Advances in neural information processing systems , volume=

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Showing 17 of 17 citing papers.

• Base Models Look Human To AI Detectors cs.CL · 2026-05-19 · unverdicted · none · ref 36

  Base model text evades AI detectors better than instruction-tuned text, and the HIP method strengthens this trade-off across model sizes.

• Evolutionary Negative Module Pruning for Better LoRA Merging cs.AI · 2026-04-20 · conditional · none · ref 22

  ENMP prunes negative LoRA modules via evolutionary search to boost merging performance to new state-of-the-art levels across language and vision tasks.

• MixTTA: Low-Rank Cross-Channel Mixing for Reliable Test-Time Adaptation cs.LG · 2026-06-26 · unverdicted · none · ref 36

  MixTTA equips normalization layers with low-rank cross-channel transformations plus decoupling and spectral projections to correct cross-channel structural changes during test-time adaptation.

• LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection cs.LG · 2026-06-02 · unverdicted · none · ref 50

  LiftQuant enables continuous bit-width LLM quantization via dimensional lifting and projection from a 1-bit lattice, allowing 2.4-bit compression of 70B models that outperforms fixed 2-bit baselines on identical hardware.

• Stories in Space: In-Context Learning Trajectories in Conceptual Belief Space cs.CL · 2026-05-12 · unverdicted · none · ref 190

  LLMs perform in-context learning as trajectories through a structured low-dimensional conceptual belief space, with the structure visible in both behavior and internal representations and causally manipulable via interventions.

• PERSA: Reinforcement Learning for Professor-Style Personalized Feedback with LLMs cs.AI · 2026-05-01 · unverdicted · none · ref 1

  PERSA combines RLHF with selective parameter-efficient updates to top transformer layers, raising style alignment scores from 35% to 96% on code feedback benchmarks while holding correctness near 100%.

• AGoQ: Activation and Gradient Quantization for Memory-Efficient Distributed Training of LLMs cs.CL · 2026-05-01 · unverdicted · none · ref 121 · 2 links

  AGoQ delivers up to 52% lower memory use and 1.34x faster training for 8B-32B LLaMA models by using near-4-bit adaptive activations and 8-bit gradients while preserving pretraining convergence and downstream accuracy.

• Are Large Language Models Economically Viable for Industry Deployment? cs.CL · 2026-04-21 · unverdicted · none · ref 41

  Small LLMs under 2B parameters achieve better economic break-even, energy efficiency, and hardware density than larger models on legacy GPUs for industrial tasks.

• Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling cs.LG · 2026-04-20 · unverdicted · none · ref 31

  AdaLeZO uses a non-stationary multi-armed bandit to adaptively allocate perturbation budget across layers in zeroth-order optimization and applies inverse probability weighting to reduce variance while preserving unbiased gradients, delivering 1.7x-3.0x wall-clock speedup on LLaMA and OPT models.

• TLoRA: Task-aware Low Rank Adaptation of Large Language Models cs.CL · 2026-04-20 · unverdicted · none · ref 29

  TLoRA jointly optimizes LoRA initialization via task-data SVD and sensitivity-driven rank allocation, delivering stronger results than standard LoRA across NLU, reasoning, math, code, and chat tasks while using fewer trainable parameters.

• The Illusion of Equivalency: Statistical Characterization of Quantization Effects in LLMs cs.AI · 2026-07-09 · conditional · none · ref 95

  Quantized LLMs diverge from their base models at the decision level even when accuracy is preserved, with query and key attention projections showing the greatest structural distortion under low-bit compression.

• Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR cs.LG · 2026-06-30 · unverdicted · none · ref 36

  Orthonormal initialization for LoRA in RLVR achieves the minimal gap to full fine-tuning, stabilizes training, and outperforms standard LoRA and prior variants on mathematical reasoning benchmarks.

• Text-Guided Multi-Scale Frequency Representation Adaptation cs.CV · 2026-05-05 · unverdicted · none · ref 36

  FreqAdapter adapts multimodal models by text-guided multi-scale fine-tuning in the frequency domain, claiming better performance and efficiency than signal-space PEFT methods.

• Commonsense Knowledge with Negation: A Resource to Enhance Negation Understanding cs.CL · 2026-04-21 · unverdicted · none · ref 15

  Augmenting commonsense knowledge corpora with negation produces over 2M new triples that benefit LLM negation understanding when used for pre-training.

• Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety cs.CL · 2026-05-13 · unverdicted · none · ref 28

  Fine-tuned 8B LLMs produce children's English reading stories with better difficulty control and safety than zero-shot GPT-4o and Llama 3.3 70B.

• Unlocking the Edge deployment and ondevice acceleration of multi-LoRA enabled one-for-all foundational LLM cs.DC · 2026-04-20 · unverdicted · none · ref 12

  A framework combines multi-LoRA runtime switching, multi-stream stylistic decoding, and Dynamic Self-Speculative Decoding with INT4 quantization to achieve 4-6x memory and latency gains for on-device inference of a one-for-all foundational LLM on Qualcomm chipsets.

• ICT-NLP at SemEval-2026 Task 3: Less Is More -- Multilingual Encoder with Joint Training and Adaptive Ensemble for Dimensional Aspect Sentiment Regression cs.CL · 2026-05-11 · unverdicted · none · ref 9

  A lightweight multilingual encoder system with joint training and adaptive ensemble achieves top-half rankings across datasets in SemEval-2026 dimensional aspect sentiment regression.