE · 2026-06-11 · unverdicted · novelty 7.0

Proves sharp threshold on mutation parameter χ for (1+1)-EA on Dynamic Binary Value and Uniform weight dynamic linear problems, yielding O(n log n) runtime below threshold and 2^Ω(n) above, plus a second stagnation-distance threshold for the former.

Towards Event-Robust Acoustic Scene Classification

cs.SD · 2026-06-05 · unverdicted · novelty 7.0

Introduces ESAS benchmark dataset using LLM-assisted event injection into acoustic scenes, showing significant performance drops in existing ASC models.

The geometry of lunar gravitational wave detection

gr-qc · 2026-06-03 · unverdicted · novelty 7.0

Optimal SSB frame origin for LGWA cuts sampling time by 10x and tightens chirp mass and sky position constraints for stellar-mass binaries beyond LVK performance.

SMT-Based Active Learning of Weighted Automata

cs.FL · 2026-05-08 · unverdicted · novelty 7.0

An SMT-based active learning algorithm learns minimal nondeterministic weighted automata over arbitrary semirings, with partial correctness proofs, a sufficient termination condition, and experiments showing smaller models and fewer queries than baselines.

citing papers explorer

Showing 3 of 3 citing papers after filters.

  • Direct Preference Optimization: Your Language Model is Secretly a Reward Model cs.LG · 2023-05-29 · accept · none · ref 46

    DPO derives the optimal policy directly from human preferences via a reparameterized reward model, solving the RLHF objective with only a binary classification loss and no sampling or separate reward model.

  • RWKV: Reinventing RNNs for the Transformer Era cs.CL · 2023-05-22 · unverdicted · none · ref 2

    RWKV uses a linear attention mechanism to deliver Transformer-level performance with RNN-style inference efficiency, demonstrated at up to 14 billion parameters.

  • CodeT5+: Open Code Large Language Models for Code Understanding and Generation cs.CL · 2023-05-13 · conditional · none · ref 4

    CodeT5+ is a flexible encoder-decoder LLM family for code pretrained with diverse objectives on multilingual corpora and initialized from existing LLMs, achieving state-of-the-art results on code generation, completion, math programming, and retrieval tasks including new SoTA on HumanEval with the 1