Sumi is an openly released 7B parameter uniform diffusion language model pretrained from scratch on 1.5T tokens that matches autoregressive models on several benchmarks.
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88
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representative citing papers
BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.
Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
DPG lets researchers shape language model properties like weight patterns or output behaviors by optimizing synthetic training data via higher-order gradient rewards in a policy gradient setup.
LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
TASA improves task-aware mixed-precision LLM quantization by searching calibration data mixtures via gradient-trace alignment and aggregating perplexity plus reasoning sensitivity signals, enabling 3.5-bit models to match or beat 4-bit baselines with over 20-point gains on GSM8K.
DiscoLoop adds a decoded token-embedding channel to looped transformers, fixing a representation mismatch that limited implicit multi-hop reasoning and improving OOD generalization.
FlashMorph formulates hybrid layer selection as budget-constrained optimization, trains per-layer gates on synthetic retrieval data with linearization regularization, then discretizes and distills to produce efficient hybrid architectures.
AR-OPD disentangles privileged supervision via anchored residual guidance to reduce hindsight leakage in on-policy distillation, reporting gains of 2.3 points over full privileged OPD and 7.9 over SFT on reasoning tasks.
STAR-KV applies differentiable soft thresholding for per-head and per-block adaptive low-rank KV cache compression, combined with hybrid decomposition and low-rank-aware quantization, achieving up to 75% compression and 3.1x throughput gains.
ATWU jointly optimizes model parameters and token weights via a linear scorer on hidden states, recovering oracle forget-specific tokens under a separation condition and achieving SOTA forget-retain trade-offs on TOFU and RWKU.
SubFit enables better LLM compression by fitting residual bypasses to non-contiguously selected submodules, outperforming layer-granularity baselines in accuracy-perplexity trade-offs at 12.5-37.5% sparsity.
A hybrid first-order then zeroth-order optimization approach improves robustness of safety-aligned LLMs while preserving utility, with layer-wise sensitivity estimation for efficiency.
Logit composition of autoregressive models is projective under factorized conditionals, preserved under smooth reparameterizations, and maintains length generalization when assumptions hold uniformly.
Decoupling prefix source from token-level KL direction in autoregressive sequence KL yields four objectives unifying SFT, DAgger, offline RL and OPD, with KL mixing and entropy-gated curriculum improving math reasoning accuracy and shortening responses.
CR^2 matches full-information routing performance for device-edge LLM inference using only device-side signals and cuts normalized deployment cost by up to 16.9% at matched accuracy.
A binomial multibit watermarking scheme encodes every payload bit at each LLM token with dynamic redirection, outperforming baselines in accuracy and robustness for large payloads.
A new watermarking method for closed LLMs boosts random word-pair co-occurrences via rephrasing and detects the signal statistically in outputs, working reliably even when the watermarked data is only 1% of fine-tuning tokens while preserving utility.
RangeGuard uses range identifiers to enable bounded approximate error correction, tolerating 64+ bit flips with 16-bit parity and no noticeable accuracy loss in DNN inference.
Retrieving structured thinking traces as a corpus improves reasoning performance on AIME, LiveCodeBench, and GPQA over standard RAG or no retrieval.
A single 15B supernet checkpoint supports runtime switching between attention mixer placements for multiple decode speed presets while retaining 77-96% quality relative to the teacher model.
DEMASK adds a lightweight pairwise-dependency predictor to dLLMs and uses greedy selection to enable parallel unmasking whose total-variation error is provably bounded under sub-additivity.
Top-W applies Wasserstein-regularized truncation on token-embedding geometry to create a closed-form optimal crop for LLM sampling that outperforms prior methods by up to 33.7% on GSM8K, GPQA, AlpacaEval, and MT-Bench.
PRIMETIME generator reveals that LLM datetime parsing and arithmetic primitives are individually unreliable but fully learnable via fine-tuning, enabling frontier-level accuracy on event planning with small LoRA models.
citing papers explorer
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Sumi: Open Uniform Diffusion Language Model from Scratch
Sumi is an openly released 7B parameter uniform diffusion language model pretrained from scratch on 1.5T tokens that matches autoregressive models on several benchmarks.
