EADP: Entropy-aware visual token pruning for efficient VLMs under dense instructions
A new arXiv preprint introduces Entropy-Aware Dense Pruning (EADP), a framework for compressing visual tokens in vision-language models (VLMs) that addresses two failure modes in existing methods: textual noise corrupting cross-modal scoring and feature fragmentation from naive Top-K selection. EADP uses statistical entropy to filter textual noise and reformulates token selection as a submodular maximization problem with a spatial prior. The authors report state-of-the-art accuracy-efficiency trade-offs on multimodal benchmarks under strict token budgets.
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VEPO: Vision-anchored token selection improves RL for visual reasoning
A new arXiv paper identifies a failure mode of entropy-based credit assignment in multimodal reinforcement learning: vision-sensitive tokens with naturally low entropy are systematically ignored, causing the mechanism to collapse in visual reasoning tasks. The authors propose VEPO (Vision-Entropy token-selection for Policy Optimization), which couples visual sensitivity with token entropy via a multiplicative scheme to redirect gradient credit toward tokens that are both visually grounded and semantically informative. VEPO outperforms entropy-only baselines by 2.28 points at 7B scale and 3.15 points at 3B scale on visual reasoning benchmarks.
Reroute: Training-free recoverable visual token routing for vision-language models
A new arXiv preprint proposes Reroute, a training-free plug-in that replaces the standard rank-and-remove visual token pruning paradigm in VLMs with a recoverable routing mechanism. Instead of permanently discarding low-ranked tokens, Reroute defers them to re-enter the candidate pool at later decoder stages, addressing the problem that token importance shifts across decoder depth. Evaluated on LLaVA-1.5 and Qwen backbones augmented with FastV, PDrop, and Nüwa pruning methods, Reroute improves grounding performance under aggressive token reduction without sacrificing general VQA accuracy. The approach preserves the theoretical compute and KV-cache budget of the underlying pruning method.
AdaCodec: Predictive Visual Coding for Efficient Video MLLMs
AdaCodec introduces a predictive visual code interface for video multimodal large language models that exploits temporal redundancy in video. Instead of encoding every sampled frame as an independent RGB image, it sends full visual tokens only for reference frames with high conditional predictive cost, and encodes inter-frame changes as compact P-tokens. Evaluated against a Qwen3-VL-8B per-frame baseline across eleven benchmarks, AdaCodec at 1/7 the token budget (32k vs 224k tokens) surpasses the baseline on all long-video benchmarks while reducing time-to-first-token from 9.26s to 1.62s.
Predictor-gated bank-wise sparsity recipe for dense-to-sparse LLM upcycling from Qwen2.5-8B
A new arXiv preprint introduces a continual training recipe to convert dense LLMs into channel-sparse models without post-hoc pruning. Starting from a Qwen2.5-8B checkpoint, the method uses a low-rank predictor to gate FFN channel routing, achieving 4x sparsity in FFN intermediate activations via a bank-wise top-k rule at 32K context. The routing module is trained on the main language modeling path, making the resulting sparsity hardware-oriented rather than approximate. The authors also identify and patch a layer-local long-context failure mode on the RULER-CWE benchmark.
CHERRY: Compressed Hierarchical Experts with Recurrent Representational Yield — three compute-efficient LM training techniques
A preprint from arXiv introduces CHERRY, a suite of three complementary techniques for compute-efficient language model training: Selective Ground Truth Token Training (SGT) that concentrates supervision on ~15% of semantically loaded tokens while recovering ~67% of full-sequence loss reduction; depth compression that shrinks a 48-layer 1B-parameter model to 6 layers (227M) via layer averaging and recurrent unrolling, matching a 566M dense model's loss; and a Mixture of Efficient Experts (MoEE) assembly that outperforms individual compressed models at comparable active parameters. The techniques are validated on CHERRY-1.8B, a Korean-language foundation model trained entirely from scratch using these methods. Authors are transparent about scope limitations: one model family, Korean data, and loss-based metrics only.
Variance-Calibrated Modulation (VCM): training-free decoding intervention to address LLM likelihood trap
Researchers propose Variance-Calibrated Modulation (VCM), a training-free pre-decoding method that reshapes LLM probability distributions before truncation to combat repetitive degeneration and vocabulary dullness. VCM combines two mechanisms: Contextual Searchlight via PMI (suppressing stopwords, elevating context-relevant tokens) and Adaptive Self-Debiasing (scale-invariant penalization using real-time logit standard deviation). Evaluated across open-ended generation, factual QA, and mathematical reasoning, VCM improves diversity, coherence, and reasoning accuracy at higher temperatures with negligible overhead. The method is compatible with existing decoding strategies like Top-p and Min-p.
Sparsity regularizers improve interpretability of Top-k sparse autoencoders for vision models
A new arXiv preprint proposes two sparsity regularizers compatible with Top-k sparse autoencoders (SAEs), a standard tool for mechanistic interpretability of vision foundation models. The regularizers — an ℓ1 penalty on off-support units and a scale-invariant ℓ1/ℓ2-ratio penalty — are applied before Top-k selection and consistently improve monosemanticity without degrading reconstruction quality across two datasets and three vision models. The central finding is that hard architectural sparsity and soft regularization are complementary, addressing known limitations of fixed-budget Top-k SAEs such as overfitting to training k values.
CLP: Lightweight collocation-length predictor achieves zero-loss multi-token inference speedup
Researchers propose CLP (Collocation-Length Predictor), a span-level decision layer for accelerating LLM inference via multi-token prediction without quality degradation. The key insight is 'Backbone-as-Architect': the backbone LM head always generates the first token while MTP heads handle only subsequent tokens, eliminating head-backbone competition that causes repetitive outputs in prior methods. CLP uses a single linear layer (~4.6K–7.7K parameters) versus 1M-parameter gate networks in prior work, achieving 1.14x–1.29x speedup on Qwen2.5 models with near-zero repetition ratio. The paper also establishes that shorter prediction horizons improve MTP head accuracy on larger models, offering a scaling-aware design principle.