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BoLT: A Benchmark to Democratize Black-box Optimization Research for Expensive LLM Tasks
BoLT is a benchmark of surrogate models fitted to real LLM experiment data that enables evaluation of Bayesian and black-box optimization methods on multi-fidelity, multi-objective, high-dimensional LLM tasks.
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Grid Games: The Power of Multiple Grids for Quantizing Large Language Models
Allowing each quantization group to select among multiple 4-bit grids improves accuracy over single-grid FP4 for both post-training and pre-training of LLMs.
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Synthetic Data for any Differentiable Target
DPG lets researchers shape language model properties like weight patterns or output behaviors by optimizing synthetic training data via higher-order gradient rewards in a policy gradient setup.
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Large Language Diffusion Models
LLaDA is a scalable diffusion-based language model that matches autoregressive LLMs like LLaMA3 8B on tasks and surpasses GPT-4o on reversal poem completion.
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Beyond Activation Alignment:The Alignment-Diversity Tradeoff in Task-Aware LLM Quantization
TASA improves task-aware mixed-precision LLM quantization by searching calibration data mixtures via gradient-trace alignment and aggregating perplexity plus reasoning sensitivity signals, enabling 3.5-bit models to match or beat 4-bit baselines with over 20-point gains on GSM8K.
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DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoning
DiscoLoop adds a decoded token-embedding channel to looped transformers, fixing a representation mismatch that limited implicit multi-hop reasoning and improving OOD generalization.
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Morphing into Hybrid Attention Models
FlashMorph formulates hybrid layer selection as budget-constrained optimization, trains per-layer gates on synthetic retrieval data with linearization regularization, then discretizes and distills to produce efficient hybrid architectures.
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Beyond Absolute Imitation: Anchored Residual Guidance for Privileged On-Policy Distillation
AR-OPD disentangles privileged supervision via anchored residual guidance to reduce hindsight leakage in on-policy distillation, reporting gains of 2.3 points over full privileged OPD and 7.9 over SFT on reasoning tasks.
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STAR-KV: Low-Rank KV Cache Compression via Soft Thresholding for Adaptive Rank Control
STAR-KV applies differentiable soft thresholding for per-head and per-block adaptive low-rank KV cache compression, combined with hybrid decomposition and low-rank-aware quantization, achieving up to 75% compression and 3.1x throughput gains.
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Learning What to Forget: Improving LLM Unlearning via Learned Token-Level Importance
ATWU jointly optimizes model parameters and token weights via a linear scorer on hidden states, recovering oracle forget-specific tokens under a separation condition and achieving SOTA forget-retain trade-offs on TOFU and RWKU.
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From Layers to Submodules: Rethinking Granularity in Replacement-Based LLM Compression
SubFit enables better LLM compression by fitting residual bypasses to non-contiguously selected submodules, outperforming layer-granularity baselines in accuracy-perplexity trade-offs at 12.5-37.5% sparsity.
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Aligned but Fragile: Enhancing LLM Safety Robustness via Zeroth-Order Optimization
A hybrid first-order then zeroth-order optimization approach improves robustness of safety-aligned LLMs while preserving utility, with layer-wise sensitivity estimation for efficiency.
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Compositional Generalization in Autoregressive Models via Logit Composition
Logit composition of autoregressive models is projective under factorized conditionals, preserved under smooth reparameterizations, and maintains length generalization when assumptions hold uniformly.
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Decoupling KL and Trajectories: A Unified Perspective for SFT, DAgger, Offline RL, and OPD in LLM Distillation
Decoupling prefix source from token-level KL direction in autoregressive sequence KL yields four objectives unifying SFT, DAgger, offline RL and OPD, with KL mixing and entropy-gated curriculum improving math reasoning accuracy and shortening responses.
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CR^2: Cost-Aware Risk-Controlled Routing for Wireless Device-Edge LLM Inference
CR^2 matches full-information routing performance for device-edge LLM inference using only device-side signals and cuts normalized deployment cost by up to 16.9% at matched accuracy.
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Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark
A binomial multibit watermarking scheme encodes every payload bit at each LLM token with dynamic redirection, outperforming baselines in accuracy and robustness for large payloads.
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Dataset Watermarking for Closed LLMs with Provable Detection
A new watermarking method for closed LLMs boosts random word-pair co-occurrences via rephrasing and detects the signal statistically in outputs, working reliably even when the watermarked data is only 1% of fine-tuning tokens while preserving utility.
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RangeGuard: Efficient, Bounded Approximate Error Correction for Reliable DNNs
RangeGuard uses range identifiers to enable bounded approximate error correction, tolerating 64+ bit flips with 16-bit parity and no noticeable accuracy loss in DNN inference.
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RAG over Thinking Traces Can Improve Reasoning Tasks
Retrieving structured thinking traces as a corpus improves reasoning performance on AIME, LiveCodeBench, and GPQA over standard RAG or no retrieval.
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Super Apriel: One Checkpoint, Many Speeds
A single 15B supernet checkpoint supports runtime switching between attention mixer placements for multiple decode speed presets while retaining 77-96% quality relative to the teacher model.
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Dependency-Guided Parallel Decoding in Discrete Diffusion Language Models
DEMASK adds a lightweight pairwise-dependency predictor to dLLMs and uses greedy selection to enable parallel unmasking whose total-variation error is provably bounded under sub-additivity.
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Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models
Top-W applies Wasserstein-regularized truncation on token-embedding geometry to create a closed-form optimal crop for LLM sampling that outperforms prior methods by up to 33.7% on GSM8K, GPQA, AlpacaEval, and MT-Bench.
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PRIMETIME : Limits of LLMs in Temporal Primitives
PRIMETIME generator reveals that LLM datetime parsing and arithmetic primitives are individually unreliable but fully learnable via fine-tuning, enabling frontier-level accuracy on event planning with small LoRA models.
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Improving LLM Unlearning Robustness via Random Perturbations
LLM unlearning is reframed as inadvertently installing backdoor triggers on forget-tokens; Random Noise Augmentation is introduced as a defense that improves robustness with theoretical guarantees.
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Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision
Fixed counterfactual explanation datasets train LMs such that generated explanations track the model's evolving behavior rather than the fixed targets, due to persistent correlation during training.
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End-to-End Dynamic Sparsity for Resource-Adaptive LLM Inference
L2A trains one LLM with input-and-budget-conditioned gates to adapt sparsity across layers, heads, and tokens, tracing the compute-accuracy frontier while staying within 0.6% of dense performance at 34% layer sparsity on tested models.
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Bifocal Diffusion Language Models: Asymmetric Bidirectional Context for Parallel Generation
R2LM combines causal attention with a reverse Mamba SSM sidecar to supply right-side context in dLLMs, claiming 2.4x-12.9x throughput gains over bidirectional dLLMs and 1.9x-2.9x over AR baselines while matching or exceeding quality.
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SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference
SharQ combines input-adaptive N:M sparsity and FP4 quantization via sparse backbone plus dense residual, recovering 43-63% of the NVFP4-to-FP16 accuracy gap on Llama and Qwen models without calibration or retraining.
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Bridging Spherical Black-Box Optimizers
Unifies ES, CBO, and OVI black-box optimizers via two design axes and proposes hybrid methods that outperform base algorithms on benchmarks.
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Don't Go Breaking My LLM: The Impact of Pruning Attention Layers on Explanation Faithfulness and Confidence Calibration
Pruning attention layers in five LLMs across eight datasets maintains accuracy but degrades faithfulness and calibration.
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Variable-Width Transformers
×-shaped variable-width transformers outperform parameter-matched uniform baselines on language modeling loss with 22% fewer FLOPs and 15% smaller KV cache.
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AlphaQ: Calibration-Free Bit Allocation for Mixture-of-Experts Quantization
AlphaQ performs calibration-free mixed-precision quantization of MoE models by allocating higher bits to experts whose weight spectra exhibit stronger heavy-tailed structure according to HT-SR theory, outperforming calibration-based methods and reaching near full-precision accuracy at 3.5 average bi
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Fixed-Point Masked Generative Modeling
FP-MGMs with consistency loss and three-state reuse (CoFRe) reduce parameters by up to 38.8% and improve low-budget perplexity and FID versus standard masked generative models on text and images.
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GDSD: Reinforcement Learning as Guided Denoiser Self-Distillation for Diffusion Language Models
GDSD reduces RL for dLLMs to likelihood-free self-distillation via a normalization-free logit-matching objective, outperforming ELBO methods with more stable training on LLaDA-8B and Dream-7B.
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MobileMoE: Scaling On-Device Mixture of Experts
MobileMoE introduces on-device MoE LLMs that match dense models with 2-4x fewer FLOPs and provide efficient smartphone inference.
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GEMQ: Global Expert-Level Mixed-Precision Quantization for MoE LLMs
GEMQ applies global LP-based expert importance estimation and router fine-tuning within progressive quantization to cut memory and speed inference in MoE LLMs with little accuracy loss.
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Full Attention Strikes Back: Transferring Full Attention into Sparse within Hundred Training Steps
RTPurbo converts full-attention LLMs to sparse attention by retaining full KV for retrieval heads and using a low-dimensional dynamic indexer, achieving near-lossless accuracy after minimal adaptation.
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EMO: Frustratingly Easy Progressive Training of Extendable MoE
EMO progressively expands the expert pool in MoE models during training to match fixed-expert performance with improved wall-clock efficiency.
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Not How Many, But Which: Parameter Placement in Low-Rank Adaptation
Gradient-informed placement of LoRA parameters recovers full performance under GRPO while random placement does not, due to differences in gradient rank and stability across training regimes.
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Compute Where it Counts: Self Optimizing Language Models
SOL trains a policy to dynamically control multiple efficiency mechanisms per token via group-relative policy optimization on teacher-forced episodes, yielding better quality at matched average budget than static or random allocation.
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StoSignSGD: Unbiased Structural Stochasticity Fixes SignSGD for Training Large Language Models
StoSignSGD resolves SignSGD divergence on non-smooth objectives via structural stochasticity, matching optimal convex rates and improving non-convex bounds while delivering 1.44-2.14x speedups in FP8 LLM pretraining.
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(How) Learning Rates Regulate Catastrophic Overtraining
Pretraining LR decay sharpens LLMs, and that sharpness—not just token count—drives catastrophic forgetting during supervised fine-tuning.
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In-Place Test-Time Training
In-Place TTT adapts LLM MLP projection matrices at test time with a next-token-aligned objective and chunk-wise updates, enabling better long-context performance as a drop-in enhancement.
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Exclusive Unlearning
Exclusive Unlearning makes LLMs safe by forgetting all but retained domain knowledge, protecting against jailbreaks while preserving useful responses in areas like medicine and math.
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M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling
M²RNN achieves perfect state tracking at unseen lengths and outperforms Gated DeltaNet hybrids by 0.4-0.5 perplexity on 7B models with 3x smaller recurrent states.
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InnerQ: Hardware-Aware Tuning-Free Quantization of KV Cache for Large Language Models
InnerQ delivers 1.3x average speedup over prior KV cache quantization and 2.7x over baseline by inner-dimension grouping, hybrid symmetric/asymmetric quantization, high-precision windows for recent and sink tokens, and prefold per-channel key normalization.
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RAT+: Train Dense, Infer Sparse -- Recurrence Augmented Attention for Dilated Inference
A single dense-pretrained RAT+ model can be adapted with 1B tokens to run at dilation sizes D=2..128, matching dense accuracy at D=16 and losing only 1-3 points at D=64 on reasoning and long-context benchmarks, though retrieval tasks drop far more.
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CrispEdit: Low-Curvature Projections for Scalable Non-Destructive LLM Editing
CrispEdit edits LLMs via low-curvature projections using Bregman divergence and K-FAC approximations, achieving high edit success with under 1% average capability degradation.
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When LLMs get significantly worse: A statistical approach to detect model degradations
A McNemar-based statistical test detects real degradations in optimized LLMs with controlled false positives, even for accuracy changes as small as 0.3%.